December 25 - Indian Society of Pulses Research and Development

Transcription

Volume 25 Number 4
ISSN
0970-6380
Online ISSN
0976-2434
Journal
of
Food Legumes
Journal of Food Legumes
Volume 25
Number 4
December 2012
December 2012
Indian Society of Pulses Research and Development
I SPR D
1987
Indian Institute of Pulses Research
Kanpur, India
www.isprd.in
INDIAN SOCIETY OF PULSES RESEARCH AND DEVELOPMENT
(Regn. No.877)
The Indian Society of Pulses Research and
Development (ISPRD) was founded in April 1987 with the
following objectives:
 To advance the cause of pulses research
 To promote research and development, teaching and
extension activities in pulses
 To facilitate close association among pulse workers
in India and abroad
 To publish “Journal of Food Legumes” which is the
official publication of the Society, published four times
a year.
Membership : Any person in India and abroad interested
in pulses research and development shall be eligible for
membership of the Society by becoming ordinary, life or
corporate member by paying respective membership fee.
Membership Fee Indian (Rs.)
Foreign (US $)
Ordinary (Annual)
350
25
Life Member
3500
200
Admission Fee
20
10
Library/ Institution
3000
100
Corporate Member
5000
-
The contribution to the Journal, except in case of
invited articles, is open to the members of the Society
only. Any non-member submitting a manuscript will be
required to become annual member. Members will be
entitled to receive the Journal and other communications
issued by the Society.
Renewal of subscription should be done in January
each year. If the subscription is not received by February
15, the membership would stand cancelled. The
membership can be revived by paying readmission fee of
Rs. 10/-. Membership fee drawn in favour of Treasurer,
Indian Society of Pulses Research and Development,
through M.O./D.D. may be sent to the Treasurer,
Indian Society of Pulses Research and Development,
Indian Institute of Pulses Research, Kanpur 208 024,
India. In case of outstation cheques, an extra amount of
Rs. 40/- may be paid as clearance charges.
EXECUTIVE COUNCIL : 2010-2012
Chief Patron
Dr S Ayyappan
Co-patron
Dr N Nadarajan
President
Dr JS Sandhu (Acting)
Joint Secretary
Mr Brahm Prakash
Patron
Dr SK Datta
Vice President
Dr JS Sandhu
Secretary
Dr AK Choudhary
Treasurer
Dr KK Singh
Councillors
Zone I
:
Zone II
:
Zone III
Zone IV
:
:
Dr (Mrs) Livinder Kaur
PAU, Ludhiana
Dr HK Dixit
IARI, New Delhi
Vacant
Dr Vijay Prakash
ARS, Sriganganagar
Zone V
:
Zone VI
:
Zone VII
Zone VIII
:
:
Dr KK Nema
RAK College, Sehore
Dr Ch Srinivasa Rao
CRIDA, Hyderabad
Vacant
Dr Anoop Singh Sachan
IIPR, Kanpur
Editor-in-Chief : Dr. NP Singh
Editors
Dr
Dr
Dr
Dr
Dr
Dr
Dr
Dr
Dr
A Amarendra Reddy, ICRISAT, Hyderabad
AB Rai, IIVR, Varanasi
AK Tripathi, CSAUAT, Kanpur
CS Praharaj, IIPR, Kanpur
IP Singh, IIPR, Kanpur
Jagdish Singh, IIPR, Kanpur
KB Saxena, ICRISAT, Hyderabad
Li Zhenghong, RIRI, PRC China
MK Singh, IIPR, Kanpur
Dr
Dr
Dr
Dr
Dr
Dr
Dr
Dr
Dr
MA Iquebal, IASRI, New Delhi
Mohd Akram, IIPR, Kanpur
P Duraimurugan, DRR, Hyderabad
Rajindar Peshin, SKUAT, Srinagar
RK Varshney, ICRISAT, Hyderabad
RS Raje, IARI, New Delhi
Sarvjeet Singh, PAU, Ludhiana
SC Gupta, ARS, Durgapura
VK Shahi, RAU, Pusa
(Formerly Indian Journal of Pulses Research)
Vol. 25 (4)
December 2012
CONTENTS
RESEARCH PAPERS
1
Status, scope and strategies of arid legumes research in India- A review
255
D. Kumar and A.B. Rodge
2.
Transferability of cowpea and azuki bean derived SSR markers to other Vigna species
273
Ravindra Bansal, Sudhir Kumar Gupta and T. Gopalakrishna
3.
Genetic diversity studies in blackgram (Vigna mungo L. Hepper)
279
M. Srimathy, M. Sathya and P. Jayamani
4.
Stability analysis for seed yield and its component traits in rice bean (Vigna umbellata)
282
Wungsem Rungsung and S.A.P.U. Changkija
5.
Sequence comparison of coat protein gene of Mungbean yellow mosaic India virus isolates infecting
mungbean and urdbean crops
286
Naimuddin and M. Akram
6.
Bio-efficacy of some insecticides against H. armigera (Hubner) on chickpea (Cicer arietinum L.)
291
P.S. Singh, R.K. Shukla and N.K. Yadav
7.
Comparison of nodC and 16S rDNA gene analysis of rhizobia associated with legumes of arid and
semi-arid regions of Haryana
294
Rajesh Gera, Ranjana Bhatia and Varun Kumar
8.
Phenology, dry matter distribution and yield attributes under normal and drought stress conditions
in Lentil (Lens culinaris Medik.)
300
Vijay Laxmi
9.
Efficacy of post emergence herbicides on weed control and seed yield of rajmash (Phaseolus vulgaris L.)
306
Baldev Ram, S.S. Punia, D.S. Meena and J.P. Tetarwal
10.
Enhancing water use efficiency and production potential of chickpea and fieldpea through seed bed
configurations and irrigation regimes in North Indian Plains
310
J.P. Mishra, C.S. Praharaj and K.K. Singh
11.
Variability in the nutrients, antinutrients and other bioactive compounds in soybean
(Glycine max (L.) Merrill) genotypes
314
Reeti Goyal, Sucheta Sharma and B.S. Gill
12.
Effect of presoak treatment on cooking characteristics and nutritional functionality of rice bean
V.D. Pawar , M.K. Akkena, P.M. Kotecha, S.S. Thorat and V.V. Bansode
321
13.
Factors associated with economic motivation of legume growers in desert area of Rajasthan
326
Subhash Chandra, P.Singh and J.P. Lakhera
14.
Farmers participatory approach in seed multiplication of pulses in Bundelkhand region - A case study
330
Purushottam, S.K. Singh, C.S. Praharaj and Lakhan Singh
15.
Tropical Legumes 2 pigeonpea seed system in India: An analysis
334
M.E. Holmesheoran, M.G. Mula, C.V.S. Kumar, R.P. Mula and K.B. Saxena
16.
Dissemination of pulse production technologies in Uttar Pradesh : A micro-level analysis
340
Rajesh Kumar, S.K. Singh, Purushottam and Uma Sah
17.
Pigeonpea (Cajanus cajan L.) price movement across major markets of India
344
D.J. Chaudhari and A.S. Tingre
SHORT COMMUNICATIONS
18.
Genetic variability, character association and path coefficient analysis in faba bean
348
B.K. Chaubey, C.B. Yadav, K. Kumar and R.K. Srivastava
19.
A comparative study of hybrid and inbred cultivars for germination and other related traits of pigeonpea
351
M. Bharathi and K.B. Saxena
20.
Screening of chickpea (Cicer arientinum L.) genotypes for identification of source of resistance to
Botrytis grey mould
355
Lajja Vati, K.P.S. Kushwaha and Abhijeet Ghata
21.
Management of Fusarium wilt of lentil using antagonistic microorganisms in Tarai region of Uttarakhand
358
Ankita Garkoti and H.S. Tripathi
22.
Effect of phosphorus and zinc on yield and economics of mothbean under semiarid conditions
361
L.R. Yadav, Poonam Choudhary, Santosh, O.P. Sharma and Meenu Choudhary
List of Referees for Vol. 25 (4)
i
Reviewer Index (2012)
ii
Author Index (2012)
iv
Subject Index (2012)
vi
ISPRD Fellowship awards, 2012
Obituary
viii
x
Journal of Food Legumes 25(4): 255-272, 2012
Status, scope and strategies of arid legumes research in India- A review
D. KUMAR and A.B. RODGE1
Central Arid Zone Research Institute, Jodhpur-342 003, India; 1Department of Food Chemistry and Nutrition, MAU,
Pabhani-431 402, India; E-mail: dkumarcazri@gmail.com
(Received : May 25, 2012 ; Accepted : November 15, 2012)
ABSTRACT
Arid legumes are adjudged for sustaining growth and
production, especially in driere co-systems encountered with
harsh and hostile growing environments. Sustained breeding
efforts have been made to improve these crops so as to make
them more productive. In guar [Cyamopsis tetragonoloba (L.)
Taub.], early maturing (85-90 days) varieties like, ‘RGC-936’,
‘HG-365’ and ‘HG-563’ suited to 300-400 mm rainfall, medium
maturing (90-105 days) varieties like, ‘RG-1002’, ‘RGC-1003’,
‘RGC-1038’, ‘HG-870’ and ‘HG-884’ suited to 400-450 mm
rainfall and long duration (110-120 days) varieties like, ‘H-220’ and ‘RGC-986’ suited to 500-550 mm rainfall have been
developed. Guar cultivar ‘RGC-1066’ is also suitable for
mechanical harvesting. In cowpea [Vigna unguiculata (L.) Walp.],
varieties with improved plant types and early maturity (60-62
days) like, ‘RC-101’, medium maturity like, ‘Co (CP-7)’ (67-73
days) and ‘GC-3’ (90-95 days and adapted to whole country)
and late maturity (>95 days) like, ‘V-585’, ‘V-240’ and ‘KBC-2’
with dual purpose have also been developed. In moth bean [Vigna
aconitifolia (Jacq.) Marechal], varieties with erect upright
growth (‘FMO-96’ matured in 58-60 days), erect growth (‘RMO40’, ‘RMO-225’ and ‘CAZRI-Moth-3’ in 60-63 days), semi-erect
growth (‘CAZRI Moth-2’ in 66-68 days) and semi-spreading
growth (‘CAZRI Moth-1’ in 72-74 days) suiting to 150-400 mm
rainfall have been developed. In horsegram [Macrotyloma
uniflorum (Lam.) Verd], promising varieties maturing in 85-90
days (‘AK-21’ and ‘AK-42’), 90-100 days (‘PHG-9’, ‘CRIDA-118R’ and ‘BJPL-1’) and 105-111 days (‘VLG-15’ and ‘VLG-19’)
have also been released. Besides this, protocols for rapid callus
induction and regeneration systems have also been developed
in arid legumes. QTLs for resistance to thrips damage have
been identified in cowpea. Efficient cropping sequences like,
guar-mustard and guar-wheat are widely followed in northern
India. In southern states, growing short duration horsegram
with rice by replacing cowpea is beneficial. In Orissa and parts
of Andhra Pradesh, horsegram is successfully cultivated in rice/
maize/sorghum sequences. Beneficial effects of intercropping
guar with pearl millet/sorghum/maize/castor have also been
observed. Moth bean+pearl millet (3:1) intercropping system
is effective in arid situations. However, cowpea+castor (6:1) is
found to have highest monetary return over other intercropping
systems. Foliar application of ZnSO4 @ 0.5% at 25 or 45 days of
sowing (DAS) has been useful with 15-20% higher seed yield
in guar, cowpea and moth bean. Basalin @ 1.0 kg a.i./ha in
moth bean and guar; and pendamethalin @ 0.75 kg a.i./ha +
one hand weeding in cowpea are useful for effective weed control
with leas t weed index. Orga nic, inorganic, biocontrol
management and IPM strategies have also been developed
against major pests and diseases. Guar gum (galactomannan) -
Dr. D. Kumar, Emeritus Scientist at Central Arid
Zone Research Institute (CAZRI), Jodhpur, obtained
M. Sc. (Ag) and Ph. D. Degrees from Banaras Hindu
University. He served R.B.S. College Bichpuri, Agra as
Jr Plant Breeder during September 1976 to 24
November, 1981 and Haryana Agricultural University
Reg. Res. Sta., Bawal as Assistant Oilseeds Botanist
during 1981-1984. He joined CAZRI as Scientist S-2 on 26, November
1984. He served CAZRI in different capacities including Principal
Scientist and Project Coordinator, National Network Research
Project on Arid Legumes upto superannuation (31 July, 2010). He
founded the “Indian Arid Legumes Society” in February, 2000 and
remained Secretary upto superannuation. In capacity of Organizing
Secretary, he organized 4 National Symposia on Arid Legumes. He
has authored/edited 19 technical books, brought out 12 production
technology bulletins, authored more than 50 chapters in books and
proceedings and has more than 150 full length research papers in
refereed journals. He has guided 7 Ph. D students. He was a member
of Institute Management Committee of NRC on Rapeseed-Mustard,
Bharatpur for 6 years and NRC for Groundnuts, Junagadh for 3
years. In individual capacity, he has developed 3 promising varieties
of moth bean, CAZRI Moth-1, CAZRI Moth-2 and CAZRI Moth3 and rigistred two unique germplasm of moth bean. He has visited
Israel, Zambia, Kenya and Ethiopia and Tanzania and successfully
introduced moong bean (K-851) in summer season of 2011 in
Gambella region of Ethiopia, and guar in Tanzania, during 2012.
Ch. Devi Lal Award for outstanding performance of AICRP was
bestowed upon him on 16 July, 2009 for excellent performance of
National Network Project on Arid Legumes.He is recipient of
Haldhar Times Ratan Award, 2012. He is involved in introduction
of guar in non-traditional regions of Anantapur, Karnool, Mahboob
Nagar, RangaReddi (A.P.), Yavatmal (M.S.), Raipur (CS) and Madurai
(T.N).
Dr. A. B. Rodge obtained M.Sc. degree (Food Science)
from University of Saskatchewan, Saskatoon, Canada
and Ph. D. (Food Science) from Marathwada Agril.
University (MAU), Parbhani. Presently he is working
as Head, Dept. of Food Chemistry and Nutrition,
College of Food Technology, MAU Parbhani. He has
about three decades of experience in research, teaching
and extension activities. As a Principal Scientist, he has been
monitoring 2 National Projects, 1 National Network Project on
Arid Legumes and 2 National Network Project on Harvesting,
Processing and Value Addition of Natural Resins and Gums. He has
published more than 60 research papers in reputed journals, and 30
other technical publications, one book and written six book chapters.
He is recipient of travel grant from American Association of Cereal
Chemists, Nashville, USA, President Honor roll from the American
Oil Chemist Society, Annual meeting USA; and recipient of common
Wealth Scholarship for higher studies in Canada. He is recipient of
prestigious George F. Stewart International award as a finalist in
Institute of Food Technologist Annual Meeting Chicago USA. He
has been acting as Vice President of Indian Arid Legume Society,
Jodhpur and as Research Advisory Committee member at Indian
Institute of Natural Resins and Gums.
256
Journal of Food Legumes 25(4), 2012
a polysaccharide organic compound - is used in a number of
industrial products where water is an important factor. However,
varieties having more than 32.0% gum content and higher
viscosity of guar gum (4000-5000 cP) are more preferred for
export. Guar meal (a by-product of guar gum industries) can
also replace edible oil cakes due to its higher crude protein
(40-45%). Future strategies for these arid legumes are also
discussed.
Key words :
Arid legumes, Cowpea, Guar, Horsegram, Moth bean
Deep rooted, summer annual legumes grown under
resource constraint situations are generally referred to as arid
legumes. Commonly known as the crops of dry habitats, these
are characterized with low cost source of livelihood of
financially ridden arid farmers. For convenience, four legumes
viz., guar or clusterbean, moth bean or dew bean, cowpea or
lobia and horsegram or kulthi form a group of crops generally
referred to as arid legumes in India. These crops adapted to
specific set of environments are basically known for taming
drought, sustaining soil productivity, stabilizing agricultural
system and providing nutritional security. The crops provide
nutritious green fodder/vegetables and are being used in many
secondary and tertiary products. These virtues have made
these crops from being locally and regionally important to
front running dryland crops of great economic significance in
India.
In spite of a series of merits attached, these crops also
suffer from certain biological bottlenecks which need to be
addressed. Main obstacle is poor productivity of arid legumes
resulting from poor source-sink relationship. The plant type
is generally suiting for survival values but little for higher
productivity. Second most important biological weakness is
their long maturity subjecting these to terminal stress leading
to poor adaptation and production. There are few but important
plant diseases which may also cause heavy yield losses during
congenial conditions. Thus, to bridge the gaps between
potential and realized yields of these crops, immediate
biological and management remedies may be taken up requiring
insight to research information available for assessing existing
status and necessary impetus in genetic improvement, crop
husbandry, plant protection and biochemistry. In view of
stagnated growth of these crops in reference to area,
production, productivity and quality aspects, it appears
imminent for a strategically move in required direction which
would help in increasing productivity of arid regions
contributing to self sufficiency in production of these pulses,
increasing export potential of industrial products and income
generation.
This paper provides an insight to relook on its existing
strategic research and development in arid legumes to provide
a more sensible road map to bridge the research gaps in
productivity at different levels.
Genetic Resource
Guar: More than 5000 accessions have been collected by
National Bureau of Plant Genetics Resources (NBPGR) New
Delhi, mainly from dry habitats of northern India. Two wild
species viz., C. serrata and C. senegalensis were also
introduced from USA. A total of 3714 accessions have been
put for ex-situ conservation. Additionally, 4878 accessions
with indigenous origin have also been conserved in mediumterm storages (Mishra et al. 2009). Evaluation of more than
730 indigenous and 20 exotic lines led to selection of lines
maturing in less than 90 days. Effective evaluation of more
than 375 accessions at S.K.Nagar, Gujarat, India against
important diseases have resulted in promising resistance lines
against bacterial leaf blight (‘GAU-9406’, ‘GG-1’, ‘RGC-1027’),
alternaria leaf blight (‘GAUG-9406’, ‘GAUG-9005’, ‘GG-1’,
‘GAUG-9003’) and RootRot (‘GG-1’, ‘HGS-844’, ‘GAUG-9406’)
were identified (Kumar 2008). Certain guar lines viz., ‘Sona’,
‘Suvidha’, ‘IC-09229/P3’, ‘Naveen’, ‘PLG-85’ and ‘RGC-471’
for seed type, and others like, ‘Pusa Mausmi’, ‘Pusa
Sadabahar’, ‘Pusa Navbahar’, ‘IC-11388’, ‘PLG-850’ and
‘Sharad Bahar’ were released as promising varieties for
vegetable purposes.
Cowpea: A total of 2139 accessions have been evaluated for
24 descriptors and 3422 lines have been conserved ex situ.
More than 67% varieties developed in cowpea owe their origin
from evaluated germplasm lines. Some of promising varieties
released following evaluation of exotic germplasm includes
‘Aseem’, ‘Bundel Lobia-1’, ‘C-152’, ‘Shweta’, ‘Co-2’, ‘Co-4’,
‘Pusa Phalguni’, ‘Pusa Sawni’, ‘Rituraj’, ‘S-288’ and ‘S-488’,
‘Charodi’, ‘Co-1’, ‘Co-5’, ‘cowpea-78’, ‘cowpea-88’, ‘FS-68’,
‘GC-1’, ‘Gomti’, ‘JC-2’, ‘JC-10’, ‘BBC-1’, ‘Bundel Lobia-1’ and
‘Paiyur-1’.
Moth bean: Evaluation of more than 2011 accessions has
exhibited wide diversity in growth, yield, quality traits and
disease resistance (Gautam et al. 2000). However, 43
accessions introduced from Ceylon (Agarwal 1964), Mexico
(Agarwal 1964), USA (Mishra et al. 2009), former USSR
(Agarwal 1964) and Taiwan (Agarwal et al. 1987) are maintained
and more than 1100 accession of moth bean have been
evaluated at NBPGR. A total of 1540 moth bean accessions
have also been conserved ex-situ in the national repository.
Almost 2143 accessions have been maintained as active
collections in medium-terms-storage facilities available at
different centres of NBPGR. The varieties like, ‘Type-3’, ‘T-9’,
‘Baleswar-12’, ‘MG-1’, ‘Jadia’, ‘Jawala’, ‘IPCMO-880’ and
‘IPCMO-912’ have been developed directly or through
selections made from local germplasm (Kumar 2005).
Horsegram: More than 1500 accessions collected by NBPGR
are being maintained at New Delhi (> 500 accessions), Akola
(650 accessions) and Thrissur (500 accessions). TNAU,
Coimbatore is also maintaining almost 320 accessions at RARS,
Piyaur. Similarly, GKVK Bangalore is maintaining almost 120
accessions. Majority of these improved varieties released at
Kumar & Rodge : Status, scope and strategies of arid legumes research in India- A review
National and/or State level owe their origin from local
germplasm directly or selection thereafter. These varieties are
for Bihar (‘BR-5’, ‘BR-10’ and ‘Madhu’), Himachal Pradesh
(‘HPK-2’, ‘HPK-4’, ‘HPK-5’ and ‘HPK-6’), Andhra Pradesh
(‘PDM-1’and ‘VZM-1’), Jharkhand (‘K-82’ and ‘Birsa Kulthi’),
Orissa (‘S-27’, ‘S-28’, ‘S-39’ and ‘S-1264’), Rajasthan (‘Maru
Kulthi’, ‘KS-2’, ‘AK-21’and ‘AK-42’), Uttranchal (‘VL Ghat1’), Karnatka (‘Hebbal Hurali-2’, ‘PHG-9’ and ‘KBH-1’) and
Tamil Nadu (‘Co-1’, ‘35-5-122’ and ‘35-5-123’).
Genetic Improvement
Interspecific hybridization: Earlyness is required to be
transferred from wild species (S. serrata and S. senegalenesis)
to cultivated species (C. tetragonolaba) of guar. Hence,
Sandhu (1988) while studying the details of hybridization
between 2 species (C. tetragonoloba and C. serrata) reported
failure of hydridization through conventional methods. Other
approaches like, bud pollination, amputation of stigma and
style, use of organic solvents also failed to overcome the
stigmatic incompatibility barriers. Scope for interspecific
hybridization is, therefore, limited in guar unless specific tools
are used. This prompted application of nonconventional
methods of hybridization including ovary rescue, protoplast
fusion etc. for genetic improvement.
Interspecific hybridization in cowpea is required for
transfer of resistance against pod-borer and sucking bugs
from wild to cultivated species. Ng (1990) reported fertile
hybrids by crossing cultivated cowpea [Vigna unguiculata
sp. unguiculata (L.) Walp.] with wild relatives viz.,V.
unguiculata spp dekind tiana (Harms) Verdc., Vig na
unguiculata spp stenophylla (Harvey) M.M. & S., and Vigna
unguiculata spp tenuis (E. Mey.) M.M. & S. At IITA, Nigeria,
crosses between wild species (Vigna unguiculata ssp
dekindtiana) and cultivated cowpea were made to transfer
resistance against pod-borer and pod-sucking bugs. The
introgressive backcrossing (IBC) resulted in the traits of
recurrent parent being fixed as early as in IBC2 . Fertility
increased with each IBC generation. A new method was termed
congruity backcrossing (CBC) which involved crossing with
each parent species in alternate generations (Kumar and Dixit
2004). Increase in fertility of the species used as female in the
original crosses were observed in subsequent generations.
Mahudeswaran et al. (1973) derived a ‘Co 2’ variety from 3way crosses involving V. catjang (Burm. f.) Walp., V.
unguiculata and V. sesquipedalis (L.) Fruw.
Improved plant types and earliness: Arid legumes known for
high biomass production suffer from poor partitioning towards
economic parts and are therefore, branded as poor yielders.
Alteration in their plant types in concomitant with early
maturity in reference to the agro-ecoregion and rainfall
distribution is desired. Research carried out in moth bean has
resulted in the development of new genotypes characterized
with erect growth habit and early maturity (58-62 days)
257
compared with traditional genotypes with spreading and late
maturing habits (90-100 days). These altered genotypes, in
spite of curtailment in growth period by about 30-35 days,
have great yield advantages. For instance, ‘CZM 32’ and ‘CZM
18’ mutant lines flower in 28 days, take 63 days to maturity,
have 12.5 g/day productivity and may yield almost 780 kg/ha
compared to late maturing spreading types (with 51 days, 94
days, 3.72 g/day and 380 kg/ha, respectively) (Kumar 2002).
Thus, ‘CZM-32’ has been registered as a new potential
genotype (IGNR no. 01024) at the NBPGR (Kumar 2003).
Similarly, ‘RMM 12’ an early and synchronous-maturing type
having only main stem has also been registered as new plant
material (IGNR No. 04095) with maximum daily uptake of Na
(0.08 × 10-9 mole) as compared to others with 0.04-0.09 × 10-9
mol (Tarafdar and Kumar 2003). ‘CAZRI Moth-1’- an improved
semi-erect type - is also known for (Kumar 2001) in built
resist ance against yel low mosaic viru s (YMV). A
comprehensive list of promising varieties of guar, moth bean,
cowpea and horsegram is also given in Table 1.
Drought tolerance: Efforts have been made to make arid
legumes more productive by inducing earliness so as to escape
terminal drought by manipulating sowing dates and bringing
in uniformity in flowering for matching these with irrigated
crops. In moth bean for instance, dry-matter distribution
towards economic parts, nutrients movement from soil to root
(Tarafdar and Kumar 2003) and retention of flowering period
(30-32 days) are of practical significance and are exploited. In
cowpea, higher total plant dry matter in season (Agarwal 1987),
larger tap root system under wilt (Itani 1992), lower value of
13
C in concomitant with higher levels of water-use efficiency,
leaf area and xylem ABA (Nagugi et al. 1999), detached leaf
test (Shekhawat et al. 2002) and screening during summer
season at 3-4% available soil moisture have been successful.
However, morphological and physiological characters
contributing for drought tolerance have varied inheritance
pattern (monogenic and polygenic) and gene action. Stomatal
sensitivity and osmotic adjustment (OA) are dominant and
drought tolerance is controlled by single dominant gene (Rds1
and Rds2) in cowpea (Mai-Kodomi et al. 1999). Breeding for
high yielding varieties with drought tolerance is complex due
to difficulties in combining drought tolerance and seed yield
traits as both are governed by polygenes. There is also a
need to identify high yielding genotype giving high yield
under stress conditions. Hence, increasing yield potential
under nonstress situation is being explored as a simple method
with other approaches viz., collection of germplasm from
dro ught habit ats representing bot h dro ught and heat
environments, screening genotypes under summer season
representing drought complex, predicting seed yield of
drought-tolerant lines during rainfed rainy season situation,
hybridization between high-yielding and drought-tolerant
lines, and hybridization between traditional lines representing
avoidance phenomenon and early-maturing lines representing
escape mechanisms. Since expression of yield and yield traits
258
Table 1. Improved varieties of arid legumes suitable for different cropping regions
Sl. Average
No. Rainfall*
(mm)
Rajasthan
1
170-200
2
3
200-250
250-300
Region/
district
Cropped Produ- Varieties
area*
ctivity* (Year of
(000 ha) (kg/ha) release)
Churu
315.00
235
Jaisalmer
190.00
100
Bikaner
Barmer
411.00
325.00
215
135
Ganganagar
Hanumangarh
180.00
319.00
RGC-936
(1992)
HG-365
(1998)
RGC-936
RGC-365
RGC-563
(2005)
5
6
300-350
375-400
400-450
Remarks
85-90
Suitable for arid Rajasthan
80-85
85-90
80-85
85-90
675
RGC-1031
(2005)
105-108 Seed whitish in color, leaves wide, field
tolerant to BLB and ALB
Suitable for summer
cultivation and wide spacing
Suitable for irrigated
conditions
RGC-1002
HG-20-2
110-112 Suitable to high rainfall, high gum content,
moderately resistant to BLB, RR and ALB
with seed yield 1300-1600 kg/ha
115-120 Tall growing, requiring better management,
seed yield 1100-1200 kg/ha
Gum content 31.41% suited
for wider spacing and
irrigated conditions
Dual purpose and suitable
for canal command areas,
HG-365
RGM-112
(2005)
Nagaur
155.00
420
Jodhpur
183.00
180
Sikar
78.00
311
Jhunjnu
62.00
280
Pali
67.45
708
Jalore
69-50
600-650
Jaipur
Bhilwara
55.14
37.00
780
600
8
700-800
Alwar
34.66
1000 RGC-986
Bhiwani
Mohindergarh
Sarsa
Hisar
90.00
30.00
101.00
70.00
900
985
1400
1200
HG-365
HG-563
HG-884
HG-2-20
80-85
80-85
95-100
108-112
Banaskantha
61.60
604
GG-2
95-100
Kutch
58.10
610
HG-563
HG-365
RGC-936
Churu
293.00
470
Jaisalmer
170.00
121
Bikaner
283.00
215
Barmer
208.00
194
Rajasthan
1
170-200
2
200-250
High viscosity (3500 cP) and
ruling variety of Haryana
-
RGC-1038
(2006)
HG-884
(2007)
RGC-1002
(1999)
RGC-1017
(2002)
RGC-1038
807
870
7
Haryana
9
200-250
10
200-225
11
300-350
12
250-300
Gujarat
13
Most drought hardy, flowers light pink in
color and seed yield 900-1200 kg/ha
Medium height, spreading type and seed
yield 1000-1200 kg/ha
-
Suited to low rainfall zones, heavy podding
behavior, improved in gum content and its
quality
80-85
85-90
Moderately susceptibility to BLB disease,
leaves light green and seed yield 1100-1300
kg/ha
100-105 Suitable for Rajasthan state, single stem type,
tall growing, brisk podding and seed yield of
1200-1500 kg/ha
95-100 Somewhat photo insensitive and seed yield
of 1200-1500 kg/ha
95-100 Improved in gum and quality and seed yield
potential 1400-1500 kg/ha
100-105 Moderately resistant to BLB, improved in
gum content and seed yield 800-1300 kg/ha
95-100 Moderately resistant to BLB, PM and seed
95-100 -
RGC-1066
(2007)
4
Guar
Maturity Important traits
(days)
FMO-96
(1996)
CAZRI
Moth-3
(2003)
RMO-40
(1994)
RMO-225
(1999)
-
Branched type, determinate growth and
Moderately resistance to BLB disease
80-85
80-85
85-90
Mothbean
58-59
60-62
61-62
64-65
Suitable for Haryana, high
gum and viscosity profile
(4050 cP).
-
Suitable to mechanical
harvesting in canal command
areas with close planting
Suitable to summer season
and wide spacing (40-50 cm)
High gum content (30-31%)
and viscosity (3000-3500 cP)
-
-
-
Erect upright and synchronized growth
Released for whole Rajasthan.
Suitable for intercropping
Erect and synchronized growth, escapes Suitable for extreme drought
YMV and seed yield 700-800 kg/ha
situations
Less biomass, erect growth and seed
yield 600-750 kg/ha
Field tolerance to YMV, synchronized
growth and seed yield 650-700 kg/ha
Suitable for extreme drought
situation
Suitable for low rainfall
Sl. Average Region/
No. Rainfall* district
(mm)
3
250-300 Ganganagar
4
300-350
5
350-450
Sl.
No.
Region/
district
Cropped
area* (000
ha)
0.23
Productivity*
(kg/ha)
446
Hanumangarh
39.00
417
Jodhpur
159.00
251
Nagaur
215.00
218
Sikar
Pali
Jalore
0.93
0.32
0.32
289
239
470
Rajasthan
1
Andhra Pradesh
2
3
Tamil Nadu
Karnatka
Varieties
(Year of
release)
Maturity
(days)
Co(CP-7)
(2006)
67-72
Co(CP-7)
Vamban-1
(1999)
GC-3 (1997)
67-72
90-100
90-95
KBC-2 (1998) 95-105
GC-3
Vamban-1
90-95
90-100
4
Kerala
Subhra
70-90
5
Madha Pradesh,
Chattisgarh, Orissa
GC-3
V-130 (1994)
90-95
95-100
6
Rajasthan (Sikar,
RC-101 (2001) 60-62
Jodhpur, Jaipur region)
V-240 (1994) 90-95
7
GC-5 (2003)
Gujarat (North Gujarat, GC-5
Mehsana, Banaskantha) GC-3
Sl.
No.
Region/
district
Cropped Produarea* (000 ctivity*
ha)
(kg/ha)
Rajasthan
1
Andhra Pradesh
39.31
444
2
206.41
460
3
Karnatka
Tamil Nadu
75-80
75-80
90-95
44-79
500
Varieties
(Year of
release)
CAZRI
Moth-3
RMO-435
(2002)
FMO-96
RMO-40
CAZRIMoth-2
(2002)
RMO-435
RMO-257
(2005)
CAZRIMoth-1
(1999)
Maturity Important traits
(days)
60-62
259
Remarks
-
-
64-65
Leaves dark green and seed yield 600- Suitable for Rajasthan
650 kg/ha.
-
66-68
First variety from hybridization, dark M ost high yielding and
green color, seed yield 800-1200 kg/ha suitable for high inputs and
better soils
Good for seed and fodder, seed yield
Suitable for intercropping and
600-650 kg/ha
dual purpose
Inputs responsive, natural source of
High protein 25.0%, suitable
YMV, seed yield 500-550 kg/ha
to medium June planting and
good source of fodder
64-65
63-65
73-75
Cowpea
Important traits
Remarks
Moderately resistance to LCV, erect,
synchronized growth habit and seed yield 9501200 kg/ha
Seed contain 28.0% crude protein with
minimum tannin content (0.75 mg/g)
Long pods and seed yield 950-1000 kg/ha
Release for whole Rajasthan, dual
purpose
High digestibility (90.0%), with less
cooking time 44.0%
Dual purpose
Adapted to whole country, short stature, YMV
resistant and seed yield 750-1150 kg/ha
Asynchronous growth, long pods, tolerant to rust
and seed yield 650-1100 kg/ha
Very promising for Kerala, medium tall height,
MR to anthracenose and seed yield 800-1000
kg/ha
Adapted to low rain fall situations, seed red colour
and seed yield 900-1100 kg/ha
Synchronized growth, non-viny, escapes CYMV
and seed yield 750-800 kg/ha, white seed color
Red seed, dark green leaves and seed yield 10001200 kg/ha
Bold seeds and seed yield 1200-1400 kg/ha
Horsegram
Varieties Maturity
Important traits
(Year of period
release)
(days)
CRIDA
1-18R
(2007)
PHG-9
(1997)
BGM-1
(1990)
CRIDA
1-18R
Paiyur-2
(1994)
CRIDA
1-18R
BJPL-1
(2009)
95-100
95-105
100-110
Released for whole Karnataka, dual
purpose. Seed cP 25-26%
Released for whole Kerala, dual purpose
Dual purpose
Released for whole Rajasthan and for
mechanical harvesting.
Dual purpose
Suitable to kharif and summer
Remarks
Synchronized, 20% more yield over Most promising for southern states
PHG-9 and seed yield 750-1150
kg/ha.
Winy type, photo sensitive, suitable to
southern states
Bears tendrils, tolerant to YMV and Dual purpose
seed yield 600-700 kg/ha
95-100
105-110
More suited to rabi season
95-100
95-98
27% more yield over PHG-9 and seed Dry fodder yield of 1200-1500
kg/ha with 1.70 mg/g tannin
content
260
Sl.
No.
Region/
district
4
Madhya Pradesh
Chattishgarh and
Maharashtra
Cropped Produarea*
ctivity*
(000 ha) (kg/ha)
62.00
310
Varieties
(Year of
release)
AK-42 (2005)
Maturity
period
(days)
90-95
AK-21 (1999) 85-90
5
6
Orissa
Hilly regions
58.79
11.76
300
580
AK-21
85-90
VLG-10 (2006) 115-130
VLG-15 (2008) 105-115
VLG-19 (2010) 100-105
Important traits
Remarks
Suited for moderate rainfalls, seed
color brisk red and seed yield 600-850
kg/ha
Suitable to northern, western and
central India and seed yield 650-800
kg/ha
Field tolerance to anthracnose and
stem rot and seed yield 1000-1200
kg/ha
30% higher yield over AK-21, field
tolerance to anthracnose, seed yield
800-1400 kg/ha
Moderately resistance to anthracnose
and RR, seed yield 1000-1300 kg/ha
CP 30.0%, Fat 3.65, grain type
Most drought hardy and suitable to
low rains
Suitable for June sowing, dual
purpose
Suitable for June sowing, dual
purpose and 86.2% digestibility
Seed protein 26.6%,
83.4%digestibility
*Long range statistics on rainfall, area and productivity
are usually poorly expressed under stress situations, selection
of drought related traits may be carried out under drought
conditions while their yield potential is assessed under non
stressed situations (Kumar 2008).
Improved seed yield: Arid legumes show increased seed yield
under existing situations with little or no fertility and poor
agronomic inputs. Improved guar varieties like, ‘RGC 1002’,
‘RGC 1071’, ‘HGS 365’, ‘RGC 1038’ and ‘HG 884’ have yield
potential of 1000-1400 kg/ha compared to traditional varieties
(800-900 kg/ha). Earlyand high yielding varieties of guar like,
‘HG-365’, ‘HG-563’ and ‘HG 884’ have higher average
productivity (1200 kg/ha) in Haryana state in comparison to
their counterparts (370 kg/ha) in Rajasthan state of India.
Similarly, varieties like, ‘Co(CP-7), ‘V585’, ‘GC 3’and ‘KBC 2’
in cowpea, ‘CAZRI Moth 3’, ‘CAZRI Moth 2’, ‘RMO 435’,
‘RMO 40’ and ‘RMO 225’ in moth bean, and ‘AK 42’, ‘AK 21’,
‘CRIDA-1-18’ and ‘VLG-19’ in horsegram have higher yield
potential (Table 1).
Improved quality: Guar needs quality improvement in respect
of industrial guar gum production having potential to export.
Similarly, guar meal is also an important animal feed rich in
crude protein (40-50%) and green pods are largely used as a
rich source of Fe and Zn in the form of delicious green
vegetable. However, significant strides in enhancement of gum
and protein content in guar seeds has not been made due to
certain inverse relationships. There is a positive correlation
between seed yield and per cent gum content (Mittal et al.
1971), while there is a negative association between seed
weight and gum content hampering breeding efforts for large
seeds. Seed color is also not associated with seed gum content.
Both additive and non-additive gene effects are involved in
determination of gum content. Viscosity is maximum in ‘HG365’ (3500-4000 cP) while genotype ‘HG-884’ has maximum
gum content (31.41%). In moth bean, cowpea and horsegram,
systematic evaluation have resulted in isolation of some
promising lines viz., cowpea strain ‘PGCP-1’ and ‘Co (CP-7)’
with maximum seed protein (28%) and lower tannin content
(0.75 mg/g) in the latter. In case of horsegram, maximum crude
protein (31.12%) in ‘CRHG-7’ and minimum tannin content
(1.44 mg/g) in ‘AK-21’ are observed (Table 2) (Chinnaswamy
et al. 2011and Rodge 2009). In general, more tannin content is
related to more cooking time in case of horsegram. Maximum
seed protein (25%) is also observed in moth bean ‘CAZRI
Moth-1’ (Kumar 2001).
Table 2. Arid Legume varieties with improved quality traits
Seed crude Seed cooking Seed tannin
protein (%) time (min) contents (mg/g)
Cowpea
Co(CP-7)
28.2
28.2
0.75
PGCP-1
28.0
28.0
0.70
Moth bean CAZRI Moth-1
25.0
13.0
0.33
RMO-225
24.5
15.0
0.24
Horsegram CRHG-7
31.1
15.0
2.27
AK-21
30.2
13.0
1.44
Crop
Varieties
Biotechnology
1. Callus induction and regeneration protocols: In case of
guar, MS media containing 6-benzylaminopurine (13.3 µM or
3 mg/L) in combination with indole-3-acetic acid (11.4 µM or 2
mg/L) with cotyledon node explants gives the highest
frequency of multiple shoot regeneration (Deepak et al. 2003).
More efficient regeneration is reported following culturing
callus on MS medium containing 1-napthlenacetic acid (13.0
µM) in combination with 6-benzylaminopurine (5.0 mM) with
a range of 82.1-88.4% of callus clumps producing 20-25 shoots
(Deepak et al. 2005). The medium containing 2, 4-D (10.0 mM)
in combination with 6-benzylaminopurime (5.0 mM) with
embryo or cotyledon explants is the most suitable for
induction of callus in guar. In moth bean, embryogenic callus
cultures are established from the cotyledonarynode as explant
on semi-solid MS medium supplemented with 0.75 mg/L 2,4-D
and 1.5 mg/L 6-benzylaminopurine (BA) and with various
additives (50 mg/L ascorbic acid and 25 mg/L each of adenine
sulphate, citric acid and L - arginine). Numerous somatic
embryos are differentiated on basal nutrient medium
supplemented with 0.25 mg/L 2,4 – D and 0.5 mg/L of kinetic
(Kin). Transfer of these embryos onto fresh MS basal medium
containing 0.2 mg/L BA and 2.0 mg/L gibberellic acid enable
these to achieve complete maturation (Choudhary et al. 2009).
In vitro regeneration of plants via somatic embryogenesis
through cell suspensions culture is achieved in horsegram.
Embryogenic calluses are induced on leaf segments on solid
MS medium with 9.0 mM 2, 4-D. Differentiation of somatic
embryos occur on transfer of embryogenic calli to liquid MS
medium containing 2,4-D. Conclusive results indicates that a
medium supplemented with 7.9 mM 2,4-D, 3.0% sucrose, 40
mg 121 L-glutamine and 1.0 m Mabscisic acid is effective to
achieve high frequency of somatic embryo induction,
maturation and further development (Mohamedi et al. 2004).
Similarly, cowpea plants are regenerated from in vitro cultured
explants of primary leaves. Primary leaves, including the intact
petiole, is excised from three-day-old seedlings and cultured
on Gamborg’s B5 basal medium containing 8×10-7M 2,4,5trichlorophenoxyacetic acid, 1×10-2 M L-glutamine and 1 × 104
M adenine sulfate. Callus is formed at petiole end and prolific
shoot regeneration occurs when this callus is transferred to
B5 basal medium containing 5×10-6 M 6-benzyl-aminopurine
(BAP). Regenerated shoots rooted in growth-regulator-free
B5 basal medium are also established in soil (Muthukumar et
al. 1995).
261
for isolation of drought tolerant gene P5 Cs encoding 1pyrroline 5-carboxylate synthesis protein through proline
biosynthesis pathway for transfer of drought tolerance in
Nicotiana tabacum (Hong et al. 2000). The same gene is also
utilized for development of transgenic rice for salt tolerance
(Karthikeyan et al. 2011). Hence, P5Cs is an important source
of drought and salt tolerance capable of being effectively
transferred through Agrobacterium tumefaciens strain LBA
4404 harbouring the binary vector pCAMBIA 1301/P5Cs.
Guar: A 1.6 kb guar mannam synthase (MS) promoter region
has been isolated. This MS promoter sequence is over
expressed in alfalfa (Medicago sativa L.). The quantitative
GUS assay reveales that the MS promoter directs GUS
expression especially in the endosperm in transgenic alfa,
hence guar MS promoter could prove useful in directing
endosperm-specific expression of transgenes in legumes
(Naoumkina and Dixon 2011). The procedure of transformation
of large seeded endospermous guar is also reported. Using
Agrobacterium tumefaciens with T-DNA construct harbouring
a-glucuronidase gene (uid A) and a neomycin phosphotrans
ferase gene, maximum transformation frequency with
cotyledomary explants are obtained using 145 mg/l kanamycin
sulfate as selective agent. Carbenicillin and cefotaxine used
for elimination of Agrobacterium after co-culture increases
transformation frequency up to 10-folds in total. The presence
of transgenes in the primary transformations is demonstrated
by genomic DNA analysis of GUS-positive shoots. The high
influx of energy into storage protein synthesis in guar seed
has also been reflected by a high representation of genes
annonated as involved in signal transduction carbohydrate
metabolism, translation and ribosome structure. Thus, guar
unigenes involved in galactomannan metabolism are identified.
It is reported that among storage proteins, the most abundant
protein is a conglutin accounting for 3.7% of the total ESTs
(Naoumkina et al. 2007).
Cowpea: QTL for economic traits have been identified to make
the selection procedures more effective through MAS loci
(Citadin et al. 2011). For instance, three quantitative trait loci
(QTL) for resistance to Thrips tabaci and Frankliniella
schultzei are identified using a cowpea recombination inbred
population. The AFLP genetic linkage map and foliar feeding
damage ratings are used to identify genomi c regions
contributing towards resistance to thrips damage. Three QTLs
(Thr-1, Thr-2 and Thr-3) are identified on linkage groups 5
and 7 accounting for between 9.1 and 32.1% of the phenotypic
variance. AFLP markers ACC-CAT7, ACG-CTC-5, and AGGCAT1 are co-located with QTL peaks for Thr-1, Thr-2 and
Thr-3, respectively. These results will provide a resource for
mo lecu lar marker devel opment and t he genet ic
characterization of foliar thrips resistance in cowpea (Muchero
et al. 2010). In another case, a genetic linkage map is
constructed using SSR markers and a recombinant inbred (RI)
population derived from a cross between the breeding line
524B, and 219-01. Polymorphic SSRs obtained are used to
construct a genetic map consisting of 11 linkage groups (LGs)
spanning 677 cM with an average distance between markers
of 3 cM. Six QTL for seed size reveal phenotypic variation
ranging from 8.9 to 19.1%. Four QTL for pod shattering are
also identified with the phenotypic variation ranging from 6.4
to 17.2%. The QTLs for seed size and pod shattering have
been identified in two areas LGs-1 and 10 facilitating the use
of MAS to eliminate undesirable wild phenotypes in breeding
activities involving utilization of traits from wild germplasm
(Andargie et al. 2011). A highly efficient Agrobacteriummediated cowpea transformation method for introduction of
the cowpea á-amylase inhibitor-1 (áAI-1) gene into a
commercially important cowpea cultivar, Pusa Komal generates
fertile transgenic plants. The use of constitutive expression
of addi tional vir genes in resi dent pSBI vector in
Agrobacterium strain LBA4404, thiol compounds during cocultivation and a geneticin based selection system also results
in two-fold increase in stable transformation frequency.
Expression of áAI-1 gene under bean phytohemagglutinin
promoter results in accumulation of áAI-1 in transgenic seeds.
The transgenic protein is active as an inhibitor of porcine áamylase in vitro. Transgenic cowpeas expressing áAI-1
strongly inhibit the development of C. maculates and C.
chinensis in insect bioassays (Solleti et al. 2008).
Moth bean: Being extremely drought hardy crop, it is utilized
Horsegram: Based on rDNA, IGS, RFLP by means of three
2. Identification and transfer of genes and QTL
262
restriction enzymes, 69 isolates of rhizobia in horsegram are
grouped in five clusters. By sequence analysis of 16S-23S,
rDNA, IGS identifies the genotypes of rhizobia distributed
into five different lineages of Brady rhizobium genus. Nearly
87% of indigenous horsegram isolates (IGS types I, II, III & V)
could not be related to any other species within the genus
Bradyrhizobium. Phylogeny based on house keeping ginll
and recA genes confirms those results found by the analysis
of the IGS sequence. All these isolated rhizobia nodulate
Macrotyloma and Vigna spp. The isolates within each IGS
type varied in their ability to fix nitrogen. Selection for high
symbiotic effective strains could reward horsegram production
(Chinnaswamy et al. 2011).
Crop Production
Guar and moth bean require well drained light textured
sandy to loamy plain lands having even sloppy profiles (of 510% area) in Rajasthan, Haryana and Gujarat. Cowpea and
horsegram are adapted to large range of climates from arid to
semi-arid regions of western dry tracts of Gujarat to humid
region in eastern parts of Orissa and West Bengal, and from
north plains of Jammu and hill region of Himachal to deep
south in Tamil Nadu and Kerala. The details of agrotechnologies are discussed here.
Sowing windows: Early planting of guar and moth bean may
cause profuse growth resulting in poor harvest but late planting
may not attain usual growth and result in severe yield
reductions (Kumar 2009). However, results reported byYadav
et al. (2003) and Yadav et al. (2002) on early maturing guar
varieties (‘HG 365’, ‘HG 563’ and ‘RGC 936’) revealed that
planting after 25th June resulted in higher seed yield. In
Southern India, winter cowpea is planted in OctoberNovember following N-E monsoon. Horsegram in rainy and
winter seasons is sown from August to October and sometimes
during November particularly in southern states (Table 3).
Planting in row spacing as paired rows (Singh et al.
2003) or solid rows is better over broadcasting method. Thus,
optimum plant stand is desired (Yadav et al. 1989a) for higher
yield and both inter and intra-row spacing are different for
different plant types (branched and unbranched). Under late
sowing condition, closer spacing may be recommended. Early
maturing varieties (‘HG-563’ and ‘RGC-936’) perform better
under 60 cm inter row spacing than that of 40 cm (Kumar et al.
2003a). Usually 15 and 10 cm intrarow spacing are optimum
for branched and unbranced guar varieties. Location specific
trial on cowpea at Bangalore and Pattambi also indicates that
30 cm wider spacing is better over 45 cm (Table 3). However, in
case of horsegram, seed yield is less influenced by row spacing
as usually a seed rate of 40 kg/ha is adequate for higher seed
yield. In plateau region of Bihar, 20 cm interrow spacing (50 kg
seed/ha) proved optimum with seed yield of 2.06 t/ha (Rafey
and Srivastva 1989).
Cropping system: Cultivation of legumes improves N status
of the soil thereby, reducing its requirement for the succeeding
crops (Faroda and Singh 2003). Guar fixes around 30-70 kg N/
ha through biological N fixation and leaves a residual effect
equivalent to about 15-20 kg N/ha (Rao 1995). In rainfed
conditions, most prominent rotations viz., pearl millet + guar fallow, guar- fallow/mustard, sesame + guartaramira, guartaramira, guar-mustard, guar + grass- fallow etc are dominant
(Table 3). A long term study on cropping system reveals that
pearl millet-guar cropping system gives 11% higher seed yield
over monocropping of pearl millet only. Contents of organic
carbon and available soil phosphorus may considerably
improve (Saxena et al. 1997) through proper cropping systems.
Location specific studies on intercrops at Bawal,
Haryana indicates that intercropping one row of pearl millet
and paired rows of guar is better for realizing guar yield by
27.2% (with 8.7 q/ha additional yield of pearl millet) compared
to sole stand in paired row (Singh et al. 2003). At Bikaner,
Rajasthan, strip-cropping of pearl millet and guar in 4 : 4 row
ratio gives highest pearl millet yield over other combinations
(4 : 6 or 4 : 8 ratio) (Yadav 1992), whereas alternate row planting
(1:1) of pearl millet : guar is superior compared to other planting
systems (Singh and Joshi 1994). Although guar yield was
highest in 4:4 row strip-cropping of pearl millet: guar, but the
net returns were highest in alternate-row intercropping system
(Singh and Joshi, 1994a). At Bikaner, growing of guar and
pearl millet in 2:2 planting system has the highest yield
advantage (Yadav and Beniwal 2003). Similarly, uniform
planting of castor+guar in 1:1 ratio and application of 10 kg N
and 30 kg P2O5/ha to guar component has significantly yield
advantage in terms of yield and additional gross income (‘1564/
ha) over the sole crop of guar (Kumar 2009). Cowpea grown
with 1 or 2 rows between 2 rows of pigeonpea/maize/sorghum/
pearl millet may give 0.5-0.7 t seed yield/ha of cowpea without
affecting the yield of companion crop. However, seed yield of
pulses can be increased considerably (by 25%) by cultivating
1 or 2 rows of cowpea between paired rows of main crop
(Yadav 1992). Studies also reveals that highest land equivalent
ratio (1.532) was recorded with cowpea + amaranth (4:1) and is
at par with other intercropping systems viz., cowpea + okra
(4:1), cowpea + amaranth (5:1 and cowpea + okra (5.1) in Kerala
(Table 3). Trials at Bangalore during 2007-08 and 2008-09 also
reveals that cowpea + castor (6:1) has higher gross monetary
returns per ha (‘ 18020/- and ‘18090/- during both years)
compared to sole cowpea (‘15760/- and ‘15080/-) and other
cowpea based intercropping systems (Kumar 2009). A study
at Fatehpur-Shekhawati (Rajasthan) reveals that moth bean
mixed with pearl millet (moth bean at 2/3 seed rate + pearl
millet at 1/3 seed rate) gives the highest moth bean-equivalent
seed yield compared to other crops when mixed with pearl
millet (Shekhawat et al. 2002).
Input management: Based on two years’ data at CAZRI,
Jodhpur, application of 20 kg N and 40 kg P2O5 /ha has
263
Table 3: Region wise high input and low input technologies and common cropping systems for arid legumes
Guar
Sl. Regions/
No. districts
Technology Optimum
LITs/ HITs sowing window
Protection Improved Planting
priority
variety
inputs
Fertility
inputs
Cropping
sequences/rotation
Inter/Mixed
cropping
3
4
5
6
7
8
9
10
1 Churu
Jaisalmer
Bikaner
Badmer
LITs
June end to 25
July (within 2-3
days of 30-40
mm rainfall)
(ST-RR)
‘RGC-936’
‘RGM112’
‘ HG-365’
‘HG-563’
Line sowings, 60 × 10 cm,
hand weeding,
deep
interculture
up to 30-40
dos
Monocropping,
Guar – Guar,
Guar – Guar – Bajra,
Guar - Bajra
Guar + Bajra (3:1)
If delayed rains up to
1st week of August
Guar + Moth, Bean +
Bajra + Til + Cowpea
(25% seed of each
crop)
2 Ganganagar
Hanumangah
HITs
June end to midJuly
(within 2-3 days
of 30-40 mm
rainfall).
Beginning to mid
of March for
summer crop
ST-RR
ST-BLB
Full
protection
package
‘RGC1066’
‘RGC1031’
Line sowing, Full
fertility
25 × 5 cm,
package
deep
interculture
up to
30-40 days
full
production
package
Sole cropping
Guar – Mustard
Guar – Wheat
Crop substitutions:
Groundnuts Cotton,
Bajra
Limited mix or
intercropping
1 2
Rajasthan
3 Nagaur
HITs
Jodhpur Sikar
Jhaunjnu
June end to mid ST-RR
July
ST-BLB
(within 2-3 days
of 30-40 mm
rainfall).
Beginning to mid
of March for
summer crop
‘RGC1002’
‘RGC1003’
‘RGC1017’
‘RGC-936’
Line sowing,
45 × 10 cm,
deep
interculture
up to 40 days
4 Pali
Jalore
Jaipur
Bheelwara
Alwar
HTs
End of June to
mid of July (with
rains or preirrigation).
Beginning to mid
of March for
summer crop
ST-RR
ST-BLB
Full
protection
package
‘RGC1038’
‘HG-884’
‘HG-1031’
‘RGC-986’
Lime sowing, Fertility
40 × 10 cm, package
Deep
interculture
up to 40 days,
Production
package
Guar-Wheat
Guar-Mustard
HIT
End of June to
mid July,
Beginning to mid
of March for
summer crop
ST-RR
ST-BLB
Full
protection
package
‘HG-365’
‘HG-563’
‘HG-884’
Line sowing, Full
35-40×10 cm Fertility
Deep
package
interculture
up to 40 days
Production
package
Guar + Bajra (3:1)
Substitution: Cotton,
Bajra, Til, Groundnuts Guar + Sorghum (3:1)
Crop sequence:
Guar (‘HG-884’, ‘HG2-20’) – Wheat,
Guar (‘HG-365’, ‘HG563’) – Mustard,
for saline water
Guar - Wheat
LTTs
End of July to
ST-RR
mid of August. ST-BLB
Beginning to mid
of March for
summer crop
‘HG-365’
‘RGC-936’
‘GG-2’
‘GG-1’
Line sowing,
60 × 10 cm,
Deep
intercullure
upto 40 dos
Haryana
5 Sarsa
Hisar
Bhiwani
Rewari
Gujarat
6 Katch
Banaskantha
Urea spray Guar- Mustard
@1-2% at Guar – Guar
50-60
Guar – Bajra
DOS
Guar – Guar –
Bajra/Sorghum
Guar-B. tournifortii
Spray of Guar-Bajra , Guar
urea @1- (GG-1-Mustard
2% at 50- (irrigated)
60 DAS
North Gujrat :
Guar-Potato, Agrisilviculture system
Guar with P. cineraria
Guar : Bajra (3:1)
Guar + Bajra /
Sorghum (3:1)
Guar + Bajra (3:1)
Guar + Sorghum (2:1)
Mixed cropping for
North Gujrat
Bajra + Moth bean +
Cowpea + Guar (0.40,
3.0, 5.0 & 3.75 kg/ha
respectively)
264
Mothbean
1
2
Rajasthan
1 Jaisalmer
Bikaner
Barmer
Jodhpur
Pali
Sikar
3
4
5
6
7
8
9
LITs 5 July to 25 July (2-3
days of 30-35 mm rain
falls)
ST- ‘RMO-40’
RR ‘RMO-225’
‘RMO-435’
‘FMO-96’
‘CAZRI’
‘Moth-3’
Line sowing, 60×5 cm 1-2% urea spray
spacing
at 35-45 DAS
Deep interculture up to
30 DAS and hand
weeding
2 Churu
Nagaur
Ganganagar
Hanumangarh
Jalore
HITs End of June to mid July
(2-3 days of 30-40 mm
rainfall)
ST- ‘CAZRI’
RR ‘Moth-2’
‘CAZRI’
‘Moth-1’
Line sowing,
Full fertility
package
45 × 5 cm
Deep interculture,
Production full planting
package
3 Gujarat
Haryana
LTTs Mid July to end of July
ST- RMO-40
RR CAZRI
Moth-3
Line sowing,
40 × 10 cm
deep interculture
10
Sole cropping
Moth beanGuar
Moth bean Mustard
Mono cropping
Strip cropping
Moth bean + C.
ciliaris
Mixed cropping Bajra
(? ) + Moth bean (? )
seed
Sole cropping Moth bean + Bajra
Moth bean - and Castor (1:3 and
1:1)
Mustard
Moth bean – Moth : Bajra
Wheat
(3:1 or 2:2)
Moth bean Sunflower
Urea spray
@1.0% at
flowering
Horsegram
1
Southern states
Andhra
LIT/
Pradesh
HIT
2
Karnatka
3
Beginning to mid ST against
July
anthracmose
(ST-Anth)
ST-RR
‘CRIDA-118R’
‘Palm-1’
‘Palm-2’
HIT
First fortnight of ST-Anth
August
ST-RR
‘PHG-9’
‘CRIDA-118R’
Tamil Nadu
LIT
Second fortnight ST-Anth
of Oct. to second ST-RR
fortnight of Nov.
‘Paiyur-2’
4
Madhya
Pradesh and
Chattisgarh
LIT
Second fortnight ST- Anth
of June to first
ST-RR
fortnight of July
‘AK-42’
‘AK-21’
5
Orissa &
Jharkhand
LIT
3rd week of
August to Ist
week of Sept.
(late kharif)
‘AK-21’
‘AK-42’
6
Hill region
HIT
First fortnight of ST-Amilha
June
with
complete
package
ST-Anth
ST-RR
VLG-15
VLG-19
Line sowing,
35 × 5 cm
late planting
30 × 5 cm with
production package
Line sowing,
35 × 5 cm
Deep interculture up
to 40 days with
production package
Line sowing,
37.5 × 10 cm
spacing Preimergence herbi
cide (Basalin/ @ 1.5
kg ai/ha)
Line sowing,
40
× 10 cm spacing
to 40 days and
hand weeding
Protection
package
Kharif Maize –
winter Horsegram
Kharif Sorghum –
winter Horsegram
Protection
package
Horsegram – Finger
millet
Ragi - Horsegram
-
Horsegram – Finger
millet
Ragi - Horsegram
-
Kharif Maize –
winter Horsegram
Kharif Sorghum –
winter Horsegram
Kharif Finger millet
– winter Horsegram
Upland RiceHorsegram
Maize-Horsegram
Ragi-Horsegram
Line sowing,
40
× 10 cm spacing
to 40 days and
hand weeding
Line sowing,
Full
30 × 5 cm spacing fertility
package
with full planting
package
Mono crop
Hybrid Sorghum +
Groundnut (2:2) + 1
row of Horsegram
when grounds at
flowering
Maize + Horsegram
(1:1)
Horsegram + Ragi
(6:1)
Horsegram + Castor
(6:1)
Maize + Horsegram
(1:1)
Horsegram + Ragi
(6:1)
Horsegram + Castor
(6:1)
Niger + Horsegram
(2:1)
Castor (VI-1) +
Horsegram (1:1)
Marvel grass +
Horsegram (1:1)
Amaranthus+
Horsegram
Horsegram + Finger
millet + Horsegram
+ Groundnuts
Cowpea
1
Southern States
Andhra
LIT
Pradesh
HIT
HIT
Kharif- June end
to July end
RabiOct-Nov
Summer
Whole March
ST-RR
‘Co(CP-7)’
ST-Anth
Full package
plant
‘Co(CP-7)’
protection
‘Co (CP-7)’
Line sowing,
40 × 15 cm
Fertility
Deep interculture and package
hand weeding
Line sowing:
30 × 10 cm whole
package
Rice-Cowpea
Rice-fellow-summer
Cowpea
Rice-Rice-Cowpea
Pigeon pea (PP) +
Maize + Sorghum
(2 rows) + Cowpea
(2 rows)
1
2
2
Tamil Nadu
3
LIT
4
5
Kharif: June end ST-RR
to July end
ST-Anth
Rabi:
Oct-Nov to
Summer
Whole March
6
‘Co (CP-7)’
7
Line sowing,
40 × 15 cm
Deep interculture and
hand weeding
Line sowing:
30 × 10 cm whole
package
8
-
9
Rice-Rice-Cowpea
3
Karnatka
LITs
June end to July ST-RR
end coconut floor ST-Anth
– May – Sept.
‘KBC-2’
Line sowing,
30 × 15 cm spacing
deep interculture and
hand weeding
-
4
Kerala
LITs
Homestead
garden whole
year
‘Subhra’
Line sowing,
45 × 10 cm spacing
deep interculture and
hand weeding
-
Kharif- Sole
Cowpea
Rabi: Sole Cowpea
Summer- Sole
Cowpea
Maize + Cowpea –
Maize
Cowpea – Maize +
Cowpea
‘V-585’
‘V-240’
Line sowing,
30×10 cm
deep interculture
Production package
‘RC-101’
‘GC-31’
Line sowing,
30x5 cm
hand weeding
‘RC-101’
‘GC-3’
Line sowing,
60 × 10 cm
hand weeding
5
Northern and Eastern
Hills, UP,
HITs Kharif:
Punjab and
Beginning to
Bihar
mid-July
LITs
Summer18-31 March
ST-RR
ST-Anth
with
protection
package
ST-ALS
6
Rajasthan
LIT
KharifFirst week of
July to end of
July
Summer- Mid
Feb to end of
March
ST-RR
7
Gujarat
HIT
Mid July to mid
August Summer
Mid Feb
ST-RR
ST-Anth
GC-3
GC-5
Fertility
package
Maize – Wheat –
Cowpea
Cowpea – Mustard/
Wheat – Moong
bean
1-2% urea Berseem +
Japanirye – Jowar +
spray at
flowering Cowpea
Maize + Cowpea
Jowar + Cowpea
Bajra + Cowpea
1-2% urea Cowpea – Mustard –
spray
summer fellow
Cowpea – Mustard
summer Moong
bean
Line sowing,
Fertility
package
40 × 10 cm Deep
interculture
Production package
Cowpea – Wheat –
Moong bean
Maize – Wheat Cowpea
265
10
Paired rows of PP
+ Maize +
Sorghum + ½ rows
of Cowpea
Bhindi + Cowpea
(1:1)
Amaranthus +
Cowpea (1:4)
Cowpea + Ragi
(5:1)
Cowpea + Castor
(6:1)
Cowpea +
Amaranth (4:1)
Maize paired rows
+ Cowpea
Cowpea +
Sorghum (1:1)
Cowpea + Pigeon
pea (2:1)
Bajra + Moth bean
+ Cowpea + Til +
Guar (0.90 + 3.0 +
5.0 + 3.75 + 0.10
kg/ha resp.)
Paired row’s of
Maize + 1 row of
Cowpea
Intercropping
Pigeon pea +
Cowpea (2:1 or
2:2)
ST- RR: Seed treatment against root-rot: 2-3 g Thiram/Bavistin per kg seed, ST-Anth: Seed treatment against Anthracenose fungal disease of
horsegram and cowpea: Mancozeb/Benomyl @ 2-3 g/kg seed; ST-BLB: Seed treatment against Bacterial leaf blight disease of guar: 100 ppm
streptocycline for one hr; Ful l package of plant protecti on: Alternaria Leaf Spot: Spray of Mencozeb @0.2%. BLB: Spray of streptocycline @
150 ppm + 0.2% vitavax. Cercospora Leaf Spot (CLS): Spray of Dithane M-45 or Dithane Z-78 (ai) @ 0.2%. White flies: Spray rogor @0.02%;Full
package of fertility: 20 kg N + 40 kg P 2O5 + 20 kg ZnSO4 per ha, + PSB, 1-2% urea spray at flowering; Ful l package of planting: Chemical
weedicide: basalin @ 1 to 1.5 kg/ha ai pre-plant application by dissolving in 700 liter water/ha + 1 handweeding, one light suppl. irrigation at pod
formation stage
increased seed yield of guar substantially (25.2%) while water
use efficiency was much higher (21.7%) over that of control
(Singh and Khan 2003). Work at Bikaner reveals that 40 kg
P2O5/ha increases seed yield of guar (by 38.4 and 22.7%
compared to control and 20 kg P2O5/ha, respectively). Similarly,
three years (1992-94) study at S. K. Nagar reveals that guar
fertilized with 40 kg P2O5/ha records the highest seed yield
(by 28% over control). Similarly, at Agra, application of 60 kg
P2O5/ha increases seed yield of guar (by 38.1 and 13.6% over
control and 30 kg P2O5/ha respectively). At Gwalior, guar yield
increases with each successive increase of 20 kg P2O2 up to a
level of 60 kg P2O5/ha and increase in yield at this level is
much higher (44.9%) than control. Besides this, gum and
protein contents are also increased (Bhadoria et al. 1997). It is
also reported that application of 60 kg P2O5/ha is optimum for
higher seed yield by 27.3 and 11.9% over control and 30 kg
P2O5/ha, respectively (Kumawat and Khangarot 2001).
Guar grown on S-deficient soil of Gwalior, responds up
to a level of 40 kg S/ha in terms of growth, yield attributes and
seed yield. The yield increase at 40 kg S/ha is shown to be
optimum, respectively. Based on three years’ (1992-1994)
results at Bawal, application of 20 kg S/ha in guar crop resulted
in significantly higher seed yield (1.212 t/ha) over control
(10.82 q/ha) (1 q=100 kg).
266
Micronutrients studies conducted at Hisar, Bawal,
Durgapura and Gwalior on guar reveals that at all the locations,
one spray of 0.5% ZnSO4 either at 25 or 45 DAS gives higher
seed yield over control but is statistically equivalent to soil
application of ZnSO4 @ 25 kg/ha. A result from S. K. Nagar
also shows that cowpea may be applied with 25 kg ZnSO4/ha
as basal dose to obtain higher net returns and BCR. If Zn is
not applied at sowing, it can be alternatively applied in 2 sprays
at 25 and 45 DAS. At Bangalore, soil application of 25 kg
ZnSO4/ha, gives higher yield, followed bycombined application
of ZnSO4 and FeSO4 and 2 sprays of 0.5% ZnSO4 at 25 and 45
DAS. Application of Fe and Zn in moth bean also brings
significant increase in yield at Hisar. Optimum doses of Fe
and Zn also help in less infection of guar against Alternaria
leaf spot (Gupta and Gupta 1999).
Maximum seed yield of guar at Gwalior is obtained when
the crop receives 3 irrigations each at vegetative, flowering
and seed development stage. However, 2 irrigations at
flowering and seed development stages are optimum at
Durgapura, whereas one irrigation at seed development at S.
K. Nagar while that at flowering stage at Bawal is sufficient to
obtain higher seed yield.
Weed control: Season-long competition of weeds in guar may
cause reduction in yield by 30 to 50% and the losses may also
go up to 70-90%. Critical crop-weed competition lies between
15 to 30 DAS in loamy sand and sandy loam soils. Thus,
removing weeds at 20 or 30 DAS is helpful in raising pods/
plant, water use efficiency and seed yield (Yadav 1998).
Studies conducted in different agro-climatic zones/soil types
have established the superiority of mechanical/cultural means
over chemicals (Kumar et al. 1996). The work on chemical
weed control also indicates that pre-plant incorporation of
fluchloralin 1.0 kg a.i./ha (Basalin) is an effective herbicide to
control weeds in guar (Yadav et al. 1998) under rainfed
condition at Bawal, Haryana and is at par with hand weeding
at 30 DAS (Kumar et al.1996). Increase in seed yield due to
one hand weeding and fluchloralin is 49 and 45% respectively,
over weedy check. Two hoeings and fluchloralin at 1.5 kg/ha
are also useful at Hisar, Haryana with higher seed yields of
1.89 and 1.84 t/ha and net income of ‘2900 and 2845/ha,
respectively over control (Yadav et al. 1997). However, the
weed control efficiency of the two hoeings (80.7%) and
fluchloralin at 1.5 kg/ha (79.9%) are better over those of than
one hoeing (64.2%). Pre-plant incorporation of fluchloralin at
1.0 kg a.i./ha is beneficial for 84% reduction in weed population
but seed yield was at par with one or two hand weedings,
whereas pendimethalin causes phytotoxic effect in guar
(Yadav et al. 1998).
Seed inoculation: Inoculation of guar seed with VAM fungi
has improved dry matter production and seed yield (Rao and
Tarafdar 1993). Similarly, inoculation of seed with arbuscular
mycorrhizal fungus (AMF) and Rhizobium culture also
significantly improve nodules, dry weight and seed yield of
guar over no inoculation, and further application of FYM @ 4
t/ha along with inoculation of AMF and Rhizobium culture
has beneficial effect on seed yield over NP application (20 kg
N and 40 kg P2O5/ha) (Tarafdar and Rao 2001). Increase in
yield through Rhizobium ranges from 8 to 15% depending
upon the intensity and distribution of rainfall (Rao 1995). In a
field study conducted at Jaipur, Kumawat and Khangarot (2001)
gets higher guar seed yield by Rhizobium inoculation along
with increased N, P, S, protein and gum content (in guar seed)
and higher total uptake of N, P and S (Kumawat and Khangarot
2002). Three years study reveals that sole FYM or sole seed
treatment with PSB may not influence seed yield of cowpea,
bu t their combined applicatio n increases seed yield
significantly over control. Results of multilocation trials
conducted on integrated nutrient management at Hisar,
Gwalior, Durgapura, S. K. Nagar and Bawal to know the
response of Rhizobium inoculation and PSB in guar indicates
that inoculation with both Rhizobium and PSB is helpful in
increasing seed yield (by 22.35%).
Plant Protection
Arid legume diseases: Powdery mildew [Erysiphe polygoni
DC, Sphaerotheca fuliginea (Schlect) Pollacci, Leveillula
taurica (Lev) Arnaud] is an important disease of cowpea and
horsegram, particularly, in southern India. Sometimes guar
may also show infection particularly, in late sown conditions.
Appearance of dirty-white, circular, floury patches at the time
of pod formation and maturity are common symptoms (Gupta
and Rohilla, 2008). Similarly, root rot complex [Macrophomina
phaseolina (Tass) Goid, R. solani, Fusarium caeruleum,
Sclerotium rolfsii, Neocosmospora vasinfecta] is also
common. Among root diseases, root rot and wilt are major
diseases. In case of the former, root tissues are decomposed,
preventing normal growth of host plants. In case of latter,
leaves and green parts lose their turgidity, become flaccid and
ultimately fall down. R. solani is highly pathogenic in guar
and may kill 90% seedlings within three weeks. F. caeruleum
may cause wilting to almost 78% plants. Dry root rot is also
attributed to high soil temperature (35-40°C) and low soil
moisture content (0.5-1%).
Yield losses due to anthracnose [Colletotrichum
lindemuthianum Sacc & Mogn. (Bri & Cav.)] may range from
35 to 50% (Gupta, 2009). Initially water soaked lesions appear
on the pods, later-on, become brown and enlarging to form
circular spots of varying size. In more severe conditions, the
infected leaf petiole and stem parts may wither off. Seedlings
very often are blighted due to infection soon after the seeds
germinate (Gupta and Rohilla, 2008). Similarly, in Cercospora
leaf spot [Cercospora canescens Ell. & Martin, C. Dolichi Ell
& EVr], two pathogens viz., C. canescens and C. cruenta have
been reported to cause leaf spot in cowpea growing areas of
Fiji, Kenya, Brazil, Nigeria, Zimbabwe, India, Bangla Desh,
Iran, Malaysia, Thialand (Saxena et al. 1998). The disease is
favoured by humid weather and the spots is spread by wind
and water splash.
Fusarium wilt [Fusarium oxysporum f sp trachciphilum
(Smith) Syn. & Han.] in another important disease where more
than 6 species of Fusarium is reportedly associated with
cowpea seeds, seedlings and adult plants that cause wilt at
different stages of plant growth. Cation exchange-capacity
(CEC) may play an important role in management of this
disease in soil. It is more severe in nutrient-deficient soils
with CEC 4.9. The higher CEC nullifies the adverse effect of
this fungus when used as seed treatment (Monga and Grover
1993). Similarly, in alternaria leaf spot [Alternaria cyamopsidis
Rangaswami & Rao. A. cyamopsidis (Ell. & Ev.) Elliot], heavy
yield losses to the tune of 50-55% are reported. It appears
every year in mild to severe form and the pathogen is seed
born in nature. Disease infection is observed more in late
sown conditions, high humidity (70-90%) and moderate
temperature range of 25-31°C. Early sown crops particularly,
during last June may escape from this disease. Bacterial
disease like, Xanthomonas axonopodis pv. vignicola; X.
campestris pv. cyamopsidis Dye and X. phaseoli has the
favourable temperature range of 25-30% and relative humidity
of 70-90%. As the bacterial pathogen is seed born, the disease
appears both as leaf spot and blight simultaneously. Disease
spots appear intraveinal and are round, water soaked or oily
in appearance and well defined on the dorsal surface of the
leaves. Black streaks which develop later on, may lead to
cracking of stem and these black streaks may also develop on
pods.
With regards to viral diseases, cowpea mosaic virus
(CMV) is very important as yield reductions to the extent of
40-100% is reported Two strains of this virus i.e., CPMV-Sb
and CPMV-Vu are reported (Agarwal 1964). Under natural
condition, two distinct types of necrotic local lesions or
chlorotic local lesions are observed. The virus is readily sap
transmissible. The diagnostic sp includes cowpea cv. Black
Eye and Early Ramshon and Chenopodium quinoa. Similarly,
mungbean yellow mosaic (MYM) disease may appear at any
stage of plant growth. If it appears in initial stage, plants may
not flower and yield losses may reach as high as 90%. High
incidence of yellow mosaic in early sown and late sown moth
bean is probably due to higher population of white fly in the
field. Guar is found most effective trap crop for checking the
white fly. Moth bean variety CAZRI Moth-1 is also reported
possessing resistance to yellow mosaic.
Arid legume pests: Colonies of aphids (Aphis craccivora
Kock) are found on leaves, stems and pods of guar and
cowpea. The pest is most effective in early growth of plants
(July-September), having several generations during the
cropping season. Nymphs and adults suck cell sap from lower
surface of the leaves, top shoots; consequently plants become
discolored and weak. Similarly, in case of Pod borer
[Helicoverpa armigera or H. Vitrata (Hübner)], young larvae
267
feed on the foliage and later on damage flowers buds, pods
and feed on developing seeds inside pods and may reduce
seed yield to the tune of 60%. A single larva may destroy 3040 pods before reaching maturity. There may be as many as 8
generations in one year. Other pests include white flys [Bemisia
tabaci (Gennadius)] where, both nymphs and adults feed on
cell sap from under surface of the leaves. White flies also act
as vectors for spread of viral diseases. These flies also excrete
honeydew on which black moulds grow interfering with
photosynthesis. Leaf perforator [Dichomeris ianthest (Mery)]
is also an important pest of guar as the newly emerged larvae
crawl and ultimately settle down near mid rib or near thick leaf
vein, spinning a web there. There is more damage due to this
pest in early stage of crop growth. White grub (Holotrichia
consanguinea Blanch.) is destructive and a polyphagous pest
for which killing of adults through trapping in light lamp is
easiest way. The fungus Metarrhizum anisopliae (Mestch)
has been fo und pathogenic o n the adults. Bruchi ds
[Collosobruchus maculates Fab., C. chinensis (L.), C. analis
Fab.] are also seen in cowpea, moth bean and horsegram seeds.
Remedial measures against the above major diseases and
insect pests is given in Table 4.
Quality of arid legumes
Cooking is known to improve the palatability and
nutritional quality of food legumes. However, prolonged
cooking may result in decreased protein quality and loss of
vitamins and minerals. Dehusking and splitting of pulses into
cotyledons reduce the cooking time considerably. In certain
legumes like horsegram, this practice is also not effective.
Pressure cooking, addition of chemicals to cooking water or
soaking of common legumes in solution of chemicals have
been recommended for reducing the cooking time or producing
instant cooking of beans. Soaking and germination of
horsegram and moth bean reduced the levels of tannin and
phytase. Native and germinated legumes can be incorporated
in bakery products to increase the protein level (Deshpande
et al. 2005, Ghatage et al. 2005, Rodge and Wankhede 2003).
Resistant Starch (RS): Legumes contain substantially higher
levels of resistant starch (RS) than do cereal seeds, flours and
seed-based food products. They (RS) have drawn broad
interest worldwide for both their functional properties and
potential health benefits. Initial clinical studies have
demonstrated that the same have properties similar to dietary
fiber and promising physiological benefits in human beings
which may result in prevention of various diseases. There are
four types of resistant starch RS1, RS2, RS3, and RS4. Legumes
come under RS1 which resists digestion, protects from
digestive enzymes by other components normally present in
a matri x of typical starch sources. Among the bestcharacterized forms of RS are those which are derived from
legumes. The RS provide many health benefits such as
improving bowel health, blood lipid profile, and increased
268
Table 4: Important diseases and insect pests in arid legumes and their remedial measures
Sl. No. Diseases
1
Root rot complex
Dry root rot
Wilt
Crops
Guar
Cowpea
2
Cowpea
Horsegram
3
4
5
6
7
Anlhraconose
Remedial measures
Seed treatment in
Bavistin (0.2%),
Vitavax (0.2%)
Fungicide spray of
Brassicol (0.2%)
Foltaf (0.2%)
Tiride (0.2%)
Seed treatment for
Carbendazim @ 2 g/kg
seed
Mancozeb /Thiram/
Benomyl @ 2-3 g/kg
seed
Foliar spray of
Benomyl (0.2%)
Mancozeb (0.2%)
Cercospora leaf spot Cowpea
Seed treatment of
Horsegram Moth Captan/thiram @ 2.5
bean
g/kg seed
Foliar spray of
Benomyl (0.2% ai)
Dithane M-4.5 (0.2% ai)
Dithane Z-78 (0.2% ai)
Powdery mildew
Cowpea
Foliar spray of
Horsegram (in
Tridemorph (0.05%)
south India, in
Kerthane (0.5%)
late sown
Calixin (0.1%)
condition)
Benomyl (0.2%)
Alternaria leaf spot Guar
Seed treatment of
Cowpea
Thiram/Dithane Z-78/
Horsegram
illex @ 0.3%
Foliar application of
Iprodione (0.2%) soil
application and foliar
application of or Zn in
combination
Seed treatment of
Bacterial leaf blight Guar
(BLB),
Cowpea
Ceresin wet (0.2%)
Bacterial Leaf Spot
Thiram (0.2%)
(BLS)
Streptocycline (0.1%)
Foliar spray:
Two spray of ZnSO4
at25 & 45 DOS (0.5%)
Streptocycline spray
(250 ppm)
B. subtilis + white
sterile fungs
Mung bean
Cowpea Moth
Foliar spray of
yellow mosaic virus bean
Rogor (0.2%)
(MYV)
Methaldemeton (0.1%)
micronutrient absorption (magnesium and calcium) in the
colon. These factors may affect the risk of developing diseases
su ch as co lorectal cancer, cardio vascular diseases,
osteoporosis and inflammatory bowel diseases. Increase in
fecal bulk RS are important in preventing constipation and
hemorrhoids and in diluting potentially toxic compounds that
might promote the formation of cancer cells. RS may be of
benefit to healthy individuals who are trying to achieve and
maintain a healthy body weight. RS containing foods have
low glycogenic index thus release of glucose is slow, resulting
References
Lodha and
Sharma (2000)
Insect pests
Aphids
Crops
Guar
Cowpea
Remedial measures
Malathion spray (0.05%)
Rogor spray (0.02%)
Use of entomogenous
fungi
Entomophthora,
Cephalosporium
Use of predators:
Coccinellids, syrphids
lace wings.
Neem based insecticide
like nimbecidine
Release of egg parasitoid
Trichodermona chilonii
@ 0.3 million adults/ha
Release of larval
parasitoid Campolestis
chloridaeuchida
Gupta and
Rohilla (2008)
Pod borer
Cowpea
Moth bean
Gupta et al.
(1998)
Leaf perforator Guar
Spray malathion @0.05%
Endosulfan @ 0.07%
Gupta and
Rohilla (2008)
Whitfly
Cowpea
Moth bean
Horsegram
Foliage spray of
Dimethoate 30 EC @
250 ml or oxydemeton 25
EC @ 300 ml/0.4 ha
Gupta et al.
(1999)
White grub
Guar
Dusting with methyl
parathion @ 2% or
Spraying quinolphos @
0.05% for killing beetles
Use of pathogenic fungus
Metarrhizum anisopliae
(Meslch)
Gupta et al.
(2007)
Lodha (2001)
Bruchids
Cowpea
Moth bean
Horsegram
Store seed at moisture
content (10.0%)
Seed treatment with
neem leaves
Seed treatment with
edible oil content @5-6
g/kg seed
in lowered insulin response and greater access and use of
stored fat. This helps in management of diabetes and impaired
glucose tolerance. Hence, it is used in the treatment of obesity
and weight management (Rodge 2009).
Guar Gum: Guar gum is a white to yellowish-white powder
and is nearly odorless. Fine finished guar gum powder is
available in different viscosities and granulometries depending
on the desired viscosity development and applications. Guar
gum is a natural high molecular weight hydrocolloidal
polysaccharide composed of galactan and mannan units
combined through glycosidic linkages, which may be
chemically described as galactomannan. Guar gum is a cold
water soluble polysaccharide. This ability to hydrate without
heating makes it very useful in many industrial uses. Dissolved
guar gum forms a line of high viscosity and viscosity is a
function of temperature, time and concentration. Solution with
different gum concentrations can be used as emulsifiers and
stabilizers because they prevent oil droplets from coalescing.
Guar gum is also used as suspension stabilizer and is an
economical thickener and stabilizer. Different guar gum
powders are manufactured as per their industrial uses such as
thickening, texturing, stabilizing and enhancing suspension.
There are two types of guar powders viz., food grade guar
gum powder which is used in industries like food, cosmetics,
pharma etc. Here the particle size of food grade gum powders
are 200 mesh having 2000-5000 viscosity (cP) and with 300
mesh having 3500-5000 cP values. The other one is industrial
grade gum powder which is used mainly in industries like
paper, mining, explosive, fire fighting, oil drilling etc and are
available with particle size of 100 mesh with viscosity values
of 3000-7000 cP.
Minimum standard for good quality guar gum as defined
by US FCC and by European Union Specification includes
moisture 14.0% maximum (max.) ash 1.5% max., acid insoluble
residue 47% max., galactomannan 75.0% min, protein 7.0%
max., arsenic 3 ppm max., lead 10 ppm max., zinc 25 ppm max.,
copper and zinc 50 ppm max (Rodge 2008).
Guar gum is used in almost all systems where water is
an important factor. Food industries share almost 30-40%,
petroleum and mining industries share around 20-25% and
textile industries share almost 18-20% of total guar gum
consumption (Table 5).
Export of guar gum: India exports almost 75-85% of its guar
gum including other derivatives annually. India exported 0.258
million tonnes of guar gum worth ‘ 139 million during 2008-09.
The same dramaticallyincreased to almost 0.35 m t worth ‘28000
Table 5. Use of hydrocolloids in food products
Dairy industry
Beverages
Bakery products
Confectionery
Meat and fish
products
Ice-cream stabilizer, milk shake, Ice milk (Soft icecream), ice pops and water ice chocolate, milk
drink, flavoured milk drinks, instant desert
puddings, cooked desert puddings, cottage cheese,
cheese spread, whipped cream yoghurt
Soft drinks with fruit pulp, soft drinks, fruit juices
and nectars, foam stabilizer, beer clarification,
wines, juices and vinegar, aging of spirits
Bread doughs, cake batters, fruit cakes, yeast raised
dough nuts, pipe fillings, fruit fillings, bakery
jellies, flat icings, cookies, frozen pies fillings
Candy jells and jellies, Frozen confection, candy
glaze, chewing gums, cough drops, gum drops,
candy mints
Fish preservation, canned fish, meat and poultry
Coated jellied meat, preservative coating for meat
and poultry, synthetic meat fibers and products
Source: Halis and Feramuz (2007)
269
million during 2010-11 (Fig 1). It is due to increased demand
of guar gum from USA to 0.45 m t annually against 0.075 m t
earlier due to increased demands from drilling of petroleum
fields in middle east countries. During 2006-07, top importing
countries of guar products were USA followed by China,
Germany, Italy and Netherlands.
3000
2500
2000
1500
1000
500
0
Fig 1. Exports of guar gum from India (Source APEDA)
Guar meal: The germ and outer seed-coat of guar seed
together constitute guar meal. Removal of gum from guar seeds
increases the protein content of the residual byproduct, i.e.
guar meal. It is light, grayish material with beany flavour. The
guar seeds result in 62-68% of guar meal having a rich source
of protein content by about 35-46%. It contains about 1.5
times more protein than guar seed and is compared well with
other vegetable protein sources like oilseed cake used in
poultry diets. The proximate composition and nutritive value
of defatted guar meal, protein isolates and protein concentrate
are best for monogastric animals. It is observed that the guar
oil contained 55.1% linoleic acid compared to 51.8% in
sunflower oil. The total unsaturated fatty acids are 78.7 and
92.0% in gaur and sunflower oil, respectively. Guar oil
contained 3.36% linolenic acid. The iodine value and refractive
index of the guar oil is well comparable with that of sunflower
oil.
Arid legumes in non-traditional areas: Frequent climate
changes, need for more production and enhanced international
demands for industrial products have prompted to explore
the possibility of introducing these crops in newer regions
and seasons. Development of early maturing varieties (60-65
days) of cowpea (RC 101, PGCP-3) has helped in introduction
of this legume as summer cowpea in northern western plain
zones and foot-hills with 3-4 irrigations only. Seed yield
obtained to the tune of 700-1000 kg/ha support their successful
acceptance in these regions. Similarly, field trials of guar during
rainfed rainy season of 2011 and summer season (5 irrigations
including pre-sowing) of 2012 at CAZRI, Jodhpur have given
new concept of guar cultivation in summer season (Table 6).
Seed yield of guar can be increased by 2.5-3 times, gum content
by 1.7%, guar gum yield by 320 kg/ha and viscosity of guar
gum by 339 cP in summer over rainy season. It has been due
to favorable climatic conditions and controlled availability of
soil moisture, proved useful in reducing diseases and insect
270
Table 6. Seed yield, gum content and viscosity of guar genotypes during rainfed conditions and summer irrigated conditions
at CAZRI, Jodhpur during two seasons
Genotypes
RGC-1066
RGC-986
RGC-936-1-5-1
HG-884
HGS-563
RGC-936 (Ch.)
Mean
C.D.(0.05)
*
Seed yield (kg/ha)
Kharif 2011
Summer* 2012
487.19
1021.0
483.75
1275.0
495.94
1916.67
534.38
1520.83
482.81
1950.00
546.25
1439.58
505.0
1520.5
36.11
584.41
Kharif
Gum content (%)
2011 Summer* 2012
29.41
30.57
28.70
30.05
27.90
30.98
29.09
31.29
29.14
29.70
29.24
31.17
28.9
30.60
NS
1.36
Viscosity of guar gum (cP)
Kharif 2011
Summer* 2012
3522
3639
3402
3440
3169
3231
3130
3474
3015
3226
3336
4600
3262.3
3601.6
NS
902.34
5 irrigations including pre-sowing irrigation, no fertility and plant protection measures were adopted
pests infection and leading to better source sink relationship.
Guar is successfully supplementing rainfed groundnut in
Annantpur and other parts of Rayalsema region, Government
of Andhra Pradesh is supporting guar in traditional rainfed
groundnut regions. It has been successfully adopted during
summer 2012 in Yavatmal and nearbydistricts of Maharashtra.
There are still other examples like Vadodra (Gujarat), Raipur
(Chattisgarh) where guar is being cultivated.
Future strategies


Increasing productivity of arid legumes by about 3-5
folds over existing levels for each drop of water, unit of
time, area and inputs, to face the ever increasing demands
of these crops.
Development of guar varieties maturing in 70-75 days
so as to prevent from losses caused due to terminal
stress and varieties must have inbuilt resistance
potential against root rot and bacterial leaf blight
diseases.

Development of cowpea varieties wit h mo re
synchronous maturity and compact plant types having
60-70 days maturity, resistance against CYMV, with
colorless, bold seeds for table purpose.

Curtailing maturity period of horsegram to 75-80 days
with improved conversion having resistance against
anthracnose and powdery mildew diseases in addition
to showing thermo-in sensitivity.



The value added products/derivatives like hydroxyl
propyl used in oil drilling field may be taken up. Similarly
in food applications hydrocolloids, stabilizer food
additives may be developed and exported.
Developing sustained production technology for
subsistence farming community and corporate world
as well.
Finding out remedial measures against anti nutritional
factors in horsegram seed, making it alternative source
of edible food pulses particularly for tribal areas and
popularization of its medicinal and food values.

Exploiting Bt technology against pod borer (Armiigera
vitrata) and developing Bt cowpea lines.

Developing non-conventional breeding strategies for
transferring desired traits from wild sp. C. serreta L.
into cultivated sp. C. tetragonoloba L. for early maturity,
more pods and resistance against BLB.

Diversifications of agriculture through arid legumes and
introduction of these crops in non-traditional regions
and seasons like guar in Ananthpur district, guar is
summer season in canal command area and summer
cowpea in northern and hilly region.
REFERENCES
Agarwal HO. 1964. Identification of cowpea mosaic virus isolates.
Mededalingen van de Landbouwhoge School te Wageningen 64: 5363.
Aggarwal VD. 1987. Selecting copwpea varieties for harsh, semi-arid
sahilian areas. Tropical Agriculture 8: 2.
Andargie M, Pasquet RS, Gowda BS, Muluvi GM and Timko MP. 2011.
Construction of a SSR-based genetic map and identification of QTL
for domestication traits using recombinant inbred lines from a cross
between wild and cultivated cowpea [V. unguiculata (L.) Walp.].
Molecular Breeding 28: 413-420.
Bhadoria RBS, Tomer RAS, Khan H and Sharma MK. 1997. Effect of
phosphorus and sulphur on yield and quality of clusterbean
(Cyamopsis tetragonoloba). Indian Journal of Agronomy 42: 131134.
Chinnaswamy A, Govindan G, Michal K, Raghvan K, Balamurugan S,
Vaiyapuri P and Nair S. 2011. Phylogenetic diversity of Rhizobia
associated with horsegram [M. uniflorum (Lamm) Verdc] grown in
south India based on ginll, recA and 16S-23S intergenic sequence
analysis. Current Microbiology 62: 1230-1238.
Choudhary K, Singh M, Rathore MS and Shekhawat NS. 2009. Somatic
embryogenesis and in vitro plant regeneration in moth bean (V.
aconitifolia Jacq Marechal): a recalcitrant grain legume. Plant
Biotechnology Reports 3: 205-211.
Citadin CT, Ibrahim AB and Aragao FJ. 2011. Genetic engineering in
cowpea (V. unguiculata) history, status and prospects. GM Crops 2:
144-149.
Deepak P, Singh S, Gupta PP, Singh J and Yadav G. 2003. High frequency
multiple shoot regeneration from colyleclonary nodes of guar [C.
tetragonoloba (L.) Taub]. In vitro Cellular & Developmental
Biology 39: 384-387.
Deepak P, Singh Subhdra, Gupta PP, Singh J and Kadyan SPS. 2005.
Callus introduction and de novo regeneration from callus in guar
[C. tetragonoloba (L.) Taub]. Plant Cell Tissue & Organ Culture
80: 209-214.
271
Kumar D. 2003. Drought tolerant germplasm of moth bean CZM-32
(IGNR No. 01024, IC No. 296803). ICAR Newsletter 9: 6.
Kumar D. 2005. Status and direction of arid legumes research in India.
Indian Journal of Agricultural Sciences 77: 375-391.
Deshpande HW, Wankhede DB, Ghatge PU and Rodge AB. 2005. Studies
on sensory qualities of cake supplemented with native moth bean,
horsegram and cowpea flours. Journal of Arid Legumes 2: 251-253.
Kumar D. 2008. Arid legumes-an introduction. In: D Kumar and A
Henry (Eds), A Souvenir, 3rd National Symposium on Arid Legumes,
June 28-30, 2008, Indian Arid Legumes Society, CAZRI, Jodhpur,
India. Pp 25-44.
Faroda AS and Singh Raj 2003. Crop production strategies for arid
legumes with remunerative efforts and cost. In: A Henry, D Kumar
and NB Singh (Eds), Advances in Arid Legumes Research, Scientific
Publisher, Jodhpur, India. Pp 161-171.
Kumar D and Dixit GP. 2004. Genetic improvement of minor pulse
crops - retrospect and prospects. In: M Ali, BB Singh, S Kumar And
V Dhar (Eds), Pulses in New Perspective, Indian Society of Pulses
Research & Development, IIRR, Kanpur, India. Pp 112-131.
Gautam PL, Sharma GD, Srivastva YC and Singh BM. 2000. Twenty
glories years of NBPGR (1976-1996). National Bureau of Plant
Genetic Resources, New Delhi. 333 pp.
Kumar Virender 2009. Perspective production technology of arid
legumes. In: D Kumar and A Henry (Eds),A Perspective Research
Activities of Arid Leguems, Indian Arid Legumes Society, CAZRI,
Jodhpur, India. Pp 119-155.
Ghatage PU, Wankhede DB, Rodge AB and Deshpandey HW. 2005.
Studies on utilization of moth bean, horsegram and cowpea malt
flour in baked products (cookies). Journal Arid Legumes 2: 348350.
Gupta PP. 2009. Status and direction of Plant Pathology research in
India. In: D Kumar and A Henry (Eds), Perspective Research
Activities of Arid Legumes in India, Indian Arid Legumes Society,
CAZRI, Jodhpur, India. Pp 156-200.
Gupta PP, Arora RN and Gandhi SK. 1998. Disease of kharif fodder
crops and their management. In: TS Thind (Ed), Disease of field
crops and their Management National Agricultural Technology
Information Centre, Ludhiana, Punjab, India. Pp 373-404.
Gupta PP, Kumar Rajesh and Jalali BL.1999. Response of different
vesicular arbuscular mycorrhizal fungi on cowpea. Plant Disease
Research 14: 25-30.
Gupta PP and Rohilla HR. 2008. Plant protection in arid legumes. In:
D Kumar and A Henry (Eds), Souvenir at 3 rd National Symposium
on Arid Legumes, June 28-30, 2008, Indian Arid Legumes Society,
Gupta PP, Yadav BD and Joshi UN. 2007. Effect of micronutrient on
major diseases of clusterbean [Cyamopsis tetragonoloba (L.) Taub.].
Journal of Arid Legumes 4: 47-51.
Gupta Vivek and Gupta PP. 1999. Role of micro-nutrients in the
management of Alternaria leaf spot of clusterbean. Plant Diseases
Reporter 14: 187-190.
Halis Sahin and Feramuz Ozdemir 2007. Effect of some hydrocolloids
on the rheological properties of different formulated ketchups.
Food Hydrocolloids 18: 1015-22.
Hong ZK, Lakkineni Z and Verma DPS. 2000. Removal of feed back
inhibition of 1-pyrroline 5-carboxylate synthetase results in
increased proline accumulation and protection of plants from
osmotic stress. Plant Physiology 122: 1129-1136.
Itani J. 1992. Studies on water absorption ability of cowpea. Japanese
Journal of Tropical Agriculture 36: 37-44.
Karthikeyan A, Pandian SK and Ramesh M. 2011. Transgenic indica
rice cv. ADT43 expressing a -pyrroline-5-carboxylate synthetase
(P5Cs) gene from V. aconitifolia salt tolerance. Plant Cell Tissue
and Organ Culture 107: 383-395.
Kumar D. 2001. CAZRI Moth-1: a new variety of moth bean for arid
and semi-arid areas. Indian Farming 11:11, 15-16.
Kumar D. 2002. New concepts of moth bean improvement. In: D
Kumar and NB Singh (Eds), Moth bean in India, Scientific Publishers,
Jodhpur, Rajasthan, India. Pp. 31-48.
Kumar V, Yadav BD and Agarwal SK. 1996. Performance of fluchloralin
and pendimethalin to control weeds in clusterbean under rainfed
conditions. Haryana Journal of Agronomy 12: 43-46.
Kumar V, Yadav BD and Yadav JS. 2003a. Spatial-temporal management
for grain production of clusterbean genotypes HG 563 in light
textured soil of Southern Haryana. In: Proceedings of National
Symposium on Sustainability, Advancement and Future Thrust Areas
of Research on Forage, March 5-6, 2003,CCSHAU, Hisar, India.
Pp 77.
Kumawat PD and Khangarot SS. 2001. Response of sulphur, phosphate
and Rhizobium inoculation on growth and seed yield of clusterbean
[Cyamopsis tetragonoloba (L.) Taub.]. Annals of Biology 17: 189191.
Kumawat PD and Khangarot SS. 2002. Response of sulphur, phosphorus
and Rhizobium inoculation in content and uptake of nutrients of
clusterbean [Cyamopsis tetragonoloba (L.) Taub.]. Environmental
Ecology 20: 803-805.
Lodha S. 2001. Efficacy of antagonist spray of bacterial blight of
clusterbean. Indian Phytopathology 54: 133-134.
Lodha S and Sharma SK. 2000. Inactivation of Macrophomina
phaseolina propagules during composting and effect of composts
on dry rot severity and on seed yield of clusterbean. European
Journal of Plant Pathology 108: 253-261.
Mahudeswaran K, Veeraswamy R, Palaniswamy GA, Rathnaswamy R,
Rajasckaran VPA and Marappan PV. 1973. Co2 - a vegetable-cumgrain cowpea. Madras Agricultural Journal 60: 1844-1845.
Mai-Kodomi Y, Singh BB, Terao (Jr) Myers O, Yopp JH and Gibson PJ.
1999. Inheritance of drought tolerance in cowpea. Indian Journal
of Genetics & Plant Breeding 53: 317-323.
Mishra SK, Singh Neeta and Sharma SK. 2009. Status and utilization of
genetic resources of Arid Legumes in India. In: D Kumar and A
Henry (eds), Perspective Research Activities of Arid Legumes in
India, Indian Arid Legumes Society, CAZRI, Jodhpur, India. Pp 2330.
Mittal SP, Thomas TA, Dabas BS and Lal SM. 1971. Gum content as
related to seed yield and other characters in guar. Indian Journal of
Genetics and Plant Breeding 31: 228-232.
Mohamedi SV, Wangi CS, Thiruvengadam and Jayabalan N. 2004. In
vitro plant regeneration via somatic embryogenesis through cell
suspension culture of horsegram [M. uniflorum (Lam.) Verdc]. In
vitro Cell Developmental Biology-Plant 40: 284-289.
Monga D and Grover RK. 1993. Effect of cation exchange capacity on
272
chemical control and population changes of Fusarium solani
affecting cowpea. Plant Diseases Research 8: 54-58
agronomy. In: D Kumar and Henry A, Moth Bean in India, Scientific
Publishers, Jodhpur, Rajasthan, India. Pp 65-85.
Muchero W, Ehlers JD and Roberts PA. 2010. QTL analysis for resistance
to foliar damage caused by Thrips tabaci and Frankliniella scbultzei
(Thysanoptera: Thripidae) feeding in cowpea [V. unguiculata (L.)
Walp]. Molecular Breeding 25: 47-56.
Singh J, Kumar A, and Singh B. 2003. Production potential and economics
of clusterbean under various geometries and intercropping systems.
In: A Henry, D Kumar and NB Singh (Eds), Advances in Arid
Legumes Research, Scientific Publishers, Jodhpur, India. Pp 20711.
Muthukumar B, Mariamma M and Gnanan A. 1995. Regeneration of
plants from primary leaves of cowpea. Plant Cell, Tissue and Organ
Culture 42: 153-155.
Nagugi ECK, Galwey NW and Austin RB. 1999. Genotypes ×
environment interaction in carbon isotype discrimination and seed
yield in cowpea. Euphytica 73: 213-14.
Naoumkina M, Dixon RA. 2011. Characterization of the mannan
synthase promoter from guar (C. tetragonoloba). Plant Cell
Reporter 30: 997-1006.
Naoumkina M, Torres-Jerez T, Allen S, He Ji Zhao PX, Dixon RA and
May GD. 2007. Analysis of cDNA libraries from developing seeds
of guar [C. tetragonoloba (L.) Taub.]. BMC Plant Biology 7: 62.
Ng NQ. 1990. Recent development in cowpea germplasm collection,
conservation and research at genetics resources Unit, IITA. In: NQ
Ng and LM Monti (Eds), Cowpea Genetic Resources, International
Institute of Tropical Agriculture, Nigeria. Pp 13-28.
Rafey A and Srivastva VC. 1989. Agronomic management of late sown
horsegram. Indian Journal of Agronomy 34: 461-463.
Rao AV. 1995. Biological nitrogen fixation in arid ecosystem. In: RK
Behl et al. (Eds), Plant Microbe Interactions in Sustaianble
Agriculture, Bio-science Publishers, Hisar, India. Pp 89-101.
Rao AV and Tarafdar JC. 1993. Role of VAM fungi in nutrient uptake
and growth of clusterbean in an arid soil. Arid Soils Research
Rehabilitation 7: 275-280.
Rodge AB. 2008. Quality and export potential of arid legumes. In: D
Kumar and Henry A (Eds) Souvenir, Indian Arid Legumes Society,
Rodge AB. 2009. Status and direction of quality oriented research. In: D
Kumar and Henry A (Eds) Perspective Research Activities of Arid
Legumes in India, Indian Arid Legumes Society, CAZRI, Jodhpur,
India. Pp 71-95.
Rodge AB and Wankhede DB. 2003. Composite flour in cooking
marketing. In: D Kumar and Henry A (Eds) Advances in Arid Legumes
Research, Indian Arid Legumes Society, CAZRI, Jodhpur, India. Pp
307-314.
Sandhu HS. 1988. Interspecific hybridization studies in genera
Cyamopsis. Ph. D. thesis, Chaudhary Charan Singh Haryana
Agricultural University, Hisar, India.
Saxena A, Singh DV and Johsi NL. 1997. Effect of tillage and cropping
systems on soil moisture balance and pearl millet yield. Journal of
Agronomy of Crop Sciences 178: 251-257.
Saxena Moly, Saxena DR, Bhale MS and Khare MN. 1998. Diseases of
cowpea and their management. In: T.S. Thind (Ed), Diseases of
field crops and their management. Pp 239-252.
Shadeya L, Akurdabweni M, Monti L and Fanzo D. 2000. Detached leaf
test as rapid stress appraisal in cowpea germaplam for drought
resistance. Discovery and Innovation 12: 3-4.
Shekhawat BS, Sharma RC and Shekhawat RPS. 2002. Moth bean
Singh M and Joshi NL. 1994. Effect of mixed and intercropping systems
on dry matter and grain yields of companion crops in arid
environment. Annals of Arid Zone 33: 125-128.
Singh M and Joshi NL. 1994a. Performance of pearl millet based
intercropping system under drought conditions. Arid Soil Research
Rehabilitation 8: 313-319.
Singh Raj and Khan MA. 2003. Response of clusterbean varieties to
fertility levels and cropping systems under arid conditions. In: A
Henry, D Kumar and NB Singh (Eds), Advances in Arid Legumes
Research, Scientific Publishers, Jodhpur, India. Pp 225-228.
Solleti SK, Bakshi S, Purkayastha J, Panda SK and Sahoo L. 2008.
Genetic transformation and hybridization. Plant Cell Reporter 27:
1841-1850.
Tarafdar JC and Rao AV. 2001. Response of clusterbean to Glomus
mosseae and Rhizobium in an arid soil fertilized with nitrogen and
farm yard manure. Journal of Indian Society of Soil Science 49:
751-755.
Tarafdar JC and Kumar D. 2003. Moth bean (V. aconitifolia) cultivars
compared for root nutrients uptake efficiency in arid NW India.
Journal of Arid Environment 55: 215-222.
Tripathi AB. 1999. Availability in horsegram. Annals of Agricultural
Research 20: 382-383.
Yadav A, Budhiraja M, Rathee SS, Yadav IS and Malik RK. 1998.
Selectivity of clusterbean varieties to dinitroanilines in different
soils. Indian Journal of Weed Science 30: 108-112.
Yadav BD, Agarwal SK and Arora SK. 1989a. Yield and quality of new
cultivars of clusterbean as affected by row spacing and fertilizer
application. Guar Research Annals 5: 24-27.
Yadav BD, Joon RK and Kuamr V. 2003. Response of early maturing
guar variety to dates of sowing and seed rates. In: A Henry, D Kumar
and NB Singh (Eds), Advances in Arid Legumes Research, Scientific
Publishers, Jodhpur, India. Pp 199-202.
Yadav BD, Joon RK and Lodhi GP. 1997. Chemical weed control in
clusterbean [Cyamopsis tetragonoloba (L.) Taub.]. Forage Research
23: 189-192.
Yadav, BD, Kumar Virendra and Saxena A. 2002. Agronomy. In: D
Kumar and NB Singh (Eds), Guar in India, Scientific Publishers,
Jodhpur, India. Pp 35-62.
Yadav ND. 1992. Studies on strip cropping of pearl millet + arid legumes
in rainfed arid western Rajasthan. Current Agriculture 16: 89-92.
Yadav ND and Beniwal RK. 2003. Competitive relationship of arid
legumes and pearl millet intercropping system under rainfed
condition of western Rajasthan. In: A Henry, D Kumar and NB
Singh (Eds), Advances in Arid Legumes Research, Scientific
Publishers, Jodhpur, India. Pp 272-274.
Yadav RS. 1998. Effects of weed removal in clusterbean (Cyamopsis
tetragonoloba) under different rainfall situations in an arid region.
Journal of Crop Sciences 181: 209-214.
Transferability of cowpea and azuki bean derived SSR markers to other Vigna
species
RAVINDRA BANSAL, SUDHIR KUMAR GUPTA and T. GOPALAKRISHNA
Nuclear Agriculture and Biotechnology Division, Bhabha Atomic Research Centre, Mumbai – 400 085, India;
E-mail: ravindra@barc.gov.in
(Received: July 10, 2012; Accepted: December 18, 2012)
ABSTRACT
The genus Vigna contains many important grain legumes in
the tropical and sub-tropical regions. Among the tropical grain
legumes, the most studied species of Vigna are the cowpea and
azuki bean. Also genomic resources of only these two Vigna
species are available that can be further used for genomic
analysis of other Vigna species. This study represents the
transferability study of cowpea and azuki bean SSR markers
to other Vigna species. Fifty SSR markers developed in cowpea
using sequences available in cowpea CGKB database and 95
azuki bean SSR markers from other literature were checked
for transferability to nine other Vigna species. It was found
that 17 (34%) cowpea SSR and 61 (64.21%) azuki SSR markers
were transferable to other Vigna species. These SSR markers
derived from cowpea and azuki bean would serve as a valuable
tool for genetic analysis and marker assisted selection of
agronomically important traits in other Vigna species like
blackgram, mungbean, rice bean etc. for which there is either
little or no sequence information is available.
Key words:
Azuki bean, Cowpea, SSR markers, Transferability,
Vigna species
The genus Vigna contains the most important legumes
in the sub-tropical and tropical regions. The legume genus
Vigna comprises about 75-80 species originating from regions
of Africa and Asia. The genus has been sub-divided into 7
subgenera based on their centers of origin (Marechal et al.
1981). Many Vigna species are cultivated for food. Many of
them are valued as forage, cover and green manure crops in
many parts of the world. Annual worldwide production of the
various Vigna species is likely to approach 20 million hectares
and virtually all of this production is in developing countries
(Richard 2002).
Azuki bean (Vigna angularis) is an annual food legume
and is considered to have been domesticated in China, Korea
or Japan from its wild ancestral form, V. angularis var.
nipponensis (Ohwi) Ohwi & Ohashi. A number of SSRs have
been developed in adzuki bean. Cowpea [Vigna unguiculata
(L.) Walp] is also one of the most important crops and
predominantly a hot weather crop. It is more tolerant than the
other legume to drought, water logging, infertile soils, and soil
acidity stress. They are widely grown in eastern Africa and
south-east Asia primarily as a leafy vegetable (Richard 2002).
Simple sequence repeats or microsatellite repeats are
defined as regions within DNA sequences where short
sequences (1-6 bp; monomers to hexamers) are repeated in
tandem array. These markers are also termed simple sequence
length polymorphism (SSLP), short tandem repeats (STRs),
and simple sequence repeats (SSRs) or sequence-tagged
microsatellite site (STMS). SSR markers are widely used in
gene mapping (Tanksley et al 1995), analysis of genetic
diversity (Wang et al 2006), and marker-assisted selection in
breeding programs (Sun et al 2006). Uniform abundance in
the genome, codominant in nature, locus specificity and high
reproducibility of these markers make them very suitable to
check the transferability. Genomic resources are very limited
in Vigna species (Gupta and Gopalakrishna 2008; Datta and
Gupta 2009) and are main hurdle in their improvement. However,
because of high rate of transferability, SSR markers developed
in one species can be effectively used in other related species
(Souframanien and Gopalakrishna 2 009, Gupta and
Gopalakrishna 2010).
Therefore, aim of the present study was to develop the
SSR markers in cowpea, and evaluate the transferability of
these cowpea SSRs and already reported azuki bean SSR
markers to other Vigna species.
MATERIALS AND METHODS
Sequences retrieval and primer designing: Cowpea gene
space sequences were downloaded from Cowpea Genespace/
Genomi cs
Knowl edge
Base
(http: //
cowpeagenomics.med.virginia.edu/CGKB/). SSR locator
programme were used to find tandem nucleotide repeat in the
sequencese. Primer3 software (http://frodo.wi.mit.edu/primer3/)
was used to design the PCR primer. For designing primers,
user defined parameters was used viz. optimum primer length
was 20 mer (range was 18-25 mer), optimum annealing
temperature was 60°C (range was 55-62°C), optimum GC
content was 50% (range was 30-80%) and rest of the
parameters had the default value. The azuki bean SSR primers
used in the study were taken from Han et al. (2005).
Plant material and DNA extraction: Ten Vigna spp.
genotypes were used in the study are listed in Table 1. Total
genomic DNA was extracted from young seedlings using the
modified CTAB method (Doyle and Doyle 1987). The quality
274
Table 1. List of Vigna species used in the study
S. N. Vigna species used in the study*
Source
1 Vigna unguiculata (V-240)
NBPGR, India
2 Vigna vexillata
NBPGR, India
3 Vigna aconitifolia
TNAU, India
4 Vigna trilobata
NBPGR, India
5 Vigna glabrescence
NBPGR, India
6 Vigna umbellata (EC-634639)
NBPGR, India
7 Vigna angularis (EC-634633)
AVRDC, Taiwan
8 Vigna mungo (TU 94-2)
BARC, India
9 Vigna radiata var. radiata (TMB-37)
BARC, India
10 Vigna radiata var. setulosa
NBPGR, India
* Number in the paranthesis indicate accession number/cultivar
of DNA was checked on 1% agarose gel and the quantity was
determined using UV spectrophotometer (Unicam UV 300, UK).
SSR marker analysis: PCR reactions were performed in 20 µl
volume containing 10 mM Tris-HCl (pH 9.0), 50 mM KCl, 1.5
mM MgCl 2, 0.2 mM of each dNTP, 0.5 unit Taq DNA
polymerase (Bangalore Genei, Bangalore, India), 50 ng template
DNA, 20 ng each of forward and reverse primer. PCR
amplifications were performed in an Eppendorf Mastercycler
Gradient (Eppendorf, Hamburg, Germany) using the following
thermal profile: 1 cycle of 95°C for 2 min, followed by 39 cycles
of 94°C for 30 s, 50-65°C for 30 s (depending on primer), 72°C
Table 2. List of cowpea SSR markers developed and used in the study
Primer name
VuM-01F
VuM-01R
VuM-02F
VuM-02R
VuM-03F
VuM-03R
VuM-04F
VuM-04R
VuM-05F
VuM-05R
VuM-06F
VuM-06R
VuM-07F
VuM-07R
VuM-08F
VuM-08R
VuM-09F
VuM-09R
VuM-10F
VuM-10R
VuM-11F
VuM-11R
VuM-12F
VuM-12R
VuM-13F
VuM-13R
VuM-14F
VuM-14R
VuM-15F
VuM-15R
VuM-16F
VuM-16R
VuM-17F
VuM-17R
VuM-18F
VuM-18R
VuM-19F
VuM-19R
VuM-20F
VuM-20R
VuM-21F
VuM-21R
VuM-22F
VuM-22R
VuM-23F
VuM-23R
VuM-24F
VuM-24R
VuM-25F
VuM-25R
Primer sequence (5’….3’)
AACAAGATGTGGCATGCTGA
TGAAAACGGAAAAGGGATCA
GAAACTAGCACCAAATCCAACA
GAGCAAAAGCCTCCATCACT
GCACCCAATCAAACACACAC
GAAGCGGATTTGAGAGTTGG
GCAGGGGCAACAATACATTA
GTTGGACTACCCCAAATGCT
GCGGGATTCTATTCCAGTGA
TCCATTGGGTTTCTCAACCT
TGAAAGTTGAGAAGGGGACAA
CATTCAGGTTCAGCTCACGA
TGTTTCCAACAGGATTAGCC
AAGGCCAATAATTGCACAAG
TCAAAAACACAGGTCCTCCA
CATCCCGTGAAATTCAACAA
TTGAGCACAAGTTCATCGAG
TGATTGCCTAAACGACACAC
TCAAAACTTCAACCCAGACA
AAAAAGGAAGTCCATTGCTC
GGGCAGGAGCTGCATATAAC
CCTGCAACAACAAAAATGGA
CATGGCAATTTGCAACAAAG
CTAAAGTGCCGTGACGATGA
ACTCAACGTGTGTGAATAGGC
CCCTCACAAGAAGAAACAGAA
CGGGCAAGATAACCAATTAGAC
AGTTGTCAGACCAACCTGCAT
ATGTTGCTGGACAAATCTCTGA
TGTGCCAACTGATTCTCTGC
GGACATTTCCGGATGTCAAC
CTTTGCCATTCACTTTCACG
GGATATCATAGCAAGTCGAA
AAGGAGTGCATCCTAAACTC
GGCACCCCAGTTCAGGAT
TTGCGAACTTGTTCATGTGG
AGAACCCAGCATACCTGCAT
CCTCGCCAATGATTCTGAG
CCAAGAGGAAAAGGTATCAGACA
GCATTCTTGCACAAGGAGTCT
AAACCAGATGTCTCTGTTTCTTCTC
GCGTAACACAGGCGTTATCA
CAATCACCATTCACCAAACA
TATTGGGACTCAGGTCTTGG
CGTACCTAATGTGAAGGTTCGTT
AAGGCAAAAAGCTCTTGCAG
GGTTTATCACCACCTCAACA
CGATGAATTTTAGCCATCAG
AGGGATGAGTTCCTTCAACG
AAGAAGTGGTGAGGGCACAG
Primer name
VuM-26F
VuM-26R
VuM-27F
VuM-27R
VuM-28F
VuM-28R
VuM-29F
VuM-29R
VuM-30F
VuM-30R
VuM-31F
VuM-31R
VuM-32F
VuM-32R
VuM-33F
VuM-33R
VuM-34F
VuM-34R
VuM-35F
VuM-35R
VuM-36F
VuM-36R
VuM-37F
VuM-37R
VuM-38F
VuM-38R
VuM-39F
VuM-39R
VuM-40F
VuM-40R
VuM-41F
VuM-41R
VuM-42F
VuM-42R
VuM-43F
VuM-43R
VuM-44F
VuM-44R
VuM-45F
VuM-45R
VuM-46F
VuM-46R
VuM-47F
VuM-47R
VuM-48F
VuM-48R
VuM-49F
VuM-49R
VuM-50F
VuM-50R
Primer sequence (5’….3’)
TTTTAAGCATTGCCACCAGA
AACAACAACCGCATATCCCTA
TCCATCCACCATTTTCCATC
ATGGGAATGCCCGAGAGT
TAGAACACTCTTGGGGGTTA
CGGAGAAAGAGGAAGTACAA
TTTTTCTCGACACACGGTGA
TTTCCCCCTCTCTCACACAC
TGCAACATCCACTAATAGACCA
TTGCTCAACATAAAGGACGAC
TGGTTCACTTCCCATATTGTCA
AGGCAGAGACGAAGGAGTGA
AATGAAATCAGCCCAAGGAA
ATGGCTTTTGTCTTGCCTTC
AAAGGTGGGGGATTATGAGG
TGTCCAATCCTGATGGATGA
CCTGATGGATTTACAGACATGC
GGTGAGGGCAATACCTGTGT
AAGACTTTCGTGGTGCAGGT
AAGTGGCATGGAAGATGGAG
TGTGCCAAAAGGAAAAGACA
GGGATGGTATGTTCCTCACG
TCATTGGTACGTTCAAAGCAA
TGGATCCCTACTCAATTTCTCC
TGCTTAAAGGAGAAATACTCGACTT
CTGTCCTCATGTTGAAAACCTCT
CGAAAAAGCATGATCAACCA
CCCCTTTCGCTAAAATTTCC
TTCTACATGGTTTTGGGGTCA
GAGCTTGCCCTCAAGAATTG
TGAGGTGTGCACTTTTAACTCC
TTCTCACACATACACACGCAAT
ATGTTATACGCCGGCAAAGT
TCTGGGTGCTTTGGAAAATC
AGCTTTGCACTAATCCATCTTAGTC
CAAGATCATTTTTCGCGACTC
ACCAAACCATCCGTGAAGTG
TGGTTGTCCACGAATATGTGTC
TGGTTGGAAGTCTCACATCAA
GCATATGCATCTCGTATGTAGGTC
TTTGGTTTCACATGTTGAGG
TTCTTGGGAATATGTTCAGG
TGTTTTTCGCTATGCCTCAA
GGCAGCTAGATTCGTCCTTG
ACCTACTCACGAATATCCACAG
CACCGATAATCTCCAAAACA
TGAGATTGGTGTTGAATGCT
CAATGAACTAAACCCCTTCTTC
TTAGGGACCAAAAGGAATGA
TAATCGCACACATCAGCCTA
Bansal et al.: Transferability of cowpea and azuki bean derived SSR markers to other Vigna species
275
Table 3. Cross-species transferability of cowpea SSR markers to other Vigna species
S.N. Primer
V.
vexillata
name
1. VuM5
+
2. VuM7
3. VuM8
4. VuM11
+
5. VuM15
+
+
6. VuM18
7. VuM19
+
+
8. VuM25
9. VuM37
10. VuM40
+
11. VuM44
+
+
12. VuM51
13. VuM57
+
14. VuM58
+
15. VuM59
+
16. VuM60
17. VuM61
+
V.
aconitifolia
+
+
+
+
+
+
+
+
+
+
+
+
V.
trilobata
+
+
+
+
+
+
+
+
+
+
+
+
V.
glabrescence
+
+
+
+
+
+
+
+
+
+
+
+
+
+
V.
umbellata
+
+
+
+
+
+
+
+
+
+
+
+
+
V.
angularis
+
+
+
+
+
+
+
+
+
+
+
+
V.
mungo
+
+
+
+
+
+
+
+
+
+
+
+
+
+
V. radiata var.
radiata
+
+
+
+
+
+
+
+
+
+
+
V. radiata var.
setulosaa
+
+
+
+
+
+
+
+
+
+
+
Table 4. List of azuki bean SSR markers used in the study
S. N.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
Primer No.
CEDCAA1
CEDG2
CEDAAT2
CEDAAG4
CEDG5
CEDGAT8
CEDC8
CEDG11
CEDC11
CEDC14
CEDG15
CEDG16
CEDC16
CEDG18
CEDG21
CEDG22
CEDG23
CEDG24
CEDC28
CEDG30
CEDG33
CEDC33
CEDG35
CEDC35
CEDG36
CEDG37
CEDG40
CEDG41
CEDG42
CEDC55
CEDG59
CEDG63
Serial No.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
Primer No.
CEDG66
CEDG71
CEDG73
CEDG80
CEDG81
CEDG98
CEDG99
CEDG100
CEDG106
CEDG107
CEDG112
CEDG114
CEDG117
CEDG121
CEDG124
CEDG125
CEDG131
CEDG132
CEDG134
CEDG146
CEDG147
CEDG150
CEDG158
CEDG165
CEDG171
CEDG172
CEDG174
CEDG184
CEDG186
CEDG187
CEDG191
CEDG193
for 1 min and a final extension of 72°C for 7 min. PCR products
were separated on 2% agarose gel using 1X Tris-borate-EDTA
(TBE) buffer, stained with ethidium bromide and photographed
in a gel documentation system (Syngene, UK).
Serial No.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
Primer No.
CEDG195
CEDG196
CEDG197
CEDG198
CEDG201
CEDG202
CEDG203
CEDG205
CEDG212
CEDG215
CEDG232
CEDG238
CEDG243
CEDG244
CEDG245
CEDG247
CEDG251
CEDG253
CEDG257
CEDG259
CEDG261
CEDG262
CEDG265
CEDG269
CEDG270
CEDG280
CEDG285
CEDG286
CEDG294
CEDG298
CEDG305
RESULTS AND DISCUSSION
Transferability of cowpea SSRs within genus Vigna: A total
of 50 cowpea SSR markers were developed in this study (Table
2) and were screened on nine other Vigna species to determine
276
Table 5. Cross-species transferability of azuki bean SSR markers to other Vigna species
S. N. Primer name V. unguiculata V. vexillata V. aconitifolia V. trilobata V. glabrescence V. umbellata V. mungo
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
CEDG2
CEDAAG4
CEDGAT8
CEDC8
CEDC11
CEDG11
CEDG15
CEDG21
CEDG24
CEDG35
CEDG36
CEDG37
CEDG40
CEDG42
CEDC55
CEDG59
CEDG66
CEDG71
CEDG73
CEDG81
CEDG98
CEDG99
CEDG100
CEDG106
CEDG107
CEDG114
CEDG117
CEDG121
CEDG124
CEDG132
CEDG146
CEDG147
CEDG165
CEDG171
CEDG172
CEDG174
CEDG186
CEDG196
CEDG197
CEDG198
CEDG201
CEDG202
CEDG203
CEDG205
CEDG212
CEDG215
CEDG243
CEDG245
CEDG247
CEDG251
CEDG257
CEDG259
CEDG261
CEDG262
CEDG265
CEDG269
CEDG270
CEDG285
CEDG298
CEDG305
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
V. radiata
var. radiata
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
V. radiata
var. setulosa
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Bansal et al.: Transferability of cowpea and azuki bean derived SSR markers to other Vigna species
Fig 1.
Azuki bean markers SSR markers CEDGAT8 &
CEDC550 showing trans ferability to other Vig na
species (The species number is according to Table 2).
their transferability. Out of 50 primer pairs examined, only 17
primer pairs were found to give clear amplification. Out of 17
cowpea SSR markers tested, 4 gave amplification in all Vigna
species. Around 9 gave amplification in 8 species and 5 gave
amplification in 7 species. Whereas, only 2 sho wed
amplification in 6-5 species and 4 were found to be transferable
in less than 5 species. The cross species transferability pattern
of cowpea SSR markers is given in Table 3 and PCR
amplification of cowpea SSR markers on Vigna species is
shown in Fig 1.
Transferabilty of azuki bean SSRs within genus Vigna: To
determine azuki SSR marker transferability, 95 SSR markers
(Table 4) were screened on nine other Vigna species. Out of
95 primer pairs examined, 61 primer pairs were found to give
clear amplification. Out of 61 azuki bean SSR markers, 28
amplified in all Vigna species, thirteen gave amplification in 8
species and nine showed amplification in 7 species, and 8
primer pairs gave amplification in 6-5 species. Only 3 primers
showed amplification in less than 5 species. The cross species
transferability pattern of azuki bean SSR markers is given in
Table 5 and PCR amplification of azuki bean SSR markers on
Vigna species is shown in Figure 2.
In many genetic studies, one of the major rate limiting
steps is the development of markers for use in a new study
system. The study gets more complicated when there is no or
very little information about the genome sequence is available
for the development of markers. Moreover many marker
systems are more or less species specific, limiting their use in
other related species. Because of high transferability of SSR
markers, these problems can be minimized. The ability to use
the same microsatellite primers in different plant species
depends on the extent of sequence conservation in the primer
binding sites flanking the microsatellite loci and the stability
of these sequences during evolution (Choumane et al. 2000,
Decroocq et al. 2003). Microsatellite primer pairs used in the
current study originated from cowpea and azuki bean, and
were found to be transferable to other Vigna species. This
indicates the conservation of microsatellite sequences
between the Vigna species during evolution. The conservation
of microsatellite sequences also has been observed across
other legumes (Choumane et al. 2000, Ford et al. 2002, Phansak
et al. 2005). However, the transferability of azuki derived SSR
Fig 2.
277
Cowp ea SSR markers VuM7 & VuM18 sho wing
transferability to other Vigna species (The species
number is according to Table 2).
markers was high (63%) compared to cowpea SSR markers
(34%). This may be because cowpea belongs to the sub genus
Vigna, and azuki bean and other species like mungbean,
blackgram, rice bean, moth bean belongs to the subgenus
Ceratotropis of the genus Vigna,. Thus, compared to cowpea,
phylogenetically azuki bean is closer to other Vigna species
and therefore, markers developed from azuki bean showed
more transferability.
These results indicate that these microsatellite markers
will be helpful for genetic analysis of other Vigna species like
mungbean, black gram, rice bean etc. for which sequence
information is not available for SSR marker development. The
transferability of microsatellite markers across species may
increase their utility and potentially decrease the development
cost. The microsatellite markers conserved between the
species also serve as a valuable tool for comparative mapping
studies (Dirlewanger et al. 2004, Yu et al. 2004, Gupta et al.
2008).
REFERENCES
Choumane W, Winter P, Baum M and Kahl G. 2000. Conservation of
microsatellite flanking sequences in different taxa of family
leguminosae. Euphytica 138: 239-245.
Datta S and Gupta S. 2009. Use of genomic resources in improvement
of Vigna species. Journal of Food Legumes 22: 1-10.
Decroocq V, Fave MG, Hagen L, Bordenave L and Decroocq S. 2003.
Development and transferability of apricot and grape EST
microsatellite markers across taxa. Theoretical and Applied Genetics
106: 912–922.
Dirlewanger Z, Graziano E, Joobeur T, Garriga-Caldere F, Cosson P,
Howard W and Arus P. 2004. Comparative mapping and markerassisted selection in Rosaceae fruit crops. In: Proceedings of the
National Academy of Science of the United States of America 101:
9891-9896.
Doyle JJ and Doyle JL. 1987. A rapid DNA isolation procedure for
small quantities of fresh leaf tissue. Phytochemical Bulletin 19: 1115.
Gupta SK and Gopalakrishna T. 2008. Molecular markers and their
application in grain legumes breeding. Journal of Food Legumes 21:
1-14.
Gupta SK and Gopalakrishna T. 2010. Development of unigene-derived
SSR markers in cowpea (Vigna unguiculata) and their transferability
to other Vigna species. Genome 53: 508-523.
Gupta VS, Ramakrishna W, Rawat SR and Ranjekar PK. 1994. (CAC)5
278
detects DNA fingerprints and sequences homologous to gene
transcripts in rice. Biochemical Genetics 32: 1-8.
Han OK, Kaga A, Isemura T, Wang XW, Tomooka N and Vaughan DA.
2005. A genetic linkage map for azuki bean [Vigna angularis (Willd.)
Ohwi & Ohashi]. Theoretical and Applied Genetics 111:1278-87.
Marechal R, Mascherpa JM and Stanier F. 1981. Taxonometric study
of the Phaseolus-Vigna complex related genera. In: RM Polhill and
PH Rven (Eds), Advances in Legume Systematics. The Royal
Botanic Garden, Kew. Pp. 329-335.
Souframanien J and Gopalakrishna T. 2010. Cross-species amplification
of microsatellite loci and diversity analyses in blackgram. Journal
of Food Legumes 22: 11-17.
Sun LH, Wang CM, Su CC, Liu YQ, Zhai HQ and Wan JM. 2006.
Mapping and marker-assisted selection of a brown plant hopper
resistance gene bph2 in rice (Oryza sativa L.). Acta Genetics Sinica
33: 717–723.
Tanksley SD and Ganal MW. 1995. Abundance, variability and
chromosomal location of microsatellites in wheat. Molecular and
General Genetics 246: 327–333.
Phansak P, Taylor PWJ and Mongkolporn O. 2005. Genetic diversity
in yardlong bean (Vigna unguiculata ssp. sesquipedalis) and related
Vigna species using sequence tagged microsatellite site analysis.
Scientia Horticulturae 106: 137-146.
Wang LX, Guan RX, Liu ZX, Chang RZ and Qiu LJ. 2006. Genetic
diversity of Chinese cultivated soybean revealed by SSR markers.
Crop Science 46: 1032–1038.
Richard F. 2002. New opportunities in Vigna. In: J. Janick and A.
Whipkey (Eds.), Trends in new crops and new uses, ASHA press. Pp
424-428.
Yu YG, Saghai Maroof MA, Buss GR, Maughan PJ and Tolin SA. 1994.
RFLP and microsatellite mapping of a gene for soybean mosaic
virus resistance. Phytopathology 84: 60-64.
Genetic diversity studies in blackgram (Vigna mungo L. Hepper)
M. SRIMATHY, M. SATHYA and P. JAYAMANI*
Department of Pulses, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore
641003, India; E-mail: jayamani1108@hotmail.com
(Received: February 18, 2012 ; Accepted :
January 05, 2013)
ABSTRACT
Divergence analysis of 46 genotypes including 20 genotypes of
blackgram and 26 accessions of V. mungo var. silvestris, a wild
progenitor species for eleven biometrical traits was carried out
using Mahalanobis D2 statistics. The genotypes were grouped
into twelve clusters. The cluster I was the largest with 25
accessions of V. mungo var. silvestris while, other clusters consisted
of two cultivated genotypes. Cluster XII had only one accession
viz., V. mungo. var. silvestris acc 10. This study showed clear
grouping of V. mungo var. silvestris accessions from the cultivated
blackgram (V. mungo) genotypes. Cluster XI recorded the
maximum intra cluster distance of 12.58 followed by cluster X
with a distance of 11.39. The highest inter cluster distance was
found between cluster IX and cluster XII (28.71) followed by
cluster XI and XII (23.79) and cluster V and XII (23.30). Based
on cluster mean and divergence, it was concluded that the
hybridization between accessions of V. mungo var. silvestris in
clusters I and XII and cultivated genotypes in the other clusters
could produce desirable recombinants for plant type, important
economic traits and grain yield.
Key words:
Blackgram, Cluster analysis, D2 analysis, Genetic
divergence, V.mungo var. silvestris
Blackgram (Vigna mungo (L.) Hepper) popularly known
as urdbean or mash, is a grain legume domesticated from V.
mungo var silvestris (Chandel, 1984). This wild progenitor is
resistant to bruchid infestation and also tolerant against
abiotic stresses. Blackgram is a rich source of protein (20.8 to
30.5 per cent) with total carbohydrates ranging from 56.5 to
63.7 per cent. It is also a good source of phosphoric acid and
calcium. India is the largest producer and consumer of
blackgram in the world. It is the fourth important pulse crop in
India, cultivated as a sole crop and intercrop covering an area
of about 3.24 million hectares and producing 1.46 million tons.
However, the productivity is very low with 526 kg/ha
(Anonymous, 2010). Many breeding efforts have been carried
out to improve the yield level of this crop and to break the
yield plateau, but it could not be done because of narrow
genetic base of parents used in hybridization.
Genetic diversity is an important factor and also a
prerequisite in any hybridization programme. Inclusion of
diverse parents in hybridization programme serves the
purpose of producing desirable recombinants. Multivariate
analysis by means of Mahalanobis D2 statistic is a powerful
tool in quantifying the degree of divergence at genotypic
level. Therefore, an attempt has been made in the present
investigation with a view to estimate genetic divergence
among a set of 46 genotypes including cultivated genotypes
of blackgram and its wild progenitor accessions for eleven
biometrical traits.
Forty six genotypes of cultivated blackgram and its wild
progenitor, collected two decades ago, were evaluated at
Department of Pulses, Tamil Nadu Agricultural University,
Coimbatore in a randomized block design (RBD) with two
replications. Each genotype was sown in paired rows of four
meter length with a spacing of 30 x 10 cm. Recommended
package of practices were followed to raise a healthy crop.
Five randomly taken plants were considered to record data
for days to 50 per cent flowering, days to maturity, plant height
(cm), number of branches per plant, number of clusters per
plant, number of pods per cluster, number of pods per plant,
pod length (cm), number of seeds per pod, 100 seed weight
(g) and yield per plant (g). The mean values of five plants
were taken for the analysis of genetic divergence following
Mahalanobis (1936). The genotypes were grouped into
different clusters following Tocher’s method as described by
Rao (1952).
Genetic diversity is the basic requirement for successful
breeding programme. Collection and evaluation of germplasm
lines and genotypes of any crop is a pre-requisite for any
programme, which provides a greater scope for exploiting
genetic diversity. The multivariate analysis (D2) is a powerful
tool to measure the genetic divergence within a set of
genotypes (Murthy and Arunachalam, 1966). The present
study was planned to examine the trend of genetic divergence
in 20 genotypes of cultivated blackgram and 26 accessions of
V.mungo var. silvestris, a wild progenitor species. The
genotypes were grouped into twelve clusters indicating large
amount of genetic diversity among the genotypes (Table 1).
Elangaimannan et al. (2008) also reported grouping of 55
blackgram genotypes into seven clusters, where cluster I was
the largest (34 genotypes) followed by clusters IV (eight
genotypes), II (six genotypes), V (four genotypes), while rest
of the clusters had one genotype each. Grouping of accessions
by multivariate method in the present study is of practical
280
value to the breeders. Representative accessions may be
chosen from particular cluster for hybridization programme.
In the present study, cluster I was the largest with 25
accessions of V. mungo var silvestris while, other clusters
consisted of two genotypes each except cluster XII which
had only one accession viz., V. mungo var silvestris acc. 10.
High level of variability was observed for several morphological
and biometrical traits among the accessions of V. mungo var
silvestris in cluster I. Even though all the accessions of V.
mungo var silvestris formed a single cluster, a good level of
variability was also observed in the mean values of different
traits. Similar kind of separate grouping of cultivated
genotypes and V. mungo var silvestris accessions was
observed in a dendrogram based on SSR analysis (data not
shown). Contribution of various biometrical characters
towards genetic divergence is presented in Table 3. Among
the characters studied, yield per plant contributed maximum
towards divergence, followed by number of pods per cluster,
100 seed weight and days to 50 per cent flowering. Similar
results were also reported earlier by Ghafoor and Ahmed (2005),
Konda et al. (2007), Shanthi et al. (2006). Plant height and
number of branches per plant contributed minimum to the
genetic divergence leading to the inference that in general,
the variability for these characters are low in blackgram
genotypes used in this study.
The intra and inter cluster D2 values among the clusters
are presented in the Table 2. Cluster XI recorded the maximum
intra cluster distance of 12.58 followed by cluster X with a
distance of 11.39. There was one solitary cluster (cluster XII)
possessing no intra cluster value. This accession had smaller
seed size and found to be resistant to bruchid infestation.
The highest inter cluster distance was found between cluster
IX and cluster XII (28.71) followed by cluster XI and XII
(23.79), cluster VII and XII (23.35) and cluster V and XII (23.30).
The inter cluster distance of all other clusters with cluster XII
showed higher values when compared to the inter cluster
distance between other clusters. The least inter cluster
distance was found between cluster II and IV (6.19).
The mean values of 11 characters for twelve clusters
are presented in the Table 3. Cluster IV recorded the highest
mean value (38.00) for days to 50 % flowering followed by
cluster XII (37.50) and clusters VII and X (37.00), whereas
cluster XI recorded the lowest mean value for days to 50 %
flowering (31.50). Cluster IV recorded the highest mean value
for days to maturity (68.00) followed by cluster XII (67.50) and
clusters VII and X (67.00), while Cluster XI recorded the
minimum days to maturity (61).
Cluster IV recorded the maximum plant height of 30.88
cm followed bycluster XII (29.77 cm), while cluster XI recorded
the minimum plant height of 20.22 cm. The highest mean value
for number of branches per plant was recorded by cluster VI
and XII (1.84) and the lowest was recorded by cluster XI (1.42).
The highest mean for number of clusters per plant was
recorded by cluster V (13.00) followed by cluster VII (12.00)
Table 1. Constitution of D2 clusters of 46 genotypes of blackgram
Cluster number Number of genotypes
Name of the genotype
I
25
Vigna mungo var. silvestris acc 1, acc 2, acc 3, acc 4, acc 5, acc 6, acc 7, acc 8, acc 9, acc 11, acc 12, acc 13,
acc 14, acc 15, acc 16, acc 17, acc 18, acc 19, acc 20, acc 21, acc 22, acc 23, acc 24, acc 25, acc 26,
II
2
AC-305, PLS-44
III
2
Cotton leaf – 32, K 951
IV
2
P-169, Co 5
V
2
P-226, PLS 364/92
VI
2
P-123, T9
VII
2
P-153, VBN (Bg) 4
VIII
2
P-307/1-1/, VBN 3
IX
2
P-202, Co-02/103
X
2
AC-43, VBN (Bg) 5
XI
2
P 133/13, CoBG 653
XII
1
V. mungo var. silvestris acc 10
Table 2. Average intra (in bold) and inter cluster D2 distances
Cluster
I
II
III
IV
V
VI
VII
VIII
IX
X
XI
XII
I
8.95
II
12.86
3.21
III
11.18
6.31
4.24
IV
12.18
6.19
6.98
4.35
V
14.86
9.85
8.42
9.49
4.82
VI
10.24
9.49
7.97
9.31
10.55
5.73
VII
15.45
6.87
9.37
6.63
8.27
12.04
5.81
VIII
11.56
6.23
6.99
8.63
9.83
8.59
9.38
8.16
IX
17.53
9.21
11.55
10.79
10.45
11.60
9.19
10.86
9.94
X
12.71
9.44
7.75
8.42
11.68
10.04
11.73
10.17
13.80
11.39
XI
11.90
12.33
11.69
13.58
12.67
9.36
14.62
10.67
14.31
14.60
12.58
XII
17.71
23.16
20.63
20.16
23.30
22.77
23.35
22.88
28.71
21.42
23.79
0.00
Srimathy et al.: Genetic diversity studies in blackgram (Vigna mungo L. Hepper)
281
Table 3. Cluster mean values for 11 biometrical characters in blackgram
Cluster
I
II
III
IV
V
VI
VII
VIII
IX
X
XI
XII
Contribution
of traits
toward
divergence
Days to
50%
flowering
34.16
36.25
35.50
38.00
34.25
34.50
37.00
34.00
35.75
37.00
31.50
37.50
4.54
63.96
66.25
65.50
68.00
64.25
64.50
67.00
64.00
65.75
67.00
61.50
67.50
Plant
height
(cm)
27.47
23.33
23.00
30.88
22.43
29.20
29.67
26.96
23.58
27.71
20.22
29.77
Number of
branches/
plant
1.62
1.42
1.67
1.58
1.67
1.84
1.67
1.57
1.83
1.59
1.42
1.84
0.39
0.10
0.19
Days to
maturity
Number of Number of
Number of
clusters/
pods/
pods/ plant
cluster
plant
7.72
4.51
34.99
8.50
3.46
29.50
6.42
2.84
18.77
9.50
3.59
34.52
13.00
2.59
28.67
8.50
4.22
36.50
12.00
2.90
34.50
8.84
3.70
26.00
11.50
3.35
39.50
8.09
4.21
23.25
10.67
4.14
41.80
5.73
2.50
26.00
1.06
17.10
5.80
Pod
length
(cm)
4.04
4.86
4.64
4.44
4.58
4.54
4.54
4.46
4.72
4.55
4.59
3.15
0.48
Single
Number of 100 seed
plant yield
seeds/pod weight (g)
(g)
5.66
3.96
6.88
6.00
5.13
5.16
5.78
4.68
5.92
5.98
4.80
6.71
5.88
4.90
8.53
6.10
4.88
8.91
6.08
5.18
6.09
5.78
4.98
5.51
5.80
5.80
8.14
5.85
4.75
7.24
5.63
4.58
8.43
5.30
2.00
4.32
0.68
17.29
52.37
and maximum mean of number of pods per cluster was recorded
by cluster I (4.51) while, the lowest mean for number of pods
per cluster was recorded by cluster XII (2.50). The highest
mean value for number of pods per plant was recorded by
cluster XI (41.80) and cluster III recorded the lowest mean
(18.77).
mungo var silvestris can be used in inter sub-specific
hybridization program to transfer genes for resistance to biotic
and tolerance to abiotic stresses, improved plant type and
also to broaden the genetic base in blackgram.
Cluster II recorded the maximum mean value for pod
length (4.86 cm) followed by cluster IX (4.72 cm) and the lowest
pod length was recorded by cluster XII (3.15 cm). Cluster VI
recorded the maximum value for number of seeds per pod
(6.10) followed bycluster VII (6.08) while, cluster XII recorded
the lowest mean value for number of seeds per pod (5.30).
Cluster IX recorded the maximum hundred seed weight (5.80
g) and cluster VI recorded maximum single plant yield (8.91 g)
while, Cluster XII recorded lowest mean value for hundred
seed weight (2.0 g) and single plant yield (4.32 g). The
accession V. mungo var silvestris acc 10 recorded lowest mean
value for most of the traits viz., number of clusters/plant,
number of pods/cluster, pod length, 100 seed weight and
yield/plant. The uniqueness of the accession could be the
reason for the formation of separate cluster XII, when
compared to all other accessions of V. mungo var silvestris in
the cluster I.
Anonymous. 2010. Project Coordinator ’s Report. AICRIP on
MULLaRP. IIPR, Kanpur. Pp-20.
From the present investigation, it was concluded that
blackgram displayed a wide range of diversity for few traits
and there were few accessions with unique characters. Vigna
mungo var silvestris accessions were distinctly separated from
the other blackgram genotypes. Hence, the accessions of V.
REFERENCES
Chandel KPS. 1984. Role of wild Vigna species in the evolution and
improvement of mung (Vigna radiata (L.) Wilczek) and urdbean (V.
mungo (L.) Hepper). Annals of Agricultural Research 5: 98-111.
Elangaimannan R, Anbuselvan Y and Karthikeyan P. 2008. Genetic
diversity in blackgram (Vigna mungo (L.) Hepper). Legume Research
31 (1): 57-59.
Ghafoor A, Sharif A, Ahmed Z, Zahid MA and Rabbani MA. 2001.
Genetic diversity in blackgram (Vigna mungo (L.) Hepper). Field
Crops Research 69: 183-190.
Konda CR, Salimath PM and Mishra MN. 2007. Genetic diversity in
blackgram (Vigna mungo (L.) Hepper). Legume Research 30 (3):
212-214.
Mahalanobis PC. 1936. On the generalized distance in statistics. Proc.
Natl. Acad. Ins India. 12: 49-55.
Murthy BR and Arunachalam V.1966. The nature of genetic divergence
in relation to breeding system in crop plants. Indian Journal of
Genetics 26: 188-189.
Rao CR. 1952. Advanced statistical methods in Biometrical Research.
John Wiley and sons Inc., New York.
Shanthi P, Jebaraj S and Manivannan N. 2006. Genetic diversity in
urdbean (Vigna mungo (L.) Hepper). Legume Research 29: 181185.
Stability analysis for seed yield and its component traits in rice bean (Vigna umbellata)
WUNGSEM RUNGSUNG and S.A.P.U. CHANGKIJA
Department of Genetics and Plant Breeding, SASRD, Medziphema-797106, Nagaland, India; E-mail:
asemosem@yahoo.com
(Received: June 25, 2012; Accepted: October 25, 2012)
ABSTRACT
Fifty germplasm lines of rice bean were evaluated in a
randomized complete block design (RCBD) at three locations
during kharif season for two consecutive years. Stability analysis
was carried out as per Eberhart and Russell (1966) model. The
pooled analysis of variance due to genotypes was found highly
significant for all the characters indicating the presence of
considerable genetic variability among the genotypes. Highly
significant pooled deviation for all the characters except days
to flowering, pods/cluster, pod length, seeds/pod and seed weight
was observed suggesting that the performance of the various
genotypes under study fluctuated significantly from their
respective linear path of response to environments. The genotype
× environment interactions for most of the characters were
also highly significant revealing differential interactions of
genotypes with changes in the environments. Among all the
genotypes studied, ‘NRB-34’ and ‘NRB-35’ were found most
stable for yield/plant.
Key words:
G × E interaction, Rice bean, Seed yield, Stability
analysis
Rice bean, a leguminous crop and also known as
climbing mountain bean, mambi bean and oriental bean, is
native to south-east Asia (Bolivar and Luis 2010). The crop
possesses excellent food and fodder values and is grown for
fodder, green manure and cover crop. The dry seeds are eaten
boiled as dhal (soup) and young immature pods are consumed
as vegetables (Gupta et al. 2009). Rice bean, thus, occupies
an important place in the Indian food system, and studies
conducted so far on the bean revealed the existence of high
genetic variability. An investigation on its stability analysis
will, therefore, help in sorting out the most promising and
stable genotypes from the genetically variable populations.
Therefore, the present investigation was taken up for stability
analysis of seed yield and its component traits in some of the
rice bean germplasm lines.
The experimental materials consisted of a set of 50
germplasm lines of rice bean. The seeds of these lines were
sown and raised as kharif crop on dry terraces under rain-fed
and normal sown conditions in the randomized complete block
design (RCBD) with three replications under the same set of
agronomic and cultivation practices at three locations, namelyMedziphema, Chumukedima and Kohima (Nagaland) during
2009 and 2010. Six competitive plants (two from each
replication) from each germplasm line were randomly selected.
Data were recorded on seed yield (g) and its component traits,
viz., days to flowering, days to maturity, plant height, clusters/
plant, pods/plant, pods/cluster, pod length (cm), seeds/pod,
biomass/plant (g) and 100-seed weight (g), were observed
and recorded at different phenological events. Stability
analysis was carried out as per Eberhart and Russell (1966)
using SPAR-2 (Statistical Package for Agricultural Research)
developed at the Indian Agricultural Statistics Research
Institute, New Delhi by Ahuja et al. (2005). The mean and
deviation from regression of each genotype were considered
for stability, and linear regression was used for testing the
varietal response: (i) genotypes with high mean, bi =1 and
non-significant S2d (not significantly deviating from zero) were
considered ‘average responsive’ (adaptable or suitable over
all environmental conditions), (ii) genotypes with high mean,
regression coefficient greater than unity (bi>1) and nonsignificant S2d were rated ‘highly responsive’ (suitable for
favourable environments but yielding poor in unfavourable
environments), (iii) genotypes with high mean, bi<1 with nonsignificant S2 d were ‘low responsive’ (not favourably
responsive to improved environmental conditions and hence
could be regarded as specifically adapted to poor/unfavourable
environments) and (iv) genotypes with any bi value with
significant S2d were unstable.
The development of varieties/genotypes, which can be
adapted to a wide range of diversified environments, is the
ultimate goal of plant breeders in any crop improvement
programme (Danyali et al. 2012) and it is imperative to evaluate
varieties/genotypes over different environments to ascertain
their consistency and stability of performance before some
potential genotypes are released for commercial cultivation
(Patel and Acharya 2011). The pooled variance due to
genotypes was found highly significant for all the characters
(Table 1). Highly significant pooled deviation for all the
characters except days to flowering, pods/cluster, pod length,
seeds/pod and seed weight was o bserved. These
observations were in close agreement with the findings of
Yan et al. (1995) who observed highly significant pooled
deviation for days to maturity, clusters/plant, pods/cluster,
pods/plant and yield in French bean genotypes evaluated
under contrasting soils. This highly significant pooled
Rungsung and Changkija: Stability analysis for seed yield and its component traits in rice bean (Vigna umbellata)
283
Table 1. Pooled analysis of variance (mean squares) for stability of various characters in rice bean genotypes
Sources of
Variation
Df
Genotype (G)
49
5
Environment(E)
G x E (linear)
245
Pooled deviation 200
Pooled error
600
Days to
Days to Plant height Pods/
(cm)
flowering maturity
Cluster
425.00**
53.04**
1.11
1.00
2.32
888.37** 12137.83**
77.01**
165.91**
1.89**
18.57**
2.38**
14.77**
2.90
18.58
2.29**
0.76
0.01
0.01
0.02
Mean squares (MS)
Clusters/
Pods/
Pod
plant
length
plant
(cm)
419.88** 8450.27** 13.76**
34.77** 2276.67**
0.67
1.96**
37.48**
0.01
1.69**
24.79**
0.02
1.84
42.41
0.02
Seeds/ Biomass/ 100-seed Seed yield/
plant
weight
Pod
Plant
(gm)
(gm)
(gm)
6.55** 252.25** 365.12** 40016.45**
0.39 119.39**
0.71
4333.92**
0.03
4.35**
0.06
169.50**
0.02
5.31**
0.10
75.77**
0.03
5.58
0.11
86.92
*, **: Significant at P = 0.05 & 0.01, respectively
Table 2A. Estimates of stability parameters of rice bean genotypes for various characters
Genotype
NRB-1
NRB-2
NRB-3
NRB-4
NRB-5
NRB-6
NRB-7
NRB-8
NRB-9
NRB-10
NRB-11
NRB-12
NRB-13
NRB-14
NRB-15
NRB-16
NRB-17
NRB-18
NRB-19
NRB-20
NRB-21
NRB-22
NRB-23
NRB-24
NRB-25
NRB-26
NRB-27
NRB-28
NRB-29
NRB-30
NRB-31
NRB-32
NRB-33
NRB-34
NRB-35
NRB-36
NRB-37
NRB-38
NRB-39
NRB-40
NRB-41
NRB-42
NRB-43
NRB-44
NRB-45
NRB-46
NRB-47
NRB-48
NRB-49
NRB-50
Pop.
mean
Days to flowering
Mean
bi
S2d
122.06 1.13** -1.70
115.83 0.72 -1.08
97.72 0.23 2.54
111.95 0.41 -1.24
125.06 0.90* -1.53
124.11 0.76 -0.89
109.28 0.13 -0.93
123.95 1.17** -2.04
125.39 1.04* -0.62
115.94 0.88* -1.89
108.89 1.34** -1.43
126.00 0.48 -1.68
125.72 0.33 -1.72
102.61 1.09* -1.60
107.28 0.46 -1.30
132.56 0.84 -1.54
114.89 0.79 -1.72
124.89 0.40 -1.09
118.78 0.37 -0.46
122.67 0.65 -1.60
105.11 0.77 -1.80
123.89 0.56 -1.97
123.28 0.94* -1.92
113.83 0.28 -2.16
120.72 1.22** -2.00
122.17 0.97* -2.13
121.89 0.89* -1.18
121.28 1.14** -2.02
121.28 1.21** -1.98
122.39 0.78 -1.32
121.06 1.23** -0.63
103.45 1.07* -1.48
128.39 1.47** -1.05
114.44 1.35** -1.82
126.61 1.89** -1.10
105.50 0.61 -1.56
106.06 0.61 -2.06
113.00 0.95* -1.56
124.50 1.99** -1.97
125.17 0.69 0.31
122.72 1.20** -1.65
115.50 1.70** -2.09
123.50 1.13** -2.10
121.00 0.95* -1.46
122.56 1.78** -2.09
122.56 1.29** -1.87
126.61 1.54** 0.27
95.11 1.19** -1.39
115.72 1.28** -1.92
121.06 1.51** -1.73
118.12 0.54 -1.42
Days to maturity
Mean
bi
S2d
158.61 1.07 -1.64
142.33 1.30* -1.05
121.78 0.97 -2.52
147.33 0.89 -1.85
163.83 0.45 -1.74
165.67 1.11* -2.31
136.22 0.81 -2.79
165.06 0.50 -1.31
165.17 0.82 -2.17
154.89 0.75 -1.50
145.22 0.95 -1.94
162.17 1.21* 0.09
159.44 1.41* -1.69
132.22 1.47** -2.64
141.67 1.53** -2.30
168.11 1.73** -2.43
145.72 0.83 -1.63
164.11 0.54 -2.62
161.11 1.83** -0.31
163.94 0.60 -1.00
141.78 1.38* -2.43
162.56 1.17* -2.82
164.72 0.96 -1.91
139.22 1.20* -1.78
152.50 0.44 -2.23
158.28 1.32* -0.88
148.44 1.31* -0.24
163.33 0.57 -2.52
153.72 0.48 -1.88
161.94 1.49** -2.06
159.89 1.48** 1.36
130.11 -0.33 -0.75
165.06 0.46 -1.51
145.94 0.72 -1.94
151.33 2.22** -0.77
136.94 0.85 -1.28
139.06 0.19 3.77
135.72 1.10* 0.84
161.56 0.50 -1.01
162.22 0.43 -0.67
144.72 1.03 -2.37
157.22 1.16* 4.37*
147.06 1.72** -1.10
161.28 1.18* 0.00
145.61 1.46** -2.52
145.78 0.21 1.77
150.56 1.15* 1.66
120.83 1.82** -0.78
141.50 1.09 1.81
163.67 0.84 -1.48
151.54 0.65 -1.21
Plant height (cm)
Mean
bi
S2 d
117.20 0.61 -16.08
129.33 0.59 -16.63
178.11 0.94 -15.59
68.14 0.75 -7.56
118.81 0.60 -17.17
192.64 1.32 -9.20
76.36 0.70 -6.63
272.72 0.56 -9.47
211.14 1.02 -14.49
254.70 -0.54 25.22
145.25 0.63 -10.59
137.78 0.99 -17.30
179.17 1.56 -17.86
167.72 0.30 -5.88
185.67 1.62 -13.52
211.47 1.54 -7.64
154.97 -0.41 0.97
172.67 0.11 -9.17
164.36 0.41
2.85
5.39
196.06 1.42
98.70 0.40 -14.31
143.83 2.22* -6.86
192.81 -0.28 4.13
181.70 1.03 -9.88
204.67 1.71 -8.49
185.97 2.04* -17.88
193.61 0.71
0.48
177.97 2.16* 4.08
244.22 2.78** -8.20
223.92 1.77 -4.69
107.06 1.32 -15.05
184.72 0.67 -1.21
165.67 1.53 -13.17
199.67 0.42 -10.53
187.80 -0.15 -13.18
106.16 -0.23 -7.58
107.64 1.28 -15.10
99.61 0.14 -15.15
124.00 0.30 -3.30
173.89 0.99 -3.39
183.22 1.67 -2.66
191.92 1.35 -13.98
154.33 -1.10 2.42
252.22 0.70 -2.74
203.30 2.65** 2.21
185.47 0.16 -15.95
176.97 1.34
6.45
117.44 0.16 -18.05
150.11 1.32 -14.44
161.50 1.27 -4.13
168.29 0.74 -7.61
Pods/Cluster
Mean
bi
S2 d
2.54 1.15** -0.01
2.47 0.75* -0.01
3.45 0.78* 0.00
2.50 1.26** -0.05
1.79 1.05** -0.01
3.46 1.24** -0.01
2.42 1.19** -0.01
3.55 1.13** -0.01
3.56 0.94* -0.01
2.56 1.18** -0.02
2.98 0.96** 0.00
3.38 0.59 0.00
3.35 1.25** -0.01
3.53 0.86* -0.01
3.37 0.70 -0.02
3.52 0.93* -0.01
3.09 2.05** 0.00
3.32 0.78* -0.01
3.44 0.88* -0.02
3.61 0.90* -0.01
1.64 0.83* -0.01
2.61 1.06** -0.01
3.23 0.75* -0.01
3.56 1.05** 0.00
3.35 0.85* -0.02
1.63 0.92* -0.02
2.48 1.13** -0.01
3.51 1.10** -0.02
2.69 0.84* -0.01
2.98 0.23 -0.01
1.97 1.38** -0.01
2.94 1.31** 0.00
3.49 1.04** -0.01
3.51 0.96** 0.00
3.56 0.75* -0.01
2.80 0.87* 0.00
2.65 0.76* -0.01
2.60 0.64 -0.02
2.50 0.99** -0.01
2.79 1.58** 0.00
2.52 1.57** -0.01
2.33 0.69 -0.01
3.45 1.19** -0.01
4.16 1.18** 0.00
3.53 0.69 -0.01
3.03 0.75* 0.00
2.46 0.84* -0.01
3.27 1.03** -0.01
4.20 0.95* -0.02
2.61 1.16** -0.01
3.00 0.59 -0.01
Clusters/plant
Mean
bi
S2d
36.72 1.33 -1.43
34.81 2.80** 0.56
40.94 2.00** -1.76
35.94 0.87
2.08
30.50 1.99** -0.61
30.08 3.04** 2.03
36.69 0.75 -1.42
43.83 2.37** -0.12
37.67 1.44* -1.68
36.33 2.04** 0.74
44.22 1.06 -1.68
35.44 1.15
0.42
33.11 1.69* -1.03
25.42 1.75* -1.50
23.06 1.08 -1.34
38.53 0.96 -0.72
32.61 -0.26 -1.15
42.39 0.83
2.04
31.86 0.53
2.34
34.14 0.67 -0.49
34.11 0.39 -0.97
24.56 0.96 -1.74
35.17 -0.55 -1.24
34.92 1.82** 0.58
51.25 0.66 -1.47
31.56 0.68 -0.16
27.06 0.76
0.24
35.14 0.69 -0.62
49.19 1.05 3.86*
43.97 0.10
1.31
27.14 0.77 -0.71
28.86 0.95 -0.21
34.50 0.46 -0.19
45.53 0.14 -0.92
41.72 0.53 -1.32
44.06 1.73* -0.15
26.97 1.73* 0.65
22.17 0.59
2.49
17.61 0.63 -1.17
37.94 1.25 -1.28
18.75 0.45 -1.08
35.47 1.02 -0.43
26.53 0.55 -0.51
34.36 0.35 -0.42
25.89 2.07** 0.29
12.81 0.15 -0.93
26.33 0.96 -0.35
48.78 0.27 -1.31
41.86 -0.04 -0.40
25.86 0.72 5.18**
33.89 0.63 -0.35
Pods/plant
Mean
bi
S 2d
87.91 1.17** -28.09
80.58 1.17** -12.69
135.83 1.24** -8.81
84.52 1.18** 6.98
49.39 0.96** -36.13
98.87 2.02** -17.60
83.26 1.07** -40.06
150.22 2.05** -12.54
128.47 1.30** -35.46
87.77 1.41** -27.01
126.33 1.19** 7.10
114.02 0.89** -29.74
105.42 1.47** -25.98
84.49 1.20** -35.32
72.23 0.88** -36.09
130.24 1.19** -19.59
95.32 1.31** -34.79
135.29 1.22** -11.36
103.94 0.73* 8.30
118.01 1.05** -15.20
50.43 0.73* -35.98
58.84 0.81* -37.74
108.24 0.37 -27.40
119.05 1.64** 20.77
165.97 1.05** -39.52
45.75 0.71* -36.09
61.57 0.72* -22.91
117.91 0.98** -25.26
126.88 0.98** -11.36
125.26 -0.04 -0.35
48.02 0.83* -41.10
79.41 1.02** -24.09
115.07 0.88** -4.63
154.11 0.74* -7.57
143.33 0.92** -24.39
118.18 1.28** 51.85
65.84 0.93** -13.49
52.19
0.58 -13.90
0.56 -34.83
38.65
100.69 1.71** -29.95
41.81 0.73* -37.41
77.01
0.58 -34.25
86.00 0.85** -7.41
137.51 1.09** -18.64
85.88 1.03** -10.52
33.17
0.16 -40.20
59.52 0.74* -26.64
154.00 0.98** -22.51
168.95 0.53 -8.57
63.63 0.73* 11.93
97.50
0.39 -19.12
284
Table 2B. Estimates of stability parameters of rice bean genotypes for various characters
Genotype
Pod length (cm)
Mean
bi
S2 d
NRB-1
-0.02
9.61
0.75
NRB-2
-0.02
9.29
0.98
NRB-3
-0.01
9.30
0.61
NRB-4
-0.02
8.28
1.16*
NRB-5
-0.02
8.23
0.79
NRB-6
-0.02
8.62
0.57
NRB-7
-0.01
8.43
0.87
NRB-8
-0.01
9.51
1.17*
NRB-9
-0.02
10.24
0.72
NRB-10
-0.02
11.24
0.76
NRB-11
-0.02
9.26
0.83
NRB-12
-0.02
7.36
1.10
NRB-13
8.05 1.73**
-0.01
NRB-14
-0.02
9.25
1.19*
NRB-15
-0.01
8.45
0.30
NRB-16
-0.02
7.41
1.08
NRB-17
-0.02
7.39
0.99
NRB-18
11.25 1.19*
-0.01
NRB-19
8.47
-0.02
1.02
NRB-20
8.81 2.01** 0.07**
NRB-21
7.35
-0.02
1.22*
NRB-22
8.39
-0.02
0.93
NRB-23
12.69
-0.02
0.84
NRB-24
9.28
-0.02
0.80
NRB-25
10.49 1.25*
-0.02
NRB-26
10.60
-0.02
0.72
NRB-27
8.91
0.01
1.01
NRB-28
11.08 2.02**
-0.01
NRB-29
12.57
-0.02
0.83
NRB-30
13.12 1.31*
-0.01
NRB-31
11.03 1.45*
-0.01
NRB-32
6.44
-0.02
0.56
NRB-33
-0.02
8.50
0.97
NRB-34
-0.01
10.08 1.12*
NRB-35
-0.02
8.41
1.07
NRB-36
-0.01
8.23
0.91
NRB-37
-0.02
6.51
0.92
NRB-38
-0.02
7.54
0.91
NRB-39
-0.01
7.55
1.08
NRB-40
-0.02
8.56
0.87
NRB-41
-0.02
8.29
0.70
NRB-42
-0.01
10.52
0.72
NRB-43
-0.01
10.45
0.94
NRB-44
0.00
10.15 1.25*
NRB-45
0.00
9.52
0.66
NRB-46
0.00
9.09
0.45
NRB-47
-0.01
10.04 1.65**
NRB-48
-0.01
10.15
0.79
NRB-49
-0.01
10.42
0.81
1.14*
NRB-50
-0.02
7.46
Pop. mean 9.24
0.82
-0.01
Mean
8.32
7.87
8.01
6.52
5.94
4.55
6.51
6.39
6.50
6.83
7.51
6.50
6.24
8.13
7.01
6.23
6.32
7.42
7.22
7.40
6.30
4.47
8.92
7.79
7.24
7.36
6.33
6.71
8.01
8.77
6.68
5.55
7.57
6.43
5.54
7.31
5.22
6.26
6.03
7.69
6.75
6.90
7.17
6.36
5.61
5.50
6.07
8.35
9.03
5.65
6.82
Seeds/pod
bi
0.99
2.01**
-0.51
1.37
1.23
1.96**
1.07
0.88
1.64*
1.46
0.25
3.77**
0.79
0.30
2.46**
1.38
0.59
-0.10
-0.19
0.49
1.81*
1.17
0.30
2.24**
0.04
0.60
0.91
2.67**
0.30
1.66*
-0.28
-0.34
0.48
1.48
0.30
1.31
0.41
0.65
2.13**
1.59*
0.18
2.82**
0.23
-0.05
0.98
1.18
0.34
0.51
1.03
1.15
0.60
S2d
-0.03
0.00
-0.02
-0.03
-0.01
-0.03
-0.01
-0.02
-0.03
-0.02
0.01
0.08**
-0.02
-0.01
-0.01
-0.02
-0.02
-0.01
-0.03
-0.03
-0.03
-0.02
-0.01
0.01
-0.02
-0.02
0.00
0.11**
-0.01
-0.03
0.00
-0.02
-0.03
-0.03
0.02
-0.03
-0.01
-0.02
-0.02
-0.03
0.01
-0.02
0.00
0.00
-0.01
-0.01
-0.01
-0.02
-0.02
-0.01
-0.02
Biomass/plant (g)
Mean
bi
S2d
24.17
0.86
-4.66
27.22
0.85
-3.32
31.70
0.16
-0.83
18.42
1.42
-3.43
24.39
0.68
-3.09
31.80
0.77 14.25**
26.97
-0.64 47.86**
43.14 2.08** 29.06**
37.64
0.15
-1.14
42.61
0.80
-4.99
26.03
0.03
-3.27
28.92
0.71
-3.64
30.48
1.33 20.24**
25.42
0.93
7.35
34.39
0.50
0.98
39.75
1.33
-3.12
26.92
0.73
-5.17
35.42
1.25
-3.79
32.56
0.53
-1.88
34.56
1.54*
-5.39
19.81
0.63
-3.89
27.06
0.27
-0.83
34.28
1.03
-3.05
32.67
1.70*
-4.43
37.03
1.07
-5.26
32.72
1.41
-3.91
36.39
0.44
-0.07
33.64
1.30
-3.37
38.97
0.47
-2.22
42.61
1.38
-3.82
23.86
1.26
-5.52
35.05
0.41
-1.99
33.00
1.61*
-4.16
34.22
1.08
-4.36
35.42
1.13
5.32
22.67
0.71
-2.46
26.50
0.84
-5.20
18.06
1.31
-3.94
22.61
1.52*
-3.61
35.19
1.85*
-3.38
33.20
1.46
-2.56
34.72 2.19**
0.13
29.17
1.33
-4.33
48.89
1.80*
-3.49
35.45
1.52*
-3.36
31.86
0.13
6.04
33.00
1.70*
-3.79
25.11
1.39
-2.51
27.97
-0.02
-1.41
31.86
1.92*
-0.37
31.51
0.81
-2.50
deviation also indicates that considerable genetic diversity is
locked up in the bean. Such non-linear deviation might also
be of practical value to construct and test the utility of the
multiple regression models to know more critically the complex
mechanism of adaptation. The genotype × environment
interactions for most of the characters were also highly
significant which further substantiated differences among
genotypes and their inconsistent response to different
environments (Table 2A,B). However, the magnitude of
100-seed weight (g)
Mean
bi
S 2d
7.37
0.70
-0.11
8.02
-0.45
-0.08
7.62
0.76
-0.09
10.35
0.19
-0.16
14.65
0.87
-0.06
26.98
0.61
-0.03
12.23
1.91
-0.06
23.23
0.90
-0.07
25.58
1.68
-0.06
29.04
1.65
-0.07
8.37
1.06
-0.11
7.42
0.04
-0.07
15.40
1.17
-0.09
11.87
0.00
-0.08
14.11
1.15
-0.05
6.34
0.28
-0.10
7.79
0.56
-0.05
21.22
0.90
-0.06
14.36
0.24
-0.10
11.34
0.88
-0.10
7.36
0.24
-0.10
25.79
1.36
-0.08
22.38
1.09
-0.09
18.32
1.11
-0.10
26.23
0.67
-0.08
20.09
1.13
-0.08
23.19
1.26
0.00
27.24
0.98
-0.07
28.36
1.24
-0.10
29.35
0.95
-0.10
22.18
-0.07
0.32
-0.10
7.31
1.17
7.33
-0.10
0.70
18.51
-0.03
0.34
20.39
-0.10
1.11
7.42
1.47
-0.10
11.34
-0.10
1.43
13.79
-0.08
1.89
11.24
-0.08
0.80
7.39
1.10
-0.09
11.95
-0.04
0.72
-0.02
25.75
1.89
-0.08
24.23
0.71
25.41
-0.09
0.74
-0.04
28.18
0.97
-0.08
27.71 2.06*
-0.09
23.01
-0.49
-0.04
8.21
1.13
-0.10
11.37
1.29
-0.10
12.34
0.65
16.73
0.88
-0.08
Seed yield/plant (g)
Mean
bi
S2d
50.01
0.62
-79.28
46.98
0.55
-66.80
79.11
0.65
-58.18
53.27
0.84
-75.98
39.39
0.69
-79.24
117.41 2.12**
-29.05
62.60
0.87*
-80.76
219.41 2.74**
-73.10
209.53 2.22**
-68.99
170.85 2.50**
-16.03
75.77
0.68
-74.62
51.06
0.63
-80.28
97.54 1.24**
-56.74
77.71
0.75
-71.86
67.80
0.96*
-75.77
47.68
0.49
-80.84
43.09
0.59
-82.24
209.06 0.84
-68.71
103.95 0.62
-41.17
95.11
0.82
-64.65
19.68
0.34
-86.10
64.15
0.89*
-73.43
212.10 0.79
-5.66
166.50 2.39**
87.09
311.27 1.65** 302.31**
63.72
-73.14
0.87*
86.64
-1.23
0.77
212.10 2.13** 190.55*
284.08 1.77** 195.62*
318.69 0.63
731.22**
67.13
-71.57
0.83*
28.34
-81.71
0.34
59.91
-78.32
0.44
179.84 0.98*
-11.16
158.10 1.01*
-5.23
60.31
-51.93
0.70
35.17
-75.24
0.46
-61.40
41.14
0.48
22.48
-83.04
0.39
53.44
-83.11
0.95*
30.00
-83.03
0.41
-62.21
133.33 1.34**
145.60 1.29**
5.34
218.12 1.48**
-2.28
131.95 1.35**
71.51
46.63
-77.68
0.26
78.55
-75.78
0.72
101.48 0.85*
-74.79
169.35 0.80
-67.46
40.72
-64.62
0.40
109.16 0.60
-54.58
genotype × environment interaction for flowering, pods/
cluster, pod length, seeds/pod and seed weight was low and
non-significant indicating the consistent performance of
genotypes over variable environments for these characters.
As the stability of the genotypes for pod length was
concerned, ‘NRB-2’, ‘NRB-34’ and ‘NRB-43’ were rated the
most stable genotypes since they possessed good pod length
and showed non-significant mean square deviations from zero
along with regression coefficient values very close to unity
Rungsung and Changkija: Stability analysis for seed yield and its component traits in rice bean (Vigna umbellata)
(+0.98, +1.12 and +0.94, respectively). Similarly, ‘NRB-24’ and
‘NRB-34’ were found most stable for pods/cluster; ‘NRB-1’
and ‘NRB-49’ for seeds/pod; ‘NRB-12’, ‘NRB-16’ and ‘NRB42’ for clusters/plant; ‘NRB-14’, ‘NRB-32’ and ‘NRB-38’ for
flowering; ‘NRB-3’, ‘NRB-11’, ‘NRB-38’, ‘NRB-41’ and ‘NRB49’ for maturity; ‘NRB-23’, ‘NRB-25’, ‘NRB-34’ and ‘NRB-35’
for biomass; ‘NRB-20’, ‘NRB-25’, ‘NRB-28’, ‘NRB-29’, ‘NRB35’, ‘NRB-44’ and ‘NRB-48’ for pods/plant and ‘NRB-8’, ‘NRB18’, ‘NRB-23’, ‘NRB-24’, ‘NRB-26’, ‘NRB-27’, ‘NRB-28’, ‘NRB30’, ‘NRB-35’ and ‘NRB-45’ for seed weight. A perusal of
stability parameters for grain yield/plant shows that ‘NRB18’, ‘NRB-23’ and ‘NRB-49’ registered promising average grain
yield and regression coefficients less than unity (+0.84, +0.79
and +0.80, respectively) with non-significant mean square
deviations from regression, thus, revealing their less
sensitivity to environmental changes and hence will be better
adapted to poor environmental conditions. However, ‘NRB6’, ‘NRB-8’, ‘NRB-9’, ‘NRB-10’, ‘NRB-24’, ‘NRB-42’, ‘NRB43’, ‘NRB-44’ and ‘NRB-45’, which were also good performers
for grain yield showed highly significant regression coefficient
values (bi=+2.12, +2.74, +2.22, +2.50, +2.39, +1.34, +1.29, +1.48
and +1.35, respectively) and non-significant mean square
deviations. This clearly implies that these genotypes are highly
sensitive to environmental changes and therefore will be
suitable for favourable environments. ‘NRB-25’ and ‘NRB30’, despite the fact of being the highest-yielding genotypes,
had unpredictable performance across the environments as
indicated by their highly significant deviations from regression
and thus, were unstable for yield. The stability of yield is an
important characteristic to be considered when judging the
value of a cropping system relative to others (Piepho
2008). Among all the genotypes studied, ‘NRB-34’ and ‘NRB35’ were found most stable for yield/plant as they showed
285
high degree of stability irrespective of the environments. These
genotypes may be suitable for cultivation in Nagaland during
kharif season irrespective of locations, and may also be used
directly for breeding stable genotypes of rice bean. In like
manner, ‘NRB-3’, ‘NRB-11’, ‘NRB-38’, ‘NRB-41’and ‘NRB-49’
can be used in the breeding of rice bean for developing stable
and early-maturing genotypes.
REFERENCES
Ahuja A, Malhotra PK, Bhatia VK and Parsad R. 2005. Statistical
package for agricultural research (SPAR-2). Indian Agricultural
Statistics Research Institute (IASRI), New Delhi.
Bolivar A and Luis CZ. 2010. Impact of germination on phenolic
content and antioxidant activity of 13 edible seed species. Food
Chemistry 119: 1485–1490.
Danyali SF, Razavi F, Segherloo AE, Dehghani H and Sabaghpour SH.
2012. Yield stability in chickpea (Cicer arietinum L.) and study of
relationship among the univariate and multivariate stability
parameters. Research in Plant Biology 2: 46-61.
Eberhart SA and Russell WA. 1966. Stability parameters for comparing
varieties. Crop Science 6: 36-40.
Gupta S, Kozak M, Sahay G, Durrai AA, Mitra J, Verma MR, Pattnayak
A, Thongbam PD and Das A. 2009. Genetic parameters of selection
and stability and indication of divergent parents for hybridization
in rice bean [Vigna umbellata (Thunb) Ohwi and Ohashi] in India.
Journal of Agricultural Science 147: 581-588.
Patel JB and Acharya S. 2011. Stability analysis for grain yield in
fieldpea (Pisum sativum L.). Journal of Food Legumes 24: 150151.
Piepho HP. 2008. Methods for comparing the yield stability of cropping
systems. Journal of Agronomy and Crop Science 180: 193–213.
Yan X, Bube BE and Lynch JP. 1995. Genetic variation for phosphorus
efficiency of French bean in contrasting soil types II: yield response.
Crop Science 35: 1074-1099.
Sequence comparison of coat protein gene of Mungbean yellow mosaic India virus
isolates infecting mungbean and urdbean crops
NAIMUDDIN and M. AKRAM
Division of Crop Protection, Indian Institute of Pulses Research, Kalyanpur, Kanpur 208024, India; E-mail:
naimk@rediffmail.com
(Received: July 02, 2012 ;Accepted: November 14, 2012)
ABSTRACT
The coat protein (CP) gene sequence of ten isolates of
Mungbean yellow mosaic India virus (MYMIV), five from
different genotypes of each mungbean and urdbean were used
to study the intra-field variation in MYMIV during kharif 2009.
The CP gene was successfully amplified using primer pair NM1
5’ GTA TTT GCA (GT)CA (AT)GT TCA 3’ / NM2 5’ AGG DGT
CAT TAG CTT AGC 3’ designed using DNA sequence of
MYMIV isolates. The complete nucleotide sequence of the CP
gene of all the MYMIV isolates had single open reading frame
(ORF) of 774 bp and 257 amino acids. Analysis of CP gene
sequences of isolates from mungbean and urdbean genotypes
revealed that all the isolates among themselves had 99-100%
homology at amino acid level and 97-99% similarity at
nucleotide level. Isolates differed in amino acid composition
only at four locations. These isolates had 96-100% similarity
at amino acid level and 95-99% similarity at nucleotide level
with known MYMIV isolates. Results indicated that CP gene
was highly conserved among the isolates of MYMIV infecting
different genotypes in a field at Kanpur.
Key words:
Amino acids, Coat protein, MYMIV, Nucleotides,
PCR, Variation
Yellow mosaic disease occurs across the Indian subcontinent and is a major constraint to the production of most
of the warm-season legumes, particularly mungbean, urdbean
and soybean. Estimation of actual losses due to yellow mosaic
disease (YMD) in farmers’ field is difficult as these losses
vary from year to year and from variety to variety. However,
based on the incidence of YMD in mungbean, urdbean and
soybean, an annual loss of over US $ 300 million is estimated
in these crops (Varma et al. 1992). Yellow mosaic disease
occurs in a number of leguminous plants such as mungbean,
urdbean, cowpea (Nariani 1960, Nene 1973), soybean (Suteri
1974), horsegram (Muniyappa et al. 1975), lablab bean (Capoor
and Varma 1948) and French bean (Singh 1979). The YMD in
pulse crops is caused by whitefly transmitted Begomoviruses
such as Mungbean yellow mosaic virus (MYMV), Mungbean
yellow mosaic India virus (MYMIV) and Horsegram yellow
mosaic virus (HgYMV) across India (Malathi and John 2008).
These viruses are distinct species of the genus Begomovirus
under the family Geminiviridae and have bipartite genome
that consists of two components, viz., DNA A and DNA B. In
DNA A there are five to seven ORFs; two in virion sense
(AV1-coat protein and AV2-pre-coat protein) and others in
complementary sense (AC1-replication initiation protein, AC2transcription activator protein, AC3-replication enhancer
protein, AC4 and AC5). Coat protein (CP) is a multifunctional
protein and is involved besides encapsidation in three major
functions of viral pathogenicity-viral DNA replication, intra-,
inter-cellular movement and long distance transport of viral
genome. Most importantly, the vector transmission specificity
is controlled by CP gene.
Differences in the disease reaction of genotypes of
mungbean and urdbean in different locations particularly in
southern and northern zones of India are commonly reported.
This difference can be to some extent due to the different
virus species operating in these areas, MYMIV in north
(Usharani et al. 2004) and MYMV in south (Karthikayan et al.
2004). Difference in the severity of yellow mosaic in mungbean
and urdbean in a given location is also observed. To address
this we have tried to look whether there is a possibility of
intra-field variability among MYMIV isolates at Kanpur. CP
gene i s the mo st conserved sequence in the genus
Begomovirus (Harrison et al. 2002) and is also known to control
functions of virus pathogenicity. Also, this gene has long
been considered important for establishing the identity of a
distinct geminivirus (Rybicki 1998). CP gene was, therefore,
selected to analyze the variability in the isolates of MYMIV
infecting mungbean and urdbean in a field at Kanpur.
MYMIV isolates: During kharif 2009, young leaves showing
characteristic yellow mosaic symptoms were collected from
the field-infected plants of five mungbean (T44, Meha, LGG
407, PDM 54, Kopergaon) and five urdbean (Shekhar, PDU 1,
AKU 9904, Barabanki Local, TPU 4) genotypes and brought
to the laboratory and used to isolate total DNA using E.Z.N.A.®
Pl ant DNA Mini prep Kit (U.S.A.) according to t he
manufacturer’s instructions. DNA from one healthy plant of
each genotype was also isolated. Total DNA was used as
template in PCR reactions.
Primers: A set of degenerate primers (NM1 5’GTA TTT GCA
(GT)CA (AT)GT TCA AGA 3’/NM2 5’AGG (AGT)GT CAT
TAG CTT AGC 3’) was designed using DNA sequence of
different MYMIV isolates. Primers were designed to get the
complete coat protein gene of MYMIV without cloning by
Naimuddin et al.: Sequence comparison of coat protein gene of MYMI virus isolates
taking about 100 extra nucleotides on both sides of the gene.
The complete CP gene sequence was extracted from the direct
sequencing data of the PCR product.
Thermal conditions: PCR was performed in T1 Thermalcycler,
Biometra® programmed for 35 cycles with one step of initial
denaturation for 2.5 min., and denaturation for 45 s at 94°C, 1
minute annealing at 54°C and 1 min for extension at 72°C
followed by one step final extension for 10 min at 72°C. PCR
assays were conducted with Easy-DoTM PCR PreMix (SBS
Greentech Co., Ltd.) in total reaction mixture volume of 50 µl.
Experimental control was a PCR master mix, in which the
template DNA was 2 µl (50ng/µl) of healthy leaves of
corresponding genotypes. Following inputs were added to
make a total reaction mixture volume of 50 µl : DNA template
(50ng/µl)-2µl, Primer NM1 (50 pmole/µl)-1µl, primer NM2 (50
pmole/µl)-1µl and dH2O - 46µl.
Electrophoresis, sequencing and analysis: PCR amplicons
were analyzed in 1% agarose gel in Tris-acetate EDTA (TAE)
containing ethidium bromide @ 0.1%. The gel was observed
under UV trans-illuminator and photographed. PCR product
of CP gene was purified using E.Z.N.A®. Gel Extraction Kit
(USA) and sequenced through M/S R.D. Applied Biosciences,
Ltd. New Delhi. Sequence data were blasted using NCBI data
base. Multiple sequence alignment and phylogram were
generated using CLUSTAL W version 1.8 3 (http: //
www.genome.jp/tools/clustalw). Cluster phylogram illustrating
the phylo geneti c rel ationship was inferred using the
Neighbor-Joining method (Saitou and Nei 1987) based on the
multiple alignments of nucleotide sequences of CP genes of
the MYMIV isolates under study with corresponding gene of
known isolates of MYMIV and MYMV. The bootstrap
consensus tree was inferred from 1000 replicates (Felsenstein
1985). The evolutionary distances were computed using the
p-distance method (Nei and Kumar 2000) and are in the units
of the number of transitional differences per site. The analysis
involved 26 nucleotide sequences conducted in MEGA5
(Tamura et al. 2011). Sequences of CP gene of the isolates of
MYMIV infecting five genotypes of mungbean and urdbean
in an experimental field at Kanpur were compared.
287
Symptomatology and PCR amplification: The first symptom
of YMD in mungbean and urdbean appeared as small interveinal yellow spots in young leaves. In subsequently
emerging leaves symptoms became more conspicuous and
appeared as irregular yellow and green patches. Inter-veinal
area or even the whole lamina often turned completely yellow
in highly susceptible genotypes. The puckering in lamina
which is some times reported in YMD (Malathi and John 2008)
was not observed in any of the genotypes used in the present
study. There was however, observed some reduction in lamina
size which was more in urdbean than in mungbean genotypes.
Yellow patches on pods were observed more in the case of
mungbean than in the urdbean genotypes.
PCR products from all the YMD-affected samples of
mungbean (5 nos.) and urdbean (5 nos.) when analyzed in gel
yielded amplicon of expected size (~950bp), indicating
involvement of MYMIV (Fig. 1). No amplicon was observed
in PCR products from healthy plants indicating no infection
by MYMIV in plants that were free from yellow mosaic
symptoms. These results indicate that the primers designed
worked well to amplify the fragment (containing CP gene) of
DNA A of the virus genome. The primers used in this study
have also been exploited successfully to detect the MYMIV
infection in cowpea (Naimuddin and Akram 2009) and in
accessions of wild Vigna (Naimuddin et al. 2011). These
primers may be routinely used for the PCR-based detection of
MYMIV in pulses.
Fig 1.
Gel electrophoresis of PCR amplified products of CP
gene of MYMIV using NM1/NM2 primer pair. Lane
M=1 kb DNA ladd er, lane 1-5= CP amp lified
products with DNA of diseased mungbean plants, lane
6-10= amplified products with DNA of dis eased
urdbean plants.
Table 1. Per cent identity of coat protein gene of MYMIV isolates at amino acid (below) and nucleotide levels (above)
MYMIV-Ub01
MYMIV-Ub02
MYMIV-Mb03
MYMIV-Mb01
MYMIV-Mb02
MYMIV-Ub05
MYMIV-Mb04
MYMIV-Ub04
MYMIV-Mb05
MYMIV-Ub03
MYMIVUb01
100
99
99
99
99
99
99
100
99
100
MYMIVUb02
97
100
99
99
99
99
99
99
99
99
MYMIVMb03
98
97
100
99
99
99
99
99
99
99
MYMIVMb01
97
98
97
100
99
100
100
99
100
99
MYMIVMb02
98
99
97
99
100
99
99
99
99
99
MYMIVUb05
98
98
98
98
98
100
100
99
100
99
MYMIVMb04
97
98
97
98
98
98
100
99
100
99
MYMIVUB04
99
98
99
98
98
98
99
100
99
100
MYMIVMb05
98
98
97
98
98
98
98
98
100
99
MYMIVUb03
97
98
97
98
98
97
97
97
97
100
288
Comparison of CP gene sequences: The PCR products were
purified and got directly sequenced. The CP gene sequences
were easily extracted from the sequence data. The isolates
from mungbean genotypes LGG 407, Meha, Kopergaon, PDM
54 and T 44 were designated as MYMIV-Mb01, -Mb02, -Mb03,
-Mb04 and -Mb05 and isolates from urdbean genotypes AKU
9904, Barabanki Local, TPU 4, Shekhar and PDU 1 were
designated as MYMIV-Ub01, -Ub02, -Ub03, -Ub04 and -Ub05
and their sequences were submitted to NCBI data base under
the accession nos. GQ387501, GQ387502, GQ387503,
GQ387504, GQ387505 andGQ387506, GQ387507, GQ387508,
GQ387509, GQ387510, respectively. The complete nucleotide
sequence of the CP gene of all the MYMIV isolates had single
open reading frame (ORF) of 774 base pairs and 257 amino
acids. This is in conformity with the earlier reports (Ilyas et al.
2010). The cluster phylogram based on multiple alignment of
the nucleotide sequence of the CP gene of 10 isolates under
study and 8 isolates of each MYMIV and MYMV indicated
that all the 10 isolates belonged to MYMIV species as they
formed cluster with other known isolates of MYMIV (Fig. 2).
Fig 2.
Cluster phylogram illustrating the phylogenetic
relationship between MYMIV and MYMV isolates
Blast search results showed that the isolates under
study had 95-99% identity at nucleotide level and 96-100%
identity at amino acid level with other isolates of MYMIV
available at NCBI database. Comparison of CP gene of all the
ten isolates under study revealed among themselves 97-99%
similarity at nucleotide level and 99-100% similarity at amino
acid level (Table 1). Isolate MYMIV-Ub01 had identical amino
acids with MYMIV-Ub04 and MYMIV-Ub03. Amino acids
sequence of isolate MYMIV-Mb01 was identical to MYMIVUb05, MYMIV-Mb04, and MYMIV-Mb05. Review of literature
indicated non-availability of information on intra-field
diversity in MYMIV based on CP gene sequence. However,
recently Ilyas et al. (2010) studied diversity based on full
sequence of DNA A of MYMIV isolates from different parts
of Pakistan and indicated 93.8-99.5% nucleotide sequence
identities with MYMIV sequences available in the database.
CP gene of MYMIV isolates from different hosts was shown
to have 97-99% nucleotide sequence similarity (Sachan et al.
2010).
The amino acids of CP gene of the isolates under study
differed among themselves at 4 positions (Fig. 3). All the
isolates have threonine at position 5 of amino acids except
MYMIV-[Mb02], which had proline at this position. Amino
acid at position no. 6 was tyrosine in seven isolates, whereas
in three isolates (Ub01, Ub03 and Ub04), this position had
phenylalanine. The other differences in amino acids were
observed at position no. 209 and 232. At position no. 209 all
isolates had tyrosine except MYMIV-Mb02 in which this
position had histidine. Similarly, at position no. 232, all isolates
had methionine except MYMIV-Ub02 isolate, which had
isoleucine. These results showed that there was little variation
in coat protein. Harrison et al. (2002) also reported that the
amino acid sequences of the coat protein of whitefly
transmitted begomoviruses were more conserved than the
remainder of the genome. In an earlier study, comparison of
amino acids of CP gene of MYMIV isolates from four different
hosts (cowpea, mungbean, urdbean and rajmash) revealed
differences in amino acids at six positions (Sachan et al. 2010).
Since CP gene is known to have multiple functions in
viral pathogenesis and also in vector specificity, comparison
of isolates operating in a field may yield some important
information pertaining to the diversity in them. Deletion of
two amino acids (75th and 150th) in N-terminal has been shown
to affect the systemic spread and pathogenicity of MYMIV
isolate in cowpea, mungbean and urdbean but not in rajmash
(Haq et al. 2011). Coat protein gene offers an opportunity to
decipher not only inter- and intra-field diversity in MYMIV
isolates but also in the host preference (Haq et al. 2011).
Further, it would be interesting to investigate the impact of
natural change in amino acids at these positions on virus
pathogenicity in different hosts. It may however, be concluded
that the changes in amino acids in domains of coat protein
that regulate viral pathogenesis may also be responsible for
Naimuddin et al.: Sequence comparison of coat protein gene of MYMI virus isolates
Fig 3.
289
Multiple alignment of amino acids sequences of MYMIV isolates. The total length of deduced amino acid in CP gene of
MYMIV is 257. MYMIV isolates of mungbean (Mb01=LGG407, Mb02=Meha, Mb03=Kopergaon, Mb04=PDM54,
Mb05=T44) and isolates of urdbean (Ub01=AKU9904, Ub02=Barabanki local, Ub03=TPU4, Ub04=Shakher, Ub05=PDU1).
Dots indicate the similar amino acid.
290
the temporal variation in disease expression in mungbean and
urdbean in a given location.
REFERENCES
Capoor SP and Varma PM. 1948. A new virus disease of Dolichos
lablab. Current Science 19:248-249.
Felsenstein J. 1985. Confidence limits on phylogenies: An approach
using the bootstrap. Evolution 39:783-791.
Haque QMI, Jyothsna P, Ali A and Malathi VG. 2011. Coat protein
deletion mutation of Mungbean yellow mosaic India virus
(MYMIV). Journal of Plant Biochemistry and Biotechnology 20:
182-189.
Harrison BD, Swanson MM and Fargette D. 2002. Begomovirus coat
protein: serology, variation and functions. Physiological and
Molecular Plant Pathology 60: 257-271.
Ilyas M, Qazi J, Mansoor S and Briddon RW. 2010. Genetic diversity
and phylogeography of begomoviruses infecting legumes in
Pakistan. Journal of General Virology 91: 2091-2101.
Karthikeyan AS, Vanitharani R, Balaji V, Anuradha S, Thillaichidambaram
P, Shivaprasad PV, Parameswari C, Balamani V, Saminathan M and
Veluthambi K. 2004. Analysis of an isolate of Mungbean yellow
mosaic virus (MYMV) with a highly variable DNA B component.
Archives of Virology 149: 1643-1652.
Malathi VG and John P. 2008. Geminiviruses infecting legumes. In: Rao
GP, Lava Kumar P, Holguin-Pena RJ eds. Characterization Diagnosis
and Management of Plant Viruses-Vegetables and Pulses Crops,
Studium Press LLC, USA, 97-123.
Muniyappa V, Reddy HR and Shivshankar C. 1975. Yellow mosaic
disease of Dolichos biflorus Linn. Current Research 4: 176.
Naimuddin and Akram M. 2010. Detection of mixed infection of
begomoviruses in cowpea and their molecular characterization based
on CP gene sequences. Journal of Food Legumes 23: 191-195.
Naimuddin, Akram M, Aditya Pratap, Chaubey BR and John JK. 2011.
PCR based identification of the virus causing yellow mosaic disease
in wild Vigna accessions. Journal of Food Legumes 24:14-17.
Nariani TK. 1960. Yellow mosaic of mung (Phaseolus aureus L.).
Indian Phytopathology 13: 24-29.
Nene YL. 1973. Viral diseases of warm weather pulse crops in India.
Plant Disease Reporter 57: 463-467.
Nei M. and Kumar S. 2000. Molecular Evolution and Phylogenetics.
Oxford University Press, New York.
Rybicki EP. 1998. A proposal for naming geminiviruses: A reply by the
Geminiviridae Study Group Chair. Archives of Virology 143:421424.
Sachan M, Mishra M, Naimuddin and Akram M. 2010. Amino acid
variability in coat protein gene of Mungbean yellow mosaic India
virus infecting pulses crops. Trends in Biosciences 3:166-168.
Saitou N and Nei M. 1987. The neighbor-joining method: A new method
for reconstructing phylogenetic trees. Molecular Biology and
Evolution 4:406-425.
Singh RN. 1979. Natural infection of bean (Phaseolus vulgaris) by
Mungbean yellow mosaic virus. Indian Journal of Mycology and
Plant Pathology 9:124-126.
Suteri BD. 1974. Occurrence of soybean yellow mosaic virus in Uttar
Pradesh. Current Science 43:689-690.
Tamura K, Peterson D, Peterson N, Stecher G, Nei M and Kumar S.
2011. MEGA5: Molecular Evolutionary Genetics Analysis using
Maximum Likelihood, Evolutionary Distance, and Maximum
Parsimony Methods. Molecular Biology and Evolution 28:27312739.
Usharani KS, Surendranath B, Haq QMR, Malathi VG. 2004. Yellow
mosaic virus infecting soybean in Northern India is distinct from
the species infecting soybean in southern and western India. Current
Science 86: 845-850.
Varma A, Dhar AK, Mandal B. 1992. MYMV transmission and control
in India. In: SK Green and D Kim (Eds), Mungbean Yellow Mosaic
Disease, Taipei Taiwan: Asian Vegetable Research and Development
Centre. Pp 8-27.
Bio-efficacy of some insecticides against H. armigera (Hubner) on chickpea (Cicer
arietinum L.)
P.S. SINGH, R.K. SHUKLA and N.K. YADAV
Department of Entomology and Agricultural Zoology, Institute of Agricultural Sciences, Banaras Hindu University,
Varanasi-221005, Uttar Pradesh, India; E-mail: pss_ento@yahoo.co.in
(Received: August 28, 2012; Accepted: November 13, 2012)
ABSTRACT
Chickpea ‘SAKI 9516’ was sown on Agricultural Research Farm
of Banaras Hindu University during Rabi season 2010-11 and
2011-12 for the bio-efficac y of certain new molecules
insecticides viz., HaNPV@ 250 LE/ha, spinosad 45 SC @ 100 g
a.i./ha, fenvalerate 20 EC@300 g a.i./ha, quinalphos 25EC@450
g a.i./ha, emamectin benzoate 5SG @ 11 g a.i./ha, azadirachtin
1500 ppm@5ml/lit, cartap hydrochloride 50 SP @ 500 g a.i./ha,
fipronil5 SC@50g a.i./ha and indoxacarb 14.5 EC @ 75 g were
applied twice at 15 days interval against gram pod borer, H.
armigera. Spinosad was found best among all the treatments
with 81.2% reduction in larval population over control followed
by indoxac arb, fipronil, e mamectin be nzoa te, cartap
hydrochloride, fenvalerate, and quinalphos, azadirachtin and
HaNPV. After 7 days of spraying of second application spinosad
was found best again in reduceding 79.8% larval population
followed by indoxacarb (with 78.3% reduction in larval
populations). The highest yield was obtained in spinosad (1.79
t/ha) while low in azadirachtin (1.06 t/ha). The cost: benefit
ratio was high in treatment fipronil (1: 8.2) while low in
treatment indoxacarb (1: 5.3).
The experiments were carried out under field conditions
at the Agriculture Research Farm of Institute of Agricultural
Sciences, BHU, Varanasi during the years 2010-12 on chickpea
variety ‘SAKI 9516’ in randomized complete block design
(RBD) having 10 treatments replicated thrice with plot size 7.2
m2 having 4 rows with 4 m long in each plot. The plant spacing
between row to row and plants to plant were maintained at 40
cm and 15 cm, respectively. The crop received two sprays, the
first being given at pod formation when the population crossed
Economic Threshold Level while the second spray was
imposed on the basis of insect population. The spray mixture
of each treatment was prepared by mixing of required quantity
of the insecticides formulations in water to make it equivalent
to 500 liters. Insecticides were applied during early hours of
the day where wind velocity was suitable for spraying. This
helped in avoiding the drift of spray fluid to the adjacent
plots. Due care was also taken to spray each plot uniformly
and the sprayer was thoroughly washed after spraying of
each insecticides.
Key words:
Bio-efficacy, Chickpea, H. armigera, Insecticides
Insect pests are one of the major constraints which limit
the production of chickpea. In India, it is attacked by 57 insect
species and about half a dozen of them are considered to be
of economic importance. Pod borer [Helicoverpa armigera
(Hubner)] is the most prominent insect species that causes
major economic damage to this crop. The yield loss in chickpea
due to pod borer was reported as 10-60 per cent in normal
weather conditions, while it was 50-100 per cent in favourable
weather conditions, particularly in the states where frequent
rains and cloudy weather prevailed during the crop season
(Patel 1979). Reports showed that H. armigera has developed
resistance to all the major insecticides classes and it has
become increasingly difficult to control its population in India.
H. armigera alone accounts for the consumption of half of
the total pesticides used in India for the protection of different
crops (Suryavanshi et al. 2008). For managing the insecticides
resistance in better way, different group of insecticides should
be preferred in lieu of a single group of insecticides. In this
study, an attempt was made to study the bio-efficacy of certain
new molecules against H. armigera in chickpea.
The number of H. armigera larvae was counted on 5
randomly selected plants in each plot. Pretreatment larval count
on 5 plants was made a day before spraying while post
treatment counts were taken 7 days after applications. The
per cent reduction in larval population was calculated on the
basis of number of larvae recorded in treated and control plots.
The dat a were stati stically analyzed after arc sin
transformation. The significance was tested by referring to
‘F’ tables of Fisher and Yates (1963).
To study the relative efficacy of various chemical
treatments, the percentage pod damage and plot yield were
recorded after the crop harvest in both cropping years. After
harvesting, all the pod of 5 plants of individual plot were
collected and pooled together. Finally, 100 pods were picked
up randomly and per cent pod damage was recorded. For
recording the yield, all the pods from individual treatment
were threshed and grain weight obtained were converted into
q/h. The cost: benefit ratio was calculated with the help of
costs of inputs and yield obtained.
The percentage reduction of the pod borer over
untreated check in different treatments was calculated using
292
Henderson and Tilton‘s (1955) formula as given below:
T C 
Per cent efficacy = 1   a  b  100
 Tb C a 
Where,
Ta = Population in the treated plot after spray
Tb= Population in the treated plot before spray
Ca = Population in the control plot after spray
Cb= Population in the control plot before spray
Per cent reduction in larval population: The pooled mean
after 7 days of 1st application showed that all treatments were
found significantly superior to control (Table 1). Spinosad 45
SC @ 100 g a.i./ha was the best treatment that reduced 81.2%
larval population; however, it did not differ significantly with
indoxacarb 14.5 EC @ 75 g a.i./ha, fipronil 5 SC@ 50 g/ha,
emamectin benzoate 5 SG @ 11 g/ha1, cartap hydrochloride 50
SP @ 500 g a.i./ha, and fenvalerate 20 EC@300 g a.i./ha which
reduced larval population by 79.7, 77.9, 76.9, 76.3 and 75.6 per
cents, respectively. Quinalphos 25EC @450 g a.i./ha also
reduced larval population by 74.4% while azadirachtin 1500
ppm and HaNPV @ 250 LE/ha reduced the population by 52.4
and 48.8%, respectively. The least effective and significantly
inferior treatment in comparison to others was HaNPV @250
LE/ha (with 48.8% reduction in population).
However, on the basis of pooled data, the reduction in
larval population at 7 days after second spray showed that all
the treatments were found significantly superior to control.
Thus, spinosad 45 SC @ 100 g a.i/ha was the best treatment
that reduced 79.8% larval population followed by indoxacarb
14.5 EC @ 75 g a.i./ha (with 78.3% reduction in larval
population). The other treatments viz., fipronil 5 SC@ 50 g
a.i./ha, emamaectin benzoate 5 SG @ 11 g a.i./ha, cartap
hydrochloride 50 SP @ 500 g/ha, quinalphos 25EC@450 g
a.i./ha, fenvalerate 20 EC@300 g a.i./ha, HaNPV @ 250 LE/ha
and azadirachtin 1500ppm reduced larval population by 75.2,
74.5, 72.6, 70.1, 70.0, 58.1 and 54.1 per cent, respectively. The
minimum reduction in larval population noted after second
spray in azadirachtin was 54.1%. The present findings were in
conformity with the findings of Kambrekar et al. (2012), Anandi
et al. (2011), Deshmukh et al. (2010) and Singh et al. (2008).
Pod damage: The bio-efficacy of different insecticidal
treatment on per cent pod damage by H. armigera was
evaluated under field condition. On the basis of pooled mean,
it showed that all the treatments were found significantly
superior to the control. The minimum pod damage was
observed in spinosad 45 SC @ 100 g a.i. /ha (2.3%) while
maximum damage was observed in azadirachtin 1500 ppm
treatment (12.8%). The other treatments showing increased
per cent pod damage included indoxacarb (3.6%), cartap
hydrochloride (4.7%), fipronil (5.4%), emamectin benzoate
(6.1%), fenvalerate (6.0%), quinalphos (8.3%) and HaNPV
(10.7%). Present findings were in conformity with the findings
of Singh et al. (2004) and Singh et al. (2008).
Seed Yield: During both years when efficacy was tested in
terms of grain yield, all the treatments proved significantly
Table 1. Efficacy of insecticidal treatment on H.armigera larval population during 2010-12*
Treatment details
HaNPV@ 0.5ml/lit.
Spinosad@0.44 ml/lit.
Fenvarlate @3ml/lit.
Quinolphos@3.6 ml/lit.
Emamactin Benzoate@0.44 gm/lit.
Azadirachtin@5 ml/lit.
Cartap hydrochloride @ 2gm/lit.
Fipronil@2ml/lit.
Indoxacarb@1.03ml/lit.
Untreated control
SEm(±)
CD (P=0.05)
CV (%)
Dosage
g a.i./ha
Per cent reduction in larval population over control
Pod damage (%)
Yield t/ha
7 days after first spraying
7 days after second spraying 2010-11 2011-12
Mean
2010-11 2011-12 Mean
2010-11 2011-12
Mean
2010-11 2011-12
Mean
250 LE
49.2
48.8
59.4
56.8
10.9
10.5
48.8 (44.3)
58.1 (49.7)
10.7 (19.3)
1.11
1.12
1.11
(44.5)
(44.3)
(50.4)
(48.9)
(19.3)
(19.2)
100
81.7
81.2
80.3
79.3
2.3
2.2
81.2 (64.3)
79.8 (63.3)
2.3 (8.6)
1.74
1.84
1.79
(64.7)
(64.3)
(63.6)
(62.9)
(8.7)
(8.5)
300
75.4
75.6
73.6
66.4
6.5
5.6
75.6 (60.4)
70.0 (56.8)
6.0 (14.2)
1.30
1.30
1.30
(60.3)
(60.4)
(59.1)
(54.5)
(14.8)
(13.6)
450
73.9
74.4
72.1
67.9
8.8
7.7
74.4 (59.6)
70.1 (56.9)
8.3 (16.7)
1.42
1.41
1.41
(59.3)
(59.6)
(58.1)
(55.5)
(17.3)
(16.1)
11
77.2
76.9
74.0
75.1
5.6
6.7
76.9 (61.3)
74.5 (59.7)
6.1 (14.3)
1.41
1.60
1.51
(61.5)
(61.2)
(53.3)
(60.0)
(13.6)
(14.9)
1500
54.2
52.4
57.8
50.2
13.3
12.4
52.4 (46.4)
54.1 (47.4)
12.8 (21.0)
1.06
1.07
1.06
ppm
(47.4)
(46.4)
(49.5)
(45.1)
(21.4)
(20.7)
500
76.4
76.3
72.4
72.8
4.5
4.9
76.3 (60.9)
72.6 (58.4)
4.7 (12.5)
1.37
1.61
1.49
(61.0)
(60.9)
(58.3)
(58.6)
(12.2)
(12.8)
50
78.5
77.9
76.2
74.3
5.4
5.4
77.9 (62.0)
75.2 (60.1)
5.4 (13.4)
1.55
1.62
1.58
(62.4)
(62.0)
(57.2)
(59.5)
(13.4)
(13.4)
75
79.5
79.7
78.8
77.8
4.0
3.2
79.7 (63.2)
78.3 (62.2)
3.6 (10.9)
1.57
1.67
1.62
(63.1)
(63.2)
(62.6)
(61.8)
(11.5)
(10.3)
28.4
28.4
28.4 (32.2)
0.82
0.83
0.83
(32.2)
(32.2)
1.6
1.6
1.6
1.5
1.5
1.5
0.5
0.5
0.5
0.01
0.01
0.01
4.8
4.8
4.8
4.5
4.6
4.6
1.4
1.4
1.4
0.03
0.03
0.03
5.4
5.4
5.4
5.3
5.2
5.3
5.0
5.0
5.0
4.9
4.8
4.9
*Figures in parentheses are Arc sine transformation
Singh et al.: Bio-efficacy of some insecticides against H. armigera (Hubner) on chickpea (Cicer arietinum L.)
293
Table 2. Benefit cost ratio of various insecticidal treatment on chickpea during 2010-12*
Treatments detail
HaNPV
Spinosad
Fenvarlate
Quinolphos
Emamactin
Benzoate
Azadirachtin
Cartap
hydrochloride
Fipronil
Indoxacarb
Untreated control
Increase in yield (t/ha) over
control
10-11
11-12
Mean
Cost of increased yield over control
(`/ha)#
10-11
11-12
Mean
Total cost of plant protection
(`/ha)*
10-11
11-12
Mean
Net Profit (`/ha)
ICBR
1112
1:4.3
1:4.9
1:8.4
1:4.5
RANK
0.28
0.91
0.48
0.60
0.29
1.01
0.46
0.58
0.29
0.96
0.47
0.59
8520
27420
14370
17970
12138
42294
19446
24192
10329
34857
16908
21081
2100
6936
1860
4200
2300
7136
2060
4400
2200
7036
1960
4300
10-11 11-12 Mean 1011
6420 9838 8129 1:3.1
20484 35158 27821 1:3.0
12510 17386 14948 1:6.7
13770 19792 16781 1:3.3
Mean
1:3.7
1:3.9
1:7.6
1:3.9
IX
VI
II
VIII
0.59
0.76
0.68
17610
32046
24828
4912
5112
5012
12698 26934 19816 1:2.6 1:5.3 1:3.9
VII
0.23
0.23
0.23
7020
10038
8529
1500
1700
1600
5520 8338 6929 1:3.7 1:4.9 1:4.3
V
0.54
0.78
0.66
16320
32634
24477
2800
3000
2900
13520 29634 21577 1:4.8 1:9.9 1:7.4
III
0.72
0.75
-
0.78
0.84
-
0.75
0.79
-
21720
22530
-
32928
35154
-
27324
28842
-
2860
4442
-
3060
4006
-
2960
4224
-
18860 29868 24364 1:6.6 1:9.8 1:8.2
18088 30512 24300 1:4.1 1:6.6 1:5.3
-
I
IV
X
*Includes cost of Sprayer, Labours for two applications. (Charges : Sprayer @`15/day/sprayer In 2010-11 and @`20/day/sprayer 2011-12, Labour
charges @`125/day/labour In 2010-11 and @`200/day/labour in 2011-12), Quantity of water per ha: 500 lit., No. of labours per ha: 2/spray,
# Price of chickpea @`30 /kg (2010-11) and @`42/kg (2011-12)
superior to control (untreated). The maximum and minimum
yield was obtained in both years from the plot treated with
spinosad and azadirchtin, respectively. On the basis of mean
data, the maximum yield obtained among the all treated plots
was under spinosad 45 SC @ 100 g a.i./ ha (1.79 t /ha) while
the minimum yield was obtained with azadirachtin 1500 ppm
(1.06 t /ha). Present findings are in conformity with the findings
of Kambrekar et al. (2012), Deshmukh et al. (2010) and Ladaji
(2004) who reported that maximum yield could be obtained
with spinosad in comparison to other treatments.
Economics: The cost benefit ratio of various insecticidal
treatments on chickpea during both years showed that use of
insecticides against H. armigera larvae increased the yield.
The pooled mean data showed maximum cost benefit ratio
(1:8.2) in the plots treated with fipronil 5 SC@ 50 g a.i. /ha
while the lowest cost benefit ratio (1: 3.7) was obtained in
HaNPV @ 250 LE/ha treated plots. Present findings are in
conformity with the findings of Singh et al. (2004) and Singh
et al. (2008).
Based on the two years study, it was inferred that
spinosad 45 SC @ 100 g a.i. /ha was the best treatment so far
to due maximum larval population reduction, minimum per cent
of pod damage and maximum seed yield. However, the maximum
cost: benefit ratio was under fipronil 5 SC@ 50 g a.i. /ha.
REFERENCES
Anandhi DMP, Elamathi S and Simon S. 2011. Evaluation of biorational insecticides for management of Helicoverpa armigera in
chickpea: Annals of Plant Protection Sciences. 19: 207-209.
Chaudhary RRP and Sachan RB. 1999. Comparative efficacy and
economics of some insecticides against gram podborer Helicoverpa
armigera (Hubner) in chickpea in western plain of Uttar Pradesh.
Bhartiya Krishi Anusandhan Patrika 10: 159-164.
Deshmukh SG, Sureja BV, Jethva DM and Chatar VP. 2010. Estimation
of yield losses by pod borer Helicoverpa armigera (Hubner) on
chickpea. Legume Research 33: 1, 67-69.
Fisher RA and Yates F. 1963. Statistical tables for biological, agricultural
and medical research. 6. Aufl. Oliver & Boyd, London. 146 S. Preis
30 s.
Henderson CF and Tilton EW. 1955. Pests with acaricides against the
brown wheat mite. J. Economic Entomology 48: 157-161.
Kambrekar DN, Somanagouda G, Basavarajappa MP and Halagalimath
SP. 2012. Effect of different dosages of Emamectin benzoate 5 SG
and Indoxacarb 14.5 SC on pod borer (Helicoverpa armigera)
infesting chickpea. Legume Research 35: 13-17.
Ladaji RN. 2004. Management of chickpea pod borer, Helicoverpa
armigera (Hubner) using indigenous materials and newer insecticides.
M. Sc. (Ag) Thesis, UAS, Dharwad (India).
Panse VG and Sukhatme PV. 1985, Statistical methods for Agricultural
workers. ICAR, New Delhi. 381 pp.
Patel RK.1979. Unusual outbreak of gram pod borer on gram in Madhya
Pradesh. Science and Root cheek culture 45: 335-336.
Singh H, Mahajan G and Singh I. 2004. Efficacy of different insecticides
against the gram pod borer (Helicoverpa armigera) on chickpea
(Cicer arietinum). Legume Research 27: 233-234.
Singh S, Choudhary DP, Sharma C, Mahla RS and Mathur YS. 2008.
Efficacy of different insecticides against Helicoverpa armigera
(Hubner) on chickpea Cicer arietinum. Indian Journal of
Entomology 70: 177-181.
Suryavanshi DS, Bhede BV, Bosale SV and More DG. 2008. Insecticide
resistance in field population of H. armigera (Hub.) (Lepidoptera :
Noctuidae). Indian Journal of Entomology 70: 44-46.
Comparison of nodC and 16S rDNA gene analysis of rhizobia associated with legumes
of arid and semi-arid regions of Haryana
RAJESH GERA, RANJANA BHATIA and VARUN KUMAR
Department of Microbiology, CCS Haryana Agricultural University, Hisar-125004, India; E-mail: rgera1967@yahoo.com
(Received: February 29, 2012; Accepted: October 06, 2012)
ABSTRACT
In this study, nodC and 16 rDNA gene analysis of rhizobia
isolated from legumes growing in arid and semi-arid regions
of Haryana, India were compared. A total of 90 rhizobial isolates
were obtained from nodules of different leguminous crops of
arid and semi-arid zones of Haryana (India) and tested for
nodulation efficiency in their respective crops. Out of the 90
isolates, 33 isolates forming better nodules were selected for
the investigation of genetic diversity. The results of nodC
restriction analysis of these isolates showed that Vigna
unguiculata gro up lay s eparately from the Tri foli um
alexandrinum and Cicer arietinum groups. However, restriction
analysis of 16S rDNA revealed that the T. alexandrinum and
the C. arietinum isolates formed separate phylotypes within
the V. unguiculata group but with less similarity between them.
From the comparison of nodC and 16S rDNA gene analysis of
these isolates it has been concluded that nodC pattern shows
some correlation with host plant range whereas 16S rDNA
analysis results in wide diversity. Five of the isolates, one from
each phylogenetic group, were identified on the basis of partial
16S rDNA gene sequencing.
Key words:
16S rDNA, nodC, Phylogeny, RFLP, Rhizobia
Haryana is a small state which falls under arid and semi
arid zones of Northern India. Agriculture under arid zones is
low input agriculture with minimum application of chemical
fertilizers and pesticides. Role of microorganisms, especially
in nitrogen fixation, plant health promotion and phosphate
solubilization is very important under such conditions. Abiotic
stresses like high temperature and drought during summer
determine the microflora of soils. Cropping pattern and
management practices such as fertilization, crop rotation and
application of organic amendments may favor some
microorganisms over the others. Crop production under
rainfed conditions is solely dependent on microbial resources
for nutrients.
In arid and semi-arid zones of Haryana, the legumes are
in rotation with non-leguminous crops under low input
agriculture. Leguminous crops fix nitrogen and benefit the
succeeding crops by providing residual nitrogen. Nonsymbiotic microorganisms have been known to benefit the
cereals by increasing nutrient uptake. Symbiotic rhizobia have
been classified as belonging to the á-subgroup of the
Proteobacteria and to one of the six genera: Rhizobium,
Sin orhi zobi um, Bradyrhizobiu m, Azorh izo biu m,
Mesorhizobium and Allorhizobium (Garrity et al. 2003).
However, recent studies have shown that the bacteria capable
of nodulating legumes may also belong to other genera lying
wi thin á- and â-Proteo bact eria (Chen et al. 200 3,
Rasolomampianina et al. 2005, Sy et al. 2001, Trujillo et al.
2005 and Zakhia et al. 2004)). ã-Proteobacteria were found
associated with legume nodules, although their presence and
role is yet to be defined (Benhizia et al. 2004). Rhizobia are
proteobacteria which establish symbiotic relationship with
leguminous plants leading to the formation of root nodules
where they fix atmospheric nitrogen (Dubey et al. 2010).
Rhizobia that nodulate Trifolium alexandrinum, Cicer
arietinum and Vigna unguiculata (Vigna radiata, Vigna
aconitifolia, Cajanus cajan, Cyamopsis tetragonoloba and
Vigna unguiculata) legumes are Rhizobium leguminosarum
bv. trifolii, Rhi zobi um sp. and Brad yrhizob ium sp,
respectively. Soil types, host and environmental conditions
are the key factors to study the diversity of rhizobia.
A better understanding of phylogenetic relationships
among different rhizobia may be gained by using the 16S rRNA
gene. Host specificity may not be related to 16S rRNA but
phylogenies of nod genes have often been correlated with
the host plant (Ueda et al. 1995, Zeze et al. 2001). To form an
effective symbiosis, rhizobia require several classes of specific
genes involved in the morphogenesis of nodules. The nod
genes are unique to rhizobia and the phylogenies of nod A,
nod B, nod C and nod D resemble each other. We used nod C
gene for our studies. Nucleic acid based techniques such as
nodC and 16S rDNA restriction analysis have been used to
gain a better understanding of microbial comparative
community structure and function in the edaphic component
of soils and the rhizosphere.
This study was carried out to identify rhizobia isolated
from legumes grown in the semi-arid zones of Haryana on the
basis of PCR-RFLP analysis of nodC and 16S rDNA genes.
Nodule sampling and isolation of rhizobia: In order to obtain
the nodule samples representing the spontaneous distribution
of legumes in arid and semi-arid zones of Haryana, root nodules
of Trifolium alexandrinum (berseem), Vigna radiata
(mungbean), Cicer arietinum (chickpea), Vigna unguiculata
(cowpea), Vigna aconitifolia (mothbean), Cajanus cajan
(pigeonpea) and Cyamopsis tetragonoloba (guar) legumes
Gera et al.: Comparison of nodC and 16S rDNA gene analysis of rhizobia associated with legumes of arid and semi-arid
were non-selectively sampled. Segments of roots with attached
nodules from the plants were excised and transported in
plastic bags to the laboratory. Root nodules were collected
and surface sterilized with 0.1% mercuric chloride (HgCl2),
washed with sterile distilled water followed by surface
treatment with 95% ethanol and further rinsing with sterile
distilled water. Properly washed nodules were again washed
8-9 times with sterile distilled water to remove the traces of
HgCl2. Bacteria were isolated from crushed nodules on YEMA
media plates supplemented with 2.5 mg/100 ml Congo red
(Vincent 1970). The isolates were purified by re-streaking on
fresh plates and the purified isolates were maintained on
YEMA slants at 4°C.
Plant nodulation test: All the rhizobial isolates were tested
for their nodulation efficiency in their respective crops.
Rhizobial isolates were inoculated separately in YEM broth
and incubated on rotary shaker at 28±2°C. Seeds of the
legumes varietySC 5 (Cicer arietinum), HFG 156 (Cyamopsis
tetragonoloba), HFB 600 (Trifolium alexandrinum), CS 88
(Vigna unguiculata), Manak (Cajanus cajan), RMO-225
(Vigna aconitifolia) and Basanti (Vigna radiata) were surface
sterilized and kept for germination for 24 h. River sand was
thoroughly washed with acid followed by 6-7 washings with
water and sterilized in an oven at 180°C for one hour in trays.
The sand was then filled in cups and nutrient solution was
added to it. The cups were covered with paper and held in
position with the help of a thread. These nodulation test
assemblies were sterilized in an autoclave at 15 psi for 1 h.
Germinated seedlings were transferred to the sterilized cups
containing sand, along with 1-2 ml of broth of respective
rhizobial isolates. These nodulation test assemblies were kept
in pot house and watered daily with sterilized Sloger’s nitrogen
free watering solution. After 50 days of growth, plants were
uprooted and analyzed for nodulation.
Isolation of Genomic DNA: Genomic DNA of rhizobial strains
was isolated from log phase grown cells of rhizobia using
CTAB method (Ausubel et al. 19 87). The DNA was
resuspended in 50-100 µl of TE buffer and quantified.
Amplification of nodC gene: Genomic DNA of all the rhizobia
isolated from different crops was amplified for nodC gene
using nodCF (5'-AYG THG TYG AYG ACG GTT C - 3') and
nodCI (5'-CGY GAC AGC CAN TCK CTA TTG - 3') (Sarita et
al. 2005) primers by Polymerase chain reaction (PCR). The
reaction mixture (25 µl) for PCR contained 12.5 µl of Red Taq
Ready Mix (Bangalore genei), 2.5 µl of each primer (10 pmol
each), 1µl template DNA (50-70 ng/µl) and 6.5 µl sterile water.
The conditions of thermal cycler for nodC gene amplification
were: initial denaturation at 95°C for 3 min followed by 35
cycles of denaturation at 94°C for 1 min, annealing at 55oC for
1 min and extension at 72oC for 2 min with a final extension at
72oC for 3 min. The PCR was then set on hold at 4°C. The lid
temperature was maintained at 105°C. Amplified gene was
visualized in 2% agarose after electrophoresis using gel
295
documentation system (Minibis Pro).
PCR-RFLP analysis of nodC gene: Restriction fragment
length polymorphism (RFLP) is the identification of specific
restriction patterns that reveal the difference between the DNA
fragment sizes in individual organisms. The PCR amplified
products of nodC gene were digested separately with two
restriction enzymes MspI and RsaI. For the restriction
digestion, 10 µl of amplified nodC product was treated with
1µl of both the enzymes separately and held on constant 37°C
for 12 h in thermal cycler. The digested product was checked
on 2% agarose gel and the different RFLP patterns were
recorded using gel documentation system.
Amplification of 16S rDNA: Universal primers BAC 27 F (5’AGA GTT TGA TCC TGG CTC AG - 3’) (Donachie et al. 2004)
and 1488 R (5’- CGG TTA CCT TGT TAG GAC TTC ACC - 3’)
(Herrera-Cervera et al. 1999) were used for the amplification of
16S rDNA gene in the thermal cycler. For PCR, 50 µl reaction
mixture included 50-70ng/µl as template, 2 µl of each primer
having concentration of 10 µM, 1 µl of 10 mM dNTPs, 5 µl of
Taq buffer (10X) and 3 units of Taq DNA polymerase. The
conditions of thermal cycler for 16S rDNA gene amplification
were: initial denaturation at 94°C for 3min followed by 40 cycles
of denaturation at 94°C for 30 s, annealing at 50°C for 30 s and
extension at 72°C for 1 min with a final extension at 72°C for 10
min. This was followed with a final cooling at 4°C. Amplified
gene was visualized in 1.5% agarose after electrophoresis using
gel documentation system.
PCR-RFLP analysis of 16S rDNA: PCR amplified products
of 16S rDNA gene of various rhizobial isolates were digested
separately with restriction enzymes MspI and RsaI. For the
restriction digestion, 10 µl of amplified product was treated
with 1µl of both the enzymes separately and held on constant
37°C for 12 h in thermal cycler. The digested product was
checked on 2% agarose gel and the different RFLP patterns
were recorded using gel documentation system. Banding
patterns of different isolates were analyzed by Unweighted
Pair Group Method with Arithmetic Mean (UPGMA) program
cluster analysis using NTSYS-PC program. The results were
applied to construct a dendrogram depicting the clustering of
the various isolates on the basis of the similarity index of the
nod C and 16S rRNA gene within them.
Partial 16S rDNA gene seq uencing: Representative
phylotypes from each phylogenetic group were selected from
partial 16S rDNA gene sequencing using both forward BAC
27F and reverse 1488R primers. The blastn was used to
compare the 16S rDNA gene nucleotide sequences of isolates.
Isolation of symbiotic nitrogen fixing bacteria and Plant
nodulation tests: In this study, ninety rhizobial isolates were
obtained from the nodules collected from different host
legumes grown in arid and semi-arid zones of Haryana, India.
296
These isolates included 9 from T. alexandrinum, 38 from V.
radiata, 24 from V. unguiculata, 7 from V. aconitifolia, 3 from
C. arietinum, 3 from C. cajan and 6 from C. tetragonoloba.
When tested for their nodulation efficiency in their respective
crops, all the isolates were able to form nodules on their
respective crops but with variable efficiency. Their nodulation
potential indicated that these were rhizobia. Out of the 90
isolates, only 33 were able to form better nodules and these
were selected for the phylogenetic analysis.
Amplification of nodC and 16S rDNA gene in rhizobial
isolates: DNA based methods are most suitable for the
measurement of genetic relatedness (Sikora and Redzepovic
2003). In this investigation, 33 rhizobial isolates selected for
phylogenetic analysis were tested for the presence of nodC
and 16S rDNA genes using PCR. The amplification of nodC
gene through PCR using specific primers revealed amplicons
of 620bp in all the isolates. On the basis of the presence of
nodC gene, the isolates were authenticated as rhizobia. The
16S rDNA gene of all the rhizobial isolates was amplified with
universal primers and amplicons of 1460 bp were observed
after comparison with low range ruler DNA (Bangalore Genei,
India).
Nodulation genes in rhizobia have been widely used to
perform evolutionary analysis and to estimate their host
ranges (Ueda et al. 1995, Laguerre et al. 2001, Chen et al.
2008). One of the nodulation genes, nodC, which encodes
the enzyme involved in the first step of the Nod factor assembly,
has often been chosen as a nodulation marker in different
studies because it is essential for nodulation in most of the
rhizobial species examined so far (Ueda et al. 1995, Moschetti
et al. 2005, Kalita et al. 2006, Sarita et al. 2008). The amplification
Table 1. Restriction patterns of rhizobial isolates revealed
by RFLP analysis of PCR-amplified nodC genes
Isolates
Host plant
M 1, M 2, M 3, M 5
M 7, M 12, M 13, M
15, M 20
M 8, M 14
M 16
M 17, M 10
C 2, C 4, C 5
C 11
C 12, C 13, C 14, C
15, C 17
B 5, B 13, B 14, B 23
Vigna radiata
Vigna radiata
A2
G1
G2
Mo 1, Mo 2
Rz 1
nodC
genotype
I
II
nodC RFLP
Msp I Rsa I
a
a
a
b
Vigna radiata
Vigna radiata
Vigna radiata
Vigna unguiculata
Vigna unguiculata
Vigna unguiculata
III
IV
V
VI
II
VII
b
c
d
e
a
f
b
b
c
b
b
b
Trifolium
alexandrinum
Cajanus cajan
Cyamopsis
tetragonoloba
Cyamopsis
tetragonoloba
Vigna aconitifolia
Cicer arietinum
VIII
g
d
IX
V
h
d
c
c
I
a
a
II
X
a
i
b
e
Ten nod C genotypes were revealed
of nodC gene using specific primers is a reliable and quick
method for the identification of rhizobial strains (Harrison et
al. 1992, Oliveira et al. 1999). Amplified ribosomal DNA
restriction analysis (ARDRA) has been found to be a useful
approach to study Rhizobium diversity more effectively and
more authentically than the nodC gene (Sikora and Redzepovic
2003, Laguerre et al. 1993, Moschetti et al. 2005, Ltaief et al.
2007, Rashid et al. 2009, Nandwani and Dudeja 2009). We
have used both nodC and 16S rDNA genes for diversity
analysis of Rhizobia.
PCR-RFLP analysis of nodC gene amplicons: Representative
phylotypes have been identified for different crop rhizobia
based upon RFLP analysis of the nodC gene. The PCR
amplified products of nodC gene were digested with two
restriction enzymes MspI and RsaI and different RFLP patterns
were recorded (Table 1). Maximum number of isolates, which
showed high similarity in the nodC gene, was obtained from
hosts earlier classified as Vigna unguiculata miscellary group
(Figure 1a). At the similarity value of 46%, the isolates were
Table 2. Restriction patterns of rhizobial isolates revealed
by RFLP analysis of PCR-amplified 16S rDNA genes
Isolates
Host plant
16S rDNA
genotype
M 1, M 2, M 3, Vigna radiata
I
M5
M7
Vigna radiata
II
M8
Vigna radiata
III
M 12
Vigna radiata
IV
M 13
Vigna radiata
V
M 14
Vigna radiata
VI
M 15
Vigna radiata
VII
M 16
Vigna radiata
VIII
M 17
Vigna radiata
IX
M 10
Vigna radiata
X
M 20
Vigna radiata
XI
C2
Vigna unguiculata
XII
C4
Vigna unguiculata
XIII
C5
Vigna unguiculata
XIV
C 11
Vigna unguiculata
XV
C 12
Vigna unguiculata
XVI
C 15
Vigna unguiculata
XVII
C 17
Vigna unguiculata
XVIII
C 13, C 14
Vigna unguiculata
XIX
B 5, B 23
Trifolium
XX
alexandrinum
B 13
Trifolium
XXI
alexandrinum
B 14
Trifolium
XXII
alexandrinum
A2
Cajanus cajan
XXIII
G1
Cyamopsis
XXIV
tetragonoloba
G2
Cyamopsis
XXV
tetragonoloba
MO 1
Vigna aconitifolia
XXVI
Rz 1
Cicer arietinum
XXVII
Twenty seven 16S rDNA genotypes were revealed
16S rDNA RFLP
Msp I Rsa I
a
a
a
b
a
a
a
a
c
a
d
e
f
f
f
f
f
g
f
f
h
b
c
d
e
f
g
h
i
j
k
l
m
n
o
p
q
r
s
t
i
t
j
u
k
l
v
w
m
x
n
o
y
e
297
M1
M2
M3
M5
M7
M13
M14
M8
M12
M17
M15
M10
M20
C2
C12
C4
C17
C13
C14
C15
C5
C11
B5
B23
B13
B14
A2
G2
Mo1
Rz1
M16
0.56
Fig 1.
0.67
0.78
coefficient
0.89
G1
1.00
(a)
(b)
Dendrogram depicting grouping of various rhizobial isolates on the basis of (a) nodC-RFLP and (b) 16S rDNA-RFLP after
restriction digestion with MspI and RsaI. The letters in the dendrogram represent B: Trifolium alexandrinum; Rz1: Cicer
arietinum; A: Cajanus cajan; G: Cyamopsis tetragonoloba; Mo: Vigna aconitifolia; M: Vigna radiata and C: Vigna unguiculata.
divided into two major clusters. The rhizobia from T.
alexandrinum and C. arietinum legumes formed a separate
cluster from the other legume crops indicating their different
host range. Moreover, the T. alexandrinum isolates formed a
single phylotype, the isolate Rz1 i.e. the Mesorhizobium strain
from C. arietinu m sho wed 58 % simi larity to the T.
alexandrinum isolates cluster. V. unguiculata isolates formed
2 major clusters having 3 and 5 isolates, respectively. The
isolate C11 showed 100% similarity with V. aconitifolia isolates
and few of the V. radiata isolates. On the other hand, V.
radi ata iso lates showed 5 different phylo types with
divergence started at about 90% similarity coefficient and
formed 4 major clusters having 5, 3, 8 and 2 isolates,
respectively. Isolates G1 and G2 from C. tetragonoloba
exhibited 92% similarity among themselves but showed 100%
similarity to V. radiata isolates M10, M17 and M1, M2, M3,
M5, respectively. The isolate A2 from C. cajan showed a single
phylotype with a 92% similarity coefficient. Isolate M1, M2,
M3 and M5, isolates C13 & C14 and isolates B5 & B13 showed
100% similarityamong themselves, respectively, in both nodC
and 16S rDNA restriction patterns indicating that they may
represent the same strain.
PCR-RFLP analysis of 16S rDNA gene amplicons: The PCR
amplified products of 16S rDNA gene were digested with two
restriction enzymes (MspI and RsaI) and different RFLP
patterns were recorded (Table 2). Combined RFLP analysis of
16S rDNA gene of all the rhizobial isolates from different crops
using MspI and RsaI revealed high polymorphism (Figure 1b).
At the similarity level of 56%, two major groups were formed.
V. radiata and V. unguiculata isolates formed separate clusters
which were divided into various subgroups at about 90%
similarity coefficient and showed 68% similarity among them.
While all the T. alexandrinum isolates formed a single cluster
with divergence at about 93% similarity coefficient, isolates
from C. tetragonoloba, V. aconitifolia, C. cajan and
Mesorhizobium strain from C. arietinum (Rz1), formed single
phylotypes with 80% similarity coefficient. Isolates M16 from
V. radiata and G1 from C. tetragonoloba formed separate
group with 65% similarity among themselves. All the isolates
from V. unguiculata and V. radiata grouped together,
respectively.
Partial nodC gene sequencing: Five isolates M2, M16, Mo1,
C17 and B5 from V. radiata, V. aconitifolia, V. unguiculata
and T. alexandrinum from each representative group were
identified as Bradyrhizobium yuanmingense, Bradyrhizobium
sp., Bradyrhizobium sp., Bradyrhizobium sp. and Rhizobium
leguminosarum bv. trifolii on the basis of partial nodC gene
sequencing using both forward and reverse nodC primers
(Table 3).
The PCR-RFLP analysis of nodC revealed that V.
unguiculata group lay separately from the T. alexandrinum
and C. arietinum, whereas that of 16S rDNA genes showed
the T. alexandrinum and C. arietinum isolates as separate
phylotypes within the V. unguiculata group but with less
similarity between them. Rhizobial isolates from the different
crops showed much more similarity in nodC gene with most
of them exhibiting 80% similarity. On the other hand, in the
16S rDNA analysis, the rhizobial isolates from individual crops
showed high similarity (up to 90%) among them but the
diversity increased on comparison with isolates from other
crops. Restriction patterns of nodC gene using both MspI
and RsaI categorized all the tested isolates into ten groups
(Table 1) while twenty seven groups were generated with 16S
298
Table 3. Identity match of the sequenced rhizobial isolates
S.
No.
1
Isolate Crop
M2
Vigna radiata
2
3
4
5
M16
Mo1
C17
B5
Vigna radiata
Vigna aconitifolia
Vigna unguiculata
Trifolium
alexandrinum
%
Identified Match to 16S
Similarity rDNA
100%
Bradyrhizobium
yuanmingense
100%
Bradyrhizobium sp.
99%
Bradyrhizobium sp.
92%
Bradyrhizobium sp.
97%
R. leguminosarum
rDNA using both MspI and RsaI restriction enzymes (Table
2). The wide diversity of rhizobial isolates on the basis of 16S
rDNA analysis is supported by many other studies carried
out in the same region or in different areas (Mutch et al. 2003,
Moschetti et al. 2005, Ventorino et al. 2007, Shamseldin et al.
2009). Such comparative studies on nodC and 16S rDNA gene
analysis of rhizobial from arid and semi-arid regions of Haryana
state have not been reported earlier.
Thus, thirty three rhizobial isolates from seven host
legumes of arid and semi-arid regions of Haryana, India
showed good nodulation potential. From the comparison of
nodC and 16S rDNA gene analysis of these isolates, it has
been concluded that nodC pattern shows some correlation
with host plant range whereas 16S rDNA analysis results in
wide diversity.
ACKNOWLEDGEMENT
The authors acknowledge NBAIM, Mau for providing
financial support to carry out this work.
REFERENCES
Ausubel FM, Brent R, Kingston R. 1987. Current Protocols in Molecular
Biology. Wiley, New York.
Benhizia Y, Benhizia H, Benguedouar A, Muresu R, Giacomini A and
Squartini A. 2004. Gamma proteobacteria can nodulate legumes of
the genus Hedysarum. Systematic and Applied Microbiology 27:
462-468.
Chen WF, Guan SH, Zhao CT, Yan XR, Man CX, Wang ET and Chen
WX. 2008. Different Mesorhizobium species associated with
Caragana carry similar symbiotic genes and have common host
ranges. FEMS Microbiology Letters 283: 203–209.
Chen WM, Moulin L, Bontemps C, Vandamme P, Bena G and BoivinMasson C. 2003. Legume symbiotic nitrogen fixation by âProteobacteria is widwspread in nature. Journal of Bacteriology
185: 7266-7272.
Donachie SP, Hou S, Lee KS, Riley CW, Pikina A, Belisle C, Kempe S,
Gregory TS, Bossuyt A, Boerema J, Liu J, Freitas TA, Malahoff A,
Alam M. 2004. The Hawaiian Archipelago: A microbial Diversity
Hotspot. Microbial Ecology. 48: 509-520.
Dubey RC, Maheshwari DK, Kumar H and Choure K. 2010. Assessment
of diversity and plant growth promoting attributes of rhizobia
isolated from Cajanus cajan L. African Journal of Biotechnology
9: 8619-8629.
Garrity GM, Bell JA and Lilburn TG. 2003. Taxonomic outline of
Prokaryotes. In: Garrity GM (ed) Bergey’s manual of systematic
bacteriology, 2nd Ed. Springer, New York.
Harrison SP, Mytton LR, Skot L, Dye M and Cresswell A. 1992.
Characterisation of Rhizobium isolates by amplification of DNA
polymorphisms using random primers. Canadian Journal of
Microbiology 38: 1009–1015.
Herrera-Cervera JA, Caballero-Mellado J, Laguerre G, Ticky HV, Requena
N, Amarger N, Martinez-Romero E, Olivares J, Sanjuan J. 1999. At
least five rhizobial species nodulate Phaseolus vulgaris in a Spanish
soil. FEMS Microbiology Ecology. 30: 87-97.
Kalita M, Stepkowski T, Lotocka B and Malek W. 2006. Phylogeny of
nodulation genes and symbiotic properties of Genista tinctoria
bradyrhizobia. Archives of Microbiology 186: 87–97.
Laguerre G, Fernandez MP, Edel V, Normand P and Amarger N. 1993.
Genomic heterogeneity among French Rhizobium strains isolated
from Phaseolus vulgaris. International Journal of Systematic
Bacteriology 43: 761–767.
Laguerre G, Nour SM, Macheret V, Sanjuan J, Drouin P and Amarger N.
2001. Classification of rhizobia based on nodC and nifH gene analysis
reveals a close phylogenetic relationship among Phaseolus vulgaris
symbionts. Microbiology 147: 981-993.
Ltaief B, Sifi B, Gtari M, Zaman-Allah M and Lachaal M. 2007.
Phenotypic and molecular characterization of chickpea rhizobia
isolated from different areas of Tunisia. Canadian Journal of
Microbiology 53: 427-434.
Moschetti G, Peluso AL, Protopapa A, Anastasioa M, Pepea O and
Defezb R. 2005. Use of nodulation pattern, stress tolerance, nodC
gene amplification, RAPD-PCR and RFLP–16S rDNA analysis to
discriminate genotypes of Rhizobium leguminosarum biovar viciae.
Systematic and Applied Microbiology 28: 619-631.
Mutch LA, Samih M, Tamimi JPW and Young. 2003. Genotypic
characterisation of rhizobia nodulating Vicia faba from the soils of
Jordan: a comparison with UK isolates. Soil Biology and
Biochemistry 35: 709–714.
Nandwani R and Dudeja SS. 2009. Molecular diversity of a native
Mesorhizobial population chickpea (Cicer arientinum L.) in Indian
soils. Journal of Basic Microbiology 49: 463-470.
Oliveira VMDe, Coutinho HLC, Sobral BWS, Guimara CT, Elsas JDVan
and Manfio GP. 1999. Discrimination of Rhizobium tropici and R.
leguminosarum strains by PCR-specific amplification of 16S–23S
rDNA spacer region fragments and denaturing gradient gel
electrophoresis (DGGE). Letters in Applied Microbiology 28: 137–
141.
Rashid MH, Sattar MA, Uddin MI and Young JPW. 2009. Molecular
characterization of symbiotic root nodulating rhizobia isolated from
lentil (Lens culinarisMedik.). Electronic Journal of Environmental,
Agricultural and Food Chemistry 8: 602-612.
Rasolomampianina R, Bailly X, Fetiarison R, Bena G, Ramaroson L,
Raherimandimby M, Moulin L, de Lajudie P, Dreyfus B and Avarre
JC. 2005. Nitrogen-fixing nodules from rose Wood legume trees
(Dalbergia spp.) endemic to Madagascar host seven different genera
belonging to (á-and â-Proteobacteria). Molecular Ecology 14: 41354146.
Sarita S, Priefer UB, Prell J and Sharma PK. 2008. Diversity of nifH
gene amplified from rhizosphere soil DNA. Current Science 94:
109-115.
Sarita S, Sharma PK, Priefer UB and Prel J. 2005. Direct amplification
of rhizobial nodC sequences from soil total DNA and comparison
to nodC diversity of root nodule isolates. FEMS Microbiology
Ecology 54: 1–11.
Shamseldin A, El-Saadani M, Sadowsky MJ and Chung SA. 2009. Rapid
identification and discrimination among Egyptian genotypes of
Rhizobium leguminosarum bv. viciae and Sinorhizobium meliloti
nodulating faba bean (Vicia faba L.) by analysis of nodC, ARDRA,
and rDNA sequence analysis. Soil Biology and Biochemistry 41:
45–53.
Sikora S and Redzepovic S. 2003. Genotypic characterization of
indigenous soybean rhizobia by PCR-RFLP of 16S rDNA, rep-PCR
and RAPD analysis. Food Technology and Biotechnology 41: 6167.
Sy A, Giraud E, Jurad P, Garcia N, Willems A, de Lajudie P, Prin Y,
Neyra M, Gillis M, Bombín-Masson C and Dreyfus B. 2001.
Methylotrophic Methylobacterium bacteria nodulate and fix
nitrogen in simbiosis with legumes. Journal of Bacteriology 183:
214-220.
Trujillo ME, Willems A, Abril A, Planchuelo AM, Rivas R, Ludena D,
Mateos PF, Martinez-Molina E and Velazquez E. 2005. Nodulation
of Lupinus albus by strains of Ochrobactrum lupini sp. Nov. Applied
and Environmental Microbiology 71: 1318-1327.
299
Ueda T, Suga Y, Yahiro N and Matsuguchi T. 1995. Phylogeny of Sym
plasmids of rhizobia by PCR-based sequencing of a nodC segment.
Journal of Bacteriology 177: 468–472.
Ventorino V, Chiurazzi M, Aponte M, Pepe O and Moschetti G. 2007.
Genetic diversity of a natural population of Rhizobium
leguminosarum bv. viciae nodulating plants of Vicia faba in the
Vesuvian area. Current Microbiology 55: 512–517.
Vincent JM. 1970. A Manual for the Practical Study of Root Nodule
Bacteria. Blackwell Scientific, Oxford.
Zakhia F, Jeder H, Domergue O, Willems A, Cleyet-Marel JC, Gillis M,
Dreyfus B and de Lajudie P. 2004. Characterisation of wild legume
nodulating bacteria (LNB) in the infra-arid zone of Tunisia.
Systematic and Applied Microbiology 27: 380-395.
Zeze A, Mutch LA and Young JPW. 2001. Direct amplification of
nodD from community DNA reveals the genetic diversity of
Rhizobium leguminosarum in soil. Environmental Microbiology
3: 363-370.
Phenology, dry matter distribution and yield attributes under normal and drought
stress conditions in Lentil (Lens culinaris Medik.)
VIJAY LAXMI
Indian Institute of Pulses Research, Kanpur-208 024, Uttar Pradesh, India; E-mail: vijaylaxmi0@yahoo.com
(Received: November 14, 2011 ; Accepted: December 06, 2012)
ABSTRACT
Twenty lentil (Lens culinaris Medik.) genotypes were raised in
field under normal (irrigated) and drought stress (non-irrigated
condition) during rabi seasons of 2008-09 and 2009-10. The
observations on phenological days, dry matter distribution in
plant parts at different phenological stages, mean environmental
temperature and total degree days were recorded. Seed yield
were higher under normal as compared to drought condition.
Multiple regressions between seed yield and different traits
exhibited that all of them did not have significant contribution.
Seed yield and seed yield determining factors were found to be
different under normal and drought stress conditions. Under
non-irrigated condition, lentil seed yield was determined by
total dry matter production and its partitioning/day, mean
temperature and total degree-days during podding to maturity.
Under irrigated condition, lentil seed yield was determined by
root dry matter at vegetative stage, total dry matter yield, per
day dry matter production and its partitioning during seed
filling period and total degree-days during flowering to podding.
Multiple correlation (R) of traits were 0.9996 and 0.9984 and
coefficient of determination (R2) were 0.9992 and 0.9964 under
non-irrigated and irrigated conditions, respectively. Optimum
values of the traits for seed yield maximization also varied
amongst irrigated and non-irrigated conditions.
Key words:
Irrigation, Lentil, Yield component
Lentil (Lens culinaris Medik.) is an important component
of the rainfed farming system of West Asia and Africa and
source of high-quality protein for humans (Hamdi et al.1992).
Research indicated increase in lentil seed yield with increase
in irrigation frequency and total water use (Salehi et al. 2006).
There are strong linear relationships (r2 >0.90) between yield
and moisture supply in lentil (Silim et al. 1993). Salehi et al.
(2006) reported that the seedling and flowering stage were
most sensitive to water availability and drought stress.
However, a deficiency of water during any growth stages in
legume species often result in loss of seed yield. They
suggested that high seed yield is related to pods size and
number and resistance to pests and disease. Since, the lentil
is of in-determinate growth, supplying available water may
result in higher vegetative and reproductive growth periods
and drought stress during the flowering stage decreases this
period (Kusmenglu and Muehlbauer, 1998). Water deficit
highly influences seed yield components and causes reduced
pods per plant, seeds per pod and 100-seed weight. Hudak
and Patterson (1995) showed that irrigation during seed filling
period, improves yield. Water deficit also results in the decline
of number of flowers, pods, seeds per pod, and size of pods
and seed weight (Desclaus et al. 2000). As the cell losses
water, vacuole usually crumples more than cell wall which
causes the silt in the protoplasm. It seems that such damage
results in the death of cells (Lessani and Mojtahedi, 2003).
Yield loss of the plants under water deficit is one of the most
important events for the plant breeders to improve yield but
difference in the yield potential mainly relates to the adaptation
factors than merely to the stress itself in which drought
resistance indices are used to determine resistant genotypes
(Mitra, 2001). Stress appearance during the reproductive stage
reduces seed weight (Katerji et al. 2000). The amount of the
yield loss depends on the stress range and plant growth stage
at which stress occurred. In fact, plant susceptibility to stress
varies from germination to the maturity (Schmidtke et al. 2004).
One of the main drought resistance factors in plants is the
ability of cells to tolerate a large amount of lost water.
In general, water requirement in grain legumes is less as
compared to cereals but availability of soil moisture has been
reported to be the pivotal point for realization of yield potential.
There are reports for response of grain legumes to irrigation
in terms of yield and this enhancement is due to an increase in
growth attributes at different crop growth stages in chickpea
(Bhattacharya and Singh 1997) and in lentil (Bhattacharya
and Chandra 1997, Bhattacharya 1999). Variation in yielding
ability was reported in chickpea and lentil (Bhattacharya 1998)
under drought and irrigated conditions. It has been reported
that various physiological processes differ with growth stages
and their effects on seed yield of any crop plant are different
(Sinha 1973). Since major area for lentil cultivation is largely
confined to rainfed condition, and future prospect for
increasing irrigation facility to this crop is remote possibility,
therefore, increase in productivity of lentil for rainfed
sustainable land use in agro ecological system is essential.
Knowledge about yield determination factor(s) in lentil under
normal and drought conditions is scanty and therefore,
present study was undertaken to evaluate the relationship of
lentil seed yield with dry matter distribution in plant parts,
phenology, yield attributing traits, mean temperatures and total
degree-days at different crop growth stages under normal
and drought conditions.
Field experiments were conducted during Rabi of 2008-
Vijay Laxmi : Phenology, dry matter distribution and yield attributes under normal and drought stress
2009 and 2009-2010 at Indian Institute of Pulses Research,
Kanpur Farm under two planting conditions viz., normal
(irrigation) and drought (non-irrigation) involving twenty
lentils [Lens culinaris L. (Medik).] genotypes viz. IPL 59, IPL
60, IPL121, IPL522, ILL7663, EC 208355, EC 208362, EC 520204,
Bihar local, VL 4, B 77, Ranjan, WBL 58, IPL 203, IPL 403, IPL
404, IPL 517, 94/1468, P 2016, WBL 58. The soil of the
experimental site was Inceptisol having low available nitrogen
(150 kg N/ha), medium available phosphorus (22 kg P2O5/ha)
and medium in available potassium (180 kg K2O/ha).Sowing
was done under split-plot design, keeping irrigation as mainplot and genotypes as sub-plots, in three replications adopting
standard recommended agronomical practices. Phenological
observations viz., days to flowering, podding and seed filling
period were taken during crop ontogeny. Dry matter (g/plant)
in different plant parts viz., root, shoot, leaf and pod during
vegetative (30 days after sowing), flowering, podding and at
maturity were observed (mean of five plants at each stage)
from each treatment after separating the plant parts and oven
drying them at 800 C for constant dry weight. At maturity,
yield attributing traits viz., number of pod/ plant, pod and pod
wall weight (g/plant), were recorded (mean of ten plants). Per
day dry matter production during seed to seed period (kg/ha/
day), per day dry matter partitioning (kg/ha/day), total dry
matter yield (q/ha) and seed yield (kg/ha) were also recorded.
Mean temperature and total degree-days during different crop
growth stages viz., during vegetative to flowering, flowering
to podding and during podding to maturity were recorded.
Harvest index (%) for each treatment were also calculated
(Jain 1975).
Following mathematical relationships under nonirrigated and irrigated conditions are used.
Non-irrigated condition:
Y = -3106.51 – 3.352 X + 3.229X2 + 1.105 X3 + 0.195 X4 –
0.067 X5
(R = 0.9996, R2 = 0.9992)
[Where X = Total dry matter yield, X2 = per day dry
matter production, X3 = per day dry matter partitioning, X4 =
Mean temperature during podding to maturity, X5 = Total
degree-days during podding to maturity]
Irrigated condition:
Y = 166.40 – 0.023 X – 2.831X2 + 2.793 X3 + 1.031 X4 –
0.035 X5
(R = 0.9984, R2 = 0.9968)
[Where X = Root dry matter at vegetative stage, X2 =
Total dry matter yield, X3 = per day dry matter production, X4
= per day dry matter partitioning, X5 = Total degree-days
during flowering to podding]
Data of both the years were tested for their homogeneity,
301
were pooled over years, and were subjected for test of
significance for various factors and their respective interactive
effects, coefficient of correlation and regression under nonirrigated and irrigated conditions between seed yield and traits
at various crop growth stages were estimated (Snedecor and
Cochran 1978). Association percentage of the traits with seed
yield was also calculated (Hays 1955). Multiple regressions
of traits and estimation of optimum values of the traits for
seed yield maximization under drought and irrigated conditions
were worked through regression coefficients (Snedecor and
Cochran 1978).
Mean temperature as well as total degree-days during
various phenological durations was higher under normal as
compared to drought condition, excepting the total degreedays during podding to maturity where it was higher under
non-irrigated as compared to irrigated condition. Mean
temperature expressed as increase from vegetative to maturity,
whereas total degree-days were minimum during flowering to
podding. Phenological days, dry matter distribution amongst
plant parts at different growth stages, yield attributes, mean
temperature and to tal degree-days duri ng vario us
phenological durations had significant differences for various
treatments (Table 1a and 1b). Phenological days, except during
seed filling stage, were observed to be earlier under drought
as compared to irrigated condition. Dry matter in plant parts
under irrigated as compared non-irrigated condition, did not
differ significantly for irrigation, genotypes and their
interactions. Yield attributing traits were higher under irrigated
as compared to non-irrigated condition, except number of
pods/plant, pod weight/plant, harvest index which were lower
under irrigated as compared to non-irrigated condition.
Biological yield was higher under irrigated as compared to
non-irrigated, but harvest index was higher under nonirrigated as compared to irrigated condition.
Mean values observed as well as predicted (based on
mathematical relationship) for seed yield were numerically same
and were more under normal as compared to drought
condition and showed significant differences for irrigation,
genotypes and their interaction. Percent coefficient of
variation was higher for dry matter accumulation and lowest
for mean temperature and to tal degree-days duri ng
phenological durations. Multiple regression of seed yield with
phenological days, dry matter of plant parts at different stages,
yield attributing traits, mean temperature and total degreedays revealed that all traits do not express significant
relationship with yield therefore, traits having significant
relationship were considered under non-irrigated and irrigated
conditions separately. Under non irrigated condition, total
dry matter yield, per day dry mat ter producti on and
partitioning, mean temperature and total degree-days during
podding to maturity were the main traits determining lentil
302
seed yield. Multiple correlation (R) and coefficient of
determination (R2) of these traits with seed yield were 0.9996
and 0.9992, respectively. Under irrigated condition lentil seed
yield was mainly determined by root dry matter at vegetative
stage, total dry matter yield, per day dry matter production
and partitioning and total degree-days during flowering to
podding. Multipl e correlat ion (R) and coeffici ent of
determination (R2) of these traits with seed yield were 0.9984
and 0.9968, respectively. Predicted seed yield, based on
mathematical relationship were parallel to observed seed yield
under different condition. Behaviour of these traits was
different under different conditions. Under non-irrigated
condition, except for total dry matter yield had positive, whereas
total dry matter yield had negative relationship with lentil seed
yield under irrigated condition, per day dry matter during seed
to seed period and its partitioning during seed filling period
had positive, but root dry matter at vegetative stage, total
degree-days during flowering to podding had negative
relationship with seed yield.
Estimation of coefficient of correlation, regression
between seed yield and traits having significant contribution
(Table 2) revealed that under non-irrigated condition,
correlation with total dry matter yield (0.740), per day dry matter
production (0.741) and partitioning (0.961) were significant,
whereas those of mean temperature during podding and
maturity (0.160) and total degree-days (0.073) were not
significant. Under irrigated condition, significant correlation
Table 1a. Descriptive parameters of phenological days and dry matter distribution at different growth stages in lentil under nonirrigated (NI) and irrigated (I) conditions
Range
Minimum
Maximum
Mean
Phenological days
Flowering
NI
88.0
96.0
91.5
I
90.0
97.0
96.0
Podding
NI
104.0
112.0
106.22
I
110.0
118.6
116.5
Seed Filling NI
35.0
39.0
36.8
I
31.0
36.0
32.1
Root Dry Matter (g/plant)
Vegetative NI
0.02
0.14
0.05
I
0.02
0.90
0.05
Flowering
NI
0.08
0.590
0.22
I
0.10
0.37
0.21
Podding
NI
0.05
0.40
0.14
I
0.07
0.24
0.14
Maturity
NI
0.02
0.17
0.06
I
0.03
0.10
0.06
Shoot Dry Matter (g/plant)
Vegetative
NI
0.07
1.46
0.52
I
0.14
1.99
0.78
Flowering
NI
0.21
4.06
1.441
I
0.40
5.53
2.17
Podding
NI
0.16
3.25
1.15
I
0.30
4.44
1.77
Maturity
NI
0.07
1.47
0.52
I
0.03
0.10
0.06
Leaf Dry Matter (g/plant)
Vegetative NI
0.13
3.13
1.36
I
0.05
4.36
1.18
Flowering
NI
0.17
4.07
1.77
I
0.07
5.67
1.53
Podding
NI
0.08
1.90
0.83
I
0.03
2.65
0.71
Maturity
NI
0.16
3.89
1.69
I
0.06
5.42
1.51
Pod Dry Matter (g/plant)
Flowering
NI
0.03
1.19
0.33
I
0.00
3.33
0.81
Podding
NI
0.40
4.10
1.86
I
0.15
7.13
2.60
Maturity
NI
2.33
7.90
5.46
I
5.00
13.73
8.33
Figures in parenthesis are the CD at 5% and NS are “non significant
Irrigation
Variances
Genotypes
CV (%)
Interaction
445.90
(3.63)
935.51
(3.27)
504.10
(2.79)
9.85
(1.81)
20.12
(NS)
2.77
(1.26)
4.47
(NS)
4.68
(NS)
0.67
(NS)
0.00
(NS)
0.003
(NS)
0.001
(NS)
0.000
(NS)
0.00
(NS)
0.005
(NS)
0.002
(NS)
0.001
(NS)
0.000
(NS)
0.008
(NS)
0.004
(NS)
0.001
(NS)
1.531
(NS)
1.896
(NS)
7.598
(NS)
1.566
(NS)
0.090
(NS)
0.697
(NS)
0.444
(NS)
0.091
(NS)
0.084
(NS)
0.652
(NS)
0.415
(NS)
0.085
(NS)
0.764
(NS)
1.282
(NS)
0.284
(NS)
0.708
(NS)
0.487
(NS)
0.822
(NS)
0.180
(NS)
0 .773
(NS)
0.630
(NS)
1.064
(NS)
0.231
(NS)
1.063
(NS)
5.136
(NS)
12.336
(NS)
186.307
(0.67)
0.333
(NS)
1.877
(NS)
13.994
(NS)
0.476
(NS)
3.260
(NS)
14.952
(0.87)
1.70
2.58
3.22
15.45
13.42
13.15
11.24
15.15
12.54
12.23
13.14
13.15
14.22
15.14
13.50
15.03
14.91
7.93
with seed yield was expressed by total dry matter yield (0.575),
per day dry matter production (0.572) and per day dry matter
partitioning (0.985). Association percentages of these traits
were also very high. Optimum values of lentil seed yield
determining traits under non-irrigated and irrigated conditions
for seed yield maximization revealed that the values were
different from the observed mean under respective conditions.
It was interesting to note that the optimum values under both
non-irrigated and irrigated conditions were of higher
magnitudes as compared to observed mean values of the traits.
Except dry matter accumulation in different plant parts at
di fferent phenological stages expressed signi ficant
differences. Non-significant differences for dry matter
distribution amongst plant parts at stages were not properly
understood. Probably the genotypes under study had more
or less similar interaction with prevailing environmental
conditions. However, significant differences for mean
temperature and to tal degree-days duri ng vario us
phenological durations were in agreement to earlier reports
(Bhattacharya and Chandra 1997, Bhattacharya 1997). In
303
mathematical relationship for drought and irrigated conditions,
total dry matter yield had negative relationship and it
confirmed the earlier findings (Bhattacharya and Chandra 1997,
Bhattacharya 1997) that excess vegetative growth was
detrimental for realization of yield potential. It was interesting
to note that phenological days failed to express any significant
contribution for lentil seed yield under both non irrigated and
irrigated conditions. It was again interesting that under nonirrigated condition role of mean temperature and total degreedays during podding to maturity was almost negligible.
Probably during this period, numbers of pods retained/plant
were determined and/ or lower temperature during this period
was detrimental on flower and pod shedding and thus, its
effect on lentil seed yield was positive. However, a both
maximum and minimum temperature during post flowering
period had maximum effect on seed yield of normal sown
chickpea (Bhattacharya and Pandey 1999). Under irrigated
condition, mean temperature during any phenological duration
was not responsible for lentil seed yield determination.
However, importance of maximum and minimum temperature
Table 1b. Descriptive parameters of yield attributing traits, mean temperature and total- degree-days at different growth
stages, observed and predicted seed yield in lentil under non-irrigated (NI) and irrigated (I) conditions.
Range
Minimum
Maximum
Yield Attributed Traits
Pods/plant
NI
40.0
198.0
I
53.0
190.0
Pod weight (g/plant)
NI
3.55
14.51
I
3.28
14.14
Pod wall (g/plant)
NI
0.07
9.04
I
0.03
2.65
Biological yield
NI
22.08
35.42
I
23.04
47.92
Per day DM Production NI
15.66
24.43
I
15.78
32.16
Per Day DM Partitioning NI
27.41
50.60
I
34.44
68.89
Harvest Index (%)
NI
38.75
58.59
I
24.84
63.83
Observed Seed
NI
1041.67
1770.67
Yield
I
1102.08
2135.42
Predicted Seed
NI
1042.84
1779.97
Yield
I
1097.89
2139.89
Mean Temperature (oC)
Vegetative to
NI
14.37
14.55
flowering
I
14.37
14.65
Flowering to
NI
14.64
15.37
podding
I
14.83
16.20
Podding to
NI
18.98
20.88
maturity
I
20.54
23.17
Total degree-day
Vegetative to
NI
776.0
873.3
flowering
I
776.0
922.1
Flowering to
NI
156.1
333.1
Podding
I
234.8
491.7
Podding to
NI
730.7
753.7
maturity
I
711.2
850.6
Figures in parenthesis are the CD at 5% and NS are “non significant”
Irrigation
Variances
Genotypes
Interaction
114.9
105.4
8.30
7.80
2.6
0.71
28.59
38.11
20.06
25.5
37.14
52.73
47.80
45.10
1363.89
1688.75
1363.89
1688.75
2023.98
(5.44)
5.97
(0.48)
1.23
(NS)
20391.40
(7.30)
73.30
(0.52)
5464.528
(1.69)
159.53
(NS)
2374557.6
(61.02)
2374558.4
(104.37)
5921.04
(14.17)
14.82
(0.84)
4.85
(0.86)
14458.02
(1.49)
64.30
(1.05)
253.552
(3.67)
119.20
(3.88)
257971.96
(107.95)
256472.22
(108.45)
3735.71
(20.03)
26.19
(1.19)
14.80
(1.22)
3207.71
(2.11)
14.67
(1.48)
81.14
(5.48)
81.14
(5.48)
45864.12
(152.67)
46569.45
(153.37)
14.41
14.59
15.20
15.25
19.66
21.73
0.724
(0.011)
0.065
(NS)
96.825
(1.11)
0.013
(0.08)
0.041
(NS)
0.840
(0.67)
0.010
(0.11)
0.117
(0.39)
0.282
(NS)
812.37
888.11
233.92
339.17
739.80
722.53
29054.14
(55.27
249260.92
(91.28)
6711.54
(10.94)
2591.32
(32.66)
2520.44
(NS)
189.57
(NS)
1211.45
(NS)
2754.34
(NS)
217.43
(NS)
Mean
CV (%)
11.36
9.23
12.86
3.96
4.08
7.22
7.37
6.25
6.28
0.50
1.59
2.85
3.39
12.20
2.13
304
during vegetative and 1st flowering and during 1st to 50%
flowering in normal sown chickpea had been reported
(Bhattacharya and Pandey 1999). Significant differences in
total degree-days during different phenological durations were
mainly due to changes in phenological days under nonirrigated and irrigated conditions as advancement of days
increased per day temperature and also the total degree-days.
It was interesting to note that total degree-days during
flowering to podding were lower than that of during vegetative
to flowering or during podding to maturity which probably
indicated that in lentil flowering phase was under lower
temperature regime and pod filling stage was under higher
temperature regime. Per day dry matter production during seed
to seed period and per day dry matter partitioning to developing
seeds during seed filling period had expressed significant and
positive contribution for lentil seed yield under both irrigated
and non irrigated conditions. Phenological day (s) had also
non-significant contribution for lentil seed yield under both
non irrigated and irrigated conditions but they showed their
importance in conjunction with per day dry matter production
as well as their partitioning. This, phenological days had their
function in controlling the amount and/or rate of dry matter
production or it’s partitioning to developing seeds. It had
been reported that levels of dry matter accumulation and be
altered through changes in seeding dates and/or growing
environment in chickpea (Singh and Bhattacharya 1995) as
there was a strong genotype x environment interaction in
chickpea and the same was also confirmed (Bahl 1984). Positive
relationship of per day dry matter production was probably
due to the fact that under non-irrigated condition, plant tend
to complete its life cycle under lesser seed to seed period and
it also accumulated less dry matter. Therefore, increasing per
day dry matter production would lead to higher dry matter
yield. Similarly, higher magnitude of dry matter partitioning
from vegetative to reproductive plant parts would lead to
higher seed yield and therefore, per day dry matter partitioning
expressed positive relationship with lentil seed yield.
Although the relationship of total dry matter yield with seed
yield was observed to be negative, but the value of total dry
matter yield for seed yield maximization was estimated to be
higher than observed mean under both non-irrigated and
irrigated conditions. Higher temperature and/or total degreedays during reproductive stage (flowering to podding) led to
higher flower and pod abscission resulting in less number of
pods/plant and seed yield; and the same was attributed to the
negative relationship of total degree-days during flowering
to podding with seed yield. Multiple correlations of traits under
non-irrigated and irrigated conditions for seed yield were 0.9996
and 0.9984, respectively. Wide differences in calculated
optimum values and observed mean values of traits under
non-irrigated and irrigated conditions showed that there were
scope for better lentil seed yield under respective conditions
either through genetic improvement (pod number or weight/
plant) or through better crop management (days to flowering/
podding and seed filling period). Optimum value of pod wall
weight/plant was less than observed mean values and similar
observation was also reported for chickpea under different
nitrogen levels (Bhattacharya 1983).
It was concluded that physiological traits responsible
for realization of potential lentil seed yield under non-irrigated
and irrigated conditions were different. Under non-irrigated
condition, it was mainly total dry matter yield, per day dry
matter production and partitioning, mean temperature and total
degree-days during podding to maturity. Under irrigated
condition, lentil seed yield was determined by root dry matter
at vegetative stage, total dry matter yield, per day dry matter
Table 2.Optimum values of yield determining traits at different growth stages under non- irrigated and irrigated conditions in
lentil for seed yield maximization
Traits
Components of regression
Intercept
Slope
Observed
Mean
‘r’
A (%)
Maturity
Maturity
Maturity
Podding to
Maturity
Podding to
Maturity
0.740**
0.741**
0.961**
0.160
55.06
54.61
93.12
2.66
165.48
339.17
157.13
567.49
0.419
61.461
32.490
40.511
2859.1
20.0
27.14
19.66
3371.0
23.4
51.62
19.82
0.072
0.53
-1007.9
3.260
739.8
749.0
Vegetative
Maturity
Maturity
-0.147
0.575**
0.572**
2.16
32.95
32.72
1870.83
104164.46
836.15
-3958.36
0.224
33.495
0.05
3811.1
25.5
0.07
5632.0
21.8
Maturity
0.985**
97.02
105.95
30.019
52.73
62.36
Flowering to
-0.157
2.46
1952.08
podding
r = Coefficient of correlation, A (%) = Association percentage, ** are significant at 1%
-0.776
339.17
312.2
Non-irrigated condition
Total dry matter yield
Per day DM Production
Per day DM partitioning
Mean temperature
Total degree-days
Irrigated condition
Root dry matter
Total dry matter yield
Per day DM Production
Per day DM
Partitioning
Total degree-days
Growth stage
Optimum Value
305
production during seed to seed period and its partitioning
during seed filling period and total degree-days during
flowering to podding.
Katerji N, Vanhoorn J W, Hamdy A and Mastrorilli M. 2000. Salt
tolerance classification of crops to soil salinity and to water stress
day index. Agriculture and Water management 43: 99-109.
REFERENCES
Kusmenglu I and Muehlbauer F J. 1998. Genetic variation for biomass
and residue production in Lentil: II. Factors determining seed and
straw yield. Crop Science 38: 911-917.
Bahl PN, Singh KP and Singh Daljeet. 1984. Evaluation of tall chickpea
genotypes for normal and late sowings. Indian Journal of Agriculture
Science 54:110-113.
Lessani H and Mojtahedi M. 2003. Basis of the plant physiology.
Tehran University Press. Pp. 726.
Bhattacharya A. 1983. Physiological analysis of growth and yield of
Chickpea (Cicer aritenum L.) under different Nitrogen level. Ph.D.
Thesis, Division of Plant Physiology, Indian Agricultural Research
Institute, New Delhi, India.
Bhattacharya A. 1998. In “Consolidated Report of Rabi MULLaRP
Crops 1997-1998 of “All India Coordinated MULLaRP
Improvement Programme” Narendra Dev Agricultural University
Faizabad.
Bhattacharya A and Chandra S. 1997. Evaluation of tall chickpea
genotypes for normal and drought condition. Legume Research 20:
52-56.
Bhattacharya A and Pandey PS. 1999. Physiological studies in chickpea
varieties: Effect of temperature and time of sowing. Indian Journal
of Pulses Research 12: 57-64.
Bhattacharya A and Singh DN. 1997. Physiology of seed yield under
soil moisture stress and non-stress conditions in chickpea. Indian
Journal of Pulses Research 10(2): 190-194.
Desclaus D, Huynh TT and Roumet P. 2000. Identification of soybean
plant characteristics that indicate the timing of drought stress.
Hamdi A, Erskine and Gates P. 1992. Adaptation of lentil seed yield to
varying moisture supply. Crop Science 32(4): 987-990.
Hays. 1955. Methodology of plant breeding, International student
edition, McGraw Hill book company Inc. Tokyo. Pp. 442-443.
Hudak CM, and Patterson RP. 1995. Vegetative growth analysis of a
drought-resistant soybean plant introduction. Crop Science 35: 464471.
Jain HK. 1975 Breeding for yield and other attributes in grain legumes.
Indian Journal of Genetics 33:169-186.
Mitra J. 2001. Genetics and genetic improvement of drought resistance
in crop plants. Current Science 80: 758-762.
Rosielle AA and Hamblin J. 1981. Theoretical aspects of selection for
yield stress and non-stress environments. Crop Science 21: 943946.
Salehi M, Haghnazari A and Shekari F. 2006. The study of morphophysiological traits of lentil (Lens culinaris Medik) in relation with
grain yield under normal and drought conditions. The 9 th Iranian
Crop Sciences Congress. Aug. 27-28, 2006. Abourehan Campus,
University of Tehran. Pp. 534.
Schmidtke K, Neumann A and Hof G. 2004. Soil and atmospheric nitrogen
uptake by lentil and barly as monocarps and intercrops. Field Crops
Research 87: 245-256.
Silim SN, Saxena MC and Erskine W. 1993. Adaptation of lentil to the
Mediterranean environment. II. Response to moisture supply.
Experimental Agriculture 29: 21-28.
Sinha SK, Bhargava SC and Baldev B. 1988. Physiological Aspects of
pulse crops. In: Baldev B, Ramanujam S, Jain HK (eds.), Pulse
Crops. Oxford and IBH. Pp. 421-455.
Singh DN and Bhattacharya A. 1995. Consolidated Report of Rabi
Pulses 1994-1995 of All India Coordinated Pulse Improvement
Programme, Punjab Agricultural University, Ludhiana.
Singh KB and Saxena MC. 1990. Studies on drought tolerance. Annual
Report, ICARDA, Allepo, Syria.
Sinha SK. 1973. Yield of grain legumes: Problems and Prospects. Indian
Journal of Genetics 34A: 988- 994.
Sinha SK and Khanna R. 1975. Physiological, biochemical, and genetic
basis of heterosis. Advances of Agronomy 27: 123-174.
Snedecor GW and Cochran WG. 1978. Statistical Methods, 6th Edition,
Oxford and IBH Publishing Co., Calcutta.
Efficacy of post emergence herbicides on weed control and seed yield of rajmash
(Phaseolus vulgaris L.)
BALDEV RAM, S.S. PUNIA, D.S. MEENA and J.P. TETARWAL
AICRP on MULLaRP, Agricultural Research Station (Maharana Pratap University of Agriculture & Technology)
Ummedganj, Kota-324 001, Rajasthan, India; E-mail: baldev.ram@gmail.com
(Received: March 28, 2012; Accepted: December 04, 2012)
ABSTRACT
A field experiment was conducted during rabi of 2008-10 at
Agricultural Research Station, Ummedganj, Kota to evaluate
the efficacy of graded dose of imazethapyr and quizalofop ethyl
against weed complex in rajmash. Application of imazethapyr
50 g/ha applied at 20 days after sowing (DAS) significantly
reduced the weed density and their biomass recorded at 80
DAS as compared to both weedy check and graded dose of
quizalofop ethyl applied at 20 and 30 DAS. The former also
recorded significantly taller plants (43.0 cm), branches/plant
(3.7), pods/plant (18.2), seeds/pod (4.2), seed index (28.5 g) and
seed yield (710 kg/ha) over both weedy check and graded dose
of quizalofop ethyl. In addition, imazethapyr 50 g/ha at 20 DAS
had maximum net return (` 17360/ha) and B:C ratio (1.19)
with least weed index (4.2%); and was superior to all treatments
including hand weeding twice at 20 & 40 DAS.
Key words:
Imazethapyr, Quizalofop ethyl, Seed yield, Weed
control efficiency, Weed index
Rajmash (Phaseolus vulgaris L.) is grown throughout
the year in India except during winter season in hills and rainy
season in plains. Because of inefficient biological nitrogen
fixation (BNF) through poor nodulations, it requires relatively
higher dose of N fertilizer compared to other pulse crops along
with sufficient quantity of P and K for its better growth and
realization of yield potential. During its early growth stage,
weed competes with it as it emerges simultaneously with the
crop, leading to severe co mpet itio n between them
(Kandasamy 2000). Since the initial growth of rajmash is very
slow, the initial period of crop growth (30-45 DAS) is most
crucial for crop-weed competition. In addition to slow initial
crop growth, wider crop spacing also facilitates crop-weed
competition which poses a serious limitation in rajmash
production and thus, estimated seed yield loss may likely to
go to the extent of 45-65 % under unweeded condition (Mishra
2006). During winter season, broad leaved weeds may become
dominant in the early stages of crop growth because of their
fast growth and deep root system.
Usually farmers go for manual weeding under such a
situation. However, availability of labour and cost involved
make them to seek for other cheaper alternatives for weed
control. The use of post emergence herbicides for season
long weed control is thus, preferred over earlyuse of herbicides
as pre-plant incorporation (fluchloralin & trifluralin) and preemergence (alachlor and pendimethalin) as the latter control
weeds only during initial crop growth (up to 30 DAS). Hence,
an integration of both pre-emergence herbicides along with
one manual weeding is needed under a season long weed
management strategy. There is also a possibility that use of
single post emergence herbicide may replace the above and
raise the income of both farmers and farms. Since work on
post emergence herbicides especially in pulses is meager, an
attempt is thus made to evaluate the efficacy of post
emergence herbicides for effective control of weeds in
rajmash.
A field experiment was conducted at Agricultural
Research Station, Ummedganj, Kota (250 18' N, 770 23' E and
271 m above mean sea level), Rajasthan, during rabi 2008-10
to evaluate the efficacy of new molecules of post emergence
herbicide on rajmash. The clay loam (vertisols) neutral soil
(pH 7.5) of the experimental field was low in organic carbon
(4.1g/kg), medium in available P (20.5 kg/ha) and high in
available K (292.5 kg/ha). The experiment was laid out in
randomized complete block design (RCBD) comprising of 15
treatments viz., quizalofop ethyl at 40, 50 and 60 g/ha and
imazethapyr at 25, 37.5 and 50 g/ha (both herbicides) applied
at 20 and 30 DAS, pendimethalin 1.0 kg/ha as pre-emergence,
hand weeding twice at 20 & 40 DAS and a weedy check with
three replications. High yielding with semi dwarf erect type
and tolerant to bean common mosaic virus ‘HUR 137’ Rajmash
(115-125 days) was sown at a seed rate of 125 kg/ha with a row
spacing of 30 cm in line during 3rd and 2nd week of November
in 2008 and 2009, respectively. Half of recommended dose of
N (60 kg/ha), full of P (60 kg P2O5/ha) and K (40 kg K2O/ha)
through urea, diammonium phosphate and muriate of potash
were drilled in the soil before sowing and the remaining N (60
kg/ha) was top dressed in two equal splits at first irrigation
(25-30 DAS) and pod formation (65-70 DAS). The crop was
raised under irrigated condition with recommended package
of practices for the zone.
Imazethapyr 10 % SL (at 25, 37.5 and 50 g a.i./ha) and
quizalofop ethyl 5 % EC (at 40, 50 and 60 g a.i./ha) were sprayed
20 and 30 DAS, respectively by knapsack sprayer using a flat
fan nozzle at 500 l/ha spray volume by diluting with water.
Ram et al.: Efficacy of post emergence herbicides in rajmash
Weeds were removed at 20 and 40 DAS under manual weed
control (weed free). The total number of weeds from one square
m area were counted species wise (monocot, dicot and sedges)
separately with the help of iron quadrate at two places of the
sample rows in each plot at 80 DAS and analyzed after
subjecting the original data to square root transformation
(vX + 0.5). For dry matter, weeds collected from one square m
area were dried under the sun and then in an oven at 700 for 72
h, weighed (g/m2) and converted into kg/ha. Weed control
efficiency (WCE) was calculated at 80 DAS and expressed as
the percentage reduction in weed population due to weed
management practices over control using formula:
WCE (%)={Weed population in control plot (WPc)–weed
population in treated plot (WPt)}/weed population in control
plot × 100
Weed index (WI) was also calculated as per cent
reduction in yield due to the presence of weeds in comparison
with weed free plot. The economics of treatments was
computed on the basis of prevailing market prices of inputs
and outputs under each treatment. Analysis of variance was
performed on all the collected data. Pooling was made over
the years as similar trend was noticed during both the years.
Weed control: The major broad leaved and grassy weeds
observed in rajmash field included Chenopodium album
(bathua), Chenopodium murale (kharthua), Cirsium arvense
(kateli), Fumaria parviflora (gajri), Melilotus alba (senji),
Lathyrus spp. (chatri-matri), Vicia sativa (ankari), Convolvulus
arvensis (hirankhuri), Cyperus rotundus (motha) and
Cynodon dactylon (doobgrass). Thus, broad leaved weeds
(81.3%) were dominant compared to grassy(15.2%) and sedges
307
(3.5%) during both the years. All the weed control treatments
significantly curtailed weed population and their dry weight
compared to weedy check (Table 1). However, hand weeding
twice at 20 & 40 DAS recorded lowest weed (monocot, dicot
and sedges) population at 80 DAS. Amongst the herbicides,
lowest dicot population was recorded with imazethapyr 50 g/
ha at 20 DAS; and was superior over rest of the herbicide
treatments and weedy check as per cent reduction in total
weed density was 90.9 over weedy check.
Application of post emergence herbicide imazethapyr
at 25-50 g/ha and quizalofop ethyl at 40-60 g/ha sprayed at 20
and 30 DAS also significantly reduced the total weed density
to the tune of 36.0-57.9 and 18.3-30.5 % over weedy check,
respectively whereas that of pre-emergence herbicide
pendimethalin reduced the total weed density 45.7 per cent
over weedy check. Imazethapyr being freely translocated in
plants through roots and shoots could effectively controlled
broad leaved as well as grassy weeds. Tiwari et al. (2007)
observed that imazethapyr at 75 g/ha controlled broad leaved
weeds in soybean whereas, Mishra and Chandra Banu (2006)
reported efficient control of weeds by imazethapyr at 100 g/ha
over other herbicides in summer irrigated urdbean. In the current
investigation also, application of imazethapyr 50 g/ha
effectively controlled the emerged grassy, sedges and broad
leaved weeds. Thus, the results confirmed the findings of
Ram et al. (2011) in field pea.
With regards to weed biomass, imazethapyr 50 g/ha at
20 DAS recorded significantly lower weed biomass (36.8 g/
m2) and was closely followed by imazethapyr 50 g/ha at 30
DAS and imazethapyr 37.5 g/ha at 20 and 30 DAS over rest of
the treatments and weedy check, respectively. Pendimethalin
1.0 kg/ha was also found significantly superior in reduction
of total weed biomass (96.6 g/m 2 ) over weedy check.
Table 1. Effect of post emergence herbicides on diverse weed flora, total weed density and weed control efficiency at 80 DAS in
Rajmash (pooled)
Treatments
Quizalofop Ethyl 40 g/ha at 20 DAS
Quizalofop Ethyl 50 g /ha at 20 DAS
Imazethapyr 25 g a/ha at 20 DAS
Imazethapyr 37.5 g/ha at 20 DAS
Imazethapyr 50 g/ha at 20 DAS
Imazethapyr 37.5 g a/ha at 30 DAS
Pendimethalin 1.0 kg/ha PE
Hand Weeding at 20 & 40 DAS
Weedy check
SEm (±)
CD (P= 0.05)
Monocot weed
density
(no/m2)
3.4*(8.4)
3.3 (7.8)
3.0 (6.2)
4.1 (12.6)
3.7 (10.3)
3.5 (9.1)
4.8 (18.6)
4.2 (13.5)
3.9 (11.3)
5.2 (21.9)
4.8 (18.7)
4.4 (15.3)
5.4 (23.7)
3.0 (6.2)
6.7 (38.3)
0.16
0.46
Dicot weed
density
(no/m2)
11.8 (126.5)
11.4 (119.0)
10.9 (108.8)
12.6 (146.1)
12.4 (141.7)
12.0 (132.8)
8.6 (64.7)
7.1 (43.2)
5.1 (20.8)
9.1 (74.5)
7.8 (53.4)
7.1 (43.1)
7.0 (41.6)
4.2 (13.4)
14.8 (205.1)
0.27
0.77
Sedge weed
Density
(no/m2)
2.6 (4.3)
2.5 (4.1)
2.5 (3.8)
3.1 (6.7)
2.9 (6.0)
2.8 (5.2)
2.5 (3.9)
2.4 (3.6)
2.4 (3.4)
2.5 (4.1)
2.5 (3.8)
2.4 (3.6)
2.9 (5.9)
2.4 (3.5)
3.5 (8.9)
0.08
0.23
Total weed
density
(no/m2)
12.3 (139.1)
11.9 (130.9)
11.4 (118.9)
13.4 (165.5)
13.1 (157.9)
12.6 (147.0)
9.8 (87.3)
8.3 (60.3)
6.9 (41.5)
10.5 (100.5)
9.2 (75.9)
8.4 (62.0)
8.9 (71.2)
5.3 (23.1)
16.4 (252.4)
0.34
0.97
Total weed
biomass
(g/m2)
105.7
93.0
87.4
104.4
99.9
96.2
55.8
48.4
36.8
56.1
47.4
37.2
96.6
17.3
161.8
4.23
11.86
*Data subjected to square root ( x  0.5 ) transformation and figures in parentheses are original values
WCE
(%)
44.9
48.1
52.9
34.4
37.4
41.8
65.4
76.1
83.6
60.2
69.9
75.4
71.8
90.9
1.36
3.82
Weed dry
matter(kg/ha)
at 80 DAS
1057
930
874
1044
999
963
558
484
376
561
474
372
215
173
1617
42.81
118.6
Weed
index
(%)
39.7
37.3
35.2
41.0
38.2
36.3
23.5
12.3
04.2
24.4
20.1
13.4
15.4
54.4
1.02
2.84
308
Imazethapyr effectively controlled germinated broad leaved
as well as grassy weeds either through directly killed or
suppressed these (smothering effect) and thus, resulting in
least weed biomass and higher crop growth. Similar, findings
were reported by Godara and Deshmukh (2002) in soybean
and Ram et al. (2011) in field pea. On the contrary, quizalofop
ethyl was effective against grassy weeds only as it failed to
curb the population of broad leaved weeds in comparison to
imazethapyr. Nevertheless, hand weeding twice at 20 and 40
DAS recorded the lowest weed biomass (17.3 g/m2) at 80 DAS
of all the herbicide treatments including weedy check by
controlling weed population to the extent of 90.9% (Table 1).
On efficiency factor, imazethapyr 50 g/ha at 20 DAS had
maximum weed control efficiency (83.6%) recorded at 80 DAS
and was followed by imazethapyr 50 g/ha at 30 DAS (75.4%)
whereas, it was the least under quizalofop ethyl 40 g/ha at 30
DAS. This might be due to lower weed biomass and higher
efficiency of weed control under imazethapyr against both
broad leaved and grassy weeds (Table 1). Application of
pendimethalin 1.0 kg/ha as pre-emergence herbicide was also
superior over the graded dose of quizalofop ethyl at 20 and 30
DAS and weedy check. Ram et al. (2011) reported highest
weed control efficiencywith imazethapyr 50 g/ha at 20 DAS in
field pea. Use of post emergence herbicides was also found to
be superior over pre-emergence and pre-plant incorporation
applied herbicides as weeds were killed in their active growth
stage bearing 2-3 leaves (Chen et al. 1998). Similarly, minimum
weed index (4.2%) was recorded with imazethapyr 50 g/ha at
20 DAS over rest of the herbicide treatments and weedy check
(Table 1) as the treatment effectively controlled both broad
leaved and grassy weeds.
Crop growth and yield: Post emergence herbicides had
signi ficantly higher values of crop gro wth and yield
contributing characters over the weedy check. Among the
herbicide treatments, tallest plants (43.0 cm) and highest
branches/plant (3.7), pods/plant (18.2) and seeds/pod (4.2)
were recorded with imazethapyr 50 g/ha at 20 DAS; and was
statistically on par with imazethapyr 50 g/ha at 30 DAS and
pendimethalin 1.0 kg/ha in respect of plant height and seeds/
pod, respectively. Because of poor weed control efficiency
and higher weed competition index among the crop and weeds,
quizalofop ethyl was least effective for raising crop growth
and yield contributing characters of rajmash (Table 2). On the
contrary, hand weeding twice at 20 and 40 DAS (weed free)
recorded significantly higher plant height (47.2 cm), branches/
plant (5.0), pods/plant (20.9), seeds/pod (4.8) and seed index
(28.5 g) over weedy check and most of the herbicide treatments.
Seed yield of rajmash varied significantly with weed
control treatments. Maximum seed yield (741 kg/ha) was
obtained with hand weeding twice at 20 and 40 DAS and was
statistically on par with imazethapyr 37.5 and 50 g/ha at 20
DAS. The seed yield registered under the above was also
significantly higher over rest of the herbicide treatments and
weedy check. Among the herbicides, imazethapyr 50 g/ha at
20 DAS recorded maximum seed yield (710 kg/ha) which was
obviously due to its higher values of yield attributes, weed
control efficiency (83.6%) and lowest weed index (4.2%)
compared to the rest of the herbicide treatments. In addition,
imazethapyr 50 g/ha at 20 DAS also increased the seed yield
to the tune of 110% while pendimethalin at 1.0 kg/ha as preemergence raised seed yield by 85.5% over weedy check.
Effectiveness of these treatments could be attributed to better
controls of weeds during critical period of crop-weed
competition under moist soil condition which in turn reduced
Table 2. Effect of post emergence herbicides on growth, yield attributes, seed yield and economics of rajmash (pooled)
Treatments
Quizalofop Ethyl 50 g /ha at 20 DAS
Imazethapyr 25 g a/ha at 20 DAS
Imazethapyr 37.5 g/ha at 20 DAS
Imazethapyr 37.5 g a/ha at 30 DAS
Pendimethalin 1.0 kg/ha PE
Hand Weeding at 20 & 40 DAS
Weedy check
SEm (±)
CD (P= 0.05)
Plant height
(cm)
Branches/
plant
Pods/
plant
32.6
33.5
34.7
34.7
36.1
36.9
36.9
40.1
43.0
37.5
39.9
41.2
41.2
47.2
26.6
1.0
2.8
3.2
3.3
3.4
3.1
3.2
3.3
3.4
3.6
3.7
3.3
3.6
3.7
3.7
5.0
2.6
0.14
0.38
9.7
10.2
10.7
9.4
9.6
10.1
12.6
14.8
18.2
11.7
12.8
15.2
13.9
20.9
6.1
0.78
2.17
Seeds/ Seed index Seed yield *Cost of
(g)
pod
(kg/ha) cultivation
(`/ha)
3.3
27.6
447
14810
3.5
27.8
465
15200
3.5
27.7
480
15575
3.2
27.9
437
14810
3.3
27.9
458
15200
3.4
28.0
472
15575
3.7
28.1
567
14145
3.9
28.2
650
14398
4.2
28.5
710
14590
3.3
28.0
560
14145
3.7
28.1
587
14398
3.9
28.2
642
14590
3.8
28.2
627
14925
4.8
28.5
741
18900
2.6
26.8
338
13400
0.14
0.21
23.6
0.39
0.59
65.6
-
*Net
return
(`/ha)
5305
5725
6025
4855
5410
5665
11370
14852
17360
11055
12017
14300
13290
14445
1810
1067
2966
B: C
ratio
0.36
0.37
0.39
0.33
0.36
0.36
0.80
1.03
1.19
0.78
0.83
0.98
0.89
0.76
0.13
0.08
0.24
*The price of imazethapyr and quizalofop ethyl were `1680 and 1380/litre, respectively, whereas, the cost of two hand weedings (20 and 40 DAS)
were `7500 for 60 mandays and the sale price of rajmash was `45/kg
Ram et al.: Efficacy of post emergence herbicides in rajmash
biotic stress (due to weed competition) and thus, provided a
weed free environment for a better growth and development
of rajmash. These findings are in close proximity with that of
Billore et al. (1999) with imazethapyr on soybean and Ram et
al. (2011) with imazethapyr on field pea. Irrespective of its
doses and time of application, application of imazethapyr was
superior to weedy check. Lower seed yield under quizalofop
ethyl could be attributed to its poor weed control efficiency
and higher weed index against broad leaved weeds.
Economics: The highest net return (` 17,360/ha) and benefit:
cost ratio (1.19) was fetched with imazethapyr 50 g/ha at 20
DAS owing to lower cost of cultivation and effective control
of broad leaved as well as grassy weeds (Table 2); and was
followed by imazethapyr 37.5 g/ha at 20 DAS and hand
weeding twice at 20 and 40 DAS. Excellent control of dominant
broad leaved as well as grassy weeds without any adverse
effect on crop growth resulting in higher seed yield might
have caused superior economic indices in these treatments.
Least net return (` 1810/ha) and B: C ratio (0.13) was recorded
with weedy check due to both poor weed control and low
crop yield.
Thus, it was inferred from the above that post emergence
application of imazethapyr 50 g/ha at 20 days after sowing
could be recommended for effective control of broad leaved
as well as grassy weeds in rajamsh for getting higher
productivity and profitability under the existing condition.
309
REFERENCES
Billore SD, Joshi OP and Ramesh A. 1999. Herbicidal effect on
nodulation, yield and weed control in soybean [Glycine max (L.)
Merill]. Indian Journal of Agricultural Sciences 69: 329-331.
Chen TiebBao, Yong ShaoYi, Huang, ChunYan, Wang Yu, Zhang
ShanYing, Chen TB, Yang CY, Wang Y and Zhang ZY. 1998. Study
on weed control for soybean field with Jindou (AC 299263). Soybean
Science 17: 271-275.
Godara SP and Deshmukh SC. 2002. Weed biomass, weed control
efficiency and yield of soybean (Glycine max L.) as influenced by
various weed control measures. In: Proceedings of 2 nd International
Agronomy congress on Balancing Food and Environment SecurityA continuing challenge, 26-30 November 2002, IARI, New Delhi.
Pp. 1198-1200.
Kandasamy OS. 2000. Cost effective weed management strategies in
pulse production. In: Proceedings of CAS on Recent Advances in
Pulse Production Technology, 13 th September-30 t h October,
Coimbatore, Tamilnadu. Pp. 116-119.
Mishra JS. 2006. Efficacy of post emergence herbicides against wild oat
in field pea. Indian Journal of Weed Science 38: 140-142.
Mishra JS and Chandra Banu. 2006. Effect of herbicides on weeds,
nodulation and growth of Rhizobium in summer blackgram (Vigna
mungo). Indian Journal of Weed Science 38: 150-153.
Ram Baldev, Punia SS, Meena DS and Tetarwal JP. 2011. Bio-efficacy
of post emergence herbicides to manage weeds in field pea. Journal
of Food Legumes 24: 254-257.
Tiwari DK, Kewat ML, Khan JA and Khanparia NK. 2007. Evaluation
of efficacy of post emergence herbicides in soybean (Glycine max
L.). Indian Journal of Agronomy 52: 74-76.
Enhancing water use efficiency and production potential of chickpea and fieldpea
through seed bed configurations and irrigation regimes in North Indian Plains
J.P. MISHRA, C.S. PRAHARAJ1 and K.K. SINGH1
NRM Division, Krishi Anusandhan Bhawan-II, ICAR, New Delhi- 110012, India; 1Crop Production Division, Indian
Institute of Pulses Research, Kanpur-208 024, U.P., India; E-mail: mishrajaip@gmail.com
(Received: October 27, 2011; Accepted: December 12, 2012)
ABSTRACT
Chickpea and fieldpea are grown during fall in India under
diverse production systems including both rainfed and irrigated.
Moisture scarcity especially at terminal stages of these crops
results in low productivity and less farm income despite the
fact that these crops have unique adaptive mechanisms to
moisture stress. Therefore, one or two life saving irrigations at
the most critical stages is of immense relief for maintaining
plant water status in addition to other in situ water saving
measures such as land configuration and mulch. Thus, a field
trial was carried out in these pulses to study the effect of seed
bed configurations, mulch and irrigation regimes on seed yield
and water use efficiency (WUE) under North Indian Plains
during 2007-08 and 2009-10. The study revealed that pre-sowing
irrigation followed by a post-sowing irrigation depending on
critical stage of crop was optimum for realization of optimum
yield and WUE. Irrigation combined with straw mulch @ 6 t/ha
was also useful in getting maximum seed yield. Planting on a
raised bed wa s superior to flat planting. Subsequent
confirmatory field trial also revealed the yield advantage of
furrow irrigated raised beds (FIRB) planting at 60 cm with two
rows of field pea on beds over both flat and FIRB at 90 cm with
three rows of field pea on beds. Water use efficiency was however,
improved with 90 cm wide FIRBs in chickpea in comparison to
60 cm wide FIRBs in field pea. Both chickpea and field pea
responded to single irrigation at branching only.
Key words:
Chickpea, Fieldpea, Mulch, Seed bed configurations,
Seed yield, Water use efficiency
The intrinsic capacity of pulses to sustain soil health
and fertility through addition of organic matter (roots and
fallen leaves) and assimilation of atmospheric nitrogen into
ammonia and consequent economy in N fertilizer application
following BNF play a vital role in sustenance in cultivation of
pulses and its popularization among growers (Mishra et al.
2012). Furthermore, pulses do offer an attractive opportunity
through their vertical diversification for varied economic uses
such as feed, fuel and fodder (Panwar and Basu 2003, Praharaj
et al. 2011). Besides improving soil conditions, pulses have
become more remunerative in view of the recent price regime
triggered due to higher minimum support prices (MSP) and
changing domestic economy in the rural areas resulting in
increased demand for the commodity.
Chickpea and field pea are important pulses of North
Indian Plains during fall or Rabi season wherein these are
cultivated under diverse production systems including rainfed
condition. Chickpea (Cicer arietinum L.) is the premier pulse
crop in India which accounted about 44.5% of country’s total
pulse production of 18.09 million tonnes during 2010-11
(Anonymous 2011). On acreage and production front, chickpea
is grown in India in 6.67 Million hectares (Mha) with production
of 5.3 million tonnes (Mt) while field pea is grown in 0.72 Mha
with production of 0.6 Mt during 2011-12 (IIPR 2012). Being
mostly grown under rainfed condition with cessation of
monsoon rain many a time early in the season, moisture scarcity
especially at terminal stages of these crops is conspicuous
and more-frequent that results in low productivity/production
despite the crops have a unique adaptive mechanisms to
moisture stress. Thus, one or two life saving irrigations at the
most critical stages of the crop is of immense relief for
maintaining plant water status (Ali 2009, Ali et al. 2008). In
addition, resource conservation for higher input use efficiency
through appropriate seed bed configurations in conjunction
of appropriate irrigation scheduling have significant role in
enhancing both production and productivity of pulses
especially under Rabi season that usually thrived better under
limited soil moisture availability (Chaudhury et al. 2005).
Moreover, potential morpho-physiological traits in
plants viz., water use efficiency (WUE), deep root system,
higher relative biomass and harvest index, osmotic adjustment
of chickpea are advantageous under water scarce situation
(Chaudhury et al. 2005). Despite all this, crop experiences
terminal drought during seed development stage as it is
invariably grown on residual soil moisture after a preceding
rainy crop(s), thereby making the terminal moisture stress as
the major constraint in achieving potential productivity of
chickpea (Singh et al. 2010). Under such sit uations,
photosynthetic activity of leaves is hampered for the want of
nitrogen and thus, seed filling is affected (Davies et al. 2000).
Therefore, a judicious management of available soil moisture
through in-situ conservation either by a mulch practice or a
suitable land configuration viz., furrow irrigated raised bed
(FIRB) improves crop productivity (Panwar and Basu 2003).
These water saving measures may possibly render the crop in
part to get rid of water stress at least for sometime without
appreciable loss in biomass production leading to probably
higher productivity. Since the information on growing Rabi
Mishra et al.: Performance of Rabi pulses under seed bed configurations and irrigation
pulses on raised bed vis-a-vis flat system coupled with
irrigation regimes and mulches is limited, hence the present
study was undertaken on two important Rabi pulses (chickpea
and fieldpea) under modified seed bed configuration, mulch
and limited irrigation for determining the efficacy of these on
higher productivity and efficient water use under North Indian
Plains.
A field experiment was conducted at Indian Institute of
Pulses Research, Kanpur, India (26o 27/ N, 80o 14/ E and 152.4 m
above msl) under Indo-Gangetic Plains during Rabi 2007-08
and 2009-10 to evaluate the productive performance of ‘DCP
92-3’ chickpea (a small seeded semi erect and medium duration
desi variety) and ‘Akash’ field pea (a bold seeded and dwarf
variety) under different seed bed configurations, irrigation
regimes and rice straw mulch. The trial involving two seed
bed configurations (flat bed versus raised bed) in main plot
and four irrigation schedules (one irrigation, one irrigation +
rice straw mulch @6t/ha, two irrigations, and two irrigations +
rice straw mulch @6t/ha) in sub plot was carried out during 1st
year in a split plot design with three replications. During 2nd
year, precisely three seed bed configurations (flat bed, raised
bed of 60 cm width and raised bed of 90 cm width) in main plot
along with 3 irrigation schedules (rainfed, one irrigation at
branching and two irrigations at both branching and pod
formation) in sub plot were taken up at the same site in a split
plot with three replications.
The climate of the region is tropical sub-humid receiving
an annual rainfall of 722 mm with mean annual maximum and
minimum temperature of 33°C and 20°C, respectively. The
climatic situation for the representative year (2009-10) was
also given in Table 1. The soil was sandy loam (Typic
Ustochrept) with 8.1 pH, 1.43 g/cc bulk density and low in
organic carbon (SOC, 0.28 %) at the time of initiation of field
experiment. On soil fertility account, it was low in available N
(215 kg/ha), medium in P (10.5 kg/ha) and K (230 kg/ha) and S
(15.0 kg/ha). The field was prepared well and furrow irrigated
raised beds (FIRB) of 60 and 90 cm width were prepared with
tractor drawn modified conventional bed maker. Two rows of
chickpea and field pea were planted on 60 cm FIRBs (at an
inter-row spacing of 20 cm) while three rows of both the pulses
311
were planted on 90 cm FIRBs (at an inter-row spacing of 22.5
cm). Recommended seed rate was used under both system of
planting along with recommended package of practices
including use of fertilizers and appropriate Rhizobium
inoculation. The rainfall received during the crop period was
optimum and well distributed during both the years. The
moisture studies were carried out in four depths (0-15, 15-30,
30-45 and 45-60 cm) using gravimetric method; and soil
moisture depletion, water use and its efficiency (from seed
yield data) were calculated using standard procedures. Normal
practice of crop husbandry for successful crop raising was
also followed.
Seasonal variation: The distribution and intensity of rainfall
during fall in 2007-08 and 2009-10 are near optimal although it
was the most congenial during 2009-10 (Table 1) as it coincided
with active branching and late flowering stages, thereby
improving crop productivity yet nullifying the impact of tillage
and mulch treatments. The data could not be pooled due to
differences in treatments and differential trends observed for
seed yield during the seasons with different rainfall pattern.
Crop year 2007-08: During the year 2007-08, the objective
was to efficiently manage the irrigation water in chickpea
through seedbed configurations, irrigation regimes and mulch
for improving WUE in chickpea through conjunctive use of
mulch and irrigation under improved agronomic management
(FIRB i.e., furrow irrigated raised bed). Crop performance under
different land configurations indicated that FIRB planting
(2439 kg/ha) out yielded flat planting (2052 kg/ha) significantly
to the extent of 18.8% and was mainly attributed to higher
crop growth (in terms of plant height, nodule/plant, nodule
dry weight and branches/plant) and yield attributes (pod/plant)
(data not given). Though maximum seed yield was recorded
with two irrigations + mulch (2490 kg/ha), it was at par with
two irrigations alone (2450 kg/ha). However, seed yield
response to mulch in chickpea was significant up to one
irrigation only. Yet, the WUE was maximum both under raised
bed planting and one irrigation + mulch as mulch enhanced
WUE significantly (Fig. 1).
Low soil moisture availability was mainly attributed to
Table 1. Climatic situation at the location during the year 2009-10
Months
Temp max
Tempmin
RH max
RHmin
Oct, 09
Nov, 09*
Dec, 09
Jan, 10
Feb, 10
Mar,10*
Apr,10
32.3
28.2
24.9
18.4
26.6
35.7
42.1
19.5
14.5
09.8
07.8
12.2
17.9
24.3
68.6
70.5
75.7
92.0
75.0
57.7
35.2
55.2
61.1
65.9
76.8
55.5
33.2
21.2
*Planting date was early Nov, 2009 and the crop was harvested during March, 2010
R.F.
(mm)
95.8
3.8
10.0
4.4
15.6
0.0
0.0
Cum. R.F.
(mm)
3.8*
13.8
18.2
33.8
33.8*
-
Avg. Evap.
(mm/day)
4.3
2.5
1.9
1.3
3.4
7.7
12.2
312
higher soil moisture depletion due to evaporation from
undisturbed soil capillary under no mulch treatment (data not
included). Similarly, no mulch recorded higher consumptive
use over paddy straw mulch. Thus, water use efficiency was
higher in mulch treatment (with one or two irrigations at crop
critical stages). WUE, being the product of economic yield to
consumptive use of water, reflects the efficacy of a given
treatment of transforming the water used into economic
produce i.e., seed yield per unit area. The efficiency of water
use was therefore, enhanced following mulch and rice straw;
and was more conspicuous up to one irrigation at branching
only (Fig. 1).
apart as recommended) for chickpea and field pea was further
compressed to 22.5 cm on FIRBs of 90 cm containing three
rows of pulses. Under 60 cm furrow irrigated raised beds, two
rows of crops were planted so that these could get an adequate
space for horizontal spread in the left over space between two
ridges, while under 90 cm raised beds, three rows of these
crops were planted on raised beds and the middle row suffered
adversely due to increased inter- and intra-plant competitions
for resources. This could result in lower yield in 90 cm raised
beds (1.91 t/ha in chickpea and 2.01 t/ha in field pea) in
comparison to 60 cm FIRB (1.99 t/ha in chickpea and 2.68 t/ha
in fieldpea).
Crop year 2009-10: Both the rabi pulses responded
differentially to seed bed configurations as well as irrigation
scheduling (Table 2). The seed yield of chickpea did not differ
significantly due to manipulation in seed bed configurations
whether it was a flat bed or FIRB. Contrary to this, fieldpea
yielded significantly higher (2.68 t/ha) when planted on 60 cm
FIRB as it registered an increase in seed yield to the tune of
20% over flat bed and 33% over 90 cm wide FIRBs. The lowest
seed yield of both crops was recorded in 90 cm FIRBs. It was
due to the fact that normal planting distance (30 cm rows
The consumptive use of water was also the highest in
flat bed sowing under both the crops that reduced further in
furrow irrigated raised bed planting. Thus, water use efficiency
was increased under raised bed systems with the higher value
of 11.1 kg/ha-mm under 60 cm FIRBs in chickpea (and 11.8 kg/
ha-mm under 90 cm FIRBs) and 17.7 kg/ha-mm in field pea.
This indicated the fact that furrow irrigated raised bed systems
could produce higher seed yield with low water use or
application of less water (Anwar et al. 2003, Praharaj et al.
2011). Of the two crops, field pea proved more efficient in
water use under all the planting configurations over chickpea
(Table 2).
Yield
WUE
3000
20
Yield (kg/ha)
2500
18
2000
16
1500
14
1000
12
500
Fl
at
0
d
se
ai
R
d
be
ne
O
i
ir r
O
Fig 1.
g.
ne
ig
irr
ch
ul
+m
o
Tw
ig
irr
10
.
o
Tw
i rr
ig
h
ulc
+m
Seed yield and WUE as influenc ed b y planting
techniques and irrigation schedules
The wetter moisture regimes under two irrigations at
branching and pod formation also proved counterproductive
for chickpea as only a marginal increase in the seed yield of
field pea was evident (Table 2). During rabi 2009-10, the winter
rains were well distributed coinciding with the late flowering
and initiation of pod formation stages of chickpea and field
pea (Table 1) that led to no response for two irrigations in
both the crops. The consumptive use was also the highest
under two irrigations (21.3 cm in chickpea and 21.1 cm in field
pea). However, no irrigation (rainfed condition) was more
conducive for higher water use efficiency in chickpea (12.8 kg
seed/ha/mm water) and field pea (16.4 kg seed/ha/mm water).
Relatively higher water use efficiency in field pea over chickpea
was due to higher seed yield in case of former. Thus, lower
water use efficiency in wetter moisture regimes was due to
Table 2. Effect of seed bed configurations and irrigation schedules on water use and seed yield of chickpea and fieldpea
Treatments
Seed bed configurations
Flat
60 cm bed FIRB
90 cm bed FIRB
C.D. (0.05)
Irrigations schedules
No irrigation
one irrigation
Two irrigation
C.D.(0.05)
*CU: Consumptive use of water
Seed yield
(t/ha)
Chickpea
CU*
(cm)
WUE
(kg/ha-mm)
Seed yield
(t/ha)
Field pea
CU*
(cm)
WUE
(kg/ha-mm)
2.03
1.99
1.91
NS
19.6
18.2
16.3
-
10.7
11.1
11.8
-
2.24
2.68
2.01
0.28
18.7
15.6
16.6
-
12.4
17.7
12.3
-
1.90
2.04
1.99
0.11
14.8
18.0
21.3
-
12.8
11.4
9.4
-
2.14
2.38
2.40
0.21
13.0
16.8
21.1
-
16.4
14.5
11.5
-
Mishra et al.: Performance of Rabi pulses under seed bed configurations and irrigation
disproportionate increase in seed yield as compared to water
use. Thus, the role of single irrigation at the critical stage of
crop was evident from the increase in both seed yield and
water use efficiency up to the maximum level of compensation
in use of precious water as an input for crop use (Table 2).
Thus, it was inferred from the above that furrow irrigated
raised bed (60 cm width FIRBs accommodating 2 rows) could
be an effective land configuration measure in conserving
both soil moisture and enhancing productivity of chickpea
and field pea. In case of terminal moisture stress, single
irrigation at branching could be advocated for realizing higher
yield and input use efficiency.
REFERENCES
Anonymous. 2011. IV Advance Estimates, 2010-11, Directorate of
Economics & Statistics, Department of Agriculture & Cooperation,
Ministry of Agriculture, Government of India.
Anwar MR, Mckenzie BA and Hill GD. 2003. The effect of irrigation
and sowing date on crop yield and yield components of Kabuli
chickpea (Cicer arietinum L.) in a cool-temperate subhumid climate.
The Journal of Agricultural Science 141:259-271.
Chaudhury J, Mandal UK, Sharma KL, Ghosh H and Mandal B. 2005.
Assessing soil quality under long-term rice-based cropping system.
Communications in Soil Science and Plant Analysis 36: 1141-1161.
Davies SL, Turner NC, Palta JA, Siddique, KHM and Plummer JA.
313
2000. Remobilization of carbon and nitrogen supports seed filling
in desi and kabuli chickpea subject to water deficit. Australian Journal
of Agricultural Research 51: 855-866.
IIPR 2012. All Indian Coordinated Projects on Chickpea and MULLaRP,
Indian Institute of Pulses Research, Kanpur, India.
Ali Masood. 2009. 25 years of pulses research at IIPR. Indian Institute
of Pulses Research, Kanpur. Pp. 211.
Ali Masood, Ganeshamurthy AN, Singh KK and Sekhon HS. 2008.
Integrated nutrient and water management in food legumes in semiarid tropics, Vol 1: 485-502. In: Food legumes for nutritional security
and sustainable agriculture (Ed M.C.Kharwal), M/s Kamala Print-npublish, New Delhi, India.
Mishra JP, Praharaj CS, Singh, KK and Narendra Kumar 2012. Impact
of conservation practices on crop water use and productivity in
chickpea under middle Indo-Gangetic plains. Journal of Food
Legumes 25: 41-44.
Panwar JDS and Basu PS. 2003. Improving drought tolerance and water
use efficiency in chickpea. In: Masood Ali, B.B. Singh, Shiv kumar,
Vishwa Dhar (Eds), Pulses in new perspective. Indian Institute of
Pulses Research, Kanpur, India. Pp 480-488.
Praharaj CS, Sankaranarayanan K, Narendra Kumar, Singh KK and
Tripathi AK. 2011. Low-input technologies for increasing crop
productivity and sustainability. Current Advances in Agricultural
Sciences 3: 1-12.
Singh AK, Singh SB, Singh AP, Singh AK, Mishra SK and Sharma AK.
2010. Effect of different soil moisture regimes on biomass
partitioning and yield of chickpea genotypes under intermediate
zone of J & K. Journal of Food Legumes 23: 156-158.
Variability in the nutrients, antinutrients and other bioactive compounds in soybean
REETI GOYAL, SUCHETA SHARMA and B.S. GILL1
Department of Biochemistry, 1Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana141004, India; E-mail: suchetasharma_pau@yahoo.com
(Received: February 16, 2012 ; Accepted: October 09, 2012)
ABSTRACT
One hundred fourty soybean genotypes were evaluated for their
physicochemical properties and biochemical diversity in
contents of nutrients (total proteins, oil, total sugars and
sucrose), antinutrients (trypsin inhibitor activity and phytic
acid) and bioactive compounds (tannins, saponins, phenolics
and tocopherols) with the aim to identify the improved
genotypes having low antinutrient and high nutrient traits for
human/livestock consumption. Physicochemical characterization
indicated that exotic genotypes had highest mean values for
water absorption, volume expansion, hydration capacity,
swelling capacity and their indices. Soybean genotypes
exhibited 38.4-46.5% protein, 20.8-23.6% oil, 1.3-13.9% total
soluble sugars, 0.24-13.8% sucrose, 2.50-33.5 mg/g tocopherol,
8.9-20.5 mg/g tannins and 11-38.3 mg/g phenols. Genotypes SL
1000, SL 716 and SL 990 recorded minimum trypsin inhibitor
activity, phytate and tannin content respectively. Soybean
genotypes investigated differed in their nutritional and
antinutritional characteristics. Such information will be useful
for breeding purposes as well as in selecting soybean varieties
to manufacture various soy food preparations.
Key words:
Oil, Phytic acid, Protein, Soybean genotypes, Total
soluble sugars, Trypsin inhibitor
Soybean is the most important feed grain legume with a
total world production of 234.65 million tonnes and harvested
area of 94.89 million hectare (FAOSTAT 2009). In India, it is
grown on an average area of 9.79 million hectare with
production and productivity of 99.65 mt and 1,026 kg/ha,
respectively (Ram et al. 2011). The seeds of soybeans contain
a moderately high amount of calories (calorific value of 400g/
100g), protein (40%), lipid (20%), relatively high insoluble
carbohydrate content (11%), crude fibre (9%) and ash (5%)
(Varsha and Grewal 2009). In addition, soybean oil contains
approximately 14% saturated fats on an average. Reduction
of palmitic acid and stearic acid would be desirable for lowering
saturated fat content in human diet to improve cardiovascular
health (Spencer et al. 2003). Soybean oil is also a good source
of vitamin E and its content varies with variety (Lokuruka
2010). Tocopherols are known to reduce incidence of prostate
cancer and coronary heart disease (Evans et al. 2002) and
improve oil stability (Stone and Papas 2003). Soy seeds contain
high valued proteins that are used as food/feed for human
and animals. Although soybeans are deficient in methionine
but contain sufficient lysine to overcome the lysine deficiency
of cereals (Neus et al. 2005). Carbohydrate content is one of
the important quality traits in soybean. The major sugars in
soybean seeds are glucose, fructose, sucrose, and galactooligosaccharides (Blochl et al. 2007). Although soybean is
rich in nutrients but its acceptability as raw food is limited due
to the presence of anti-nutritional factors which decrease
nutritive value of grain legumes and cause health problems to
both human and the animals when taken in large amounts
(Mikic et al. 2009). Protease inhibitors represent 6% of the
protein present in soybean seed. Trypsin inhibitors cause
enlargement of the pancreas in rodents, hyper secretion of
digestive enzymes that leads to a loss of trypsin and
chymotrypsin, and reduce the hydrolysis of dietary protein,
thereby decreasing amino acid absorption and protein
synthesis (Roy et al. 2010). However, these inhibitors are
effective in preventing or suppressing carcinogen induced
transformation in vitro and demonstrates potent antiinflammatory properties (Roy et al. 2010).
Soybean seed contains phytic acid (myo-inositol
hexakis phosphate) to the extent of 1-1.5% DM and 65-80% of
total phosphorus from soybean seeds is bound to phytic acid
(Raboy et al. 2000). It chelates mineral nutrients such as copper,
zinc, manganese, iron and calcium thus reducing their
availability (Ramakrishna et al. 2006). Beside above mentioned
anti-nutri tional facto rs, soybean cont ains bio acti ve
compounds with small or unknown effects, such as tannins,
saponins, lectins, antivitamins and isoflavones (Jain et al.
2009). Raw soybeans contain between 2 to 5 g saponin/100g.
Because of presence of both hydrophobic and hydrophilic
regions, saponins are excellent emulsifiers and foaming agents
and provide functional role in foods (MacDonald et al. 2005).
The tannins and phenolic constituents are known to adversely
affect the utilization of proteins in animal and human diet due
to t heir abi lity to bind wi th and precipit ate prot eins
(Khandelwal et al. 2010).
The current focus of the breeders should be on
screening the soybean cultivars for decreasing the content of
ant inutrienal factors to a safe extent. In the present
communication, an attempt has been made to evaluate soybean
genotypes for their physical, nutritional and antinutrient
factors so as to identify the diversity in the available
germplasm in relation to their nutritional quality. Such
Goyal et al.: Variability in the nutrients, antinutrients and other bioactive compounds in soybean
information will be useful in selecting soybean varieties for
manufacture of various soy food as well as medicinal
preparations.
One hundred and forty soybean genotypes including
74 from PAU, Ludhiana collection, 51 from other parts of India
and 15 exotic genotypes were used in the present study (Table
1). The experiment was conducted in 2009 and all the genotypes
were grown in the field of Department of Plant Breeding and
Genetics, PAU, Ludhiana by following the recommended
package of practices in a randomized complete block design
with three replications. The seeds were sown in rows keeping
45 cm distance between the rows and plant to plant distance
was kept at 5 cm. The row length was 2 meter with 2 rows per
entry. The experimental soil was sandy loam in texture, pH
7.24, electrical conductivity 0.30 ds/m, organic carbon 0.34%,
available P and K (58.5 and 57.5 kg/ha, respectively). The
seeds were collected at maturity, cleaned by hand to remove
dirt and broken grains and then stored in air tight plastic
containers till further analysis. Later, seeds were crushed into
fine powder by Cemotec 1090 sample mill and then stored for
further use. The seed yield (kg/plot) for each genotype was
recorded.
Physical seed characteristics like seed weight (g), seed
volume (ml), water absorption (%), volume expansion (%),
swelling capacity (g/seed) and hydration capacity (ml/seed)
and their indices were determined by the methods of Santhan
and Shivshankar (1978). The contents of moisture, protein
and lipid in seed powder were determined by NIR Method
using Infratec TM 1241 Grain analyzer from Foss (North
America). Soluble sugars were extracted from the soybean
seed powder with 80% ethanol followed by 70% ethanol. Total
315
soluble sugar levels in the pooled extract were determined
with the phenol-sulfuric acid reagent (Dubois et al.1956) using
glucose as standard. Sucrose content was determined after
destroying the free fructose with 30% KOH by resorcinol-HCl
procedure (Roe 1934).
Trypsin inhibitor (TI) activity was determined as
described by Kakade et al. (1974). The powdered seed (1g)
was homogenized with 1% NaCl. The TI activity of the extract
was determined using casein as a substrate. One unit of trypsin
inhibitor activity is defined as the quantity of inhibitor which
reduces the activity of trypsin by one unit at 37°C.
Phytic acid in the powdered seeds was determined by
the procedure described by Vaintraub and Lapteva (1988).
Five hundred mg seed powder was stirred using a magnetic
stirrer in 3.5% HCl. The contents were centrifuged at 3000g
for 10 min to obtain supernatants. A suitable aliquot of the
supernatant was taken and 1 ml of Wade reagent (0.03% FeCl3
containing 0.3% sulfosalicylic acid) was added to it and again
centrifuged. The absorbance was measured at 500 nm using
spectrophotometer.
Phenolic compounds were extracted by refluxing the
seed powder with 80% aqueous methanol at 60oC in a water
bath with continuous shaking for 2h. The refluxed material
after filtration was used for the estimation of total phenols
(Swain and Hillis 1959). A standard curve of gallic acid (10-100
µg) was simultaneously prepared and the amount of the
phenols was calculated and expressed as mg/g seed. Tannins
were extracted from the powdered seeds and estimated. Using
a Folin–Denis Reagent, the intensity of the colour developed
was measured at 700 nm (Sadasivam and Manickam 1992). A
standard curve of tannic acid (10–100 ìg) was simultaneously
prepared.
Table 1. Soybean genotypes used in the present study
PAU, Ludhiyana collections
SL 137, SL 202, SL 255, SL 290, SL 313, SL
525, SL 568, SL 587, SL 592, SL 688, SL 707,
SL 716, SL 744, SL 773, SL 778, SL 790, SL
791, SL 793, SL 799, SL 806, SL 834, SL 871,
SL 878, SL 894, SL 900, SL 903, SL 907, SL
914, SL 917, SL 925, SL 926, SL 955, SL 958,
SL 967, SL 973, SL 976, SL 977, SL 978, SL
979, SL 980, SL 981, SL 982, SL 983, SL 984,
SL 985, SL 986, SL 987, SL 988, SL 989, SL
990, SL 991, SL 992, SL 995, SL 996, SL 997,
SL 998, SL 999, SL 1000, SL 1001,
SL 1002, SL 1003, SL 1004,
SL 1005, SL 1006, SL 1007,
SL 1008, SL 1009, SL 1010,
SL 1012, G-237XSL 295, 11-4-1
Genotypes from other parts of India
DS-76-1-139, DS-98-1, DS-98-2,
DS 2613, DS 2614, DS 143, DS 200
Origin
IARI,
New Delhi
JS 72-45, JS 81-340 , JS-84-16, JS-89-67 JNKVV, Jabalpur
Exotic Genotypes
BRAGG, EC-457156,
EC-457159, EC-457161,
EC-457286,EC-457466,
EC-457471, EC-230143
Origin
USA
PK 1026, PK-1042, PS 1414, PS 1042,
PS 1420, PS 1437, PS 1444
GB Univ. of Agri EC-103332,TG-849-309,
Sci
&
Tech, TGX825-3FF
Pantnagar
Phillipines
IC-49865, IC- 437079, IC 15977,
IC 100497
Himalayan Region EC-280148
Taiwan
G 114, GB 1587, G-18, YMV- 25,
YMV-35, YMV-36
DSR, Indore
EC-251401, EC-251498
Argentina
EC-309541
Brazil
Local collections
SEL P, SEL-37, SEL 40, SEL 41,
SEL 46, SEL 174, K-88-2629, K-B-65,
UCM 47, UPSM 124, DCB 194, DM -51336, HM 1, AK 99-67, B 86-24, R-5,
R-11, GP 650, GP 1036,GP 1037,
F-67-3975, NRC-05-976
316
For the extraction and estimation of saponins, 500 mg
of seed powder was homogenized with acetone and later with
methanol. The saponin content of soybean seeds was
estimated from the methanolic extract by the method of
Fenwick and Oakenfull (1983). A standard curve (10-40 µg) of
saponin (Himedia, Mumbai) was simultaneously prepared.
Tocopherols were extracted from the powdered seeds by
ethanol. Purified xylene was added to the supernatant and
centrifuged. Xylene layer was pipetted out and the content
was estimated using bathophenanthroline reagent, ferric
chloride and ortho-phosphoric acid (Kayden et al. 1973). The
amount of tocopherols was calculated from the standard curve
prepared by using dl-á-tocopherol(Sigma-Aldrich Corporation,
Bangalore) as standard (0.02mg/ml ethanol).
Statistical analysis: All results in this study are reported as
means of three replicates. Means, standard deviation and
correlation coefficients for different nutrients and antinutrients
were calculated using software Statgraphics Centurion Version
XV: II (Statpoint, Inc.). One way analysis of variance (ANOVA)
was carried out to determine the significant differences
between means among the different groups of genotypes at
P<0.05. The genotypes were clustered on the basis of their
biochemical similarity. Standardized matrix was used for the
Unweighted pair group method using arithmetic averages
(UPGMA method) to generate the cluster tree using NTSYS
pc 2.0 software (Rohlf 1998).
Nutritional composition of soybean seeds: The nutritional
composition of soybean genotypes was estimated and the
mean values of the contents are represented in Table 2. The
moisture content of the seeds of 140 different soybean
genotypes ranged from 7.60-12.9%. The values are comparable
to the moisture content of soybean genotypes from other
sources (9.6-13.2%) (Awadesh et al. 2003). Soybean contained
protein with a range of 38.4-46.5%, with an average value of
42.9%. Genotype PS 1444 showed the highest protein content
of 0.46 mg/g seeds. Oil content was in the range of 20.8 (EC309541) to 23.6% (SL 793). Among various genotypes studied,
5 genotypes exhibited protein content >45% and 2 genotypes
recorded lipid content >23%. Genotypes PS 1444 and EC 309541
exhibited maximum protein and minimum lipid content,
respectively as compared to other genotypes. The mean values
of protein and lipid content of seeds of various soybean
genotypes from different categories did not differ significantly.
Total soluble sugars and sucrose content ranged from 1.3013.9% and 0.24-13.8% respectively. 15 genotypes contained
sucrose content more than 10% and genotype K-B-65 showed
Table 2. Mean, standard deviation (SD), ranges of various nutrients (%), antinutrients (mg/g) and bioactive compounds (mg/g)
in soybean genotypes
Total
Genotypes
Local
Genotypes
Genotypes
from other
Parts of India
Exotic
Genotypes
Oil
20.8-23.6
21.7
0.45
21.0-23.6
21.7
0.48
21.0-22.7
21.7
0.38
20.8-22.9
21.7
0.53
0.43
Range
Mean
SD
Range
Mean
SD
Range
Mean
SD
Range
Mean
SD
CD (P=0.05)
Nutrients
Protein
38.4-46.5
42.9
1.23
38.4-45.3
42.7
1.16
40.0-46.5
43.2
1.22
39.8-44.5
43.1
1.49
0.20
Sucrose
0.24-13.8
5.9
3.01
0.24-13.6
6.8
2.71
0.60-13.8
4.3
2.92
4.00-13.8
7.0
2.35
0.48
Total Sugars
1.30-13.9
5.9
2.78
1.8-13.9
6.9
2.73
1.30-13.7
4.5
2.43
1.30-8.9
5.7
2.01
0.38
Moisture
7.60-12.9
11.0
1.42
7.8-12.9
11.3
1.37
7.60-12.7
10.5
1.42
8.30-12.6
11.0
1.30
0.46
Antinutrients and bioactive compounds
Total
Genotypes
Range
Mean
SD
Local
Range
Genotypes
Mean
SD
Genotypes
Range
from other parts of Mean
India
SD
Exotic
Range
Genotypes
Mean
SD
CD (P=0.05)
Phytate
1.2-28.5
10.5
6.10
1.2-28.5
10.5
7.22
2.1-21.2
10.8
4.62
2.3-19.0
10.3
4.57
2.27
Trypsin inhibitor
11.3- 142.5
68.8
34.8
11.2-138.7
68.9
33.5
11.3-142.5
62.3
35.9
33.8-123.7
90.8
30.1
2.42
Phenols
11.0-38.3
22.7
6.24
11.0-38.3
20.2
6.45
15.6-35.6
25.6
4.82
22.5-35.5
25.5
3.82
2.74
Tannins
8.9-20.5
14.3
3.07
8.9-20.5
15.1
2.81
10.2-20.0
13.4
3.21
9.6-19.5
13.7
2.99
0.85
Saponins
11.0-35.6
19.2
5.60
11.0-29.5
16.8
4.97
12.3-35.6
22.7
5.14
12.5-25.6
19.3
3.70
0.82
Tocopherols
2.5-33.5
14.8
5.94
2.5-33.5
14.1
6.59
9.5-28.4
15.4
4.89
8.5-26.5
16.1
5.80
1.23
the highest sucrose content of 138 mg/g seeds. Local
genotypes exhibited significantly higher mean total sugars
and sucrose content as compared to mean values for these
parameters for total genotypes and genotypes collected from
other parts of India and exotic genotypes (only total sugars
content). These genotypes can be preferred in terms of their
nutritional attributes.
Antinutrients and bioactive compounds: Large variation was
seen in soybean genotypes for antinutritional factors studied
(Table 2). Soybean genotypes were found to have a significant
variation in the phytic acid content as it varied from as low as
1.2 (SL 716) to as high as 28.5 mg/g seeds (SL 137). The mean
content of phytic acid was found to be less for exotic lines as
compared to genotypes developed locally or within India.
Mean Trypsin Inhibitor Activity (TIA) was significantly higher
in exotic genotypes as compared to those collected locally or
from other parts of India. SL 1000 had the lowest trypsin
inhibitor activity of 11.3 mg/g seeds and PS 1414 had the
highest activity of 142.5 mg/g seeds. Different authors have
reported varied ranges of TIA in soybean. Guillamon et al.
(2008) reported TI values of 43-84 TIU mg -1 sample whereas
other repo rted values for TI are 7 6.52 TIU/mg seed
(Rameshbabu and Subrahmanyam 2011) and 15.35 mg/g (Peric
et al. 2009). Variation reported in the trypsin enzyme inhibitory
activities by different authors might be because of differences
in the methods and units used.
Phenolic content of the genotypes varied from 11.0 to
38.3 mg/g with a mean value of 22.7 mg/g seeds. Among the
local genotypes, SL 987 was found to have the lowest content
of total phenols. The phenolic content ranged from 11.0 to
38.3, 15.6 to 35.6 and 22.5 to 35.5 mg/g seeds for genotypes
collected from Ludhiana, other parts of India and exotic lines,
respectively. The local genotypes showed significantly lower
mean phenolic content values as compared to mean values
for genotypes collected from other places within or outside
India. Genotypes with low phenolic content are preferred for
nutritional purpose, but genotypes with high phenols are
beneficial to plant against insect/pest resistance and also as a
source of bioactive compounds (Xu and Chang 2008). Tannin
content of 140 soybean genotypes was found to be in the
range of 8.9 (SL 990) to 20.5 mg/g seeds (SL 894) with the
mean value of 14.3 mg/g. Tannins and phenolic constituents
bind with the proteins of saliva and the mucosal membrane of
the mouth during mastication of food and adversely affect
the utilization of proteins in animal and human diets (Akinyede
et al. 2005).
Large variation was observed in the saponins and
tocopherols contents of 140 soybean genotypes (Table 3).
Saponin content varied from 11.0 to 35.6 mg/g seeds with
mean value of 19.2 mg/g seeds. Dehulled soybean and seeds
are reported to contain saponin content between 0.08% and
0.25% (Dandanell et al. 1995). Lower saponin content of a
genotype is desirable from nutritional point of view as these
317
compounds are toxic (Campos-Vega et al. 2010) and result in
retarded growth (Golawaska 2007). Tocopherol content of
140 soybean genotypes varied from 2.5-33.5 mg/g seeds. The
mean tocopherol content of exotic lines was significantly
higher than the local genotypes/total genotypes studied.
Maximum tocopherol content was observed in genotype SL
790. Tocopherols are reported to have antioxidant properties
(Evans et al. 2002) and also result in oil stability (Stone and
Papas 2003). Manipulating seed tocopherol biosynthetic
pathway in soybean to convert the less active tocopherols to
the most active á- tocopherol could have significant human
health benefits and make this crop an attractive target for the
improvement of tocopherol composition (Van Eenennaam et
al. 2003).
Cluster analysis: The genotypes were grouped into clusters
on the basis of biochemical similarity (Fig. 1-2). Local
genotypes were divided into three clusters A to C (Fig. 1).
Cluster A was composed of 41 genotypes with protein content
and TIA in the range of 39.3-45.3 % and 11.3-138.8 mg/g,
respectively. Further, the mean protein and TIA content for
cluster A was 42.6 % and 94.4 mg/g respectively. The sucrose
content of cluster A ranged from 0.24-13.6 g/100g with mean
value of 7.2 g/100g. Cluster A was further subdivided into six
sub clusters: A-I to A-VI with A-I forming the largest sub
cluster having 9 genotypes. The genotypes in sub clusters
A-I to A-VI had mean protein content of 43.4, 42.6, 42.3, 42.8,
42.9 and 41.6 %, respectively. Mean TIA of sub cluster A-I
was highest (113.3 mg/g), whereas sub cluster A-V showed
minimum TIA (68.8 mg/g) in this group. Cluster B contained
27 genotypes most of which had comparatively higher protein
content (mean 43.2%) and lower TIA (39.6 mg/g) than members
belonging to cluster A. Cluster B was subdivided into five
sub-clusters B-I to B-V. Sub-cluster B-III consisted of 3
genotypes with a higher mean TIA (51.3 mg/g) than other four
sub-clusters. The genotypes from sub-cluster B-IV had the
higher protein, phytate and tocopherol content than subclusters B-I, B-II, B-III and B-IV. Cluster C contained 6
genotypes having average protein content of 42.4 %. The
genotypes belonging to this cluster had the lowest mean TIA
(20.6 mg/g) & sucrose content (4.9g/100g) and comparatively
higher phytate content (12.1 mg/g) than those in clusters A
and B. The genotypes of all the three clusters had almost
similar mean oil content of about 21.8 %.
Genotypes from other parts of India were grouped into
two clusters A & B having 29 and 22 genotypes, respectively
(Fig. 2). Both clusters had almost similar mean protein content
of 43.2 % except genotypes PS 1444 in cluster A and PK 1026
in cluster B which had high protein content of 46.5% and
45.1%, respectively. The TIA of cluster A ranged from 11.3143.6 mg/g. Cluster A was divided into 4 sub-clusters A-I to
A-IV. The sub-clusters did not differ much for their mean protein
and oil contents. However, members of sub cluster A-I had a
much higher TIA (116.9 mg/g) and sucrose content (6.6 g/
318
100g) in comparison to other three sub-clusters. YMV-25 forms
an outlier as it has higher protein, lower TIA and sucrose
content (44.1, 30 and 1.3, respectively). Cluster B was also
divided into two sub clusters B-I & B-II composed of 6 and 10
genotypes, respectively. Both the sub clusters had almost
similar protein, oil and tocopherol contents. Mean TIA of two
sub-clusters was 41.3 & 51.4 mg/g, respectively. The
genotypes in sub cluster B-II had higher mean phytate content
(10.7 mg/g) and lower sucrose content (3.2g/100g) as compared
to sub cluster B-II. Sel-46 was an outlier with higher TIA (101.3
mg/g) and lower phytate content (2.3 mg/g). The oil content
of the outlier was also high (22.1%) as compared to mean oil
content of clusters A and B.
Exotic genotypes were also grouped into two clusters
A & B (Fig. not given). Cluster A was composed of 11
genotypes with a higher mean protein content (43.7%) and a
lower oil content (21.5%) than cluster B (41.0 & 22.4%,
respectively). The mean TIA and tocopherol content of
members of cluster A (96.5 & 26.1 %, respectively) was higher
Fig. 1
than that of cluster B (75.6 & 23.1% respectively) whereas
phytate content exhibited reverse trend (14.9 mg/g in cluster
B as compared to 8.9 mg/g in cluster A). The members of
cluster B also had a higher mean sucrose content of about 7.6
g/100g as compared to cluster A (6.8 g/100g).
Significant variation was observed with respect to
various nutritional and antinutritional characteristics among
different soybean genotypes investigated. The mean grain
yield for 140 soybean genotypes was 3.29 kg/plot. R-5 and
DS-98-2 exhibited the maximum and minimum yield (5.0 and
1.47 kg/plot, respectively). Genotypes SL 992 and PK 1026
contained protein content of 44.6 and 45.1 %, respectively
and a lower trypsin inhibitor activity and phytic acid content.
Some of the genotypes studied had very low levels of different
antinutritional factors. This diversity in soybean germplasm
will be useful to the plant breeders for using genetic resources
for the development of new cultivars with improved quality
traits, enhancement of germplasm and commercialization of
the end-products.
Dendrogram of 74 local soybean genotypes by UPGMA clustering method based on standardized matrix derived from eleven
seed quality traits. A, B, C indicate clusters; I, II, III, IV, V and VI are subclusters
Fig. 2
319
Dendrogram of 51 soybean genotypes collected from other parts of India using UPGMA clustering method based on
standardized matrix derived from eleven seed quality traits. A and B indicate clusters; I, II, III, IV, V and VI are subclusters
ACKNOWLEDGEMENT
We gratefully acknowledge Dr M. Javed, Associate
Professor of Department of Mathematics, Statistics and
Physics and Mr. D. Bhatia, Research fellow from School of
Agricultural Biotechnology for their assistance in statistical
analysis. We are also grateful to University Grants Commission
for funding this project.
Food Research International 43: 461-482.
Dandanell DY, Aman P, Betz JM and Obermeyer R. 1995. Saponin in
dehulled seeds from peas (Pisum sativum L.). In: Proceedings of
the 2nd European conference of grain legumes , Copenhagen,
Denmark. Pp. 310-311.
Dubois M, Gules KA, Hamilton JK, Roberts PA and Smith F. 1956.
Colorimetric method for the determination of sugars and related
substances. Analytical Chemistry 28: 350-356.
REFERENCES
Evans JC, Kodali DR and Addis PB. 2002. Optimal tocopherol
concentrations to inhibit soybean oil oxidation. Journal of American
Oil Chemists’ Society 79: 47-51.
Akinyede AI, Amoo IA and Eleyinmi AF. 2005. Chemical and functional
properties of full fat and defatted Dioclea reflexa seed flour.
International Journal of Food Agriculture and Environment 3: 112115.
Faostat 2009. ProdSTAT: Crops. FAO Statistical Databases (FAOSTAT),
Food and Agriculture Organization of the United Nations (FAO):
http:/faostat.fao.org.
Awadesh K, Singh G, Kumar D and Agrawal K. 2003. Physico-chemical
characteristics of some new varieties of soybean. Journal of Food
Science and Technology 40: 490-492.
Blochl A, Peterbauer T and Richter A. 2007. Inhibition of raffinose
oligosaccharide breakdown delays germination of pea seeds. Journal
of Plant Physiology 164: 1093-1096.
Campos-Vega R, Loarca-Pina GF and Oomah BD. 2010. Minor
components of pulses and their potential impact on human health.
Fenwick DE and Oakenfull D. 1983. Saponin content of food plants
and some prepared foods. Journal of the Science of Food and
Agriculture 34: 186-191.
Golawaska S. 2007. Deterrence and toxicity of plant saponins for the
pea aphid Acyrthosiphon pisum Harris. Journal of Chemical Ecology
33: 1598-1606.
Guillamon E, Pedrosa MM, Burbano C, Cuadrado C, Sanchez M and
Muzquiz M. 2008. The trypsin inhibitors present in seed of different
320
grain legume species and cultivar. Food Chemistry 107: 68-74.
Jain AK, Kumar S and Panwar JD. 2009. Antinutritional factors and
their detoxification in pulses- A review. Agricultural Reviews 30:
64-70.
Kakade ML, Rackis JJ, Mcghee JE and Puski G. 1974. Determination of
trypsin inhibitor activity of soy products: A collaborative analysis
of an improved procedure. Cereal Chemistry 51: 376-382.
Kayden HJ, Chow CK and Bjomson LK. 1973. Spectrophotometric
method for determination of tocopherol in red blood cells. Journal
of Lipid Research 14: 533-540.
Khandelwal S, Udipi SA and Ghugre P. 2010. Polyphenols and tannins
in Indian pulses: Effect of soaking, germination and pressure cooking.
Food Research International 43: 526-530.
Lokuruka MN. 2010. Soybean nutritional properties: The good and the
bad about soy foods consumption- A review. African Journal of
Food, Agriculture, Nutrition and Development 10: 2439-2458.
MacDonald RS, Guo J, Copeland J, Browning JD, Sleper D, Rottinghaus
GE and Berhow MA. 2005. Environmental influences on isoflavones
and saponins in soybeans and their role in colon cancer. Journal of
Nutrition 135: 1239-1242.
Rameshbabu S and Subrahmanyam B. 2011. Evaluation of trypsin and
chymotrypsin proteinase inhibitor in germplasm of Indian food
legumes. Journal of Food Legumes 24: 338-340.
Roe JH. 1934. A colorimetric method for the determination of fructose
in blood and urine. Journal of Biological Chemistry 1076: 152.
Rohlf FJ. 1998. NTSYS-pc: numerical taxonomy and multivariate
analysis system, version 2.0. Applied Biostatistics for Port Jefferson,
NY.
Roy F, Boye JI and Simpson BK. 2010. Bioactive proteins and peptides
in pulse crops: Pea, chickpea and lentil. Food Research International
43: 432-442.
Sadasivam S and Manickam A. 1992. Phenolics. Biochemical methods
for agricultural sciences. New Delhi, India: Wiley Eastern Ltd. Pp.
187-188.
Santhan RH and Shivshankar GC. 1978. Cooking characteristics of
Horse grain. Indian Journal of Agricultural Sciences 48: 399-401.
Spencer MM, Pantalone VR, Meyer EJ, Landau-Ellis and Hyten DL.
2003. Mapping the Fas locus controlling stearic acid content in
soybean. Theoretical and Applied Genetics, 106: 615-619.
Mikic A, Peric V, Dordevic V, Srebric M and Mihailovic V. 2009.
Antinutritional factors in some grain legumes. Biotechnology in
Animal Husbandry 25: 1181-1188.
Stone WL and Papas AM. 2003. Tocopherols, tocotrienols and vitamin
E. In: F.D. Gunstone (Ed.), Lipids for functional foods and
neutraceuticals, P J Barnes & Associates, Bridgewater, UK. Pp.5372.
Neus JD, Fehr WR and Schnebly SR. 2005. Agronomic and seed
characteristics of soybean with reduced raffinose and stachyose.
Swain T and Hillis WE. 1959. The phenolic constituents of Prunus
domestica. The quantitative analysis of phenolic constituents.
Journal of Science of Food and Agriculture 10: 63-68.
Peric V, Srebric M, Jankuloski L, Mirjana J, Zilic S, Jankulovska M,
Kandic V and Mladenovic- Drinic S. 2009. The effects of nitrogen
on protein, oil and trypsin inhibitor content of soybean. Genetika
41: 137-144.
Vaintraub IA and Lapteva NA. 1988. Colorimetric determination of
phytate in unpurified extracts of seeds and the products of their
processing. Analytical Biochemistry 175: 227–230.
Raboy V, Gerbasi PF, Young KA, Stoneberg SD, Pickett SG, Bauman AT,
Murthy PPN, Sheridan WF and Ertl DS. 2000. Origin and seed
phenotype of maize low phytic acid 1-1 and low phytic aid 2-1.
Plant Physiology 124: 355-368.
Ram H, Singh G and Aggarwal N. 2011. Effect of varieties, row spacing
and seeding rate on growth, productivity and economics of soybean
in Indo-Gangetic plains of India. Journal of Food Legumes 24: 245247.
Ramakrishna V, Jhansi RP and Ramakrishna RP. 2006. Anti-nutritional
factors during germination in Indian bean (Dolichos lablab L.)
seeds. World Journal of Dairy and Food Sciences 1: 6-11.
Van Eenennaam AL, Lincoln K, Durrett TP, Valentin HE, Shewmaker
CK, Thorne GM, Jian Jiang, Baszis SR, Charlene K, Levering CK ,
Aasen ED, Hao M, Stein JC, Norris SR and Last RL. 2003.
Engineering vitamin E content: From Arabidopsis mutant to soy
oil. Plant Cell 15: 3007-3019.
Varsha R and Grewal RB. 2009. Carbohydrate profile, dietary fibre,
antinutrients and in vitro digestibility of nine cultivars of soybean
(Glycine max L.) Merr. Legume Research 32: 31-35.
Xu B and Chang SK. 2008. Antioxidant capacity of seed coat, dehulled
bean, and whole black soybeans in relation to their distributions of
total phenolics, phenolic acids, anthocyanins and isoflavones.
Journal of Agricultural and Food Chemistry 56: 8365-8373.
Effect of presoak treatment on cooking characteristics and nutritional functionality
of rice bean
V.D. PAWAR , M.K. AKKENA, P.M. KOTECHA, S.S. THORAT and V.V. BANSODE1
Department of Food Science and Technology, Post Graduate Institute, Mahatma Phule Krishi Vidyapeeth, Rahuri
413722, India; 1Rajiv Gandhi College of Food Technology, Marathwada Agricultural University, Parbhani 431401
(M.H.), India; E-mail: bansoderaman@yahoo.co.in
(Received : May 25, 2012 ; Accepted : December 04, 2012)
ABSTRACT
The effect of soaking rice bean on proximate composition,
cooking characteristics, phytic acid, polyphenols content and
functional properties were investigated. This study aimsed at
comparing the changes that occured on soaking of blanched
rice bean seeds in distilled water for 12 h and in salt solution
(1.5% NaHCO3, 0.5% Na2CO3 and 0.75% citric acid, pH 7.0 ±
0.1) for 3, 6, 9 and 12 h each. Soaking, significantly (P < 0.05)
increased the water absorption and dispersal of solids while it
decreased the cooking time following increase in time of
soaking. The anti-nutrients viz., polyphenols and phytic acid
significantly decreased during soaking. The decrease in phytic
acid was higher in salt solution soaking as compared to distilled
water soaking. Soaking of beans for 12 h in different solutions
did not show remarkable changes in proximate composition of
rice bean flours. But foaming and emulsifying properties,
nitrogen solubility and PCMP number showed a negative
correlation, whereas, bulk density, water and oil absorption
capacity showed a positive correlation with soaking time.
Key words:
Anti-nutrients, Cooking characteristics, Functional
properties, Presoak treatment, Rice bean
Rice bean is a rich source of nutrients. There are many
reports although indicate that the protein content in rice bean
is in lower range as compared to other pulses yet its
bioavailability is high. As in other pulses, an important problem
with rice bean is that it contains various anti-nutrients, notably
phytic acid and polyphenols that reduce the uptake of several
micronutrients. Kaur and Kapoor (1992) reported in rice bean
that the poylphenolic content varied between 1279 and 1587
mg and phytic acid content between 1875 and 2270 mg/100 g.
Saikia et al (1999) observed phytic acid ranged from 1998 to
2170 mg/100 g in five varieties of rice bean.
Soaking of beans facilitates quicker cooking. Soaking
and cooking of legumes result in significant reduction in phytic
acid and tannin contents. Maximum reduction of phytic acid
(78.05%) and tannin (65.81%) was found for sodium
bicarbonate soaking followed by cooking. These treatments
also result in a slight reduction in nutrients such as protein,
minerals and total sugars (Iyer et al. 1980). The effects of
presoaking of pulses in the salt solution of several chemicals
in reducing the cooking time of pigeonpea splits (Narsimha
and Desikachar 1978a), peas (Bongirwar and Sreenivasan
1977), beans (Rockland and Mertzler 1967, Iyer et al. 1980),
winged bean (Narayana 1981), pigeon pea, chickpea, black
bean, mung bean and lentil splits (Chavan et al. 1983) and
moth bean (Pawar and Ingle 1986) were also reported. Thus,
the current investigation was carried out to study the effect
of presoak treatment with distilled water vis-à-vis salt solution
on proximat e co mpositio n, cooki ng characteristi cs,
antinutrients and nutritional functionality of rice bean.
In the present investigation, seeds of rice bean (Vigna
umbellata) were obtained from the Department of Agricultural
Botany, MPKV, Rahuri. These were cleaned for extraneous
matter and stored in clean glass bottles at 40C, until use. Quick
cooking beans were prepared as per the procedures described
by Rockland and Mertzler (1967). One lot was soaked in distilled
water for 12 h and the unsoaked sample was taken as the
control. The soaked beans were cooked traditionally in distilled
water at 100 0C (beans to water ratio was 1:4 in terms of weight/
volume) until these were softened to a uniform mass when
pressed between thumb and forefinger (Sharma et al. 1977)
for determination of cooking time. The proximate composition
of unsoaked rice bean such as moisture, protein, fat, ash and
carbohydrates of the bean was determined as per AOAC
(2000) procedures. The rate of hydration, dispersed solids
and cooking time were determined as per the procedures of
Narasimha and Desikachar (1978). The PCMP number was
calculated by the following formulae.
PCMP = pectin (Ca + Mg/2) /phytin or pectin + (Ca
+Mg/2) – phytin.
The pectin, calcium and magnesium content were
estimated according to standard procedure of AOAC (2000).
Anti-nutrient factors like, polyphenolic content was
estimated according to the method of AOAC (2000). Extraction
and precipitation of phytate phosphorus were performed
according to the method of Wheeler and Ferrel (1981). Phytate
phosphorus estimation was carried out according to the
method of Makower (1970). The iron content was measured
by the AOAC(2000) method using O-phenanthroline reagent.
On the basis of phytate phosphorus contents, the phytic acid
was calculated assuming 28.20% phosphorus in the molecule.
Functional properties such as water and oil absorption
322
capacities were determined by the procedure of Beuchat et al.
(1977).The least gelation concentration was determined by
method of Coffman and Garcia (1977) with slight modifications
as described by Deshpande et al. (1982). Nitrogen solubility
was determined as per standard procedure. The foaming
properties such as foaming capacity was determined according
to method of Coffman and Garcia (1977) while specific volume
of foams was determined as an index of air uptake during
whipping and weights were taken before and after whipping
and specific volumes calculated according to method by
Baldwin and Sinthavalai (1974).The emulsion properties such
as emulsifying activity and emulsifying stability of the samples
were determined by the method of Yasumatsu et al. (1972)
with slight modifications as described by Deshpande et al.
(1982). The bulk density was determined according to Okezie
and Bello (1988).
Statistical analysis: The analysis of triplicate data as a
function of treatment levels was done to determine significant
difference (P < 0.05) by computing standard error and critical
difference by using methods of Snedecor and Cochran (1980).
Change in proximate and chemical composition of flours:
A net loss of dry weight occurred as a result of soaking and
oxidation of stored compounds with a gradual increase of
moisture (Table 1). At the end of 12 h soaking period, per cent
moisture content of the bean increased from the initial 10.3 to
11.4 and 11.7 for distilled water and salt solutions, respectively.
There was a significant decrease in total carbohydrate content
during soaking of rice bean. Similar observations were reported
by Zacharie and Ronald (1995) on common beans. The total
ash content also gradually decreased throughout the soaking
period. Rao and Deosthale (1983) also reported loss in ash
content of mungbean and urdbean during overnight soaking
in distilled water due to leaching of total minerals in the soaking
medium. Per cent protein content of bean also decreased
significantly from 17.5 to 15.4 and 15.2 during soaking for 12 h
in distilled water and salt solution, respectively.
Little changes in the concentration of pectin, Ca and
Mg were observed due to soaking while greater change in the
concentration of phytate was found. Sample soaked in salt
solution for 12 h showed a significant decrease in pectin, Ca,
Mg, phytate and PCMP number from 3.82 to 2.92 mg/g, 2.58 to
1.79 mg/g, 0.72 to 0.51 mg/g, 21.72 to 12.39 mg/g and 0.514 to
0.479, respectively.
The hard-to-cook defect in legume seeds is based on
PCMP number which represents pectin insolubilization via
binding with divalent cations (e.g. Ca2+, Mg2+) resulting from
phytate breakdown by phytase. The effect of soaking on rice
bean in a salt solution containing Na+ cations or distilled water
decreased the amount of pectin and was associated with
divalent cations that led to insoluble form reducing the PCMP
Table 1. Effect of Soaking on Proximate composition, pectin, calcium, magnesium, phytate and PCMP number of rice bean
flours
Treatment
Moisture
(%)
10.3
11.4
Unsoaked
Soaking a
Soaking b
3h
6h
9h
12 h
SEm(±)
CD(P=0.05)
Soakinga: Soaking in
Protein
(%)
17.5
15.4
Fat
(%)
0.51
0.49
Ash
(%)
4.5
4.4
Carbohydrate
(%)
61.6
60.1
Pectin Calcium
(mg/g)
(mg/g)
3.82
2.58
3.05
1.83
Magnesium
(mg/g)
0.72
0.54
10.5
16.9
0.48
4.4
61.0
3.63
2.35
10.9
16.4
0.46
4.4
60.5
3.42
2.13
11.5
15.8
0.48
4.4
60.2
3.22
1.95
11.7
15.2
0.47
4.3
60.0
2.92
1.79
0.07
0.05
0.005 0.007
0.08
0.03
0.02
0.21
0.17
0.01
0.02
0.2
0.10
0.07
distilled water for 12 h at 27 0C; Soakingb: Soaking in mixed salt solution at 27 0 C
0.69
0.64
0.58
0.51
0.01
0.04
Phytate
(mg/g)
21.72
19.56
PCMP number
18.97
16.76
14.52
12.39
0.09
0.28
0.511
0.497
0.493
0.479
0.007
0.022
0.514
0.322
Table 2.Effect of soaking on cooking characteristics and functional properties of rice bean flours
Treatment
Rate of hydration
(g/100g)
Unsoaked
Soaking a
Soaking b
3h
6h
9h
12 h
SEm(±)
CD(P=0.05)
166.6
49.5
62.9
101.4
142.7
1.22
3.84
Dispersed solids Cooking time Reduction in
cooking time
(g/100g)
(min)
(%)
30
21.8
10
66.7
9.7
13.7
17.6
21.6
0.787
2.48
25
20
15
8
0.47
1.45
16.6
33.3
50.0
73.3
-
Water
Oil absorption
absorption
capacity
capacity (g/g)
(g/g)
2.40
1.45
2.41
1.45
2.39
2.41
2.44
2.50
0.03
0.10
Soakinga: Soaking in distilled water for 12 h at 27 0C; Soakingb: Soaking in mixed salt solution at 27 0C
1.45
1.50
1.55
1.58
0.007
0.02
Foaming
capacity
(%)
25
21
Specific
volume
(ml/g)
1.27
1.21
24
22
21
19
0.47
1.45
1.25
1.22
1.21
1.18
0.05
0.01
Pawar et al.: Presoak treatment on cooking and nutritional characteristics of rice bean
number and hardness of the bean. Bicarbonate and carbonate
of sodium salt solution have solubilizing effect on the pectic
substances that facilitates easy penetration and faster
hydration on interior starch and protein molecules resulting
into quick cooking beans.
Cha nge in cooki ng cha racteristics and functional
properties: It was observed that rate of water uptake and
leaching losses of total solids were higher when beans were
soaked in distilled water, but lower when soaked in salt
solution (Table 2). However, the rates of water uptake and
leaching losses of total solids increased progressively when
beans were soaked in salt solution for 3, 6, 9 and 12 h. Rate of
hydration and dispersed solids were also found significant in
case of 12 h distilled water soaked sample than that of salt
solution. Iyer et al. (1980) also observed increased rate of
both hydration and leaching losses with increased soaking
time with mixed salt solution from 6 to 24 h in Great Northern,
Kidney and Pinto beans.
Moreover, time required for cooking blanched rice bean
was 30 min (Table 2) when the beans to cooking water ratio
were 1.4 (wt/vol). When the beans were soaked in distilled
water for 12 h or in salt solution for 3, 6, 9 and 12 h, the time
required for cooking significantly decreased from 30 min to
10, 25, 20, 15 and 8 min, respectively. A significant decrease in
cooking time was found in 12 h salt soaked sample compared
to 12 h distilled water soaked sample. The beans soaked in
distilled water and salt solution for 12 h caused reduction in
cooking time by 66.7% and 73.3%, respectively.
In the present study, the pretreatment of seeds, duration
of soaking and type of soaking solution influenced the cooking
time. Increased reduction in cooking time observed by soak
treatment of blanched seeds in salt solutions than in distilled
water might be due to the presence of cations in the soaking
medium that increased the softening rate (as a result of ion
exchange and probably by chelation of ions resulting in
solubilization of pectin substances). Kadam et al. (1981)
reported 67% reduction in cooking time of moth bean when
soaked in a mixed salt solution for 12 h. Narayana (1981)
reported that soaking the winged bean splits in salt solution
for 2 h reduced the cooking time by nearly 50%.
323
Similarly, soaking in salt solution for 12 h had a significant
water absorption capacity (2.5 g/g flour) than soaking in
distilled water (Table 2) for 12 h (2.4 g/g flour). Although there
was apparent decrease in protein content in salt soaked flours,
yet the water absorption capacity increased significantly due
to dissociation of proteins during blanching of rice bean before
soaking in salt solution.
Change in nitrogen solubility: Unsoaked rice bean flour had
minimum N solubility of 12% at pH 5 (Fig 1). However, at pH
3.0, about 60% of nitrogen was soluble and at pH 12 it was
about 90%. In the present investigation, N solubility of raw
and 12 h distilled water soaked rice bean flours was increased
even beyond pH 10 although there was no significant
improvement in N solubility in rest of the cases (salt soaked
samples). However, it remained more or less constant up to
pH 12.0. Moreover, as the time of soaking in salt solution
increased the solubility of N decreased at all pH levels in all
soaked flours. Decrease in N solubility of 12 h water soaked
rice bean flour might be due to decrease in protein content.
Changes in foam stability: The decrease of total volumes
after soaking rice bean in distilled water and salt solution for
12 h was 9.09 and 9.2%, respectively compared to 7.2% of raw
rice bean flour (Table 3) while the corresponding decrease in
Raw
Nitrogen
solubility
Fig 1.
Effect of soaking on the nitrogen solubility (%) of rice
bean flours
Table 3. Foam stability of soaked rice bean flours
Treatment
Raw
Soaking a
Soaking b
3h
6h
9h
12 h
SEm(±)
CD(P=0.05)
0 min
Total
Foam
125
33
121
29
124
122
121
119
0.47
1.45
32
30
29
27
0.47
1.45
30 min
Total
Foam
124
31
118
27
122
120
119
116
30
28
27
25
60 min
Total
Foam
121
29
116
25
120
116
115
113
26
26
25
23
90 min
Total
Foam
118
27
113
21
118
113
113
111
24
22
21
21
120 min
Total
Foam
116
23
110
18
% decrease in volume
Total
Foam
7.20
30.3
9.09
37.9
114
111
110
108
0.52
1.62
8.06
9.01
9.09
9.20
Soakinga: Soaking in distilled water for 12 h at 27 0C ; Soakingb: Soaking in mixed salt solution at 27 0C
21
19
18
16
0.52
1.62
34.3
36.6
37.9
40.7
324
Table 4. Effect of soaking on emulsion activity and stability
of rice bean flours
Treatment
Unsoaked
Soaking a
Soaking b
3h
6h
9h
12 h
SEm(±)
CD(P=0.05)
Emulsifying
activity
(%)
52.40
42.44
Emulsifying
stability
(%)
49.22
40.44
Bulk
density
(g/ml)
0.582
0.642
% increase of
bulk density
46.58
44.76
43.24
41.82
0.47
1.46
44.23
42.46
41.28
38.77
0.08
0.27
0.592
0.614
0.638
0.651
0.005
0.012
1.72
5.17
8.62
12.06

10.34
Soakinga: Soaking in distilled water for 12 h at 27 0C; Soakingb: Soaking
in mixed salt solution at 27 0C
foam volume was 37.9, 40.7 and 30.3%, respectively. Thus, the
foam stability was comparatively more in raw rice bean flours
than water or salt soaked rice bean flours.
Change in emulsion properties and bulk density: Emulsifying
activity of unsoaked dry rice bean flour sample was 52.4%
which decreased significantly (P < 0.05) on soaking rice bean
either in distilled water or salt solution (Table 4). As the time
of soaking in salt solution increased from 3 to 12 h, the emulsion
properties decreased significantly. Bulk density of raw rice
bean flour was also increased from 0.582 to 0.651 and 0.642 g/
ml following 12 h soaking with salt solution and distilled water,
respectively.
Effect of soaking on anti-nutrients of rice bean flours: The
polyphenols expressed as tannic acid decreased significantly
from 9.62 to 6.37 and 5.77 mg/g in rice beans soaked in distilled
water and salt solution for 12 h respectively. The reduction of
pol yphenols on soaking in salt solu tion increased
progressively with time of soaking. Kaur and Kapoor (1992)
observed a remarkable reduction (27.2-36.8%) in the
polyphenolic contents of rice bean when soaked for 6 h and 8
h in distilled water.
The phytic acid content also decreased significantly
Table 5. Effect of soaking on polyphenolic content, phytate
phosphorus and phytic acid of rice bean flours
Treatment Polyphenols Reduction
Phytate Phytic Reduction
in
phosphorus acid of phytic
(mg/g)
polyphenols
acid
(mg/g)
(mg/g)
(%)
(%)
Unsoaked
9.62
6.12
21.72
Soaking a
6.37
33.7
5.51
19.56
9.94
Soaking b
3h
8.12
15.5
5.34
18.97
12.66
6h
6.62
31.1
4.72
16.76
22.83
33.14
9h
6.12
36.3
4.08
14.52
12 h
5.77
40.0
3.48
12.39
42.95
SEm(±)
0.04
0.09
CD(P=0.05)
0.13
0.28
Soakinga: Soaking in distilled water for 12 h at 27 0C; Soakingb: Soaking
in mixed salt solution at 27 0C
from 21.72 mg/g to 12.39 mg/g and 19.56 mg/g when rice beans
were soaked for 12 h in salt solution and distilled water,
respectively (Table 5). The rice beans soaked in salt solution
for 3 h also showed a remarkable decrease in phytic acid
(12.66%) when compared with beans soaked in distilled water
for 12 h. Thus, the beans soaked in salt solution for 12 h
showed a significant decrease in phytate content compared
to other salt soaked and distilled water soaked samples (Kaur
and Kapoor 1992, Deshpande and Cheryan (1983). This
decrease in phytic acid in legume seeds during soaking can
be attributed to leaching of phytate ions into soaking water
under the influence of concentration gradient.
The study suggested that the anti-nutrients such as
polyphenols and phytic acid (along with N solubility, foaming
and emulsifying properties) were decreased with soaking time
whereas, the water and oil absorption capacity and bulk density
of rice bean flours increased. The PCMP number also
decreased with increase of soaking of rice bean in salt solution
and had an advantage of improving the cooking quality
characteristics, removal of antinutrients and functional
properties over rice beans either unsoaked or soaked in distilled
water.
REFERENCES
AOAC. 2000. Official Methods of Analysis, 17 th Edition. Association
of Official Analytical Chemists. Washington, DC.
Baldwin RE and Sinthavalai S. 1974. Fish protein concentrate. Journal
of Food Science 39: 880-882.
Beuchat LR, Cherry JB and Quinn MR. 1977. Physicochemical
properties of peanut flour as affected by proteolysis. Journal of
Agricultural and Food Chemistry 23: 616-620.
Bongirwar DR and Sreenivasan A. 1977. Development of quick cooking
peas. Journal of Food Science and Technology 4: 17-23.
Chavan JK, Jawale HK, Shere DM, Jadhav SJ and Kadam SS. 1983.
Effect of presoak treatments on the cooking time of legume dhal.
Indian Food Packer 37: 78-81.
Coffman AW and Garcia VV. 1977. Functional properties and amino
acid content of protein isolate from mung bean flour. Journal of
Food Science 48: 1654-1662.
Desphande SS, Sathe SK, Cornforth D and Salunkhe DK. 1982. Effects
of dehulling on functional properties of dry bean flours. Cereal
Chemistry 59: 396-401.
Iyer V, Salunkhe DK, Sathe SK and Rockland LR. 1980. Quick-cooking
beans: Investigations on quality. Qualitas Plantarum Plant Foods
for Human Nutrition 30: 27-31.
Kadam SS, Satwadhar PN and Salunkhe DK. 1981. Effects of
germination and cooking on polyphenols and in vitro protein
digestibility of horse gram and moth bean. Qualitas Plantarum Plant
Foods for Human Nutrition 31: 71-76.
Kaur D and Kapoor AC. 1992. Nutrient composition and antinutritional
factors of rice bean. Food Chemistry 43: 119-124.
Makower RU. 1970. Extraction and determination of phytic acid in
beans. Cereal Chemistry 47: 288-295.
Narasimha HV and Desikachar HSR. 1978. Simple procedures for
Pawar et al.: Presoak treatment on cooking and nutritional characteristics of rice bean
325
reducing the cooking time of split red gram. Journal of Food Science
and Technology 15: 149-152.
antinutritional factors and effect of cooking on nutritional quality
of rice bean. Food Chemistry 67: 347-352.
Narayana K. 1981. Cooking characteristics of winged bean. Journal of
Food Science and Technology 18: 32-33.
Sharma YK, Tiwari AS, Rao KC and Mishra A. 1977. Studies of chemical
constituents and their influence on cookability of pigeon pea. Journal
of Food Science and Technology 14: 38-42.
Okezie BO and Bello AE. 1988. Physicochemical and functional
properties of winged bean flour and isolate compared with soy
isolate. Journal of Food Science 53: 450-455.
Pawar VD and Ingle UM. 1988. Functional properties of raw and cooked
moth bean flours. Journal of Food Science and Technology 25:
186-189.
Rao PV and Deosthale YG. 1983. Tannin content of pulses: varietal
differences and effect of germination and cooking. Journal of Science
of Food and Agriculture 33: 1013-1016.
Rockland LB and Mertzler EA. 1967. Quick cooking lima and other
dry beans. Food Technology 21: 345-349.
Saikia P, Sarkar CR and Borua I. 1999. Chemical composition,
Snedecor GW and Cochran WG. 1980. Statistical Methods, 7 th Ed. Iowa
State University Press, Ames, IA. Pp. 358-360.
Wheeler EL and Ferrel RE. 1981. A method for phytic acid determination
in wheat and wheat fractions. Cereal Chemistry 48: 312-316.
Yasumatsu K, Sawada K, Moritaka S, Misaki M, Toda J, Wada T. and
Ishil K. 1972. Whipping and emulsifying properties of soyabean
products. Agricultural and Biological Chemistry 36: 719-727.
Zacharie B and Ronald ES. 1995. Effects of soaking, cooking and
fermentation on composition, in-vitro starch digestibility and
nutritive value of common beans. Qualitas Plantarum Plant Foods
for Human Nutrition 48: 349-365.
Factors associated with economic motivation of legume growers in desert area of
Rajasthan
SUBHASH CHANDRA, P.SINGH1 and J.P. LAKHERA2
Krishi Vigyan Kendra; 1Agricultural Research Station, Beechwal, 2Directorate of Extension Education, SKRAU,
Bikaner-334006, Rajasthan, India; E-mail: scbalwada@gmail.com
(Received: October 20, 2012; Accepted: November 23, 2012)
ABSTRACT
The study was conducted during Kharif 2010 in arid region of
Rajasthan involving 108 legume growers to find out the
association of socioeconomic attributes with economic
motivation of legume growers. The independent variables, such
as age, education, land holding, farm power, social participation
socioeconomic status and economic motivation were measured
by the standard scale developed for this purpose. The study
revealed that about half of respondents were middle aged with
large land holding and educated upto middle standard. Majority
of them belonged to medium (66.66%) and high (17.59%)
socioeconomic status. It was observed that socioeconomic
attributes such as age, land holding, farm power and
socioeconomic status significantly associated with economic
motivation whereas education and social participation were
not associated with it. The information sources mostly utilized
by farmers were the village level worker/ agriculture supervisor/
krishakmitra followed by radio, neighbours, input dealers and
field demonstrations.
Key words:
Arid legume, Co mmunication behaviour,
Demonstration, Economic mo tivation, So cio
economic status
Pulses are grown in scattered and specific agro-climatic
input situations all over India to meet food needs of inhabitants
and fodder demand for livestock. The legumes like cluster
bean, cowpea, moth bean, mungbean and horse gram have
pivotal and unparallel role in harsh farming conditions. These
annual legumes are categorized as arid legumes and are
specially known for their sustained production under extreme
arid ecosystems, frequently encountered with harsh and
hostile growing environments with unpredicted intensity and
interval. Need-based adaptations of these legumes towards
inclement weathers have recognized them as the source of
livelihood for farmers surviving on resource constraint arid
farming. Contribution of arid legumes in combating severe
droughts, improving soil health, diversification of agriculture
and as a source of organic foods have pushed them from their
secondary status to major ones.
The status of these arid legumes is ascertained from
their vast acreage covering 4.9 million hectares (mha) in India
including Jammu and Kashmir in interior north to deep in
Kerala and from western states of Rajasthan and Gujarat to
rear eastern states of Orissa and West Bengal. The Hyperarid
partially irrigated western plain (Zone-1C) has maximum area
(1.51 mha) in Rajasthan with only 0.56 million tonnes (m t)
annual production because of very low productivity of these
crops (40.0 Kg/ha). The hot arid regions of the country are
characterized by hostile agro-climate and fragile eco-system
with an annual rainfall of 100-150 mm inclusive of both rainfall
and temperature related extreme events, low relative humidity
and high potential evapo-transpiration of 1600 to 1800 mm
especially in western part of region. Despite various
biophysical constraints, the hot arid areas of Rajasthan like,
Bikaner, Jaisalmer and Churu districts of western Rajasthan
offers very good opportunities for cultivation of these
legumes. Thus, location specific technological backup are key
to sustainability of this region. These interventions are
expected to stimulate a definite shift in cultural practices on a
farm but may encourage a shift in investment layout, farm
inventory and farm plan etc. The economic gain of farmers
also depends upon their age, education, size of holding,
socioeconomic status and their progressiveness as dynamics
in their outlook motivates them to adopt new ideas or
agricultural technology for economic gains. Keeping these in
view, the present study was conducted to study both personal
and socioeconomic characteristics of legume growers and also
to find out the association of socioeconomic attributes with
the level of economic motivation to them. The economic
motivation refers to the occupational success in terms of profit
maximization and relative values placed by the farmers on the
economic ends (Supe 1969).
The study was conducted in Hyper arid partially
irrigated Western Plains (Agro-climatic Zone-Ic) of Rajasthan
during kharif 2010. Bikaner and Churu districts were selected
on the basis of higher area and production of arid legumes
viz., cowpea, moth bean, cluster bean and mung bean. From
each of the selected district, two panchayat samities were
selected randomly from which three gram panchayats were
again selected randomly from each of them. One revenue
village was selected from each gram panchayat, randomly
making a total of 12 villages for conducting the study. A
comprehensive list of farmers who were growing arid legumes
like, mungbean, mothbean, clusterbean and cowpea at least
Chandra et al.: Factors associated with economic motivation of arid legume growers
for the last three years was prepared with the help of
agricultural supervisors. Thus, from each village, nine farmers
were selected randomly and accordingly 108 contact farmers
constituted the sample for the study. Independent variables,
such as age, education, land holding, farm power, social
participation and socioeconomic status were measured with
the help scale developed by Trivedi (1963) while for computing
economic motivation the scale developed bySupe (1969) was
used. Farmers were categorized into three groups viz., high,
medium and low on the basis of mean score and standard
deviation. Communication sources utilized was measured using
the scale developed under standard procedure. The data were
tabulated and analyzed with the help of statistical tools viz.,
frequency, percentage and chi-square.
Socioeconomic attributes: The study revealed that about
half of the farmers (47.3%) were middle aged while 25% were
young and the rest (25.9%) were old aged (Table 1). On
educational qualification, one-third (32.4%) farmers were
educated up to high school while one-fourth (26.8%) had
passed middle school and one-fifth up to primary (19.4%).
Regardi ng l and holding, more than half (51.8%) of
respondents were large farmers, while 41.6% were small farmers
Table 1. Distribution o f fa rme rs a s pe r pe rsonal
socioeconomic attributes
Characteristics
Age (Years)
Young (20-35)
Middle(36-50)
Old(above50)
Education
Illiterate
Can read only
Can read & write
Primary schooling
Middle school
High school
Graduate and above
Land holding
Marginal (< 1.0 ha.)
Small (1.0-2.0 ha.)
Medium and Big (> 2.0)
Farm power
No any farm power
Camel/Bullocks
Diesel engine/Electric motor
Tractor (with implements)
Sprinkler sets
Social participation
No Social participation
Member of one organization
Member of >one organization
Office bearers
Socio-economic status
High (score above 36)
Medium(score 20-35)
Low(score up to 20)
Frequency
Percent
29
51
28
26.8
47.2
25.9
07
09
04
21
29
35
03
6.4
8.3
3.7
19.4
26.5
32.1
2.8
07
45
56
6.4
41.6
51.8
24
42
14
13
15
22.2
38.8
12.9
12.0
13.8
44
43
14
07
40.7
39.8
12.9
6.4
19
72
17
17.5
66.6
15.7
327
and only 6.4% were marginal farmers. However, on farm power
front, less than half of the respondents (38.8%) had camel/
bullock power while 12.9% had diesel engine/electric motor
and only 12.0% had tractor for effective farm mechanization.
Similarly, only 13.8% farmers had sprinkler set for irrigation
while 22.2% respondents did not have any source of farm
power. As far as social participation is concerned, 39.8%
farmers were member of one organization while 12.9% were
member of more than one organization, thereby enabling half
of them to have a direct contact/role in village level
organization. These type of normal socioeconomic attributes
were also reported by Singh et al. (2009) and Singh et al.
(2011).
In case of socioeconomic status (Table 1), majority of
farmers possessed medium socioeconomic status (66.6%) while
one-sixth belonged to high socioeconomic status (17.5%).
Similar findings were made by Trivedi (1963), Singh et al. (2009)
and Singh et al. (2011).
Socio economic status vis-à-vis economic motivation: The
age of the farmers, their land holding and socioeconomic status
and availability of farm power were significantly associated
with economic motivation. However, education and social
participation were not associated with it (Table 2). Moreover,
the age and economic motivation were dependent on each
other. Age was an influencing and important factor in the
pursuit of economic motivation of a person because of the
fact that need and requirements were increased with the age
of individual which motivated oneself to earn more and more
(Singh and Sohal 1969 and Singh et al. 2009).
However, the education had no positive bearing on
economic motivation as both were independent attributes.
The study also revealed uneven distribution of farmers
regarding their education level. However, land holding seemed
to have positive and significant association with economic
motivation as the size of land holding affected the state of
economic motivation. It may be due to the fact that almost all
respondents were having medium and large land holdings
and were engaged themselves in intensive cultivation so as
to earn more income from farming by adopting new farm
technologies.
Farm power was found to be significantly associated
with economic motivation as sufficient number of farm
equipments and their accessories states the socioeconomic
condition of the farmers. Moreover, power showed the
progressiveness and innovativeness of the farmers and the
innovative farmers were engaged in intensive farming so as
to earn more profit from farm by using improved agriculture
through equipments (farm mechanization).
The social participation and economic motivation had
no positive and significant association with each other and
thus had no impact on economic motivation. However, the
socioeconomic status was significantly associated with
328
economic motivation. Since, majority of farmers were
socioeconomically sound, thus acted as supplementary factor
influencing level of economic motivation. Furthermore, farmers
were unevenly distributed among various socio-economic
status, yet they had difference in their perception from time to
time regarding other factors of socioeconomic status. The
overall situation reflected that the variables mentioned as
socioeconomic attributes (Table 2) complimented and
supplemented to socioeconomic motivation. These findings
are in conformity with the findings obtained by Supe (1969),
Singh et al. (2009) and Singh et al. (2011).
Communication behaviour: The study also showed that the
majority (75%) of farmers had access to VLW/Agricultural
supervisors/Krishakmitra as they met them frequently.
Moreover, radio and neighbours were next to VLW as the
known source for information by the farmers (Upadhyay and
Hansra 1986). The input dealers and demonstration jointly
(37.04%), friends (30.55%) and cooperative societies (29.63%)
were also important sources of information utilized by farmers.
However, extension officers, B.D.O, scientists, AAOs,
television, film show, leaflet, folder, farm magazine and bulletins
were not preferred as a source of information frequented by
the farmers. The occasional utilized sources included AAOs
(80.55%) followed by block officials (63.89%), cooperative
societies (53.71%), relatives (52.78%) and local leaders (50%),
input dealers (46.30%), television and field demonstrations
(35.19%). The different sources utilized by the farmers
included film shows (92.60%), scientists (91.66%), farmers fair/
Ki san Gost hi (88.3 4%), members of Panchayat and
Cooperatives, telephone talks/help lines, (79.63%), news
papers (77.78%), group meetings (75.0%), television (64.81%)
and krishi upaj mandi (50%), respectively. Thus, the primary
source of information for the respondents were village level
workers/agricult ural supervisors/krishakmitra, radio,
neighbours, input dealers and field demonstrations. These
findings are in accordance with the findings of Upadhyay
and Hansra (1988), Saravanan et al. (2009) and Meena et al.
(2010).
Conclusively, the study suggested that socioeconomic
att ributes viz., age, land holding, farm power and
socioeconomic status were associated with economic
motivation. The information source mostly utilized by farmers
included village level workers/supervisors and was followed
by inter-personal cosmopol ite sources such as radio,
neighbours, input dealers and field demonstrations.
Table 2. Association between socioeconomic attributes and economic motivation of farmers
Attributes
Age
20-35yrs
36-50yrs
Above-50yrs
Education
Illiterate
Can read only
Can read & write
Primary schooling
Middle schooling
High schooling
Graduate and above
Land holding
Marginal
Small
Medium and Big
Farm power
No any farm power
Camel/ Bullocks
Diesel engine and Electric motors
Tractor
Sprinkler set and drip system
No Social participation
Member of one organization
Member of > one organization
Office bearers
Socio-economic status(SES)
Low
Medium
High
*Significance at 0.05 level of probability, NS: Non-significant
Economic motivation total
Low (6-13)
High (13-20)
9
20
7
44
13
15
0
07
02
07
02
02
04
17
11
18
12
23
0
03
02
05
8
37
14
42
11
13
13
29
06
08
04
09
05
10
27
17
18
25
03
11
05
02
08
09
15
52
05
19
Total
29
51
28
07
09
04
21
29
35
03
07
45
56
24
42
14
13
15
44
43
14
07
17
67
24
Chi-square value
11.21*
4.03 (NS)
6.39*
6.56*
4.05 (NS)
6.67*
Chandra et al.: Factors associated with economic motivation of arid legume growers
329
Table 3. Distribution of farmers as per communication behavior*
Source of Information/Channels
Frequently
Personal/ local source
Neighbours
Friends
Relatives
Progressive farmers
Local leader
Member of panchayat & cooperative
Telephone talk(help line)
Personal cosmopolite source
Village level workers, Agrl. Sup.& krishakmitra
Assistant Agriculture officer
Group meeting
Demonstration
Farmers fairs & Kishan Gosthi
Block dev. Officials (BDO)
Scientist of ICAR & University
Impersonal cosmopolite source
Radio
Television
News paper
Film shows
Folder, farm magazine & bulletins
Commercial agencies and NGOs
Krishi Upaj Mandi
Input dealers
Cooperative societies
Extension contact
Occasionally
Never
60(55.56)
33(30.55)
23(21.30)
17(15.74)
12(11.12)
9(8.34)
9(8.34)
20(18.52)
49(45.37)
57(52.78)
40(37.04)
54(50.00)
13(12.03)
13(12.30)
28(25.92)
26(24.08)
28(25.92)
51(47.22)
42(38.88)
86(79.63)
86(79.63)
81.0(75.00)
0.00
6(5.55)
40(37.04)
8(7.40)
0.00
0.00
18.0(16.66)
87.0(80.55)
21(19.45)
38(35.18)
10(9.25)
69(63.89)
9(8.34)
9.0(8.34)
21.0(19.45)
81(75.00)
30(27.78)
90(83.35)
39(36.11)
99(91.66)
75(69.45)
0.00
7(6.48)
0.00
0.00
20(18.52)
38(35.19)
17(15.74)
08(7.40)
02(1.85)
13(12.03)
70(64.81)
84(77.78)
100(92.60)
106(98.15)
23(21.30)
40(37.04)
32(29.63)
31(28.70)
50(46.30)
58(53.71)
54(50.00)
18(16.66)
18(16.66)
*Figures in the parenthesis indicates percentage
REFERENCES
Meena SR, Sisidia SS, Punjabi NK and Sharma Chitranjan. 2010.
Information seeking behaviour of farmers about guava Production
Technology. Rajasthan Journal of Extension Education 17&18:5255.
Saravanan R, Raja P and Tayeng Sheela 2009. Information input pattern
and information needs of tribal farmers of Arunachal Pradesh. Indian
Journal of Extension Education 45: 51-54.
Singh DK, Singh AK, Yadav VP, Singh RB, Baghel RS and Singh Mayank.
2009. Association of socioeconomic status with economic
Motivation of the farmers. Indian Research Journal of Extension
Education 9: 53-56.
Singh P, Sharma SK and Singh Sangram 2011. Information seeking
behaviour of mothbean growers in western Rajasthan. Indian Journal
of Agricultural Research and extension IV: 97-100.
Singh R and Sohal T. 1969. Size of holding as related to acceptance of
crop plans, extension contacts and education of farmers. Indian
Journal of Extension Education 5: 42-48.
Supe SV. 1969. Factors related to different degree of rationality in
decision making among farmers. Ph.D. thesis, Division of
Agricultural Extension, I.A.R.I., New Delhi.
Trivedi G.1963. Measurement and analysis of socioeconomic status of
rural families. Ph.D. Thesis, Division of Agricultural Extension,
I.A.R.I., New Delhi.
Upadhyay KP and Hansra BC. 1988. Evaluation of agricultural broadcast
of radio Nepal in adoption of improved agricultural technology by
Nepalese farmers. Journal of Research 23: 143-145.
Farmers participatory approach in seed multiplication of pulses in Bundelkhand
region - A case study
PURUSHOTTAM, S.K. SINGH, C.S. PRAHARAJ and LAKHAN SINGH1
Indian Institute of Pulses Research, Kanpur-208 024; 1Zonal Project Directorate (KVK), Kanpur, U.P., India; E-mail:
purushottam1995@yahoo.com
(Received: October 20, 2012; Accepted: December 06, 2012)
ABSTRACT
A farmers participatory action research programme (FPARP)
was carried out in seed production and multiplication of major
pulses through modern seed plot techniques (SPT) to generate
awareness about SPT and ensure availability of quality seed to
farmers during 2009-2011 in Baank and Bannki villages in
Hamirpur district of Bundelkhand region of Uttar Pradesh,
India. A total of 161 farmers with 61 ha of net cultivated area
participated in this FPARP programme by sharing half of the
seed cost in three major pulses viz., chickpea, lentil and
pigeonpea. Farmers were trained through one institutional,
six field level and one special training on “know your crops
(KYC)” for acquiring initial know-how and subsequent skill/
expertise development. The study amply demonstrated that
yield advantages to the tune of 37.3, 24 and 51% in improved
varieties of chickpea, lentil and pigeonpea, respectively over
their counterparts (local variety) were obtained following above
practice. The highest yields of 1320, 1000 and 1370 kg/ha were
realized in chickpea, lentil and pigeonpea, respectively under
farmers’ condition. The average cost of cultivation improved
varieties of chickpea, lentil and pigeonpea were also reduced
to the tune of ` 7500, 9386, 7287/ha, respectively resulting in
higher benefit cost ratios (BCR) in chickpea (2.20), lentil (2.32)
and pigeonpea (3.11) over the local (1.23, 1.09 and 1.17,
respectively). Due to FPARP, farmers could able to produce
19.3, 9.0 and 14.2 tonnes of truthful level seed for the chickpea,
lentil and pigeonpea crops, respectively. In addition, significant
quantity (73.4%) of this produce could also enter into the seed
chain (31.2 t of seed materials out of 42.5 t total produce) by the
adopted farmers themselves. Chickpea seed was diffused fastest
(from 60 adopted farmers to 119 other farmers in the very first
season) and farthest (from the adopted villages to other 18
villages in a radius of 24 km). The factors affecting the overall
yield levels and variations in the yields included soil based
(nature of soil, moisture level and water stagnation), plant
based (plant population and timely weeding), climate based
(continuous fog, frost at flowering and winter rains) and pest
and diseases based (aphid, root rot and pod borer) i.e., both
abiotic and biotic in nature. Thus, the study suggested that
with awareness and knowledge upgradation through FPARP,
the farmers could ensure quality seed production and its
multiplication and thereby, making seed production system
viable and remunerative.
Key words:
Bundelkhand region, Farmers participation, FPARP,
Pulses, Quality seed production
Pulses are rich source of vegetative protein and play an
important role in nutritional security of majority of vegetarian
population in India. The country is the largest producer and
consumer of pulses occupying 33% of the world’s area and
22% of the production (FAO 2008). Pulse production in the
country has fluctuated widely between 13 and 15 million tonnes
(m t) with no significant growth trend between 1991 and 2010.
The latest estimate indicates that the present production of
pulses has reached 14.7 million tons (mt) with productivity of
637 kg/ha although the projected pulse requirement by the
year 2030 (32 mt) is estimated to be more than double the
current production level (Anonymous 2011). Thus, increasing
pulses production through either productivity increase or
allocating non-traditional areas by cultivating pulses in specific
pulses growing regions is becoming indispensable and crucial
to make the Indian subcontinent self-sufficient in pulses.
Bundelkhand region is considered to be the pulse bowl
of Uttar Pradesh as it shares about 50% area and 45% of total
pulse production of the state. The average pulses productivity
in the Bundelkhand region was low (657 kg/ha) against 725
kg/ha as the state average. The reasons are biotic, abiotic,
and socio-economic constraints causing low productivity in
pulses in this region. In addition, lack of improved varieties is
reported as the most serious constraint among all biophysical
constraints in pulses production (Roy Burman et al. 2006).
Moreover, unavai lability o f quality seed and l ack of
technological awareness as revealed by 94.2 and 74.2%
respondents from the farming community, also contributes
much towards both low production and productivity of pulses
(Purushottam et al. 2011).
In the existing scenario, seed village could be the best
approach to ensure better SRR and availability of seeds at the
farmers’ door (Chaturvedi et al. 2010). A study revealed that
ado ption of i mproved varieti es it self could enhance
productivity by 20 to 25% in rabi pulses (Samra et al. 2011).
Moreover, biotic factors including some soil borne fungal
pathogens could cause extensive damages through Fusarium
wilt and root rot complex through reduction in seed yield to
the tune of 10-50% at farmers’ field in Bundelkhand region. It
was also reported that low yield of chickpea due to wilt was
the major problem in district Mahoba of Bundelkhand (Singh
2009). Thus, improved varieties of pulses could come to the
farmers’ rescue in the event of crop failure as these were
Purushottam et al.: A case study for seed multiplication in pulses on participatory mode
basically resistant to major diseases (wilt and rust) and were
mostly high yielder. Therefore, adoption of disease resistant
improved varieties of rabi pulses could increase seed yield
between 25-70% in rainfed mono-cropping and partially
irrigated double cropping situations in Hamirpur district (Singh
et al. 2005a). Since there was meager information available on
these lines especially on remunerative seed multiplication
programme in Bundelkhand region, hence a case study was
undertaken with the objective of generating awareness and
knowledge on quality seed production and ensuring its
availability in major pulses in these region.
A farmers’ participatory action oriented research
programme was carried out in selected villages in Hamirpur
district in Bundelkhand region of Uttar Pradesh, India during
2009-2011. The district has 2 tehsils, 7 blocks and 926 villages
and the average (land) size of holding is 1.95 ha. Socioeconomic
analysis of the farmers in the district revealed that there are
46% marginal, 23% small and 31% large farmers living in the
district. Two representative pulse growing villages namely,
Baank and Baanki were selected under Bharuwa Sumerpur
block as majority of the farmers (based on land holdings)
were involved in cultivation of pulses. The total population
of the villages was 5022 with 1060 families and the cultivable
area constituted about 80.6% (1052 ha) of the total
geographical area (1306 ha). However, the cultivated area
during rabi (894 ha) was much higher over that in kharif (357
ha). Chickpea, lentil and pigeonpea were the major pulses
grown in an area of 312, 77 and 238 ha, respectively in diverse
soil types viz., Mar, Kabar, Paruwa and Rakar during 200708. Mixed cropping was usually the most common practice
adopted in pulses; and use of costly inputs like fertilizers,
insecticide and herbicide was limited. Although line sowing
was practiced in rabi pulses yet broadcasting was extensively
adopted during kharif resulting in poor plant stand and low
yield.
Farmers’ participatory action research programme
(FPARP) for seed production and multiplication was carried
out by supplying quality foundation seed (2000 kg) of
chickpea, certified seed (720 kg) of lentil and breeder seed
(390 kg) of pigeonpea from registered/certified sources viz.,
IIPR Kanpur, CSAUA&T Kanpur, NDUA&T Faizabad and
NSC, Kanpur. Recommended cultivars viz., ‘DCP 92-3’, ‘JG 16’
and ‘KGD 1168’ (chickpea), ‘Narendra Arhar 1’ (pigeonpea)
and ‘DPL 62’ (lentil) were used. The areas planted with
chickpea, lentil and pigeonpea were 20, 15 and 26 ha,
respectively with respective 60, 36 and 65 farmers (a total of
161) participation. Farmers shared only half (50%) of the seed
cost out of total cost of `88,875 (`49725, 21600, 17550 for
chickpea, lentil and pigeonpea, respectively) as per project
guidelines. For farmers’ own involvement and commitment
for successful adoption of the concept, all other inputs except
331
the quality seed were managed by the farmers themselves.
The crop was sown under rainfed condition. A base line
survey indicated that farmers were not aware of seed
pro duct ion/ mult ipli cati on t echniques and market ing
intelligence for disposal of quality produce with a premium.
Therefore, albeit their own understanding, skill (along with
awareness/knowledge) enhancements were further improved
through diverse hands on trainings both at field (six numbers)
and institutional level (one number). Individual communication
through face to face and over telephone was also maintained
to have a special training on perfect transfer of “know your
crops (KYC)” for effective FPARP. It was further strengthened
through linkage and coordination with associated line
departments viz., District Agriculture Departments, Input
agencies and NGOs. The crop was inspected periodically
and the seed produced was registered through Uttar Pradesh
Seed Certification Agency, Jalaun. Project monitoring was
made through the funding agency (NABARD) through a
separate Project Implementation and Monitoring Committee
(PIMC). To have a further boost to seed production programme,
marketing of quality produce on community/village level and
for further follow ups, a farmers’ club namely Harit Kisan
Club was constituted in association with a Government
Banking Agency (Allahabad UP- Gramin Bank).
Gain in seed yield: Despite adverse climatic condition
(continuous fog and winter rains) causing flower drop, poor
seed setting and subsequent yield reduction, 37.3% gain in
seed yield was realized as a result of improved varieties over
local cultivars of pulses (with an average yield of 590 kg/ha).
On an average, higher seed yield (1000 kg/ha) in chickpea was
realized in ‘KGD 1168’ followed by ‘DCP 92-3’ and ‘JG 16’
(Table 1) whereas, the highest yield of 1500 kg/ha under
farmers’ condition was recorded under ‘DCP 92-3’ followed
by ‘KGD 1168’ and ‘JG 16’. The incidences of root rot and wilt
was almost absent in case of ‘DCP 92-3’ and ‘KGD 1168’
chickpea. The study also revealed higher yield losses (2530%) in case of local chickpea varieties due to the above
diseases. Even the incidences of root rot and wilt in JG 16 was
Table 1. Yield levels of chickpea, lentil andpigeonpea through
FPARP
Crop
Variety
Chickpea DCP 92-3
KGD 1168
JG 16
Av.
Lentil
DPL 62
Pigeonpea Narendra
Arhar 1
* Stalk yield
Crop Average seed yield Increase Highest
area
(kg/ha)
in yield yield
(ha) Improved Local
(%) (kg/ha)
cultivars cultivars
10.0
980
590
36.7
1500
5.0
1000
590
40.9
1320
5.0
900
590
34.3
1150
960
37.5
1320
15.0
590
450
24.0
1000
26.0
550
270
51.0
1370
(3540)* (2320)* (34.0)* (45.0)*
332
low and varied (5-20%) from one to another field. Thus, the
role of improved variety over local or desi was established for
realization of higher yield under farmers’ condition.
Similar to chickpea, pigeonpea farmers also had a
perceptible additional yield advantages through growing of
improved varieties over local or desi varieties. Thus, visible
differences between local and improved variety were apparent
in terms of both average seed yield (270 kg/ha versus 550 kg/
ha) and stalk yield (2320 kg/ha versus 3540 kg/ha) resulting in
yield gain to the tune of 51 and 34%, respectively by growing
improved variety over the local.
The stu dy also revealed variabl e seed yield in
pigeonpea (300-1370 kg/ha) due to combination of several
factors including sowing in light soil with moisture stress,
water stagnation after sowing due to rainy months, poor
germination, lack of timely weeding as a result of traditional
practice of broadcasting and higher plant population, lack of
appropriate insect control practice (mostly lack of insecticide
for pod borer control), frost at low elevation and rainfed
condition with no life saving irrigation. It was observed that a
supplemental irrigation at flowering and use of insecticide
against pod borer was beneficial in terms of realization of
higher yield by the participating farmers over others. Under
Indian condition as pulses are grown in rainfed areas, mostly
under poor management conditions, the crop faces many biotic
and abiotic stresses (Ali and Kumar 2009). Thus, substantial
yield gain is possible when package of practices with modern
technology is adopted by the farmers for raising crops.
Quite similar observation was recorded in case of lentil
as on an average higher seed yield (590 kg/ha) was obtained
with improved variety in comparison to local variety (450 kg/
ha). Apparently, a yield gain to the tune of 24% (140 kg/ha)
was observed in improved variety over local. In case of the
yield level of lentil under farmers’ condition ranged from 350
to 1000 kg/ha. Moreover, large and lustrous seeds were
obtained with the improved variety of lentil over local. Lower
seed yield and higher variation in seed yield were again due
to both biotic (incidence of aphid and root rot) and abiotic
factors (soil type, poor land preparations, delayed sowing,
lack of winter rains, low soil moisture at critical stages and
frost at flowering). Thus, modification in abiotic stresses
through appropriate remedial measures (viz., low soil moisture
by life saving supplemental irrigation) and biotic stresses by
control measures could increase seed yield significantly as
observed in case of adopted farmers over others. Thus,
possibility of higher yield realization could be explored by the
farmers provided t hey adopted bett er recommended
management practices. Although they had a mind set for low
cost of cul tivation because of harvesting poor yield
consistently due to associated risk factors, yet change in
attitude and ability to learn and adopt newer technologies
could land them in progressive mode, avert risks associated
with crop husbandry and exploit yield potential of the crop(s).
This was possible in case of project farmers in the adopted
villages at least to some extent.
Study also suggested that ‘DCP 92-3’chickpea was the
best variety under the existing situation of clay loam and clay
soils as single pre-sowing irrigation could yield 400 kg/ha
more over local (951 kg/ha). Similarly, ‘DPL 62’ lentil and
‘Narendra Arhar 1’ pigeonpea grown under rainfed condition
yielded 32% (1100 kg/ha) and 52% (918 kg/ha) higher over
local check(s), respectively as reported by Dubey et al. 2011.
Singh et al. (2005) also reported that improved varieties of
chickpea, lentil and field pea had increased their seed yields
to the tune of 45, 70.4 and 61.5% over local checks, respectively.
Table 2. Eco nomics o f improv ed practice of seed
multiplication in pulses*
Crop
Variety
Chickpea Improved
Local
Lentil
Improved
Local
Pigeonepa Improved
Local
Average Improved
Local
CC
(`/ha)
7500
5300
9386
8654
7287
5482
8057
6478
Gross
Income
(`/ha)
24000
11820
20450
18093
29958
11940
24802
13951
Net
return
(`/ha)
16500
6520
21840
9440
22671
6458
20337
7472
BCR Additional Additional
CC
net return
(`/ha)
(`/ha)
2.20
2200
9980
1.23
2.32
732
12400
1.09
3.11
1805
16213
1.17
2.54
1579
12864
1.16
*Market price (improved versus local): Chickpea (`25 and `20/kg),
Lentil (`33 and `29/kg) and Pigeonepa (`35 and `30/kg; by product/
stalk (`1300/ha for lentil and `400/t for pigeonpea)
Gain in net return: Based on prevailing prices of inputs and
outputs, improved varieties were economically viable over
local varieties grown by the farmers although the cost of
cultivation (CC) in case of former was on higher side (ranging
from `732 to 2200/ha) primarily due to qualityseed cost. The
average cost of cultivation for improved varieties of chickpea,
lentil and pigeonpea were `7500, 9386, 7287/ha, respectively
while the corresponding values for local varieties were `5300,
8654, 5482/ha in chickpea, lentil and pigeonpea, respectively.
However, additional net return was increased to higher order
(ranged from ` 9980 to 16213/ha with mean return of `12864 /
ha) over local. Thus, seed multiplication resulted in higher
benefit cost ratio (BCR) of 2.20, 2.32 and 3.11 in improved
varieties as compared to 1.23, 1.09 and 1.17 in local checks in
chickpea, lentil and pigeonpea, respectively. Thus, there was
possibility of higher income per unit area and rupee invested
through seed production programme over normal crop raising
programme in pulses (meant for consumption). Similar work
of Tomar et al. (2009) revealed higher mean net income of
`9856/ha with a BCR of 2.13 in urdbean under improved
technology package as compared to local practice (with net
income of `3357/ha and BCR of 1.64) in Tikamgarh district of
Bundelkhand.
Disposal of quality Seed: The quantum of quality seed
produced by the farmers as a result of improved technology
were to the tune of 19.3, 9.0 and 14.2 t (Table 3) under chickpea,
lentil and pigeonpea, respectively (a total of 42.5 t seed). This
Purushottam et al.: A case study for seed multiplication in pulses on participatory mode
not only met their own requirements (73.4%) for seed (31.2 t)
but also fulfilled partial requirements (11.8 t) of their
neighbours, relatives, land share holders and NSC besides
meeting a few personal need for miscellaneous purposes (for
exchange and loan). More importantly, quality chickpea seed
was diffused from the adopted two villages to other 18 villages
in a radius of 24 km (from a mere 60 adopted farmers to 119 in
the very first season only). Since farmers usually do not open
the stored seed during rainy season therefore, majority of
seed was disposed off either just after harvesting or at sowing
in the next season. Additional benefit included covering the
majority of area of chickpea, lentil and pigeonpea (grown in
312, 77 and 238 ha in adopted villages) in due course of time
by improved varieties that requires quality seed (24.9, 3.8, 3.5
t, respectively) through SRR. Thus, farmers became more
progressive and their entrepreneurship behaviour led to
constitution of a Farmers’ Club under project linking to
International Traceability System Limited (ITS) for welfare
of their locality. ITS helped the farmers club in lifting 2.3 t of
lentil and 0.3 t of pigeonpea seed pooled from 20 farmers. ITS
agency is an agency working with coordination of NSC for
quality seed production and its safe disposal.
Table 3. Disposal of quality seed of pulses from the villages
Crop
Variety
Chickpea DCP 92-3
KGD 1168
JG 16
Sub-total
Lentil
DPL 62
Pigeonpea Naraendra
Arhar 1
Total
* Stalk yield
Total
Farmers’ own
use (t)
produce (t)
9.8
7.2
5.0
2.5
4.5
3.5
19.3
13.2
9.0
6.2
14.2
11.8
(95.0)*
42.5
31.2
Quantity
for sale (t)
2.6
2.5
1.5
6.6
2.8
2.4
11.8
Marketing constraints in quality seed disposal: The study
also showed that lack of seed processing plant in nearby
locality, dissatisfaction of farmers on sample checking for
quality, (delayed) payment in installments and miscellaneous
handling charges influenced speed of quality seed disposal
to Government Agencies. Due to these constraints, farmers
were often forced to sell off their produce in local market meant
for consumption resulting in jeopardizing actual seed
replacement rate (SRR). Even small and marginal farmers
initially followed their counterparts (other farmers) where they
would receive the payments after lifting the seed from the
villages that also resulted in delayed disposal of their produce.
Therefore, appropriate marketing strategies such as earliest
lifting of first lot just after threshing, disposal in small lots
instead of pooling from large number of farmers and such
other measures would boost their morale for sustenance in
farming enterprises.
Thus, it was inferred from the study that improved
333
varieties had adequate potential in enhancing quality seed
multiplication through FPARP and thus, enabling adequate
SRR for economic development in Bundelkhand region of
Uttar Pradesh. Integration of pulses technologies played a
greater role for higher social acceptability and enhancing
profitability. This was possible through various confidence
building measures for improving their awareness/knowledge
through qu alit y traini ngs, KYCs, development of
entrepreneurship and effective marketing strategy.
ACKNOWLEDGEMENT
The authors acknowledge financial support received
from NABARD for implementing the project “Farmers’
participatory seed production of major pulse crops in selected
villages of Hamirpur district in Bundelkhand region of Uttar
Pradesh” at IIPR, Kanpur.
REFERENCES
Ali M and Kumar S. 2009. Major technological advances in pulses:
Indian scenario (Eds), Indian Institute of Pulses Research, Kanpur,
India. Pp 1.
Anonymous 2011. Vision 2030, Indian Institute of Pulses Research,
Kanpur. Pp iii-vii.
Chaturvedi SK, Nadarajan N, Singh SK and Mishra JP. 2010. Strategies
for enhancing pulses production in Bundelkhand tracts of U.P. and
M.P. In: Extension strategy for Bundelkhand region, ZPD, ZoneIV, ICAR, Kanpur.
Dubey SK, Sah Uma and Singh SK. 2011. Participatory impact
assessment of technological interventions disseminated in
Bundelkhand region of Uttar Pradesh. Journal of Food Legumes 24:
36-40.
FAO 2008. Diagnosis of pulses performance of India. In: Srivastava
SK, Sivaramane N. and Mathur VC (Eds.), Agricultural Economics
Research Review 23: 137-148.
Purushottam, Kumar Rajesh and Kumar Hemant 2011. Pulse production
issues in Bundelkhand region of Uttar Pradesh. Agriculture Situation
in India. LXVII: 661-666.
Roy Burman R, Singh SK, Singh Lakhan and Singh AK. 2006. Adoption
of improved pulses production technologies and related constraints
in Uttar Pradesh. Indian Journal of Pulses Research 19: 104-106.
Samra JS. 2009. A report on drought mitigation strategies for
Bundelkhand region of U.P. and M.P. Rainfed Authority of India,
New Delhi. 12 pp.
Singh Atar and Singh AK. 2009. Yield advantages in pulses at farmers’
field. Journal of Food Legumes 22: 198-201.
Singh Atar, Lakhan Singh and Singh NP. 2005. Performance analysis of
pulses in frontline demonstrations. Indian Journal of Pulses Research
18: 202-205.
Singh SK, Roy BR, Chaudhary RG, Singh KK and Ansari S. 2005a.
Impact of usable technologies identified under pulse based rainfed
agro ecosystem. Indian Journal of Pulses Research 18: 60-63.
Tomar RKS, Sahu BL, Singh RK and Prajapati RK. 2009. Productivity
enhancement of blackgram through improved production
technologies in farmers’ field. Journal of Food Legumes 22: 202204.
Tropical Legumes 2 pigeonpea seed system in India: An analysis
M.E. HOLMESHEORAN, M.G. MULA, C.V.S. KUMAR¹, R.P. MULA and K.B. SAXENA
International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, Andhra Pradesh, India; ¹Acharya
N.G. Ranga Agricultural University (ANGRAU), Hyderabad, Andhra Pradesh, India; E-mail: m.mula@cgiar.org
(Received : January 30, 2012 ; Accepted : October 01, 2012)
ABSTRACT
Pigeonpea farmers in India have historically relied on selfsaved seed of local varieties as their seed source for upcoming
growing seasons. As improved varieties for disease resistance
and yield have been consistently developed, the challenges have
been to help farmers gain and retain access to these improved
varieties. The above objectives were tried to be accomplished
through improved agronomic practices, promoting the seed
village concept to minimize the effects of out-crossing, and
developing local seed production capacity under the aegis of
the Bill Melinda Gates Foundation funded Tropical Legumes 2
(TL 2) project operational with the pigeonpea farmers for the
last 4 years. The project was implemented in Tandur, Ranga
Reddy District of Andhra Pradesh, India, a region where the
pigeonpea is cultivated as monocropped or intercropped with
other crops. Ahandful of farmers have become truthfully labeled
seed producers, but educational programs and improved seed
have not yet reached the majority of individuals in the
communities targeted, creating a gap both in understanding
and in meeting project goals. Small hold farmers because of
their subsistence level are usually not involved in seed
production. However, improved varieties should be made
available to them for meeting the above objectives. The focus
on continuing increasing opportunity for small holders through
seed system improvement would yield more innovative methods
for community involvement and accessibility so that the gaps
in understanding can be bridged up for the welfare of the society
as a whole.
Key words:
Pigeonpea, Seed village system, Tropical legumes 2,
Truthfully labeled seed
The process through which viable seed is produced,
stored, marketed and used is known as a seed system or seed
chain. Thus, seed system includes all the channels through
which farmers acquire genetic materials, both outside of, and
in interaction with, the commercial seed industry (Tesfaye et
al. 2005). Seed systems vary widely depending on locality,
market availability, and farmer knowledge, and can be informal,
formal, or a combination of the two. An informal seed system
functions primarily through farmer’s saving and storing their
own seed for the next season. New infusions of seed stock
may be purchased every few years, but usually from a local
supplier. The formal seed sector is defined as any seed
supplied through companies or government agencies that is
registered and certified for quality. These entities usually exist
at the regional and national level. Any farmer receiving seed
supply through the formal seed channel is using the formal
seed system for cropping.
In India, self-saved seed accounts for roughly 80% of
seed cultivated for food crops in any given growing season
(Ravinder Reddy et al. 2007) indicating the fact that lower
quality seed is being grown by the majority of the farmers,
and accordingly yields are not as high as with improved
varieties. In pulses also, low seed replacement rate (SRR) similar
to cereals is a major problem. Though pigeonpea production
has seen an increase in the seed replacement rate in recent
years, farmers still self-produce and meet more than 85% of
their own seed need. An attempt is made for small holders
through improved seed system involving their community so
as to bridge the gaps in understanding and operating the
system for the welfare of the society. The seed replacement
rate expresses the percentage of seeds for a specific crop
purchased for a given season and indicates the status of
improved seed replacement over the season.
The Tropical Legumes 2 : The Tropical Legumes 2 project
was a four year project (2008-2011) funded by the Bill Melinda
Gates Foundation and has the goal of ‘improving the
livel ihoods o f small hold farmers through improved
productivity and production of tropical grain legumes in SubSaharan Africa and South Asia’. Small hold farmers in India
are ‘marginal and sub-marginal farm households that own or/
and cultivate less than 2.0 hectare of land’ (Singh et al., 2002),
which translates into 2 hectares per farm family.
ICRISAT works in collaboration with Acharya N.G.
Ranga Agricultural University (ANGRAU) located in
Hyderabad in Andhra Pradesh (India) to implement the TL 2
project in two districts (Ranga Reddy and Mahaboobnagar)
of Andhra Pradesh. Targeted areas in both districts are
historically famous for pigeonpea production with a reputation
of excellence in high dal quality produce. The TL 2 project has
also provided the opportunity for the Agriculture Research
Station, Tandur (ARS) to develop a few local seed producers,
including one in Kolkat, a village included in the assessment.
Free seed pigeonpea packets of 4-5 kg have also been given
out to 783 farmers for 4 growing seasons of the TL 2 project.
In order to assess the impacts of the TL 2 project so far,
three villages were selected in Ranga Reddy of Andhra
Pradesh, two villages that the project has been implemented
Holmesheoran et al. : Tropical Legumes 2 pigeonpea seed system in India: An analysis
in, and one outside of the project area for data collection in
April 2011 for two weeks for comparison. Large group farmer
interviews were conducted in each village followed up by
individual interviewing of key informants including village
sarpanchs (village president)] by using guide questionnaires
that included the followings key points.
1.
What were the past and current seed systems for the
village?
2.
Do farmers receive any training, and if so, by whom?
3.
What is the total economic benefit of pigeonpea
cultivation?
4.
Are communities functioning on the seed village
concept?
5.
What are FPV’s for pigeonpea?
6.
How are farmers selected as project beneficiaries?
335
food and non-food crops wherein 35,000 hectares is devoted
to pigeonpea. The Tandur area that lies approximately 160 km
west of Hyderabad is famous for pigeonpea cultivation (with
a total area of 10,000 hectares) and processing of blue slate
tiling used for home construction and concrete production.
Two of the three villages studied were in the Tandur area
(Kolkat and Gopalpur) for TL 2 project site and one in nonproject site at Godamguda for establishing comparative values
between project and non-project areas. All three villages
cultivated pigeonpea as roughly half of their total land area,
either as sole or intercropping with sorghum, black gram, or
green gram, depending on seasonal rains for the year. Yield of
pigeonpea varied substantially with new varieties as compared
to the traditional varieties (Table 1). The average land area
owned was 0.8-1.6 hectares classifying the majority of farmers
in these villages as smallholders (Table 2). Pigeonpea was
primarily used as a staple food, and all farmers surveyed stated
that they would first save food (preferred) stock and seed for
the year and then sell the surplus stock in market.
This assessment tried to ascertain what seed types were
currently being used in the Tandur area of Ranga Reddy
district, and also to understand seed saving systems in the
past, both before seeds available in market as well as before
implemented TL 2 intervention. The researcher mapped out
ANGRAU’s recommendations for seed system change.
Training materials and planned programs were also evaluated.
Cu rrent levels of true adopti on o f interventi on
recommendations was assessed and analyzed for gaps,
indicating areas of knowledge not covered and specific
populations not reached. Pigeonpea availability was also
assessed for the area in order to understand the usage of crop
at harvests both as a food and as a cash crop. Based on this
baseline data, information was gathered from the community
regarding potential improvements that could be made in
implementation frameworks for the recommended food system.
Existing Seed System Model: Farmers all over India have
traditionally relied on saved seed as their primary mode to
seed access. For pigeonpea specifically, farmers in the past
depended on cultivation of four local varieties and would
trade seed amongst themselves or between the villages when
their seed became unviable after 3-4 years of successive
cultivation (Fig. 1). The trade between the farmers and then
with neighboring villages helped to give new exposure to
existing variety in the village. When a new seed was cultivated
then new genetic material obviously entered the cycle due to
natural outcrossing which strengthened the all rounded
varietal performance.
All three villages surveyed noted that seed saving was
still their primary method of retaining access to variety and
that they only went for new seed every 2-3 years when the
The Project Site (Tandur Village, Rangareddy District) : In
Ranga Reddy District, 30.7% of land is cultivated for both
Table 1. Agro-demographic survey across study villages
Village Name
Kolkat
(4 years in project)
Gopalpur
(2 years in project)
Godamguda
(non-project)
Soil Type
Cropping Pattern
Red-Brown soil
(Alfisol)
Red-Brown soil
(Alfisol)
Heavy black
(Vertisol)
Row cropping with sole and
mixed pigeonpea cultivation
either with sorghum, black
gram, green gram
Yield
(New Varieties)
(kg/ha)
800-1000
Yield
(Local Varieties)
(kg/ha)
500-700
Potential
Difference in Yield
(kg/ha)
100-500
800-1000
500-700
100-500
N/A
600-800
N/A
Table 2. Percentage of farmers’ agricultural land use across sample villages
Village
Kolkat
Gopalpur
Godamguda
(Non-project)
Total area
(ha)
Farmers Interviewed
(no.)
1,862
486
19
24
< 2 ha (Smallholder
Farmers)
6 (32%)
17 (70%)
445
15
12 (80%)
% distribution of landholdings
2.5-3.5 ha
4-8 ha
> 8 ha
4 (21%)
2 (8%)
5 (26%)
1 (4%)
4 (21%)
4 (16%)
3 (20%)
0
0
336
Fig 1. A flow chart of Tandur seed system model
variety of their saved seed had digressed to the point of being
uncultivable. Additionally, interviewed farmers reported that
they preferred complete self-sufficiency and almost never got
new variety from outside the village. It was primarily the larger
landholders who purchased seed from outside the village.
Normally the seed system cycle operates with a certified
seed production agency (corporate seed company, ICRISAT
or ANGRAU-ARS) producing breeder and foundation seed.
The Department of Agriculture or local seed traders buy it
and then sell it to large farmers (with > 10 ha) who can afford
to purchase new seed although they constitute 10% of the
overall farming population. Neighboring smallholder (90% of
farming population) who see a decline in seed yield of their
saved seed from previous years trade go for the second
generation of new seed cultivated by above large farmers by
replacing their seed (food) at the rate of 2 kg for 1 kg improved
seed. They trade with the farmers who have a high yield in the
previous season (notably the large farmers) who could afford
to purchase first generation breeder or foundation seed.
In the surveyed project area, there were some additional
players in the seed system. ICRISAT provides breeder seed
to ANGRAU-ARS for seed producti on, and farmer
multiplication to provide additional local source for seed (Fig.
1) to farmers, a very helpful option as seed traders’ prices are
high and the Department of Agriculture frequently has a
shortage of seed. The ARS has also developed a few seed
producers who grow truthfully labeled seed.
Production of Improved varieties: During 2009-2010 per
hectare average pigeonpea yield was 510 kg for Andhra
Pradesh which was lower than that of states viz., Maharashtra,
Karnataka, and Uttar Pradesh (Gopal and Babu, 2010). The
yield level was more closely related to average yields for local
varieties although new (improved) varieties could potentially
create a seed yield differentiation of 100-500 kg/season (Table
1). Additionally, the average area cultivated for pigeonpea
was 1.8 ha i.e.,the average pigeonpea plot is categorized as
smallholder farming (Gopal and Babu, 2010).
Proposed Seed Village System An alternative to creating
isolation through distance is to encourage the majority of
neighbour farmers to cultivate the same variety of seed, thus
337
eliminating the danger of outcrossing with other varieties. If
an entire village or a large section of a village can be motivated
through extension education and community organization to
plant the same variety, yields will be maintained from season
to season and the number of year’s seed can be repetitively
saved for re-cu ltivatio n wi thou t lo ss o f desirable
characteristics. Intensive community organization is needed
to reap these highly desirable benefits. Additionally, the
development of cooperation between informal and formal seed
systems will help to maintain a system that allows the longterm benefits of improved varieties to be gleaned by
smallholder farmers for longer periods of time (Nagarajan et
al, 2007). Seeds can then be sourced according to the
community’s preference from a variety of suppliers, and any
seed traded within the community will be of the same variety,
which will remain unsullied by outcrossing (Diagram 2).
absence of these markers makes any assessment very
challenging.
Assessing the impact: At the inception stage, a clear picture
must be formed about the project targets and changes hoped
for in the targeted population. Based on these changes, the
conceptualizing team should build a set of markers which can
be easily evaluated throughout the duration of the project at
regular intervals in order to keep the project on track. The
Successes in approach and methodology: The Tandur
ANGRAU-ARS engaged in some extension education and
meeting of farmers in villages for the first time during the
rollout of the TL 2 project. This exposure was invaluable for
the fu ture as they develo p bo th a more co mplete
understanding of farmers’ needs as well as the techniques
Fig 2. A flow chart of the proposed seed village system
Thus, regular evaluation and monitoring should take
place among all partnering organizations, with optional outside
assessment as well to ensure that project goals are being met
and that adjustments in targets and approach can be made at
the appropriate times. Tesfaye et al. (2005) suggested that in
assessing the information pathways through which transfer
of innovation occurs, source of introduction, frequency of
visits by extension agents, availability of and membership in
local agricultural institution’s, and presence of local leaders
who will advocate for the innovation can all be used as
indicators of community acceptance. The crucial ingredient
in all these pathways is the presence of an individual or set of
individuals who can spend the time to identify and build local
capacities.
338
needed to transfer innovative technology to farmers. One
Farmers’ Day on the topic of pigeonpea was held at the ARS
in 2009 with more than 2000 farmers participating giving the
community an opportunity for more exposure to new
techniques and seed varieties. The ARS had also distributed
free seeds of 4-5 kgs/pack to 783 farmers, infusing the villages
with new germplasm that would give high yields for the first
season, and might add new genetic material into the local
germplasm base.
Gaps in approach and methodology
1. Improved understanding of seed village concept and farmer
perspectives and needs by project staff is necessary. Staff
should receive training to sensitize them to the situation and
needs of the population they are meant to work with as well as
human research and development skills. Many times,
unfortunately, people are not usually conscious of their
perceptions, beliefs, attitudes, and behaviour and are generally
unaware of ho w these determine and influence their
participation in social and economic activities and the benefit
they derive. This lack of consciousness has important
implications and serious consequences for the outcomes and
impacts of development projects (Ellis 1997).
2. More clearly developed targeting criteria will help
resources flow towards intended beneficiaries and achieve
intended goals. There is currently a lack of a clear rubric for
selecting sites, the amount of time to be spent in farmer visits,
and follow-up methods to ensure that full education has been
given. A rigorous procession system should be developed to
make sure that each beneficiary moves through a preprescribed set of steps to ensure maximization of benefit. Free
seed is currently given to farmers who are referred to as
‘progressive’, defined by ARS staff as those who own large
areas of land and are deemed to be cooperative by the
Department of Agriculture. It is important to remember that
progressiveness encompasses much more than the size of
land area, and also those smallholders, who may just as well
be willing to experiment with a new practice, is less likely to
have contact with the Department of Agriculture as they more
rarely seek out seed from outside their village (Ellis 1997).
3. Lack in needed exclusive focus through spreading of staff
to both jobs and projects. Farmer education in such a large
spread of villages is a specialization that must be given full
time and concentration. Age appropriate non-traditional
educational styles required for positive communication with
farmers require a certain level of expertise in human interaction,
and demand the full attention of the staff person assigned to
them.
4. More precise documentation should be provided at every
step. A complete database with detailed information about
each beneficiary farmer, their cropping patterns, and input
and return costs for the year should be maintained in order to
assess whether the target of poverty reduction in smallholders
is being reached. According to Weinberger and Lumpkin (2007),
poverty alleviation in an agricultural setting is based on the
combination of both market prices and input costs of the crop
cultivated. A fully populated data set will enable TL 2 staff to
accurately express successes in terms of this relationship.
5. Seeds are sometimes given for free with no training for
usage and no follow-up. For a farmer to receive such high
quality seed for free, demands institutional stewardship of
the opportunity they are giving along with the seed. Farmers
must understand completely the value of the seed they are
given and the intended functionality of that seed.
The TL 2 project has made some progress towards its
project goal of improving smallholder farmer access to
improved seed. The project enabled ARS Tandur to organize
Farmers Participatory Varietal selection experiments in farmers’
fields which are unique and first of its kind from this centre.
The training programs conducted, field days organized and
literature in local vernacular language distributed to the farmers
in target areas has benefited the farming community and lead
to progress towards the targeted goals of the TL 2 project in
a great way. While not all smallholders are capable of taking
the types of risk needed to try new seed varieties, and more
effort and time spent in extension education can help at least
some smallholders get access to varieties that have already
been proven to perform well. Smallholders cannot be expected
to be involved in experiments on germplasm or in seed
production because they live much closer to a subsistence
level. In spite of this, the already developed high quality seed
for pigeonpea should be made available to them for immediate
economic relief. Additionally, projects have shown the
economic benefit of improving distribution and marketing
capacities for small farmers in tandem with the provision of
seed that will ensure higher yields (Jones et al. 2002).
Continu ing to focus on increasi ng o pportuni ty for
smallholders through seed system improvement at all levels
will yield more innovative methods for cultivating community
involvement and improving accessibility.
REFERENCES
Dewey KG. 1981. Nutritional consequences of the transformation from
subsistence to commercial agriculture in Tabasco, Mexico. Human
Ecology 9:151-187.
Ellis P. 1997. Gender sensitive participatory impact assessment: Useful
lessons from the Caribbean. Knowledge and Policy: The
International Journal of Knowledge Transfer and Utilization 10:
71-82.
Gilespie S and Kadiyala S. 2011. Exploring the agriculture-nutrition
disconnect in India. IFPRI.
Gopal B and Babu KK. 2010. Agricultural Statistics at a Glance: Andhra
Pradesh. Directorate of Economics and Statistics, Government of
Andhra Pradesh, Hyderabad.
Jones R, Freeman HA and Lo Monaco G. 2002. Improving the access of
small farmers in eastern and southern Africa to global pigeonpea
markets. Agricultural Research & Extension Network, Network
Paper No. 20, January 2002.
Nadarajan N and Chaturvedi SK. 2010. Genetic options for enhancing
productivity of major pulses- retrospect, issues, and strategies.
Journal of Food Legumes 23: 1-8.
Nagarajan L, Audi P, Jones R, and Smale M. 2007. Seed provision and
dryland crops in the semi-arid regions of eastern Kenya. IFPRI
Discussion Paper 00738.
Nagarajan L, Smale M and Glewwe P. 2007. Determinants of Millet
Diversity in the Household-Farm and Village-Community Levels in
the Drylands of India: The Role of Local Seed Systems. Agricultural
Economics 36: 157-167.
Ravinder Reddy Ch, Tonapi VA, Bezkorowajnyj PG, Navi SS and
Seetharama N. 2007. Seed System Innovations in the semi-arid
Tropics of Andhra Pradesh, International Livestock Research
Institute (ILRI), ICRISAT, Patancheru, Andhra Pradesh, India. Pp
339
224.
Singh A and Singh AK. 2009. Yield advantage in pulses at farmers’
fields. Journal of Food Legumes 22: 198-201.
Singh RB, Kumar P and Woodhead T. 2002. Smallholder Farmers in
India: Food Security and Agricultural Policy. FAO Regional Office
for Asia and the Pacific, Bangkok, Thailand.
Swaminathan MS. 2006. Restoring farmers’ faith in farming. Indian
Journal of Pulses 19: 1-6.
Tesfaye A, Jemal I, Ferede S and Curran MM. 2005. Technology
Transfer Pathways and Livelihood Impact Indicators in Central
Ethiopia. Tropical Animal Health and Production 37: 101-122.
Weinberger K and Lumpkin T. 2007. Diversification into Horticulture
and Poverty Reduction: A Research Agenda. World Development
35: 1464-1480.
Dissemination of pulse production technologies in Uttar Pradesh : A micro-level
analysis
RAJESH KUMAR, S.K. SINGH, PURUSHOTTAM and UMA SAH
Division of Social Science,Indian Institute Pulses Research, Kanpur-208024 (U.P.), India; E-mail: rkm_13t2yahoo.com
(Received: December 13, 2011; Accepted: December 11, 2012)
ABSTRACT
A survey was conducted in Lakhimpur Kheri and Bahraich
districts of Uttar Pradesh, India during 2010 on 100 farmers to
ascertain the methods used for dissemination of pulse
production technologies (PPT) by extension worker, mode of
dissemination, adoption of these improved technologies and
constraints perceived in pulse production. About 24% farmers
informed that excursion trip was conducted by extension
workers for dissemination of PPT followed by group discussion
and meeting. Most of these technologies were disseminated
through progressive farmers in both the districts. Study also
indicated that growing of pulses was profitable, environment
friendly and also useful in way of improving the soil health
although adoption of PPT was found low in both districts. The
first and foremost constraint faced by these farmers was nonavailability of quality seed.
Key words:
Constraint analysis, Pulse production technologies,
Technology adoption, Technology dissemination
process
Pulses are the group of food legumes which have the
unique built-in ability of fixing atmospheric nitrogen through
symbiosis and are beneficial for enriching soil fertility. Besides,
they also make a significant contribution to human and animal
nutrition through protein supplements. On account of these
qualities, there will be no exaggeration if these crops may be
referred as “unique jewels” of Indian crop husbandry. In spite
of this importance, their acreage (20.2 to 25.5 m ha), production
(14 o 18 m t) and productivity (632 kg/ha) remained nearly
unchanged during the last several years. On the other hand,
the population has increased tremendously which has resulted
in declining intake of pulses from 75 g/capita/day during 1960
to a mere 26 g/capita/day during 2010.
The distribution of acreage under pulses in different
states/regions is also quite variable. Among 19 divisions of
the state of Uttar Pradesh, Jhansi division contributes
maximum to its production and acreage (>50%) and is followed
by Allahabad and Faizabad divisions. The area, production
and average productivity of pulses in U.P. state are 2.45 m ha,
2.43 m tonnes and 991 kg/ha, respectively. The farmers are
mainly cultivating pigeonpea, urdbean, chickpea, lentil and
pea in UP. However, pulses are cultivated on limited scale in
irrigated areas with less external input application. Various
constraints of pulse production are vagaries of weather,
insufficient irrigation, insufficient use of N and P fertilizers,
availability of rhizobium culture, proper management of pests
and diseases and use of poor quality (both yield and pests
tol erance) seeds. Technolo gy generati on, t echnol ogy
assessment and refinement, technology transfer/dissemination
and technology utilization process also contribute for reducing
the time lags between generation of pulses production
technologies (PPT) and their adoption on large scale by
farming communities. The PPT has been disseminated through
transfer of technology by KVKs, main extension agency
(Department of Agriculture), NGOs and private organization
etc. Thus, to analyze PPT disseminated in Uttar Pradesh, the
current studies was carried out.
The study was conducted in selected two districts
(Lakhimpur Kheri and Bahraich) of Uttar Pradesh, India. From
each district one block was identified; and from each identified
block, 50 farmers were selected using simple random sampling
technique. The list of pulses growers was collected from Village
Pradhan. The data was collected from farmers through
personal interview by pre-tested structured schedule and the
total sample size was 100 farmers. The collected data was
analyzed by using suitable statistical tools for arriving at valid
conclusion. Studies were made with reference to socioeconomic profile of pulse growers, methodology used for
dissemination and mode of dissemination of pulse production
technologies, opinion of the farmers about the pulse
production, adoptions behavior and constraints perceived in
pulses. The variables used in this study included age,
education, family size, type of family, land holding, land
ownership, crops grown, social participation, extension
contact and mass media exposure.
Socio Economic Profile: The surveycarried out in Lakhimpur
Kheri district (Table 1) revealed that 60 per cent farmers
belonged to middle age category while 42 % of them were
educated up to standard 5 (primary school). Two per cent of
them were graduate who were engaged in farming while
majority of them were having medium family size (50%) and
joint family system (70%). Further, it was observed that 64%
farmers had medium size land holding while 18% of them had
leased in their land for expansion of their farming activity and
Kumar et al.: Dissemination of pulse production technologies in Uttar Pradesh : A micro-level analysis
only 4% of them had leased out their land to others to optimize
the agricultural activities. In case of Bahraich, 44% farmers
belonged to middle age category while 52% of them were
educated up to standard 5. A majority of them (60%) had
medium size family and joint family (82%). It was also observed
that 48% farmers belonged to medium land holding category
with cent per cent ownership of land. All the farmers were
cultivating wheat, rice, lentil, pigeonpea, chickpea, urdbean
and mungbean. However, the farmers had low social
participation, extension contacts and mass media exposure
(Table 1).
Methods of Dissemination: It was opined by 10% farmers
that extension workers used the individual approach for
dissemination of improved PPT in Lakhimpur Kheri district,
whereas 16% farmers agreed that extension workers did visit
341
their field and home to provide the knowledge about PPT
(Table 2). Extension workers were not making contact through
individual mode either through letter, telephone or mobile
except in Bahraich where only 4% used these individual
modes. This could be attributed to inadequate representation
of extension workers per village and unavailability of modern
communication links (mobile or telephone facility) with the
farmers themselves. It was expressed by 24% farmers that
excursion trip by the extension workers was the major mode
for dissemination of PPT followed by group discussion and
meeting in Lakhimpur Kheri (Table 2). Similarly, 20 % farmers
in Bahraich district informed that extension workers were either
using group approach or meeting/group training. Participatory
approach was almost negligible in both these districts.
Mode of dissemination: It was indicated that most of
Table 1. Socioeconomic profile of farmers of Lakhimpur Kheri and Bahraich districts
Variables
Category
Age (year)
Young up to 30
Middle (31-49)
Old (50 & above)
Illiterate
Up to class 5
Up to class 10
Up to class 12
Graduation & above
Small (upto 5)
Medium (5-10)
Large (10 & above)
Nuclear
Joint
Small (2)
Medium (3-5)
Large (5 & above)
Owned
Leased in
Leased out
Wheat
Rice
Arhar
Urdbean
Mungbean
Chickpea
Lentil
Field pea
Sugarcane
Maize
Ground nut
Mustard
Low
Medium
High
Low
Medium
High
Low
Medium
High
Education
Family size
Type of family
Land holding (in acres)
Land ownership
Crops grown
Extension contact
Mass media
exposure
Lakhimpur (n = 50)
Frequency
%
12
24
20
40
18
36
11
22
21
42
09
18
05
10
02
04
05
10
25
50
20
40
15
30
35
70
10
20
32
64
08
16
50
100
09
18
02
04
50
100
50
100
05
10
15
30
01
02
05
10
35
70
01
02
13
26
00
0
10
20
18
36
26
52
12
24
12
24
35
70
13
26
02
04
25
50
20
40
05
10
Bahraich (n = 50)
Frequency
%
14
28
22
44
14
28
15
30
26
52
08
16
01
02
04
08
08
16
30
60
12
24
09
18
41
82
08
16
24
48
18
36
50
100
04
08
00
00
50
100
50
100
20
40
07
14
05
10
13
28
50
100
02
04
17
34
30
60
00
0
20
40
20
40
18
36
12
24
30
60
15
30
05
10
30
60
14
28
06
12
Pooled(N=100)
Frequency
%
26
26
42
42
32
32
26
26
47
47
17
17
6
6
6
6
13
13
55
55
32
32
24
24
76
76
18
18
56
56
26
26
100
100
13
13
2
2
100
100
100
100
25
25
22
22
6
6
18
18
85
85
3
3
30
30
30
30
10
10
38
38
46
46
30
30
24
24
65
65
28
28
7
7
55
55
34
34
11
11
342
Table 2. Methods of Dissemination of pulse production
technology
Variables
Lakhimpur (n = 50)
Frequency
%
Individual approach
Farm & home visit
Mobile
Group approach
Meeting
Group discussion
Cooperative society
Group training
Excursion trip
Participatory approach
Passive participation
Active participation
Interactive participation
Bahraich (n = 50)
Frequency
%
05
0
10
0
08
02
16
04
06
10
32
04
12
12
20
64
8
24
10
12
28
04
10
20
24
56
8
20
04
01
01
8
2
1
05
02
01
10
4
2
technologies were disseminated through progressive farmers
in both the districts as revealed by the extension functionaries
and was followed by direct contact and Village Pradhan (Table
3).
Table 3. Mode of dissemination of technologies
Variables
Direct
Village Pradhan
Progressive farmers
Lakhimpur (n = 50)
Frequency
%
16
32.0
8
16.0
32
64.0
Bahraich (n = 50)
Frequency
%
30
60.0
10
20.0
35
70.0
Table 4. Opinion of farmers about pulse production technology
Variables
Lakhimpur (n = 50) Bahraich (n = 50)
Frequency
% Frequency %
Suitability for growing of pulses
50
100
50
100
Profitability in pulse production
30
60
35
70
Environment friendliness
12
24
10
20
Soil health improvement
05
10
05
10
Enterprise possibility
0
0
02
20
Employment generation
0
0
01
2
Table 5. Adoption of improved pulse production technologies
Practices
Seed
Fertilizer
Bold seeded
Small seeded
Improved
seed
Local seed
DAP
Urea
Rhizobium
Trichoderma
Implement
Local
Improved
Processing
Local
Improved
Postharvest Local
technology
Improved
Soil testing
Lakhimpur
Bahraich
(n = 50)
(n = 50)
Frequency Percent Frequency Percent
0
0
0
50
100
50
100
05
10
03
6
45
12
0
0
0
50
0
50
0
50
90
24
0
0
0
100
0
100
0
100
47
08
0
0
1
50
0
50
0
50
94
16
0
0
0
100
0
100
0
100
0
0
0
0
0
0
0
0
Opinion of farmers about PPT: There was consensus on the
fact that pulses were suitable and profitable for farming. It
was also revealed from the study that these were both
environment friendly and were useful in improving soil health
(Table 4).
Adoption of improved PPT: The study indicated that only
10% farmer used improved seed and the rest used local seed
for farming (Table 5). Farmers preferred small seeded lentil in
both districts. In Bahraich, only 6% farmers used improved
seed due to non availability of quality seed in time. Most of
the farmers used DAP in pulses at the time of sowing, while
use of rhizobium and trichoderma was absent. All the farmers
were only using local implements and pest management
schedules. Farmers were not able to test their soil due to lack
of knowledge about laboratory/facility. These findings were
supported by result of Khatiwada (1986).
Table 6. Constraints perceived by the pulse growers
Constraints
Seed management
Lack of quality seed
Lack of knowledge about quality seed
Non availability of seed timely
Lack of quality seed of lentil
Fertilizer and manures
Lack of knowledge about use of
Rhizobium
Lack of knowledge about use of
Trichoderma
Nonavailability of Rhizobium of
different pulse crops
Nonavailability of fertilizer
High cost of fertilizer
No visible effect of fertilizers
No timely & sufficient fertilizer supply
by Govt.bodies
Nutrient Management
Deficiency of iron in soil
Plant protection management
Problem of wilt
Problem of yellow mosaic disease
Problem of pod borer
Non availability of fungicide/
insecticide
Poor effect of fungicides on crop
Weed management
Problem of weed management
Marketing
Bold seeded lentil has no demand in
market
Bold seeded lentil has no preference for
consumption
No knowledge about support price
Forced sale of lentil at lower rates
Other management
Problem of blue – bull
Problem of wild animals
Natural calamity- draught, flood ,
hailstone
Bahraich
Lakhimpur
(n = 50)
(n = 50)
Frequency % Frequency %
42
32
45
30
84
64
90
60
44
36
48
45
88
72
96
90
41
82
40
80
50
100
49
98
50
100
50
100
30
16
12
16
60
32
24
32
32
20
10
20
64
40
10
40
8
8
42
82
50
30
20
22
100
60
40
44
50
30
10
35
100
60
20
44
10
20
9
18
5
10
5
10
8
21
42
16
42
84
45
90
11
22
10
2
20
4
3
4
6
8
5
0
0
10
0
0
50
8
20
100
16
40
Kumar et al.: Dissemination of pulse production technologies in Uttar Pradesh : A micro-level analysis
Constraints analysis: The study revealed that non-availability
of seed followed by lack of quality seed of lentil in Lakhimpur
Kheri and Bahraich were the major constraints for adoption of
PPT (Table 6). In nutrient management, nonavailability of
Rhizobium culture of different pulse crops followed by lack
of knowledge about use of trichoderma and Rhizobium were
the major constraints. Problem of wilt was again the common
most problem for all the farmers of both the districts followed
by yellow mosaic diseases, non availability of pesticides.
Some farmers reported about the problem of weed management
in pulses. These finding were supported by Masood et al.
(2008) and McWilliam and Dillon (1986). Marketing is also
one of the major problems and was associated with less
preference for bold seeded lentil. There were also some
common problems including the blue bull menace in both the
districts and problem of wild animals in Bahraich.
It could be inferred from the above that progressive
farmers played the key role in dissemination of pulse
343
production technologies. Adoption of improved pulse
technologies was poor due to non-availability of quality seed
in time. There is also need to provide the inputs/trainings in
time about successful management of constraints in adoption
of these improved technologies.
REFERENCES
Masood Ali, Singh SK and Singh BB. 2008. Half Yearly Report of
ISOPOM Project Development and Popularization of Model Seed
System for Quality Seed Production of Major Legumes to ensure
Seed Sufficiency at Village Level, Indian Institute of Pulses Research,
Kanpur, U. P.
Khatiwada MK 1986. The production of food legumes in the Himalayan
range.In: Proceedings of the workshop on Food Legume
Improvement for Asian farming system, Khon Khaen, Thaialand.
McWilliam JR and Dillon JL. 1986. Improvement of food legumes:
progress and constraints. In: Proceedings of the workshop on Food
Legume Improvement for Asian farming systems, Khon Khaen,
Thaialand.
Pigeonpea (Cajanus cajan L.) price movement across major markets of India
D.J. CHAUDHARI and A.S. TINGRE
Department of Agricultural Economics and Statistics, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola- 444 104,
India; E-mail: datta1616@rediffmail.com
(Received: March 03, 2012;Accepted: November 03, 2012)
ABSTRACT
Pigeonpea is one of the major pulse crops grown across India.
The last decade witnessed large fluctuations in prices of
pigeonpea. The present study aimed to study price movement
of pigeonpea, i.e., seasonal variation, price volatility and cointegration among the major pigeonpea markets in India. Data
related to monthly average prices of pigeonpea were collected
from major markets for the period 2003-2011across different
States, viz., Akola and Latur (Maharashtra), Alwar (Rajasthan),
Sedam (Karnataka) and Thandur (Andhra Pradesh). Moving
average method was used to study seasonal variation. The
econometric tools like ADF test, Johansen’s multiple cointegration test, Granger causality test and ARCH-GARCH
model were used to analyse integration of markets across
locations. The results of the study showed that the prices of
pigeonpea were higher in the months from June to August in
all selected markets. All the price series were stationary at
first difference. The selected markets showed long run
equilibrium relationship and co-integration between them.
Most of the markets showed bidirectional influence on
pigeonpea prices of each other. Alwar, Sedam and Latur markets
were influenced by their own lag, while Akola, Latur and
Thandur markets showed high price volatility.
Key words:
ADF test, ARCH- GARCH, Granger causality test,
Co-integration, Pigeonpea, Price movement, Seasonal
variation
Pigeonpea (Cajanus cajan L.) is an important pulse crop
of India and is grown in the tropical and subtropical regions
of the world. Being rich in protein and relatively cheaper on
price, a large section of vegetarian population of the country
consumes it as ‘Dal’ in cooked form. The price of pigeonpea
showed instability and fluctuations in the last decade.
Pigeonpea displayed price volatility depending on monsoon,
production in various regions of the country, import and the
stock available with the traders. The prices of pigeonpea
showed seasonal variations. Most of the pigeonpea markets
in India are co-integrated that affected on the prices of each
other. The instability in prices of agricultural commodities like,
pigeonpea was influenced by a number of factors such as
variation in production, low price elasticity of demand and
seasonality in terms of price level, volatility and co-integration
among the prices of different markets etc. These were the
most important factors in determining competitiveness of the
commodity and to formulate long term trade strategy. Thus,
analyzing the past trend in the price of commodities was also
useful in understanding the present scenario and to formulate
appropriate strategies to improve the existing marketing
system. Therefore, the present study was undertaken with
the objectives of studying both seasonal variations in prices
of pigeonpea and to assess the price volatility and cointegration among the major pigeonpea markets in India.
The study was conducted in major pigeonpea markets
fro m di fferent states sel ected vi z., Akola and Lat ur
(Maharashtra), Alwar (Rajastan), Sedam (Karnataka) and
Thandur (Andhra Pradesh). As per the records available the
time series data on monthly average prices of pigeonpea for
the period from 2003 to 2011 were collected from Agricultural
Produce Market Committee of respective market. The method
of moving average is the most widely used method of
measuring seasonal fluctuations and the seasonal indices
were obtained from the following steps:
1.
Twelve month centered moving average value for
arrivals and prices for the markets were obtained.
2.
The original value as a percentage of centered moving
average values for all months were expressed except for
first six month and six month at the end.
3.
These percentages were arranged according to the years
and month. Primarily seasonal indices were obtained
on eliminating the irregular component by averaging
these percentages for each month. The average was
taken over different year.
The econometric tools like ADF test, Johansen’s
multiple co-integration test, Granger Causality Test and
ARCH-GARCH model were used to analyze integration of
markets across locations. In addition, various parameters were
estimated by using E-views-7 software.
Augmented Dickey-Fuller test (ADF): Before analyzing any
time series data testing for stationarity is pre-requisite. At
first, the test for stationarity of time series data on pigeonpea
prices was conducted. An Augmented Dickey-Fuller test (ADF)
is the test for the unit root in a time series sample. A stationary
series is one whose parameters are independent of time,
exhibi ting constant mean and variance and havi ng
autocorrelations that are invariant through time. If the series
is found to be non-stationary, the first differences of the series
are tested for stationarity. The number of times (d) a series is
Chaudhari & Tingre: Pigeonpea (Cajanus cajan L.) price movement across major markets of India
differenced to make it stationary is referred to as the order of
integration i.e., I(d).
ADF unit root test are based on the following three
regression forms:
Without constant and trend, Yt = Yt-1 + ut
With constant,
Yt =  + Yt-1 + ut
With constant and trend, Yt =  + T + Yt-1 + ut
The hypothesis is : H0:  = 0 (unit root)
H0:  0
t* > ADF critical value, then accept the null hypothesis,
i.e. unit root exists.
t* < ADF critical value, then reject the null hypothesis,
i.e. unit root does not exists.
Johansen’s multiple co-integration test: Johansen’s multiple
co-integration test is employed to determine the long run
relationship between the price series. The test shows whether
the selected pigeonpea markets are integrated or not. Johansen
(1988) has developed a multivariate system of equations
approach, which allows for simultaneous adjustment of both
or even more than two variables. The multivariate system of
equations approach is more efficient than single equation
approach as it allows estimating the co-integration vector with
smaller variance. The second advantage of the multivariate
approach is that in the simultaneous estimation it is not
necessary to presuppose erogeneity of either of the variables.
Granger causality test: In order to know the direction of
causation between the markets Granger Causality test was
employed. When a co-integration relationship is present for
two variables, a Granger Causality Test (Granger 1969) can be
used to analysis the direction of this co-movement relationship.
Granger causality tests come in pairs, testing weather variable
xt Granger-causes variable yt and vice versa. All permutations
are possible viz., univariate Granger causality from xt to yt or
from yt to xt , bivariate causality or absence of causality. The
Granger causality test analyses weather the unrestricted
equation,
yt =  0 + Ti = 1  1i yt-i + Tj = 1  2i xt-j +  t with 0  i , j  T
Yield better results than the restricted equation.
yt = 0 + Ti =1  1i yt-i +  t with Tj = 1 2j xt-j = 0 (The null
hypothesis)
i.e. if H0, in which  21=  22= ……=  2T = 0, is rejected then
one can state “variable xt Granger causes variable yt ”
ARCH-GARCH model: To access the presence of price
vo lati lity, ARCH-GARCH analysi s was carried ou t.
Autoregressive Conditional Heteroscedastcity (ARCH)
models are specifically designed to model and forecast
conditional variances. ARCH model was introduced by Engel
(1982) and generalized as GARCH by Bollersllev (1986). The
ARCH model have two distinct specifications, one for the
345
conditional vari ance and the standerd GARCH (1 ,1)
specification is presented as under:
Yt = 0 + 1 X1t +……..+ k Xkt + e
2
2
t-1
2
t-1
 t =  +  e + 
…………………1
………………..2
Equation (1) is the mean equation and equation (2) is
the conditional variance equation. The ARCH component ()
indicate the lag of the squared residual from the mean equation
and the GARCH term () the last period’s forecast variance
and the resultant sum of these co-efficient ( + ) are
presented. The sum of co-efficient very close to 1 indicates
that the volatility shocks are quite persistent in the series.
Seasonal variation: In major producing areas, the market
arrivals of pigeonpea started in the month of December and
lasted for four months. During the peak arrivals period, the
prices are generally low. The prices were also recorded higher
from June to August. Most of the traders released the stored
stock of pigeonpea during this period in anticipation of making
the profit. The seasonal indices of monthly average prices of
pigeonpea in different markets of India were worked out to
study seasonal variation (Table 1). It depicted that the prices
of pigeonpea were higher in the months from June to August
in Akola and Latur markets of Maharashtra and Alwar market
of Rajasthan while from July to September in the markets of
Southern India viz., Sedam (Karnataka) and Thandur (Andhra
Pradesh). The prices were highest by 7.52, 6.18, 6.26 and 7.19%
in the month of July in Akola, Latur, Alwar and Thandur market,
respectively and by 10.11 per cent in Sedam market. It was
observed that the prices were lowered by 5.2 and 3.6% in
December in Akola and Alwar market, by 5.25 and 6.54% in
January in Latur and Thandur market and by 7.13% in March
in Sedam market. Thus, the largest arrivals in these months
lowered down the prices of pigeonpea. Chaudhari and Pawar
(2010) found that the price indices were higher in the month of
July in the Osmanabad and Paranda markets.
Table 1. Seasonal indices of monthly average prices of
pigeonpea in different Indian markets
Month
January
February
March
April
May
June
July
August
September
October
November
December
Akola
(MS)
96.21
99.24
97.87
102.39
98.62
101.66
107.52
103.95
99.83
99.68
98.25
94.80
Latur
(MS)
94.75
96.44
98.87
100.05
96.62
103.18
106.18
103.49
101.84
101.68
97.60
99.29
Sedam
(KA)
97.31
95.31
92.87
94.52
94.49
98.59
105.54
106.38
106.07
110.11
100.98
97.83
Alwar
(RJ)
97.77
96.86
102.68
99.46
99.79
100.94
106.26
101.18
99.00
99.03
100.63
96.40
Thandur
(AP)
93.46
96.73
98.32
98.86
96.88
97.35
107.19
102.67
102.49
103.70
99.42
102.92
346
Aug ment ed Di ckey-Full er t est (ADF): The test for
stationarity of time series data on pigeonpea prices was
conducted. The results of ADF test (Table 2) indicated that
prices in level with lag 1, the ADF values were less than the
critical value at 1% level indicating the existence of unit root
implied non stationary nature of price series in all markets. In
first difference with lag 1, the ADF values were higher than
critical values at 1% level indicated that the price series are
free from the consequences of unit root. This implied that the
price series were stationary at first difference level. Ghosh
(2011) found the prices of rice and wheat were non-stationary
in levels but stationary in first-differences which implied that
all the series of rice and wheat prices contain a single unit root
and were integrated of order one, I (1) for both the periods.
Similarly Reddy and Reddy (2007) for groundnut and Reddy
(2007) for rice also concluded that prices were stationary at
first difference level.
Table 2. ADF test results of pigeonpea prices
Name of Market
Akola
Alwar
Latur
Sedam
Thandur
Level
First difference
-2.173171
-9.324023
-1.570010
-13.39667
-2.122809
-9.005828
-3.098783
-9.572111
-2.641566
-10.46072
Table 4. Results of pair wise Granger casuality test
-4.057528
Table 3. Results of multiple co-integration analysis
0.47
0.35
0.21
0.06
0.02
Trace
Critical
statistics value (5%)
132.9
71.8
30.6
8.4
2.4
88.8
63.8
42.9
25.8
12.5
Price volatility: To assess the presence of price fluctuations
in the prices of pigeonpea in Akola, Alwar, Latur, Sedam and
Thandur market, ARCH-GARCH analysis was carried out
(Table 5). Among the markets, the sum of alpha and beta were
near to 1, viz., 0.97, 1.46 and 0.92 as for Akola, Latur and
Thandur market respectively, which indicated the presence of
price fluctuations in the pigeonpea prices in selected markets
during the study period. Similar results were found by Sekhar
(2003) for rice and wheat, and by Lavanya (2011) for turmeric.
Critical value (1%)
Johansen’s multiple co-integration test: To determine the
long run relationship between the price series Johansen’s
multiple co-integration test was employed to all the markets.
The test showed whether the selected pigeonpea markets were
integrated or not.
Eigen
value
showed that there was a bidirectional influence on prices of
Akola and Alwar, Akola and Thandur, Alwar and Latur,
Thandur and Alwar and Sedam and Alwar market. The prices
at Akola market also influenced Latur and sedam market.
Thandur and Sedam market price were also affected by Latur
market prices. It was observed that Sedam market price
influenced Thandur market. Similar results were obtained by
Ajjan et al. (2009) for Red chilli in Tamil Nadu Moe et al.
(2008) for the pigeonpea and green gram in Myanmar.
Hypothesized
Number of
co-integration
equation
None*
At most 1*
At most 2
At most 3
At most 4
Number of
co-integration
equation
Two
The results showed that at least two co-integration
equations were significant at 5% level of significance (Table
3). Thus, the selected pigeonpea markets were having long
run equilibrium relationship and there existed a co-integration
between them. Mukim et al. (2009) found that the whole sale
prices of wheat were co-integrated in the long run. Similar
results were recorded by Gandhi and Koshy (2006) for wheat
and by Ghosh (2011) for rice and wheat markets in India.
Granger causality tests: Granger Causality test was employed
to know the direction of causation between the markets.
Theoretically, a variable is said to Granger-cause another
variable, if the current value is conditional on the past value.
The results of Pairewise Granger Causality test (Table 4)
Null hypothesis
Observed F-statistic Probability
LTR does not Granger Cause AKL
96
0.10222
0.9029
AKL does not Granger Cause LTR
19.7226
8.E-08
TND does not Granger Cause AKL
96
2.84679
0.0632
AKL does not Granger Cause TND
25.6474
1.E-09
SDM does not Granger Cause AKL
96
0.00102
0.9990
AKL does not Granger Cause SDM
3.21939
0.0446
ALR does not Granger Cause AKL
96
5.89082
0.0039
AKL does not Granger Cause ALR
12.7987
1.E-05
TND does not Granger Cause LTR
96
0.73444
0.4826
LTR does not Granger Cause TND
12.8264
1.E-05
SDM does not Granger Cause LTR
96
0.80564
0.4500
LTR does not Granger Cause SDM
2.84841
0.0631
ALR does not Granger Cause LTR
96
11.7768
3.E-05
LTR does not Granger Cause ALR
18.7670
1.E-07
SDM does not Granger Cause TND
96
4.43699
0.0145
TND does not Granger Cause SDM
2.06703
0.1325
ALR does not Granger Cause TND
96
11.2190
4.E-05
TND does not Granger Cause ALR
20.6234
4.E-08
ALR does not Granger Cause SDM
96
5.76220
0.0044
SDM does not Granger Cause ALR
3.51057
0.0340
Note : Sample: 2003M04 2011M05 Series ; AKL, LTR, ALR, SDM and
TND where, AKL : Akola market, LTR : Latur market, ALR: Alwar
market, SDM: Sedam, TND: Thandur
Table 5. Results of ARCH-GARCH analysis
Parameter
Alpha (α)
Beta (β)
Sum of α & β
Akola
Alwar
Latur
Sedam
0.982153 0.970484 0.979363 0.895793
-0.010690 -1.002528 0.487176 -0.129860
0.971463 -0.032044 1.466539 0.765933
Thandur
0.970944
-0.049272
0.921672
Policy implication: The study examined the price movement
of pigeonpea across the main markets in major pigeonpea
producing states of India. The prices of pigeonpea showed
seasonal fluctuations and were recorded higher in the months
from June to August in all selected markets. The results of
ADF test showed that all the markets having the ADF values
Chaudhari & Tingre: Pigeonpea (Cajanus cajan L.) price movement across major markets of India
higher than the critical values at 1% level, and thus, the price
series were stationary at first difference level. The analysis of
multiple co-integration depicted that the selected markets
having long run equilibrium relationship and their existed cointegration between them. Most of the markets showed the
bidirectional influence on prices of each other. As the sum of
Alpha and Beta worked out nearer to 1 for Akola, Latur and
Thandur market, this indicated high price volatility in
pigeonpea prices in these markets. The results for other
legume crops like groundnut (Reddy and Reddy, 2011) also
showed that the prices are co-integrated in the long run,
however in short run adjustment to market disequilibrium took
longer time.
It is inferred from the study that it is important to invest
in modernization of commodity markets, development of road
connectivity and marketing related infrastructure like,
warehouses etc. so as to adjust the demand and supply gap
across regions within a short period of time.
347
Agriculture Update 5: 158- 162.
Engle RF and Granger CWJ. 1987. Co-integration and Error Correction:
Representation, Estimation and Testing. Econometrica 55: 251276.
Gandhi Vasant P and Abraham Koshy 2006. Wheat Marketing and its
Efficiency in India. Working Paper No. 2006-09-03 (Indian Institute
of Management).
Gosh Madhusudan 2011. Agricultural policy reforms and spatial
integration of food grain markets in India. Journal of Economic
Development 36: 15-36.
Lavanya M. 2011. Price behaviour and marketing practices of turmeric
in erode district of Tamilnadu. M.Sc. Thesis, TNAU, Coimbatore,
India.
Megha Mukim, Singh Karan and Kanakaraj A. 2009. Market integration,
Transaction costs and the Indian wheat market: A systematic study.
Economic & Political Weekly XLIV: 149-155.
Moe AK, Yutaka T, Fukuda S and Kai S. 2008. Impact of market
liberalization on international pulses trade of Myanmar and India.
Journal of the Faculty of Agriculture, Kyushu University 53: 553561.
REFERENCES
Redddy AA. 2007. Commodity Market Integration: Case of Asian Rice
Markets. The IUP Journal of Applied Economics, VI: 21-44.
Ajjan N, Shivkumar KM, Murugananthi D. and Padmavathi P. 2009.
Red Chillies. CARDS Commodity Series - 2 (TNAU, Coimbatore).
Reddy AA and Reddy GP. 2011. Integration of Wholesale Prices of
Groundnut Complex. Indian Journal of Agricultural Marketing 25:
89-108.
Bollerslev T. 1986. Generalized Autoregressive Conditional
Heteroscedasticity. Journal of Econometrics 31:307–27.
Chaudhari DJ and Pawar ND. 2010. Growth, instability and price analysis
of pigeon pea (Cajanus Cajan Lin.) in Marathwada region.
Sekhar CSC. 2003. Volatility of agricultural prices–an analysis of
major international and domestic markets. Working Paper No. 103
(Indian Council for Research on International Economic Relations).
Short Communication
Genetic variability, character association and path coefficient analyses in faba bean
B.K. CHAUBEY, C.B. YADAV, K. KUMAR and R.K. SRIVASTAVA
Department of Genetics & Plant Breeding, N. D. University of Agriculture and Technology Kumarganj, Faizabad224 229 (U.P.) India; E-mail: ramakantsri@rediffmail.com
(Received: March 31, 2012; Accepted: November 6, 2012)
ABSTRACT
Present investigation was carried out involving seventy diverse
germplasm lines of Faba bean. Seed yield/plant showed highly
significant and positive correlation with number of pods/plant,
biological yield/plant, number of branches/plant, number of
seeds/pod, 100-seed weight and harvest index. Path analysis
identified biological yield/plant, harvest index, number of pods/
plant, 100-seed weight, number of seeds/pod and plant height
as important components having high order direct effects, while
number of pods/plant, number of branches/plant, plant height
and number of seeds/pod via biological yield/plant showed
maximum indirect effect on seed yield.
Key words:
Character association, Faba bean, Genetic variability,
Path coefficient analysis.
Faba bean (Vicia faba L.) is an important pulse crop of
the world cultivated under both irrigated and rainfed
conditions. It is grown as Rabi crop in diverse agro-ecological
si tuat ions fro m hi lls to plai ns and even under po or
management. In India, it is grown in a sizeable acreage in
Bihar, Madhya Pradesh and some parts of Uttar Pradesh. Its
green pods are used as vegetable and dry seeds are used as
split dal and in the preparation of besan. It has great
production potential which has not been realized so far.
Accordingly, the present study was carried out to evaluate
the available germplasms to work out the character association
and, direct and indirect effects of different attributes with
respect to yield.
The experimental material for the present investigation
consisted of 70 germplasm lines of Faba bean along with
check varieties e.g.,’PRT-7’, ‘PRT-12’ and ‘Vikrant’, grown in
Augmented Block Design with three checks repeated after
every 10 lines of the test entries. The experiment was carried
out at the Student’s Instructional Farm, Narendra Dev
University of Agriculture and Technology (NDUAT), Faizabad
(U.P.). Each accession was grown in double row of 4 m length
with inter row spacing of 30 cm. All the recommended cultural
practices were adopted to raise a good crop. The observations
on plant height, number of branches/plant, number of pods/
plant, number of seeds/pod, 100-seed weight (g), biological
yield/plant (g), seed yield/plant (g), harvest index (%), and
protein content, were recorded on five randomly selected
plants, while days to 50% flowering and days to maturity on
plot basis. The mean data were subjected to analysis of
variance following Federer (1956). Estimation of correlation
coefficient was done following Searle (1961). Path coefficient
analysis was done as suggested by Dewey and Lu (1959).
Analysis of variance exhibited significant differences
for all the accessions indicating presence of sufficient genetic
variability among the accessions. Correlation analysis (Table
1) revealed that seed yield/plant was significantly and
positively correlated with number of pods/ plant, biological
yield/plant, number of branches/plant, number of seeds/pod,
100-seed weight and harvest index indicating that selection
based on these characters may result in higher yield, which
was in close agreement with earlier findings of Vandana and
Dubey (1993), Abo-Elwafa and Bakheit (1999) and Patel and
Acharya (2011). Biological yield/plant was highly significant
and positively correlated with number of pods/plant, number
of branches/plant, plant height and number of seeds/pod.
Interestingly, there were significant correlations existing among
the above characters as well as seed yield/plant which,
suggested that these characters may be considered for
improvement of seed yield. Furt her, based o n these
relationships, it can be presumed that for improving yield in
faba bean, a model plant type would be that with high biological
yield, higher number of pods/plant, increased number of
branches/plant and higher seeds/per pod. Harvest index also
showed positive correlation with seed yield/plant while
negatively correlated with plant height, biological yield/plant
and days to 50% flowering. Association of these characters
with grain yield/plant elucidates the importance of proper
source to sink relationship. It would be rational to expect that
a genotype which has smaller vegetative period as in present
case i.e., early flowering will have greater ability to give more
yield than a genotype with delayed flowering. Similarly a
genotype which has more number of branches, more number
of pods, more number of seeds, higher 100-seed weight and
high biological yield is expected to fill the sink to larger extent
(Pace et al., 1979; Huang, 1983 and Ramgiry and Bansal 1997).
Path analyses showed highest positive direct effects
on the grain yield through biological yield / plant followed by
harvest index and number of pods per plant (Table 2).
Considering both correlation and path coefficient, it is crystal
clear that these characters are the most important for realizing
maximum genetic gain through selection in faba bean.
Chaubey et al.: Genetic variability, character association and path coefficient analyses in faba bean
349
Table 1. Estimates of simple correlation coefficients between different pairs of characters in faba bean
Characters
Days to
50%
flowering
Days to
maturity
Plant
height
Branches/
plant
Pods/
plant
Seeds/
pod
100seed
weight
Biological
yield/
plant
Seed
yield/
plant
Harvest
index
Protein
content
Days to 50%
flowering
(no.)
1.00
-0.046
0.068
-0.167
-0.130
0.062
0.007
-0.065
-0.066
-0.467**
-0.090
1.00
0.217
-0.071
-0.096
0.004
-0.043
0.110
-0.030
-0.210
0.355**
1.00
0.150
0.167
0.120
-0.063
0.442**
0.192
-0.406**
0.066
1.00
0.655**
0.253*
0.226
0.607**
0.633**
-0.026
-0.104
1.00
0.369**
0.208
0.663**
0.855**
0.141
-0.088
1.00
0.041
0.329**
0.429**
0.059
0.035
1.00
0.170
0.405**
0.286*
0.085
1.00
0.700**
-0.441**
0.081
1.00
0.281**
-0.017
1.00
-0.117
Days to
maturity
(no.)
Plant height
(cm)
Branches/
plant (no.)
Pods/plant
(no.)
Seeds/pod
(no.)
100-seed
weight (g)
Biological
yield/ plant
(g)
Seed yield/
plant (g)
Harvest
index (%)
Protein
content (%)
1.00
*, **: Significant at P=0.05 & 0.01, respectively
Table 2. Direct and indirect effects of different characters on seed yield/plant in faba bean
Characters
Days to
50%
flowering
Days to
maturity
Plant
height
Branches/
plant
Pods/plant
Seeds/pod
100seed
weight
Biological
yield/
plant
Harvest
index
Protein
content
Correlation
with seed
yield
Days to
50%
flowering
0.0378
-0.0015
0.0032
-0.0029
-0.0322
0.0031
0.0004
-0.0471
-0.0268
0.0003
-0.0655
Days to
maturity
-0.0017
0.0314
0.0103
-0.0012
-0.0238
-0.0002
-.0028
0.0800
-0.1202
-0.0013
-0.0295
Plant height
0.0026
0.0068
0.0477
0.0026
0.0414
0.0059
-.0041
0.3219
-0.2329
-0.0002
0.1917
Branches/
plant
-0.0063
-0.0022
0.0071
0.0173
0.1626
0.0125
0.0145
0.4421
-0.0149
0.0004
0.6331
Pods/plant
-0.0049
-0.0030
0.0080
0.0113
0.2483
0.0182
0.0133
0.4828
0.0806
0.0003
0.8551
Seeds/pod
0.0023
-0.0001
0.0057
0.0044
0.0915
0.0495
0.0026
0.2394
0.0338
-0.0001
0.4290
100-seed
weight
0.0003
-0.0013
-.0030
0.0039
0.0516
0.0020
0.0642
0.1238
0.1640
-0.0003
0.4051
Biological
yield/ plant
-0.0024
0.0035
0.0211
0.0105
0.1646
0.0163
0.0109
0.7284
-0.2526
-0.0003
0.6999
Harvest
index
-0.0018
-0.0066
-.0194
-0.0005
0.0349
0.0029
0.0184
-0.3210
0.5732
0.0004
0.2807
Protein
content
-0.0034
0.0112
0.0031
-0.0018
-0.0217
0.0017
0.0055
0.0593
-0.0670
-0.0037
-0.0169
Residual effect= 0.0529, Direct effect: diagonal (bold)
350
Simultaneously, low value of direct effects recorded in positive
direction for 100-seed weight, number of seeds/pod, plant
height, days to 50% flowering, days to maturity and number
of branches/plant indicating that direct contribution of these
traits might lead to increase in grain yield if other variables
remain constant, which was in conformity with earlier findings
of Salem (1982) and Bora et al. (1998). The indirect contribution
of biological yield/plant via number of pods/plant, 100-seed
weight via harvest index and number of branches/plant via
number of pods/plant also showed high order positive effect
indicating that these characters are important contributors of
grain yield. Biological yield/plant and plant height via harvest
index had considerable negative indirect effect on seed yield.
Some of earlier reports have also identified these characters
as important indirect contributors in the expression of seed
yield in faba bean (Reddy et al. 2002). Thus, harvest index
and number of seeds/pod emerged as most important traits to
be considered in the development of high yielding genotypes
of faba bean.
518.
Federer WT. 1956. Augmented design, “Hawain Planters” Record. 55:
191-208.
Habetinek J, Ruzickova M and Soucek J.1982. Variability and
correlations in some quantitative characters in a collection of broad
bean varieties (Faba vulgaris Moench). Sbornik Vysoke, Skolly
Zemadelske V Praze, A. 36:79-92.
Huang WT, Li FQ, Jiang XY and Li HY.1983. Correlation and path
coefficient analyses of characters in Vicia faba. Hereditas 5:21-23.
Pace C-de and Bond DA, Scarascia-Mugnozza GT and Poulsen MH.
1979. Chracteristics with significant correlations to seed yield and
path analysis in broad bean populations grown in southern Italy. In:
Proceedings of seminar in the EEC programme of coordination of
Research on Plant Proteins, held at Bari, Italy. Pp 144-167.
Patel JB and Acharya S. 2011. Genetic divergence and character
association in Indo-African derivatives of pigeonpea [Cajanus cajan
(L.) Millsp.]. Journal of Food Legumes 24:198-201.
Ramgiry SR and Bansal YK. 1997. Correlation and path coefficient
studies for yield and nodule characters in broad bean (Vicia faba L.).
Advances in Plant Sciences 10:207-211.
REFERENCES
Reddy SRR, Gupta SN, Verma PK and Rai L. 2002. Genetic variation,
correlation and path coefficient analysis under normal sown
conditions in Vicia faba L. Forage Research 28: 63-66.
Abo-Elwafa A A and Bakheit BR.1999. Performance, correlation and
path coefficient analysis in Faba bean. Asian Journal of Agricultural
Sciences 30: 77-92.
Salem SA. 1982. Variation and correlations among agronomic characters
in a collection of beans (Vicia faba L.). Journal of Agricultural
Sciences 99: 541-545.
Bora GC, Gupta SN, Tomar YS and Singh S. 1998. Genetic variability,
correlation and path analysis in Faba bean (Vicia faba). Indian
Journal of Agricultural Sciences 48:212-214.
Searle SR. 1961. Phenotypic, genotypic and environmental correlation.
Biometrics 17:474-480.
Dewey DR and Lu KH. 1959. Correlation and path coefficient analysis
of crested wheat grass seed production. Agronomy Journal 51: 515-
Vandana and Dubey DK. 1993. Path analysis in Faba bean. FABIS
Newsletter 32:23-24.
Short Communication
A comparative study of hybrid and inbred cultivars for germination and other related
traits of pigeonpea
M. BHARATHI and K.B. SAXENA
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru - 502 324(Andhra Pradesh),
India; E-mail: bharati_bth@yahoo.com
(Received: August 10, 2012; Accepted: November 27, 2012)
ABSTRACT
Eleven pigeonpea hybrids and 4 inbred cultivars were evaluated
to study seed germination and seedling growth parameters
using ‘Between Paper Towel’ (BPT) method. The test was
conducted over a period of 10 days at room temperature in a
seed germinator. Data were rec orded on test we ight,
germination, root length, shoot length, shoot to root ratio,
seedling dry weight and seedling vigour index. Significant
differences were observed among entries for all the traits
studied. Significantly greater values for germination (99.5%),
root length (11.6 cm), seedling vigour index (1832), seedling
dry weight (4.7 g) and test weight (11.4 g) were recorded for
hybrids compared to inbred lines. The presence of longer root
in the hybrids resulted in producing lengthier roots and greater
tolerance to drought during early growth stages. Thus, greater
seedling vigour of the hybrids would also contribute to their
higher plant vigour and seed yields.
Key words:
Germination, Hybrids, Inbred cultivars, Pigeonpea,
Seedling vigour index
Pigeonpea[Cajanus cajan (L.) Millsp.] is an important
pulse crop predominantly grown in the tropics and subtropics
for their high protein grains. It has been observed that despite
increase in its area and production, the national mean yield of
pigeonpea (774 kg/ha) has remained consistently low over
the last five decades. To break this yield barrier, a hybrid
breeding technology based on cytoplasmic nuclear malesterility (Saxena et al. 2005) and partial natural out-crossing
systems was developed (Saxena et al. 2009) at ICRISAT. In
comparison to other grain legumes, the growth rate of
pigeonpea seedling is slow (Brakke and Gardner 1987) and
this condition normally persists for about 30-40 days from
sowing and, thereby, makes pigeonpea less competitive to
weeds and cereal inter-crops. The cultivation of high yielding
pigeonpea hybrids could contribute in overcoming the
productivity constraints to some extent due to their better
germination, establishment of uniform plant stand, faster
seedling growth and a high yield potential. Therefore, the
present study was undertaken to compare germination and
related traits in pigeonpea hybrid and inbred cultivars.
Eleven pigeonpea hybrids and 4 inbred cultivars were
identified for this study. The experiment was conducted in 3
replications in a randomized complete block design (RCBD)
with a sample size of 100 seeds. The germination test was
conducted as per International Seed Testing Association
(ISTA) rules using ‘Between Paper Towel’ (BPT) method (ISTA
200 7).
The
seeds
were
pre-t reat ed
with
tetramethylthiuramdisulphide @ 2.5 g/kg and placed in
between layers of wet germination papers. The rolled paper
towels were placed in a tray with 1-2 cm of distilled water for
maintaining sufficient moisture. These trays were kept inside
the Percival Scientific Seed Germinator (Model I-35-LLVL) for
10 days.
Data on test weight (g) was recorded as per ISTA (2007).
The data on germination (%), root length (cm) and shoot
length (cm) were recorded on 10th day after incubation.
Subsequently, the seedlings were dried inside craft paper bags
at 60±2°C temperature for 48 h and the average dry weight
(mg) of seedlings per sample was calculated. Seedling vigour
indices were estimated using the following formula (AbdulBaki and Anderson 1973):
I.
Seedling Vigour Index = Germination % × (Total seedling
length)
Where, Total seedling length = Root length + Shoot
length
II.
Seedling Vigour Index = Germination % × Seedling dry
weight
Statistical analyses of the data were performed using
SAS version 9.2, Anon (2008).
The analysis of variance revealed highly significant
differences among the entries for all the characters studied.
The partitioning of t reatment mean squ ares revealed
significant differences among hybrids for germination per cent,
seedling dry weight, shoot length, root length, shoot: root
ratio, and test weight (Table 1). Among inbred cultivars,
significant differences were observed for germination per cent,
seedling dry weight, shoot length, and test weight. On
average, the hybrids exhibited significantly higher germination
(99.5±0.26%) over inbred cultivars (96.8±0.26%). Mercer et al.
(2006) also reported that hybrids exhibited increased seed
germination and decreased dormancy over their parents in
352
Table 1. Statistical significance of treatments on various physiological parameters
Source
df
Germination
%
Root length
(cm)
Shoot length
(cm)
Shoot: root
ratio
Seedling dry
weight
(mg)
1.38**
0.74**
0.18**
11.40**
Test weight
(g)
2.60**
1.91*
1.67
11.71**
Seedling
vigour index
(I)
80054**
53912
31074
488419**
Treatments
Hybrids
Inbred cultivars
Hybrid vs. Inbred
cultivars
Error
14
10
3
1
0.069**
0.006
0.074*
0.684**
153.22**
85.05**
6.30
1277.24**
123.77**
143.90**
71.10**
70.86**
30
0.02
13.77
10.64
0.93
25477
0.03
0.17
5.68**
3.04**
2.21**
42.47**
*, **: significant at P = 0.01 and 0.05, respectively
Table 2. Variation for different traits observed among hybrids and inbred cultivars ten days after germination
Genotype
Hybrids
ICPH 4438
ICPH 4430
ICPH 2441
ICPH 2363
ICPH 3310
ICPH 2671
ICPH 4022
ICPH 4013
ICPH 2740
ICPH 3762
ICPH 2364
Mean
Inbred cultivars
ICPL 88039
Asha
UPAS 120
Maruti
Mean
CV (%)
SEm (+)
LSD (5%)
Germination
(%)
Root length
(cm)
Shoot length
(cm)
Shoot: root
length
Seedling vigour
index
Seedling dry
weight (g)
Test weight
(g)
99.3
99.0
100.0
100.0
99.7
100.0
99.7
98.7
99.3
99.3
100.0
99.5
12.38
12.04
12.34
11.61
12.31
11.64
11.07
11.85
10.39
11.41
10.16
11.56
8.62
8.47
6.59
7.06
6.36
5.46
7.44
6.23
6.45
6.14
6.12
6.81
0.73
0.81
0.58
0.65
0.54
0.49
0.71
0.61
0.67
0.89
0.65
0.67
2072
2006
1895
1866
1855
1840
1838
1758
1728
1663
1629
1832
5.6
5.2
4.1
4.5
4.5
5.2
4.0
4.8
4.1
4.8
4.6
4.7
11.0
12.5
12.5
11.5
11.5
10.0
10.0
11.5
13.0
10.5
11.5
11.4
98.3
97.7
94.8
96.2
96.8
1.6
0.13
0.26
9.63
9.87
10.09
9.65
9.81
33.4
0.44
0.86
7.95
7.48
7.24
6.24
7.23
47.2
0.38
0.75
0.91
0.95
0.75
0.73
0.84
135.3
0.11
0.22
1700
1655
1558
1472
1596
9.0
130.47
266.16
3.9
3.5
3.3
3.4
3.5
3.8
0.13
0.28
9.5
10.0
8.0
9.0
9.13
3.8
0.33
0.68
sunflower.
Mean root length in the hybrids (11.56±0.86 cm) was
significantly greater than that of inbred cultivars (9.81±0.86
cm) indicating expression of hybrid vigour for this trait (Table
2). In contrast, the shoots were longer in the inbred cultivars
than that in hybrids. This resulted in low shoot: root ratio in
the hybrids. According to Saxena et al. (1992), one month old
seedlings of pigeonpea hybrids produced 44% more shoot
mass and 43% more root mass as compared to the inbred
cultivars. This data set indicated that the food reserves in the
hybrid seed contri buted predominantly towards root
development.
The root and shoot or total seedling length is known to
influence the vigour of seedling which can be used for
comparing the genotypes by a parameter called ‘seedling
vigour index’ that takes into account the germination per cent
of the individual genotype, giving an overall understanding
of their ability to express potential growth at the seedling
stage itself. The present study also indicated that the hybrids
were significantlyvigorous (with the index, 1832±266.2) than
the inbred cultivars (1596±266.2). Among the hybrids, ‘ICPH
4438’ had the highest seedling vigour index (2072) and ‘ICPH
2364’ had the lowest one (1629). Seedling vigour index was
also found to be highly correlated with germination per cent,
root length, seedling dry weight and test weight suggesting
the important role of these traits in the manifestation of vigour
in hybrid seedlings (Table 3).
In pigeonpea, variation for seed yield is primarily
attributed to the differences in crop growth rates. Chauhan et
al. (1995) reported that the yield in pigeonpea hybrid was
greater than inbred cultivars; and was primarily associated
with higher crop growth rates and the early vigour. They also
reported that the differences in plant vigour between hybrids
and inbred cultivars began to appear during early seedling
stage which became more pronounced with time enabling them
suitable for competitive situations. In the present set of
materials, test weight was highest in hybrids (11.4±0.33 g) as
compared to inbred cultivars (9.1±0.33 g). During the first 10
Bharathi & Saxena: A comparative study of hybrid and inbred cultivars for germination and other related traits of pigeonpea
353
Table 3. Correlation coefficients observed among different seedling traits
Characters
Seedling dry
weight (mg)
0.667**
Germination (%)
Seedling dry weight
Shoot length
Root length
Shoot: root ratio
Test weight
*, **: significant at P = 0.01 and 0.05, respectively
Shoot length
(cm)
-0.153
0.091
days of germination, relatively more quantity of reserved
carbohydrates was mobilized for the growth of root in the
hybrids. On the contrary, in the inbred cultivars, more
carbohydrates were translocated to the development of shoot.
Narayanan et al. (1981) reported that the seed reserves rather
than size of the shoot and root length were important factors
in determining seedling size. It was also evident from the
present study that the seed size was positively related to
germination % (r = 0.708**) and seedling vigour (r = 0.766**).
The data also suggested that large seeded hybrids with their
relatively vigorous roots are likely to encounter early season
drought better than the small seeded hybrids or inbred
cultivars. Olisa et al. (2010) also suggested that the germination
in pigeonpea could be enhanced through the choice of seed
size as the cultivars with higher seed weight were found to
have higher germination.
The mean dry weight of the hybrid seedlings (4.7±0.28
mg) was significantly more in comparison to inbred cultivars
(3.5±0.28 mg) indicating the fact that larger seeds had produced
greater seedling biomass and thus, higher dry matter.
Narayanan and Sheldrake (1974) demonstrated that the
seedling dry weight in pigeonpea inbred cultivars was directly
proportional to the test weight confirming again that seedling
growth was a consequence of reserve assimilates in seeds.
Seedling dry weight was found to be positively associated
with seed size, root length and seedling vigour index. The
seedling dry matter also indicated that vigour of the pigeonpea
seedling in terms of biomass was highly correlated with their
longer root and shoot (Kumar et al. 2011).
Identification of genotypes with high germination and
fast initial seedling growth is likely to result in rapid canopy
develo pment fo r greater li ght int erceptio n, great er
suppression of weeds and better seedling establishment.
Pigeonpea hybrids are more beneficial than inbred cultivars
with respect to enhanced seedling vigour, greater drought
tolerance, greater disease resistance and increased grain yield
(Saxena et al. 1992). Physiological studies have also confirmed
that pigeonpea hybrids were superior to inbred cultivars with
respect to seedling vigour, growth rate, biomass production,
drought resistance and seed yield (Saxena et al. 1996). The
present study also showed that large seeded hybrids had
better germination and more vigorous root system than those
in inbred cultivars. Significant and positive correlation
between seed size and yield was also reported by Patel and
Root length
(cm)
0.598*
0.671**
0.045
Shoot: root
ratio
-0.409
-0.429
0.552*
0.505
Test weight
(g)
0.708**
0.893**
0.063
0.718**
-0.279
Seedling vigour
index (I)
0.639**
0.738**
0.445
0.858**
0.229
0.766**
Acharya (2011). Thus, pigeonpea hybrids by virtue of their
greater root mass and depth have better abilityto mine receding
soil water and tolerate drought during its early phases of
growth. The superiority of hybrids in terms of vigour can be
identified at early growth stage which may be related to vigour
at later developmental stages associated with seed yield.
Correlation studies also indicated the association of
germination and related traits with vigour of the hybrid
seedlings. The present study also indicated the benefits of
breeding large seeded hybrids which would give better
germination and longer root with better drought tolerance at
early growth stages.
ACKNOWLEDGEMENTS
The authors are thankful for the financial support from
National Food SecurityMission (NFSM) of DAC, MOA, India.
Thanks are also due to Dr. Abhishek Rathore, Scientist
(Biometrics) for his support in statistical analysis, and to Dr.
L. Krishnamurthy of ICRISAT for his valuable comments.
REFERENCES
Abdul-Baki AA and Anderson JD. 1973. Relationship between
decarboxylation of glutamic acid and vigour in soybean seed. Crop
Science 13: 222-226.
Brakke MP and Gardner FP. 1987. Juvenile growth in pigeonpea,
soybean, and cowpea in relation to seed and seedling characteristics.
Chauhan YS, Johansen C and Saxena KB. 1995. Physiological basis of
yield variation in short-duration pigeonpea grown in different
environments of the semi-arid tropics. Journal of Agronomy and
ISTA. 2007. International Rules for Seed Testing International Seed
Testing Association (ISTA), Switzerland.
Kumar RR, Krishna K and Naik GR. 2011. Variation of sensitivity to
drought stress in pigeonpea (Cajanus cajan (L.) Millsp.) cultivars
during seed germination and early seedling growth. World Journal
of Science and Technology 1: 11-18.
Mercer KL, Shaw RG and Wyse DL. 2006. Increased germination of
diverse crop-wild hybrid sunflower seeds. Ecological Applications
16: 845-854.
Narayanan A and Sheldrake AR. 1974. Effect of seed size on growth of
seedlings in pigeonpea. In: Annual Report Pigeonpea Physiology,
International Crops Research Institute on Semi-Arid Tropics,
Patancheru, India. Pp. 39-43.
Narayanan A, Saxena NP and Sheldrake AK. 1981. Varietal differences
354
in seed size and seedling growth of pigeonpea and chickpea. Indian
Journal of Agricultural Sciences 51: 289-393.
Workshop on ‘New Frontiers in Pulses Research and Development’,
10-12 November 1989, Kanpur, India. Pp. 58-69.
Olisa BS, Ajayi SA and Akande SR. 2010. Physiological quality of seeds
of promising African yam bean (Sphenostylisstenocarpa (Hochst,
ExA, Rich) Harms) and pigeonpea (Cajanus cajan (L.) Millsp.)
landraces. Research Journal of Seed Science 3: 93-101.
Saxena KB, Chauhan YS, Laxman Singh, Kumar RV and Johansen C.
1996. Research and development of hybrid pigeonpea. Research
Bulletin 19. International Crops Research Institute for the SemiArid Tropics, Patancheru 502324, Andhra Pradesh, India. Pp.20.
Patel JB and Acharya S. 2011. Genetic divergence and charcter
association in Indo-African derivatives of pigeonpea [Cajanus cajan
(L.) Millsp.]. Journal of Food Legumes 24: 198-201.
Saxena KB, Kumar RV, Srivastava N and Shiying B. 2005.A cytoplasmicnuclear male-sterility system derived from a cross between Cajanus
cajanifolius and Cajanus cajan. Euphytica 145: 289-294.
SAS. 2008. SAS/STAT ® 9.2 Users guide, SAS Institute Inc., Cary, NC,
USA.
Saxena KB. 2009. Evolution of hybrid breeding technology in
pigeonpea. In: M Ali and Shiv Kumar (Eds), Milestones in Food
Legume Research. Indian Institute of Pulses Research, Kanpur,
India. Pp. 82-114.
Saxena KB, Chauhan YS, Johansen C and Singh L. 1992. Recent
developments in hybrid pigeonpea research. In: Proceedings of
Short communication
Screening of chickpea (Cicer arientinum L.) genotypes for identification of source
of resistance to Botrytis grey mould
LAJJA VATI, K.P.S. KUSHWAHA and ABHIJEET GHATA
Department of Plant Pathology, College of Agriculture, GBPUA&T, Pantnagar - 263 145, Uttarakhand, India;
E-mail: lajjagangwar@yahoo.com
(Received: April 04, 2012; Accepted: November 10, 2012)
ABSTRACT
In order to identify the source of genetic resistance to Botrytis
grey mould (BGM), thirty genotypes of chickpea were screened
under field condition during 2009-10 and 2010-2011. None of
the genotypes was found to be immune and highly resistant.
However, 15 genotypes namely ‘ICCV 05502’, ‘ICCV 05506’,
‘ICCV 05509’, ‘ICCV 05522’, ‘ICCV 05528’, ‘ICCV 05529’,
‘ICCV 05553’, ‘EC 516891’, ‘EC 516696’, ‘EC 516720’, ‘EC
516806’, ‘EC 516670’, ‘EC 516878’, ‘ICC 4954’ and ‘ICC 4951’
were found resistant to BGM. These genotypes can be used in
breeding programmes to develop varieties resistant to BGM.
Key words: Breeding, Chickpea, Mould, Resistance
Chickpea (Cicer arientinum L.) is a self-pollinated
diploid annual with genome size of approximately 750 Mbp
(Arumuganathan and Earle 1991). It is the third most important
grain legume crop in the world (Bakr et al., 2002; Pande et al.,
2006). Chickpea is valued for its nutritive seeds with high
protein content ranging from 5.3 to 28.9% (Hulse 1991). It is
grown in the Indian sub-continent, West Asia, North Africa
(WANA), the Mediteterranean basin, the Americas and the
Australia (Croser et al. 2003). However, the vulnerability of
this crop to biotic stresses such as BGM, Ascochyta blight,
Fusarium wilt, nematodes and pests and abiotic stresses
(drought and cold) reduce its yield substantially. Among
various fungal diseases, BGM caused by Botrytis cinerea
Pers. ex. Fr. causes considerable yield loss by reducing plant
population in the field. The wide variety of symptoms on
different plant parts may suggest that B. cinerea has a large
‘arsenal of weapons’ to attack its host plants. Among the
necrotrophic and polyphagous fungi, the grey mould agent is
one of the most – studied models (Van Kan et al., 2006). Heavy
mortality of flowers results in poor pod formation due to BGM
infection. Drooping of the infected tender terminal branches
is a common field symptom (Pande et al., 2005). It causes
extensive crop losses in most regions of the world due to the
fact that environmental condition favourable to chickpea crop
(>350 mm annual rainfall, 23-25ºC) also favour the disease.
World wide losses from this fungus account for 20% of the
harvest of the affected crops, and their cost is estimated at 101000 billion euros per year and the market size for anti botrytis
products has been 15-25 million US dollars in recent years
(Genoscope 2008). Therefore, controlling this disease is
essential to ensure stable chickpea production. Eradication
of this soil-borne pathogen is difficult because of its
polyphagous nature and its survival in the soil through its
resting structures.
As fu ngicides are costly, and unfriendly t o our
ecosystem, it is imperative to identify the source of its
resistance and exploit it to develop resistant varieties of
chickpea through breeding approaches. Therefore, in this
study efforts have been made to screen thirty genotypes
against BGM in chickpea. Beside this, such screenings were
also conducted previously. But the present study was carried
out to observe whether new races of the pathogen evolved to
cause severe damage in the locality and the screened
genotypes showing resistance in both years can exploit for
further breeding programme.
Thirty genotypes of chickpea (Table 1) were used for
screening purpose. These genotypes were obtained from the
International Crops Research Institute for the Semi-Arid
Tropics (ICRISAT), Hyderabad. Direct seeding was performed
in November 2009 and 2010 to record observations on the
disease. A basal dose of N, P and K was supplied @ 20, 60 and
50 kg/ha, respectively (Suyal 2010). Hand weeding was
performed at 2-week interval during the crop season. Irrigation
was given at 5 weeks after sowing. Plots (1.0 × 4.0 m) were
established in a randomized complete block design (RCBD)
with three replications (Gomez and Gomez 1984). Plant-to-plant
distance within the row was kept at 10 cm and row to row
distance was maintained at 30 cm. A plot with three rows (4 m
length) was kept for each genotype. Methodology for
inoculation was followed in the present investigation as
suggested by Pande (2010).
During both years, field screening was performed in the
Pulse Pathology Block, N.E. Borlaug Crop Research Center,
GBPUAT, Pantnagar. Disease severity (disease index) on each
genotype in each replication was recorded three times at the
interval of 15 days and first observation was recorded 10 days
after inoculation using the following formula:
Disease index (DI) =  of numerical ratings × 100/ No. of
plants observed × highest degree of rating
Table1. Botrytis grey mould reaction of chickpea genotypes
during 2009-11
S.N.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Genotype
ICCV04509
ICCV04512
ICCV04513
ICCV04514
ICCV05501
ICCV05502
ICCV05506
ICCV05509
ICCV05522
ICCV05526
ICCV05527
ICCV05528
ICCV05529
ICCV05530
ICCV05531
ICCV05532
ICCV05533
ICCV05535
ICCV05553
EC 516891
EC 516671
EC516696
EC 516720
EC516806
EC 516670
EC516878
EC516012
EC516755
ICC 4954
ICC4951
Cd at 5 %
(%)a Disease severity
2009-10
2010-11
23.0 (0.496) Ib
28.0 (0.553) MS
18.0 (0.438) I
23.0 (0.496) I
18.0 (0.438) I
18.0 (0.438) I
28.0 (0.553) MS
33.0 (0.611) MS
23.0 (0.496) I
23.0 (0.496) I
3.0 (0.174) R
3.0 (0.174) R
4.7 (0.211) R
3.0 (0.174) R
3.0 (0.174) R
4.7 (0.211) R
4.7 (0.211) R
4.7 (0.211) R
23.0 (0.496) I
23.0 (0.496) I
18.0 (0.438) I
23.0 (0.496) I
3.0 (0.174) R
3.0 (0.174) R
4.7 (0.211) R
3.0 (0.174) R
23.0 (0.496) I
28.0 (0.553) MS
23.0 (0.496) I
19.7 (0.445) I
28.0 (0.553) MS
23.0 (0.496) I
23.0 (0.496) I
23.0 (0.496) I
23.0 (0.496) I
28.0 (0.553) MS
3.0 (0.174) R
4.7 (0.211) R
3.0 (0.174) R
3.0 (0.174) R
23.0 (0.496) I
19.7 (0.445) I
4.7 (0.211) R
3.0 (0.174) R
4.7 (0.211) R
4.7 (0.211) R
3.0 (0.174) R
3.0 (0.174) R
4.7 (0.211) R
4.7 (0.211) R
3.0 (0.174) R
3.0 (0.174) R
19.7 (0.445) I
28.0 (0.553) MS
28.0 (0.553) MS
23.0 (0.496) I
3.3 (0.183) R
3.0 (0.174) R
3.0 (0.174) R
4.7 (0.211) R
0.12
0.12
a
Figures in parenthesis are arcsine transformed values. Averaged over
three replications
b
Level of resistant reaction; I = intermediate, MS = moderately
susceptible, R = resistant
Plants were considered diseased when drooping occur
of the infected tender terminal branches, is a common field
symptom. The data obtained on disease severity were analyzed
to determine the level of pathogenicity reaction among the
tested chickpea genotypes. The disease reaction of the
genotypes was quantified separately for two different years.
A final ANOVA was developed over the pooled value of two
years on the basis of their mean disease severity as per 1 to 9
disease rating scale suggested by Hawthorne et al., (2006).
Before analysis, values were transformed using arcsine
transformation. Statistical analyses were made using the
Statistical Package for Social Sciences Version 16.0 (SPSS 16.0,
SPSS Inc.) programme.
The two -year trial data for screeni ng chickpea
genotypes against BGM is presented in Table 1. The
observations indicated that none of the genotype was
observed without BGM infection. Similar screening of
chickpea genot ypes has been do ne at the ICRISAT,
Hyderabad, and some other State Agricultural Universities
by former workers (Verma et al. 1981, Verma and Gill 1981,
Haware and Nene 1982, Pandey et al. 1982, Chaube et al.
1983). Keeping in view of development of new races, the
present screeni ng study was necessary as previo us
screenings were done much earlier.
Disease severityranged from 3.0 to 33.0%. However, 15
genotypes (ICCV05502, ICCV05506, ICCV05509, ICCV05522,
ICCV05528, ICCV05529, ICCV05553, EC516891, EC516696,
EC516720, EC516806, EC516670, EC516878, ICC 4954 and
ICC4951) showed minimum disease severity (<5%), and thus
classified as resistant (R). In both years, genotypes under R
category showed similar reaction (Table 2). The present
findings when compared with the results of other two locations
(ICRISAT and Ludhiana), it was observed that germplasm
lines ‘ICCV 05502’, ‘ICCV 05506’, ‘ICCV 05509’, ‘ICCV 05522’,
‘ICCV 05529’, ‘ICCV 05553’, ‘EC 516891’, ‘EC 516720’, ‘EC
516806’ and ‘EC 516878’ showed resistant reaction at both
Pantnagar and ICRISAT locations (Anonymous 2010). Five
genotypes (ICCV04509, ICCV04514, ICCV05530, ICCV05535
and EC516012) were classified as moderately susceptible
(MS). Different reactions of the same genotype over years
were obtained, particularly for the intermediate reaction (I).
Therefore, the genotypes were categorised under a specific
reaction level on the basis of the pooled value (Fig. 1). The
genotypes that showed intermediate disease reaction had
possibly little scope of their use for further study. This result
was consistent during both the year of study. Similar results
with analogous methodology were reported by Singh et al.
(1982).
Disease incidence (%)
356
35
28
21
14
7
0
1 4 14 16 18 28 2 3 5 10 11 15 17 21 27 6 7 8 9 12 13 19 20 22 23 24 25 26 29 30
MS
I
R
Reaction level
Fig 1.
Reaction of chickpea genotypes against B. cinerea
(pooled value over two years). Genotypes numbers
under a specific reaction level are referred from Table
1. Error bars are standard error of the mean
Table 2. Analysis of variance for the effect of genotype on
disease severity tested in two years (2009-11)
Variable
Genotype
Reaction levela
MS
I
R
a
Year
2009-10
2010-11
2009-10
2010-11
2009-10
2010-11
SS
MS
F-value
0.2E-06b 0.1E-06 0.0000
0.0080 0.0020 0.2499
0.2526 0.0022 0.2575
0.0177 0.0019 0.1599
0.0146 0.0010 0.6120
0.0136 0.0009 0.6834
Level of resistant reaction; I = intermediate, MS = moderately
susceptible, R = resistant
b
Data transformed in arcsine prior to ANOVA.
Vati et al.: Screening of chickpea genotypes for identification of source of resistance
The genotypes grouped under MS can be eliminated
for future breeding programme. Similar type of screening for
disease severity of BGM in chickpea has been reported
(Tripathi and Rathi 1992). An analysis of variance (ANOVA)
was made including two experiments, where genotypes had
strong significant effect on disease severity over two-years
estimation. However, experiment had a poor significant effect
on disease severity. Furthermore, disease severity was
significantly affected by the interaction of genotypes ×
experiment, meaning experiments have effect on disease
severity of BGM of chickpea. Therefore, separate analyses
were performed for different reaction levels in each year (Table
2). In all reaction levels of each year showed non significant
effect. However, a strong effect of genotype reaction against
the BGM disease was detected in both years for resistant
genotypes.
Hence, the pattern showed that the genotypes under
resistance level could be cultivated over period. Resistance
genotypes showing susceptibility to BGM were often
recognised as breakdown o f gene by the pathogen or
development of a new pathogenic race of a certain region.
The present study revealed the absence of complete resistance
to B. cinerea in the used set of chickpea genotypes. These
genotypes may be utilized for improving BGM resistance in
chickpea.
REFERENCES
Anonymous. 2010. Annual Report. All India Coordinated Research
Project (AICRP) on chickpea. Pp.176.
Bakr MA, Rahman MM and Ahmed AU. 2002. Manifestation of Botrytis
grey mould of chickpea in Bangladesh. In: MA Bakr, KHM Siddique
and C Johansen (Eds), Integrated Management of Botrytis Grey
Mould of Chickpea in Bangladesh and Australia, International Crops
Research Institute for the Semi-Arid Tropics, India. Pp. 63-69.
357
Genoscope. 2008. Botrytis cinerea estimated losses for vineyards in
France amount to 15-40 % of the harvest, depending on climatic
conditions. In: Sequencing Projects of Botrytis cinerea.
Gomez KA and Gomez AA. 1984. Statistical procedures for agricultural
research. John Wiley & Sons, Singapore.
Haware MP and Nene YL. 1982. Screening of chickpea for resistance
to Botrytis grey mould. International Chickpea Newsletter 6: 18.
Hawthorne W, Davidson J, McMurray L, Lindbeck K and Brand J.
2006. Chickpea disease management strategy southern region. Pp.
4-6.
Hulse JH. 1991. Nature, composition and utilization grain legumes uses
of tropical legumes. In: Proceedings of the Consultants Meeting
(CM91), ICRISAT, Patancheru, India. Pp. 11-27.
Pande S, Krishna KG and Rao JN. 2005. Marigold: A diagnostic tool for
BGM forcasting and management in chickpea. E journal
(WWW.ICRISAT.Org) 1: 1.
Pande S, Galloway PM, Gaur, Siddique KHM and Tripathi HS. 2006.
Botrytis grey mould of chickpea: A review of biology, epidemiology
and disease management. Australian Journal of Agricultural Research
57: 1137-1150.
Pandey MP, Pandya BP, Chaubey HS, Tewari SK and Beniwal SPS.
1982. Screening cultivars and genetic stocks of chickpea for
resistance to Ascochyta blight. ICN 7: 15-16.
Pande S. 2010. Guidelines: Method of inoculation. In: International
chickpea Botrytis Gray Mold Nursery Pp. 3.
Singh G, Kapoor S and Singh K. 1982. Screening chickpea for grey
mould resistance. International Chickpea Newsletter 7: 13.
Suyal U. 2010. Studies on histopathology, molecualar charactrization
and management of Botrytis cinerea Pers. Ex. Fr., the incitant of
grey mould of chickpea. Ph.D. Thesis, GBPUA&T, Pantnagar,
India.
Tripathi, HS and Rathi YPS. 1992. Resistance to Botrytis grey mould
in chickpea: Screening technique and identification of resistance
source. Journal of Mycology and Plant Pathology 30: 231-232.
Van Kan JAL. 2006. Licensed to kill: the lifestyle of a necrotrophic
plant pathogen. Trends in Plant Science 11: 247-253.
Chaube HS, Beniwal SPS, Tripathi HS and Nene YL. 1980. Field Screening
of Chickpea for resistance to Botrytis grey mould. International
Chickpea Newsletter 2: 16.
Verma NN, Singh G, Sandhu TS, Singh H, Sandhu SS, Singh K and Bhullar
BS. 1981 Sources of resistance to gram blight and grey mould.
International Chickpea Newsletter 4: 14.
Croser JS, Clarke HJ and Siddique KHM. 2003. Utilization of wild Cicer
in chickpea improvement progress, constraints and prospects.
Australian Journal of Agricultural Research 54: 429-444.
Verma MM and Gill AS. 1981. Field screening of chickpea segregating
material in multiple disease sick plot. ICN 4:14.
Short Communication
Management of Fusarium wilt of lentil using antagonistic microorganisms in Tarai
region of Uttarakhand
ANKITA GARKOTI and H.S. TRIPATHI
Department of Plant Pathology, G.B. Pant University of Agriculture and Technology, Pantnagar - 263 145, Uttarakhand,
India; E-mail: ankita.garkoti2010@gmail.com
(Received: May 26, 2012; Accepted: October 23, 2012)
ABSTRACT
Two bio-agents, Trichoderma harzianum and Pseudomonas
fluorescence were screened in vitro against Fusarium wilt
pathogen of lentil by using dual culture technique. Observations
revealed that T. harzianum grew faster than P. fluorescens. All
the isolates of T. harzianum significantly checked the growth
of the test pathogen. Maximum inhibition (71.8%) after 96 hrs
of incubation was recorded with isolate of Th 5 followed by Th
39 (70.8%) and Th 14 (68.9%). Isolates of P. fluorescence were
found less effective in comparison to T. harzianum in inhibiting
the growth of the test pathogen (Fusarium oxysporum f.sp. lentis).
Key words:
Bio-agents, Fusarium wilt, Lentil
Lentil (Lens culinaris Medic.) is a protein-rich food
legume grown throughout northern and central India having
total area of 1.59 million ha with a total production of 0.94
million tons and productivity 591 kg/ha (AICRP on MULLaRP
2011-2012). Among the soil-borne diseases, Fusarium wilt
caused by Fusarium oxysporum f.sp. lentis (Fol) is the most
important biotic constraint to productivity of lentil worldwide
(Bhalla et al. 1992). Lentil wilt caused by Fol, is one of the
most widespread and destructive diseases. Symptoms include
wilting of top leaves followed by entire plant (Khare 1980).
The disease may cause complete crop failure under favorable
conditions for disease development, and can be the major
limiting factor for lentil cultivation in certain areas (Chaudhary
and Amarjit 2002). Biological control using antagonistic
microorganisms is an alternative method to the fungicides
and provides an opportunity for ecological based approach
to integrated pest management in sustainable agriculture in
crop production systems (Cook and Granados 1991, Singh et
al. 1999, Sutton and Peng 1993). Antagonistic Trichoderma
species are considered as promising biological control agents
against numerous phytopathogenic fungi including F.
oxysporum (Sarhan et al. 1999). Hence, in the present study
fungal and bacterial bio-agents were tried as alternative method
under organic farming for lentil wilt disease management
particularly in Uttarakhand state.
Fusarium oxysporum f.sp. lentis was isolated from
naturally infected lentil roots and thereafter same was purified
by single spore method and maintained on PDA in culture
tubes at 28+ 1°C in an incubator for further studies. Different
strains of bio-control agents viz., Trichoderma harzianum
and Pseudomonas fluorescence were procured from Biocontrol
laboratory of the Department of Plant Pathology, GBNPUA&T,
Pantnagar and initially screened by following dual culture
technique.
For fungal antagonist 20 ml of sterilized melted PDA
was poured in 90 mm diameter Petri plates. After solidification
of medium, 5 mm disc of the antagonist and test pathogen
were cut with the help of a sterilized cork borer from the edge
of 5 day old culture and placed in straight line at distance of 5
mm from the edge. Three replications were maintained for each
treatment and petri plates without antagonist served as
control. For bacterial antagonist (same amount of melted PDA
+ King’s B medium in 1:1 ratio) was used. Five mm sterilized
paper disc were dipped in bacterial suspension and were placed
at the opposite corner of the petri plate containing Fol disc on
solidified medium. The pathogen without antagonist served
as check. The inoculated petri plates were incubated at 28±10C
and linear growth of the bio-agent was observed consequently
for four days to record different stages of antagonism.
The
per cent inhibition of both antagonists was determined with
the help of mean colony diameter and calculated by using
formula described earlier (Mckinney 1923).
I
C-T
 
C
Where,
I = % inhibition
C = colony diameter in control
T = colony diameter in treated medium
The effect of bi o-agents (T. harzianu m and P.
fluorescence) for their antagonistic potential against the test
pathogen (F. oxysporum f.sp. lentis) was assessed by dual
culture technique. Observations on the colonization of the
test pathogen by the selected bio-agents indicated their varied
antagonistic potentiality (Table 1 and 2).
The strains of T. harzianum grew at a faster rate than
the test pathogen F. oxysporum f.sp. lentis. All the strains of
Trichoderma significantly checked the growth of the
pathogen. After 24 h of inoculation of antagonist and test
Garkoti & Tripathi: Management of Fusarium wilt of lentil using antagonistic microorganisms
pathogen, colony diameter of each of them developing on
PDA, individually and after 48 h of inoculation, both the
cultures came in contact of each other. The growth of
antagonist overlapped the mycelia of the test pathogen after
72 h of incubation. After 96 h of incubation, T. harzianum
completely covered the growth of the test pathogen. All the
ten isolates of T. harzianum significantly controlled the
growth of the test pathogen. Maximum inhibition 71.8% after
96 h of incubation was recorded in Th 5 followed by Th 39,
where inhibition was 70.8%. Minimum inhibition (49.3%) was
recorded in the culture treated with Th3.
A clear zone of inhibition between F. oxysporum and P.
fluorescence was observed. All the strains of P. fluorescence
were found effective in inhibiting the growth of test pathogen.
After 96 h of incubation, maximum inhibition was observed in
strain 12, in which per cent inhibition was 64.2 followed by
strain 4 where it was 61.4. Minimum per cent inhibition was
35.8 observed in strain 7.
Biological control is the best alternative, especially
359
against soil-borne pathogens such as Fusarium spp. (Akrami
et al. 2011). Trichoderma species differentially limited the
colony growth of the pathogen, overgrew the pathogen
colony and produced yellow pigment (Dolatabadi et al. 2011).
The majority of Trichoderma species is antagonist of
phytopathogenic fungi and has been broadly used as the
most important biocontrol agent (Tjamos et al. 1992, Akrami
et al. 2011). From several studies, it has been confirmed that
Trichoderma spp. have antagonistic effects against diversity
of soil-borne pathogens (Grondona et al. 1997, Bajwa et al.
2004). Results revealed that all strains of T. harzianum were
effective as it grew over and parasitized F. oxysporum f.sp.
lentis and checked the growth in dual culture technique. The
strains of T. harzianum were fast growing than pathogen.
The suppression in growth may be due to the lack of nutrition
for growth of pathogen and production of certain inhibitory
chemicals by antagonists in culture. The growth inhibition
may be due to the hyphal parasitization and production of
wall degrading enzymes by the test antagonists. However,
this needs further investigation.
Table 1. In vitro efficacy of T. harzianum isolates on growth of Fusarium oxysporum f.sp. lentis
T. harzianum
isolates
Th-1
Th-3
Th-5
Th-9
Th-14
Th-39
Th-56
Th-60
Th-66
Th-70
Check
SEm (±)
CD (P=0.05)
CV (%)
*Mean of three replications
48 h
8.3
11.4
5.3
10.5
6.6
6.1
8.8
8.8
10.2
9.6
13.8
0.47
1.39
9.08
Radial growth (mm)*
72 h
11.5
16.8
7.3
16.0
10.8
9.0
12.1
13.2
15.3
14.0
26.0
0.59
1.73
7.40
96 h
13.1
20.7
11.5
20.1
12.7
11.9
14.4
15.2
18.8
16.6
40.8
0.56
1.65
5.48
48 h
39.9
17.4
61.6
23.9
52.2
55.8
36.2
36.2
26.1
30.4
Per cent inhibition
72 h
55.8
35.4
71.9
38.5
58.5
65.4
53.5
49.2
41.2
46.2
96 h
67.9
49.3
71.8
50.8
68.9
70.8
64.7
62.7
53.9
59.31
Table 2. In in vitro efficacy of P.fluorescens isolates on growth of Fusarium oxysporum f.sp. lentis
P. fluorescens
isolates
FL P-2
FL P-4
FL P-6
FL P-7
FL P-12
FL P-18
FL P-25
FL P-27
FL P-28
FL P-31
Check
SEm (±)
CD (P=0.05)
CV (%)
*Mean of three replications
48 h
7.0
6.3
7.6
8.0
6.0
6.3
6.6
7.6
6.6
6.3
13.0
0.36
1.06
8.45
Radial growth (mm)*
72 h
11.3
10.3
12.0
13.0
9.3
10.6
11.6
11.6
11.0
11.6
21.3
0.68
2.02
9.79
96 h
19.3
14.0
20.6
23.3
13.0
14.6
19.6
21.0
17.3
17.0
36.3
0.97
2.85
8.58
48 h
46.2
51.5
41.5
38.5
53.8
51.5
49.2
41.5
49.2
51.5
Per cent inhibition
72 h
46.9
51.6
43.7
38.9
56.3
50.2
45.5
45.5
48.4
45.5
96 h
46.8
61.4
43.3
35.8
64.2
59.8
46.0
42.1
52.3
53.2
360
REFERENCES
Akrami M, Golzary H and Ahmadzadeh M. 2011. Evaluation of different
combinations of Trichoderma species for controlling Fusarium rot
of lentil. African Journal of Biotechnology 10: 2653-2658.
All India Coordinated Research Project on MULLaRP. 2011-2012.
Project Coordinator’s Report (Rabi Crops), Indian Institute of
Pulses Research, Kanpur.
Bajwa R, Mukhtar I and Anjum T. 2004. In vitro biological control of
Fusarium solani- cause of wilt in Dalbergia sissoo Roxb.
Mycopathology 2: 11-14.
Bhalla MK, Nozzolillo C and Schneider E.1992. Observation on the
responses of lentil root cells to hypha of Fusarium oxysporum.
Journal of Phytopathology 135: 335-341.
Chaudhary RG and Amarjit K. 2002. Wilt disease as a cause of shift
from lentis cultivation in Sangod Tehsil of Kota, Rajasthan. Indian
Journal of Pulses Research 15: 193–194.
Cook RJ and Granados RR. 1991. Biological control: making it work.
In: MJF MacDonald (ed). Agricultural Biotechnology At The Cross
Roads. National Agricultural Biotechnology Council, Ithaca. Pp.
213-227.
Dolatabadi HK, Goltapeh EM, Mohammadi N, Rabiey M, Rohani N
and Varma A. 2011. Evaluation of different combinations of
Trichoderma species for controlling Fusarium rot of lentil. African
Journal of Biotechnology 10: 2653-2658.
Grondona I, Hermosa R, Tejada M, Gomis MD, Mateos PF, Bridge PD,
Monte E, and Garcia Acha I. 1997. Physiological and biochemical
characterization of Trichoderma harzianum, a biological control
agent against soilborne fungal plant pathogens. Applied and
Environmental Microbiology 63: 3189–3198.
Khare MN. 1980. Wilt of Lentis. Jawaharlal Nehru Krishi Vishwa
Vidyalaya, Jabalpur, M.P., India. Pp.155.
Sarhan MM , Ezzat SM and Al- Tohamy MR. 1999. Application of
Trichoderma hamatum as a Biocontroller against Tomato Wilts
Disease Caused by Fusarium oxysporum f. lycopersici. Egyptian
Journal of Microbiology 34: 347-376.
Singh PP, Shin YC, Park CS and Chung YR. 1999. Biological control of
Fusarium wilt of cucumber by chitinolytic bacteria. Phytopathology
89: 92-99.
Tjamos EC, Papavizas GC and Cook RJ. 1992. Biological control of
plant diseases. Progress and challenges for the future. Plenum Press,
New York. USA.
Short communications
Effect of phosphorus and zinc on yield and economics of mothbean under semiarid
conditions
L.R. YADAV, POONAM CHOUDHARY, SANTOSH, O.P. SHARMA and MEENU CHOUDHARY
S.K.N. College of Agriculture (Swami Keshwanand Rajasthan Agriculture University, Bikaner), Jobner, (Rajasthan)
303329, India; E-mail: lalaram69@gmail.com
(Received: March 17, 2012; Accepted: December 06, 2012)
ABSTRACT
A field experiment was conducted during rainy (kharif) season
of 2007 at Agronomy farm, S.K.N. College of Agriculture, Jobner
(Rajasthan) to study the effect of phosphorus and zinc on
mothbean under semi arid conditions on loamy sand soil.
Twenty treatment combinations comprising five levels of
phosphorus (0, 10, 20, 30 and 40 kg P2O5/ha) and four levels of
zinc (0, 2, 4 and 6 kg Zn/ha) were tried in a RBD with three
replications. Application of 30 kg P 2 O 5/ha significantly
enhanced plant height, dry matter accumulation/plant, effective
nodules and dry weight of nodules, pods/plant, seeds/pod, seed
and straw yields, net returns, nutrient (N, P, K and Zn) content
in seed and straw and their total uptake. Zinc applied at 4 kg/ha
(on par with 6 kg/ha) was optimum as it significantly increased
plant height, dry matter accumulation/plant, chlorophyll content
in plant leaves at 40 days after sowing (DAS), per plant effective
nodules and its dry weight, number of pods, seeds/pod, seed and
straw yields, net returns, nutrient (N, P, K and Zn) content in
seed and straw and their total uptake over the lower doses
(both 2 kg Zn/ha and control).
Key words:
Mothbean, Net returns, Phosphorus levels, Zinc
levels
Mothbean (Vigna aconitifolia [Jacq.] Marechal) is a
hardy and drought tolerant crop among kharif pulses and
largely grown in arid and semi-arid regions. It is also one of
the most assured and remunerative crops in arid regions due
to i ts ext reme t olerance to moisture stress and high
temperature. Because of its very short maturity period, it is
highly suitable for low rainfall areas of western Rajasthan
(Yadav et al. 2004) under Indian subcontinent. Nutritionally it
is also good as it contains 20.5% easily digestible protein rich
in lysine and tryptophan and other essential amino acids. It
has the inherent capability for biological nitrogen fixation for
meeting its own N need and thus, helps in enriching soil
nitrogen status.
The state of Rajasthan is the largest producer of
mothbean and contributes 80 % share of country’s production.
However, its productivity is very low. Thus, harnessing its
productivity potential through use of improved agronomy
like, balanced fertilization is a viable option in the arid and
semi arid regions of the country. Amongst nutrients,
phosphorus is deficient particularly in light textured soils and
its deficiency has been recognized as a major bottleneck in
realizing the potential yield of mothbean (Patel et al. 2004). P
is also an important mineral element for grain legumes as it
helps in root development, synthesis of phosphates and
phospho-proteins, energy fixing and releasing process in
plants and improving seed quality (Singh and Yadav 2008).
Besides major and secondary nutrients, pulses require
zinc for completion of its life cycle. It is reported that majority
of pulse growing regions of India are low in zinc availability
(Singh et al. 2011). Moreover, in light textured semi arid and
arid soils of western Rajasthan, zinc deficiency is widespread
because of low soil organic carbon (SOC) and alkaline soil
reaction (pH). Zi nc also pl ays an i mpo rtant role in
photosynthesis, sugar transformation, protein synthesis,
water absorption, flowering and seed setting. Hence, the
present invest igat ion was undertaken to harness t he
productivity of mothbean by P and zinc application especially
under semi-arid conditions.
A field experiment was conducted during kharif 2007 at
Agronomy farm, S.K.N. College of Agriculture, Jobner
(Rajasthan), India. The loamy sand soil was alkaline (pH 8.2)
in reaction, low in SOC (0.14%), available N (133 kg/ha), P
(16.3 kg P2O5 /ha) and available Zn (0.4 ppm) and medium in
available K(150 kg/ha). Twenty treatments involving five
levels of P (0, 10, 20, 30 and 40 kg P2O5/ha) and four levels of
Zn (0, 2, 4 and 6 kg Zn/ha) were tried in a randomized complete
block design (RCBD) with three replications. The total rainfall
received during the crop period was 114.5 mm. Mothbean
‘RMO-40’ was sown during second week of July at 30 x 5 cm
spacing using 15 kg seed/ha and harvested in the second
week of September. A uniform basal dose of 15 kg urea-N/ha
was applied to all the plots and was adjusted with N supplied
through diammonium phosphate (DAP) as per treatment.
Whole of P through DAP and Zn through zinc sulphate were
drilled in earmarked plots as per treatment before sowing. The
amount of sulphur supplied through zinc sulphate was also
adjusted with elemental sulphur to make it uniform to all plots.
Thus, the treatments were compared with respect to P and Zn
only keeping all other factors constant. Standard methods
were used for determination of P and Zn content in plant and
their uptake were also calculated. The net monitory returns
362
were calculated on the basis of prevailing market rates.
weight of nodules over both 2 kg Zn/ha and control (Table 1).
Increasing levels of zinc up to 4 kg/ha also significantly
increased yields (seed and straw yield), yield attributes (pods/
plant and seeds/pod) and net return of mothbean. In relative
terms, application of 4 kg Zn/ha recorded 42.6, 30.4, 23.9 and
54.6 % in respect of pods/plant, seed and straw yields and
net return over control, respectively. However, test weight of
mothbean was significantly increased up to 2 kg Zn/ha (5.9 %
over control) as higher doses of Zn beyond 2 kg/ha (4 and 6
kg/ha) were found statistically at par with each other (Table
1). This might be due to increased yield attributes and seed
yield because of its pertinent role in enhanced nitrogen
metabolism thereby increasing its availability to the plants for
efficient growth and development by way of enhanced
partitioning of photosynthates towards newly formed sink
i.e. pods and seeds. These results corroborate the findings of
Singh and Sharma (2005) in mothbean.
Phosphorus application up to 30 kg P2O5/ha significantly
enhanced plant height, dry matter accumulation/plant,
effective nodules and dry weight of nodules (Table 1).
Application of higher P levels viz., 30 and 40 kg P2O5/ha (both
on par) also recorded significantly higher dry matter
accumulation and registered an increase of 50.6, and 56.1 %
over the control, respectively. However, leaf chlorophyll
content in mothbean recorded at 40 DAS was significantly
increased up to 20 kg P2O5/ha (on par with 30 and 40 kg P2O5/
ha) over control (16.2%) and 10 kg P2O5/ha (6.1%), respectively
(Table 1). These results are in close agreement with Luikham
et al. (2005). Similar to dry matter, yield attributes viz., pods/
plant and seeds/pod were significantly improved due to
application of 30 kg P2O5/ha (on par with 40 kg P2O5/ha) over
the preceding levels of phosphorus including the control.
Test weight however, increased significantly up to 20 kg P2O5/
ha over the control and 10 kg P2O5/ha as higher doses of
phosphorus failed to bring out significant variations in test
weight. Seed and straw yield of mothbean also increased
significantly with increasing levels of P up to 30 kg P2O5/ha
and per cent increases in the above yields were to the tune of
39.5, 18.9, 6.2 and 28.1, 12.0, 5.2 over control, 10 and 20 kg
P2O5/ha, respectively. Similar to yields, maximum net return
was obtained following application of 30 kg P2O5/ha (on par
with 40 kg P2O5/ha) and was superior (by ` 882/ha) over control
and other lower doses of P. Application of P might have
resulted in increased carbohydrate accumulation (higher
biomass) and their remobilization to reproductive parts of the
plants, being the closest to the sink and hence, resulted in
increased flowering, fruiting and seed formation (Nadeem et
al. 2004).
Phosphorus fertilization @ 30 kg P2O5/ha (on par with
40 kg P2O5/ha) to mothbean also significantly increased protein
and N content in seed, N content in straw and their total
uptake (12.4, 12.3, 24.5 and 59.7 % respectively, over control).
The improvement in protein content was due to higher uptake
of N in plant (Gupta et al. 2006). Application of P might have
improved nodules number and root growth which ultimately
increased nutritional environment in rhizosphere as well as in
pl ant syst em l eadi ng i n increased translo cati on and
consequently uptake of nutrients. Since protein content is
dependent on N content in seed, thus, increased N content in
seed has increased protein content in seed which reflects the
better nutritional environment in rhizosphere. These results
are in close conformity with the findings of Gupta et al. (2006)
in urdbean.
Application of 4 kg Zn/ha also significantly increased
plant height, dry matter accumulation (34.6 % over control),
leaf chlorophyll content at 40 DAS, effective nodules and dry
Zinc fertilization to mothbean also significantly
increased protein and N content in seed and straw, and its
total uptake (Table 2). Application of 4 kg Zn/ha significantly
Table 1. Effect of P and Zn fertilization on growth, seed yield and its attributes and economics of mothbean*
Treatments Plant height
at harvest
(cm)
P levels (kg P2O5 /ha)
0
22.1
10
27.8
20
30.5
30
32.7
40
33.9
CD(P=0.05)
2.13
Zn levels (kg Zn/ha)
0
24.5
2
28.7
4
31.2
6
33.1
CD(P=0.05)
1.91
*Interaction not significant
Dry matter
accumulation
at harvest
(g/plant)
Chlorophyll
content at
40 DAS
(mg/g)
Effective
nodules/
plant
Dry weight of Pods/ Seeds/
Test
Seed
nodules/
plant pod weight (g) yield
(kg/ha)
plant
(mg)
7.3
8.8
10.1
11.0
11.4
0.8
2.84
3.11
3.30
3.32
3.32
0.18
8.39
10.21
10.61
11.93
12.68
0.76
52.7
61.1
67.3
72.4
76.8
4.6
17.4
22.4
25.2
27.4
28.1
1.82
4.2
4.8
5.2
5.5
5.7
0.34
26.5
28.0
29.4
29.5
29.6
1.34
7.8
9.6
10.5
10.9
0.7
2.75
3.15
3.42
3.49
0.16
8.59
10.48
11.79
12.42
0.68
53.6
64.1
71.8
74.6
4.15
19.0
23.9
26.4
27.1
1.63
4.3
4.9
5.4
5.7
0.31
27.2
28.8
29.1
29.3
1.20
Straw
yield
(kg/ha)
Net
return
(`/ha)
668
784
878
932
938
33
1665
1903
2026
2132
2174
110
7632
9886
11573
12514
12495
755
697
830
909
924
30
1686
1977
2090
2167
98
7895
10666
12204
12515
960
Yadav et al.: Effect of P and Zn in mothbean under semiarid conditions
363
Table 2. Effect of P and Zn fertilization on protein, nutrient content and total uptake by mothbean*
Treatments
Protein
content in
seed (%)
P levels (kg P2O5 /ha)
0
10
20
30
40
CD (P=0.05)
Zn levels (kg Zn/ha)
0
2
4
6
CD (P=0.05)
N content
(%)
Seed
Straw
Total N
uptake
(kg/ha)
P content
(%)
Seed
Straw
Total P
uptake
(kg/ha)
Zn content
(ppm)
Seed
Straw
Total Zn
uptake
(g/ha)
20.2
21.5
22.2
22.7
22.9
0.7
3.23
3.44
3.55
3.63
3.67
0.11
1.65
1.82
1.92
2.06
2.08
0.07
48.6
61.8
70.2
77.6
79.4
5.0
0.38
0.43
0.48
0.50
0.50
0.01
0.23
0.25
0.27
0.28
0.28
0.01
6.28
8.10
9.66
10.57
10.88
0.51
25.1
28.3
30.7
31.8
32.8
1.3
20.0
21.0
21.7
22.2
22.6
0.9
49.5
62.5
71.2
76.9
79.9
4.0
21.2
21.8
22.3
22.3
0.6
3.39
3.49
3.57
3.57
0.10
1.84
1.90
1.93
1.94
0.06
54.9
66.8
72.7
75.0
4.5
0.41
0.46
0.48
0.48
0.01
0.23
0.27
0.27
0.28
0.01
6.74
9.10
10.07
10.45
0.46
26.8
28.0
31.7
32.6
1.2
19.5
20.7
22.5
23.0
0.8
51.6
64.3
75.6
80.0
3.6
*Interaction not significant
increased N content in seed and straw and total uptake to the
tune of 5.1, 5.0, 4.9 and 32.4 per cent over control, respectively.
However, higher dose of Zn (6 kg/ha) could not bring in
significant variation in N, P and Zn content and uptake. This
might be due to positive response of applied Zn in deficient
soils of arid and semi arid regions thereby, increased total
uptake of Zn by mothbean. Similar, finding was reported by
Saini (2003).
It could be inferred from the above that application of
30 kg P2O5/ha and 4 kg Zn/ha was found effective for higher
productivity and monetary returns of mothbean under the
existing condition of semi arid regions of Rajasthan.
REFERENCES
Gupta A, Sharma GD and Chopra P. 2006. Effect of biofertilizer and
phosphorus levels on yield attributes, yield and quality of urdbean
(Vigna mungo). Indian Journal of Agronomy 51: 142-144.
Luikham E, Lhungdim J and Singh A. 2005. Influence of sources and
levels of phosphorus on growth and yield of greengram [Vigna
radiata (L.) Wilzek]. Legume Research 28: 59-61.
Nadeem MA, Ahmad R and Ahmad MS. 2004. Effect of seed inoculation
and different fertilizer levels on growth and yield of mungbean
(Vigna radiata).Journal of Agronomy 3: 40-42.
Patel MM, Patel IC, Patel RM, Tikka SBS and Patel BS. 2004. Response
of mothbean to row spacing and phosphorus under rainfed conditions.
Indian Journal of Pulses Research 17: 91-92.
Saini RK. 2003. Effect of zinc and iron on growth and yield of mothbean
[Vigna acconitifolia (Jacq.) Marechal] in arid western Rajasthan.
M.Sc. (Ag.) Thesis, Rajasthan Agricultural University, Bikaner.
Singh KK, Praharaj CS, Choudhary AK, Kumar N and Venkatesh MS.
2011. Zinc response to pulses. Indian Journal of Fertilizers 7: 118126.
Singh RS and Yadav MK. 2008. Effect of phosphorus and biofertilizers
on growth, yield and nutrient uptake of long duration pigeonpea
under rainfed condition. Journal of Food Legumes 21: 46-48.
Singh V and Sharma SK. 2005. Response of mothbean ( Vigna
acconitifolia L.) to zinc and thiourea under dryland condition. Annals
of Agricultural Research, New Series 26: 313-314.
Yadav GL, Kumawat PD and Babel AL. 2004. Effect of nitrogen,
phosphorus and Rhizobium inoculation on mothbean. Indian Journal
of Pulses Research 17: 95-96.
i
The Editorial Board gratefully acknowledges the help rendered by following referees in reviewing manuscripts for the Vol. 25 (4),
December 2012.
Akram Dr M
Kumari Dr (Ms) Jayoti
Singh Dr NP
IIPR, Kanpur
NBPGR, New Delhi
IIPR, Kanpur
Basu Dr PS
Lakhmi Dr Swarn
Singh Dr ON
IIPR, Kanpur
IARI, New Delhi
BHU, Varanasi
Chaudhary Dr AK
Malathi Dr (Ms) VG
Singh Dr Rakesh
IIPR, Kanpur
IARI, New Delhi
NBPGR, New Delhi
Gupta Dr (Ms) Om
Panwar Dr JDS
Singh Dr Sarvjeet
JNKVV, Jabalpur
IARI New Delhi
PAU, Ludhiana
Gupta Dr SC
Praharaj Dr CS
Tripathi Dr AK
ARS, Durgapura, Rajasthan
IIPR, Kanpur
CSAUAT, Kanpur
Iquabal Dr Asif
Pratap Dr Aditya
Verma Dr Prasoon
IASRI, New Delhi
IIPR, Kanpur
IIPR, Kanpur
Jha Dr SK
Raje Dr Ranjeet
Vishwanath Dr KP
IARI, New Delhi
IARI, New Delhi
UAS, Raichur
Katiyar Dr PK
Ram Dr Baldev
Wadaskar Dr R
IIPR, Kanpur
ZARS, Kota
Akola
Kaur Dr (Mrs) Lavinder
Reddy Dr AR
Yadav Dr ND
PAU, Ludhiana
ICARISAT, Patencheru
CAZARI, Bikaner
Kumar Dr D
Singh Dr Archana
Yadav Dr SK
CAZRI, Jodhpur
IGFRI, Jhansi
CRIDA, Hyderabad
Kumar Dr Rajendra
Singh Dr IP
DSR, Mau
IIPR, Kanpur
Kumar Dr Rajesh
Singh Dr Jagdish
IIPR, Kanpur
IIPR, Kanpur
ii
REVIEWER INDEX (2012)
The Editorial Board gratefully acknowledges the help rendered by following referees in reviewing manuscripts for the
Vol. 25 (2012)
Akram Dr M
IIPR, Kanpur 208024 (U.P.)
Gabhane Dr VV
PDKV, Akola 444104
Basu Dr PS
IIPR, Kanpur 208024
Gill Dr BS
PAU,Ludhiana141004 (PB)
Bhatia Dr VS
DSR, Khandwa Road,
Indore 452 001 (M.P.)
Gupta Dr (Mrs) Om
JNKVV,Jabalpur 482004(MP)
Bhatnagar Dr Pooja
ICRISAT,
Patancheru, Hyderabad
Choudhary Dr AK
IIPR Regional Station,
Dharwad 580 005 (Karnataka)
Gupta Dr Sanjeev
IIPR, Kanpur 208024
Gupta Dr SC
ARS, Durgapura, Rajasthan
Kumar Dr D
CAZRI
Jodhpur 342 003 (Rajasthan)
Kumari Dr (Ms) Jayoti
NBPGR, New Delhi 110012
Kumar Dr. Jitendra
Kumar Dr. Narendra
IIPR,Kanpur 208024 (U.P.)
Kumar Dr Rajendra
DSR, Mau
Harer Dr PN
MPKV
Akola 444 104 (M.H.)
Kumar Dr Rajesh
IIPR, Kanpur 208024
Hussain Dr Aftab
UAS, Bangalore
Kumar Dr Ram Vinod
IGFRI, Jhansi (U.P.)
Choudhary Dr VP
PDFSR, Modipuram
Meerut 250110
Iquabal Dr Asif
IASRI, New Delhi-110012
Kumar Dr Sanjeev
ICER, Patna
Das Dr Anchal
IARI, New Delhi-110012
Jha Dr SK
IARI, New Delhi-110012
Mahapatra Dr SD
CRRI, Cuttack (Odisha)
Das Dr Manish
DMAPR
Anand 387 310 (Gujarat)
Kalaimagal Dr (Mrs) T
TNAU
Coimbatore 641003 (T.N.)
Malathi Dr (Ms) VG
IARI, New Delhi 110012
Datta Dr S
IIPR, Kanpur 208024
Kandan DrA
NBPGR, New Delhi 110012
Mallikarjuna Dr N
ICRISAT
Patancheru, Hyderabad
Dillon Dr MK
Katiyar Dr PK
IIPR, Kanpur 208024
Mandal Dr Asit B
CRIJAF, Barrackpore (WB)
Dixit Dr GP
IIPR, Kanpur 208024 (UP)
Kaur Dr (Mrs) Jagmeet
PAU, Ludhiana 141 004
Mudalagiriyappa Dr
UAS,GKVK, Bangalore 560065
Dixit Dr Harsh
IARI, New Delhi 110 012
Kaur Dr (Mrs) Lavinder
PAU, Ludhiana 141 004
Naimuddin Dr
IIPR, Kanpur 208024
Dudeja Dr SS
CCSHAU
Hisar 125 004 (Haryana)
Khan Dr MR
AMU, Aligarh
Nath Dr Rajiv
BCKVV, Kalyani (WB)
Choudhary Dr RG
IIPR, Kanpur 208024
Panda Dr PK
WTC, Bhubaneswar (Odisha)
Pandaya Dr M
IIVR, Varanasi 221 305 (U.P.)
Panwar Dr JDS
IARI New Delhi 110012
Patil Mr Prakash G
IIPR, Kanpur 208 024 (UP)
Praharaj Dr Chandra Sekhar
Prakash Dr Vijay
ARS, Sriganganagar 335 001 (Rajasthan)
Pratap Dr Aditya
IIPR, Kanpur 208024
Rai Dr AB
IIVR, Varanasi 221 305 (U.P.)
Raghvani Dr B R
JAU, Junagadh 362001
Raje Dr RS
Ram Dr Baldev
ZARS, Kota (Rajasthan)
Reddy DrA Amarender
ICRISAT
Patancheru 502 324 (AP)
Reddy Dr AR
ICARISAT
Sah Dr (Mrs) Uma
Samad Dr A
CIMAP, Lucknow
Saxena Dr KB
ICRISAT, Patancheru, Hyderabad
Sengupta Dr Kajal
BCKV, Nadia, 741 252
West Bengal
Singh Dr (Mrs) Archana
IGFRI, Jhansi 284128 (U.P.)
iii
Tripathi Dr AK
CSAUAT, Kanpur 208002 (U.P.)
Upadhyay Dr JP
RAU, Pusa, Samastipur (Bihar)
Singh Dr Awnindra
CARI, Portblair
Varshney Dr Rajeev K
ICRISAT
Singh Dr I P
Venkatesh DR MS
Singh Dr Jagdish
Verma Dr DK
IARI Regional Station,
Indore (MP)
Singh Dr NP
Singh Dr ON
BHU, Varanasi
Singh Dr PK
IAS, BHU, Varanasi
Singh Dr Rakesh
NBPGR, New Delhi
Singh Dr RC
CIAE, Bhopal 462 038
Singh Dr Sarvjeet
PAU.,Ludhiana 14100
Sohrab, Dr SS
King Abdul Aziz University
Saudi Arabia
Solanki Dr IS
IARI Regional Research Station
Pusa, Samastipur 848125
Subramanian Dr S
IARI, New Delhi -110012
Swarnlakshami Dr
Verma Mr Prasoon
Vijaylaxmi Dr (Mrs.)
Vishwanath Dr KP
UAS, Raichur (KTK)
Wadaskar Dr R
Akola (M.H.)
Waldia Dr RS
CCSHAU, Hisar 125 004 (Haryana)
Yadav Dr ND
CAZARI, Bikaner (Rajasthan)
Yadav Dr Rasmi
NBPGR, New Delhi-110012
Yadav Dr RC
CCS HAU, Hisar 125004
Yadav Dr SK
CRIDA, Hyderabad
iv
AUTHOR INDEX
A
Ahmad Shahid (175)
Akhtar J. (81)
Akken M.K. (314)
Akram Mohd (54,286)
Alipatra A. (37)
Andhalkar A.S. (128)
B
Babbar Anita (70,147)
Bairwa R.K. (211)
Balai O.P. (109)
Bandopadhyay P. (37)
Banerjee H. (37)
Bansal Ravindra (18)
Bansal Ravindra (273)
Bansode V.V. (321)
Barkhade U.P. (162)
Barpete Surendra (14)
Bhalkare S.K. (215)
Bharathi M. (96)
Bharathi M. (351)
Bhardwaj R. (234)
Bhardwaj S.K. (246)
Bhareti Priyanka (89)
Bhatia Ranjana (294)
Bhattacharya A (50)
C
Chandra Subhash (326)
Chandra Subhash (71)
Changkija S.A.P.U.(282)
Chaubey B.K. (93)
Chaubey B.K. (348)
Chaudhari D.J. (89,344)
Chaudhary R.G (61)
Chauhan Richa (187)
Chauhan V.B. (76)
Chimote, V.P. (9)
Choudhary Meenu (361)
Choudhary Poonam (361)
D
Dahiya S.S. (153)
Deb D. (112)
Dhanasekar P. (25)
Dhiman Sushil (246)
Dixit S.P. (246)
Durairaj C. (83)
G
Ganeshmurthy A.N. (116)
Gangwar S. (45)
Garg S.K. (131)
Garkoti Ankita (103)
Garkoti Ankita (358)
Gera Rajesh (294)
Gera Rajesh (39)
Ghai Navita (206)
Ghatak Abhijeet (355)
Gill B.S. (314)
Gill K.K. (125)
Gill R.K. (159)
Gill. B.S. (206)
Gopalakrishna (273)
Goud V.V. (128,243)
Gowda M. Byre (194)
Goyal Meenakshi (206)
Goyal Reeti (59)
Goyal Reeti (314)
Gupta Om (139)
Gupta S.C. (45)
Gupta S.K. (234)
Gupta Sudhir Kumar (273)
H
Holmesheoran M.E. (334)
I
Imtiaz M. (79)
Iquebal M.A. (31,147)
J
Jadhao V.P. (215)
Jadhav A.S. (9)
Jat H.L. (227)
Jat Shankar Lal (239)
Jayalakshmi V. (94)
Jayamani P. (279)
Jayaram Neetha (135)
Jyothirmayi G. (94)
K
Kale H.B. (243)
Kale A.A. (9)
Kamaluddin (175)
Kandalker V.S. (231)
Kant Rama (1,102)
Kaur Gurpreet (234)
Kaur Jagmeet (206)
Kaur Jasdeep (206)
Kaur Livinder (79)
Kaur Prabhjit (234)
Kaur Ramndeep (234)
Keval Ram (249)
Khan M.N. (175)
Khanna Veena (125)
Khare D. (200)
Khode N.M. (243)
Khulbe R.K. (89,183)
Kotecha P.M. (321)
Krishna Bal (61)
Krishna K. Ram (109)
Kumar Ajay (234)
Kumar C.V.S. (334)
Kumar D. (1)
Kumar D. (255)
Kumar Deepak (18)
Kumar Dhirendra (66)
Kumar J. (165)
Kumar K. (66,348)
Kumar Narendra (41,116,131)
Kumar Rajesh (61,85,340)
Kumar Rakesh (121)
Kumar Sanjeev (179)
Kumar Shiv (14)
Kumar Varun (294)
Kumar Y. (81)
Kumari Anupma (121)
Kumawat S.R. (156)
Kushwaha K.P.S. (355)
L
Lajjavati (100)
Lakhera J.P. (227,326)
Laxmi Vijay(300)
M
Malhotra R.S. (79)
Manjunath B. (135)
Meena D.S. (306)
Meena H.N. (239)
Meena H.P. (165)
Mishra J.P. (41,55,310)
Mishra Madhuri (139)
Mondal C. (37)
Mula M.G. (334)
Mula R.P. (334)
Muniyappa V. (135)
Murmu H. (81)
Muthaiah A.R. (171)
N
Naik Satheesh S.J. (194)
Naimuddin (31,54,286)
Naphade S.A. (344)
Narasimhamurthy G.M. (249)
P
Pannu R.K. (153)
Panwar R.K. (183)
Parihar C.M. (239)
Parmar Dinesh (14)
Parmar R.K. (231)
Parmila C.K. (194)
Patil A.N. (142,215)
Pawar V.D. (9,321)
Pawar S.V. (9)
Praharaj C.S. (41,61,310,330)
Prakash V. (147)
Prameela H.A. (135)
Punia S.S. (306)
Purushottam (61,330,340)
R
Rai V.P. (179)
Rajesh Kumar J. (83)
Ram Baldev (51,306)
Ram Hari (125)
Ramappa H.K. (194)
Ramesh S. (194)
Rathi Manisha (139)
Rathod K.S. (153)
Ravikumar R.L. (18)
Reddy C.K.K. (94)
Reedy K.S. (25)
Reena G.A. Mary (194)
Rodge A.B. (255)
Roy A.K. (112)
Roy Rina (236)
Rungsung
S
Sah Uma (340)
Sahu Pooja (200)
Saini N. (200)
Sandhu J.S. (79,150,234)
Santosh (361)
Sarika (31)
Sathya M. (279)
Satpute N.S. (162)
Savitha B.N. (18)
Saxena K.B. (334,351)
Sekhon H.S. (125)
Sepehya Swapana (246)
Sewak Shiv (31)
Sharma O.P. (361)
Sharma S.K. (227)
Sharma Sucheta (314)
Sharma N.C. (14)
Shende N.V. (222)
Shinde G. (18)
Shivay Yashbir Singh (239)
Shukla R.K. (291)
Shukla S.S. (70)
Singh A.K. (179)
Singh Amitesh Kumar (73)
Singh Anupma (183)
Singh Archana (112,187)
Singh Avtar (236)
Singh Bansa (58)
Singh D.P. (89)
Singh Guriqbal (125)
Singh Inderjit (150)
Singh J.P. (76)
Singh Johar (159)
Singh K.K. (41,58,116,310)
Singh Lakhan (330)
Singh M.K. (131)
Singh Manpreet (159)
Singh N.P. (31,97,187)
Singh O.N. (121)
Singh P. (227, 326)
Singh P.K. (81)
Singh P.S. (36, 291)
Singh Prakash (66)
Singh R.B. (76)
Singh R.S. (73)
Singh Rupinderpal (150)
Singh S.K. (61,330,340)
v
Singh Sarvjeet (150,159)
Singh U.P. (112)
Singhn Ranjeet (66)
Sirari A. (79)
Sirvastava R.K. (1)
Solanki, R.K. (31)
Srimathy M. (279)
Srinivasarao C.H. (116)
Srivasta A.N. (200)
Srivastava R.K. (348)
Srivastava A. (147)
Surinder Kaur (76)
Swarnalakshmi K. (116)
Srivastav R.K. (1,102)
Srivastava S.P. (70)
Srivastava Arpita (70)
Srivastava C.P. (179)
T
Tetarwal J.P. (306)
Thakare S.S. (222)
Thiruvengadam V. (171)
Tyagi N. (179)
Thorat S.S. (321)
Tikle A.N. (231)
Tiwari Archna (187)
Tripathi H.S. (358)
Tripathi N. (147,200)
U
Ughade Jayashri (142)
V
Vati Lajja (355)
Venkatesha S.C. (194)
Verma Prasoon (131)
Vijyalakshmi (50,45)
W
Wadaskar R.M. (142,215)
Wungsem (282)
Y
Yadav L.R. (106,361)
Yadav B.L. (156)
Yadav C.B. (348)
Yadav Indu Singh (97)
Yadav N.K. (291)
vi
SUBJECT INDEX
A
Achievement motivation (227)
ADF test (344)
Amino acids (286)
Anti-nutrients (321)
ARCH- GARCH (344)
Arid legumes (255,273,326)
Ascochyta blight (79)
Ascochyta rabiei (79)
Association (227)
Azuki bean (273)
B
B-biotype (135)
Bed planting (236)
Bio-agents (358)
Bio-efficacy (291)
Bio-fertilizer (121)
Bioinculants (73)
Blackgram (24,89)
Boron (37)
Breeding (355)
Broadcasting (131)
Bulk Method (165)
Bundelkhand region (61,330)
C
Calendar based application (142,215)
CGMS lines (231)
Character Association (93,348)
Chickpea (27,31,37,41,70,94,97,100, 139,
142,147, 150,165, 187, 234, 236, 291, 310,
355)
Chickpea regeneration (9)
Chlorophyll content (45)
Cluster analysis (24,31, 70,109,147)
Coat protein (286)
Co-integration (344)
Combining ability (231)
Communication behavior (326)
Compact dwarf (25)
Competitive incides (128)
Constraint analysis (340)
Conventional tillage (236)
Cooking characteristics (321)
Correlation (66, 70,179)
Cowpea (273,255)
Credit behavior (227)
Crop geometry (243)
Cropping Sequence (58)
D
D2 analysis (279)
Demonstration (326)
Dibbling (131)
Diseases (200)
Dolichos Bean (18)
Drought (94)
Dry matter accumulation (50)
Dry matter production (73)
Dry root rot (139)
Durable resistance (79)
E
Economic (236,239,243)
Economic motivation (326)
Embryonic axis (9)
Epistasis (1)
ET (41)
F
Faba bean (348)
Farmers participation (330)
Fenugreek (156)
Fertilizer level (159)
Field bean (18)
Fieldpea (121,310)
Fodder yield (109)
FPARP (330)
French bean (54)
Functional properties (321)
Fusarium wilt (81,358)
G
G × E interaction (282)
Garden pea (246)
Gene action (1)
General combining ability (171)
Generation mean analysis (1)
Genetic divergence (150,279)
Genetic diversity (18,89,147,194,200)
Genetic male sterility (171)
Genetic variability (70,348)
Genetics variation (109)
Genotypes (125)
Genotypic correlation (31)
Germination (351)
Germplasm (18,31)
Gibberellic acid (25)
Grain yield (73,109)
Gram pod borer (142)
Granger Causality Test (344)
Grass pea (109)
Groundnut bud necrosis virus (54)
Growth and reproductive stages (187)
Growth (37,222)
Guar (255)
H
Harvest index (159)
Helicoverpa armigera (83,291)
Heritability (66)
Heterobeltiosis (102)
Horse gram (255)
Hybrids (351)
I
Imazethapyr (306)
Inbred cultivars (351)
Inbreeding depression (102)
Induced variability (109)
Indigenous pheromone lures (83)
Inheritance (94)
Inorganic nutrient (121)
Interspecific derivatives (150)
Insecticides (36,249)
Integrated nutrient management (246)
Iron (45)
Irrigation (45)
Irrigation (300)
ISSR markers (89)
K
Kabuli chickpea (79)
L
LAI (73)
Land equivalent ratio (128)
Lathyrus sativus (109)
Leaf angle (50)
Leaf area (50)
Leaf chlorophyll (94)
Legumes – wheat rotation (116)
Lentil (66,81,300,358)
Lentil genotypes (159)
Lodging (179)
M
Mahalanobis D2 statistics (150)
Melanagromyza obtusa (249)
Meloidogyne javanica (58)
Micrografting (97)
Mid parent heterosis (102)
Milling quality (70)
Molecular marker (200)
Molecular markers (147)
Molybdenum (45)
Morpho-physiological traits (206)
Mothbean (255,361)
Mould (355)
Mulch (310)
Mulching (41)
Mungbean (37,50,89,109,135, 211,)
Mutant progenies (109)
MYMIV (286)
MYMV (135)
N
N Management (153)
NaCl (187)
Nematode (58)
Net returns (361)
Newer insecticides (142,215)
Nod C (294)
Nodulation (125)
NPK uptake (153,246)
NPKSZn (243)
NSm gene (54)
Nucleotides (286)
Nutrient accumulation (239)
Nutrient availability (239)
Nutrient uptake (128)
O
Oil (314)
Organic nutrient (121)
P
Parity Index (222)
Path analysis (70,348)
Path and cluster analysis (179)
Path coefficient analysis (348)
Path coefficient (66)
PCR (286)
Pea (179)
Pedigree Method (165)
Pendal type (18)
Phaseolus vulgaris (54)
Phenological days (50)
Phenotypic correlation (31)
Phosphorus levels (361)
Phosphorus levels (73,211,361)
Photoperiod (25)
Photosynthetic rate (50)
Phylogeny (294)
Phytic acid (314)
Pigeonpea (25,76,162,171, 194,215,
231,334,344)
Pigeonpea hybrid (243)
Pigeonpea Podfly (249)
Pigeonpea Transplanting (128)
Plant growth regulators (206)
Planter (131)
Planting time (125)
Pod borer complex (162,215)
Polymorphism (89,194)
Pra-harvest sprouting (183)
Presoak treatment (321)
Price movement (344)
Principal Component Analysis (31,109)
Progressiveness (227)
Protein (314)
PSB (45)
Pulse production technologies (340)
Pulse treatment (9)
Pulses (61,330)
Purple seed stain (200)
Q
QTLs Soybean (200)
Qualitative short day (25)
Quality seed production (75)
Quality traits (66)
Quizalofop ethyl (306)
R
Rabi (25)
Rainfed (61)
16S Rdna (294)
Regeneration (97)
Residues incorporation (116)
Resistance (139,355)
RFLP (294)
Rhizobia (294)
Rhizobium (45)
Rhizoctonia bataticola (139)
Rice bean (282,321)
Rice fallow (25)
Rice-wheat rotation (239)
Root-knot nematode (58)
RSC water (156)
Rynaxypyr (162)
S
Salinity tolerance (187)
Seasonal variation (344)
Seed bed configurations (310)
Seed drill (131)
Seed inoculation (45)
Seed size (234)
Seed village system (334)
Seed village system (334)
Seed Yield (41,116,125,156,206,211,234,
282,300,310)
Seedling vigour index (351)
Selected Bulk Method (165)
Single Seed Descrent Method (165)
Socio economic status (326)
Socio-economic status (227)
Soil application (45)
Soil fertility (128,153)
Soil Properties (116)
Sowing dates (159)
Sowing equipment (131)
vii
Soybean genotypes (314)
Soybean (128,153,175,206,222)
SPAD chlorophyll meter (94)
Specific combining ability (171)
Spinosad (249)
SSR markers (194,273)
SSR (200)
Stability analysis (175,282)
Standard heterosis (102)
Stem canker (76)
Sterility Mosaic Disease (194)
Sulphur levels (211)
Summer legumes (239)
T
Technological input assessment (61)
Technology adoption (340)
Technology dissemination process (340)
Temporel variation (222)
Thidiazuron (9)
Tillage (41)
Total soluble sugars (314)
Transferability (273)
Transgenics (97)
Transmission (135)
Trap catches (83)
Tropical legumes 2 (334)
Truthfully labeled seed (334)
Trypsin inhibitor (314)
U
Urdbean (1,58,102,125)
V
Variation (286)
Varietal improvement (81)
Vermicompost (121)
Vermicompost Zn mobility ratio (156)
Vigna (89)
Vigna mungo (1,279)
Vigna species (273)
W
Water use efficiency (41,310)
Weather parameters (83)
Weed control efficiency (306)
Weed index (306)
Whitefly (135)
Y
Yield (37,66,175,179)
Yield attributes (211,206,246)
Yield components (175,300)
Z
Zero tillage (236)
Zinc levels (361)
viii
Indian Institute of Pulses Research, Kanpur – 208 024
ISPRD Fellowship Awards 2012
To encourage pulses research and development, ISPRD admits its members as
Fellows. Applications in the prescribed proforma are invited from eligible ISPRD
members for the award of ISPRD Fellowship for the year 2012. Any member is
eligible if he/she has been the member of the Society continuously preceding
last 5 years and has at least 3 research papers related to food legumes (out of
which, one must have been published in the Journal of Food Legumes). Only
those 2 research papers, which were published in other Journals having NAAS
rating at or above par with Journal of Food Legumes, will be considered. Filledin applications along with necessary enclosures should be submitted to the Secretary,
Indian Society of Pulses Research and Development, IIPR, Kanpur 208 024
(U.P.) by February 28, 2013. Those who are already Fellows of the Society
need not apply.
-sdA K Choudhary
Secretary, ISPRD
secretary.isprd@gmail.com
ix
PROFORMA
Indian Institute of Pulses Research, Kanpur 208 024
ISPRD Fellowship Awards 2012
1.
Name in Full :
2.
Father’s Name :
3.
Date of Birth :
4.
Designation :
5.
Field of specialization :
6.
Address (with Telephone No., E-mail and Fax)
Passport Size Photo
Office : ________________________________________________________________________________________
Residence : ____________________________________________________________________________________
7.
Academic career
Degree
8.
University/Institution
Year
Distinction, if any
Employment Record and Experience
Designation
Organization
Period
9.
Enlist only three best publications indicating (a) Name of author(s), (b) year, (c) title, (d) name of journal, volume no. and
page nos.
10.
Date/year of life/ordinary membership of ISPRD.
11.
Special services rendered to ISPRD.
Signature of applicant with date
Head of Institute/Organization
(Optional)
x
Obituary
Dr Laxman Singh: Former Project Director, Directorate of Pulses Research,
Kanpur
Dr Laxman Singh (78), an eminent scientist and research manager passed away
on 19, December 2012 at his residence in Indore (Madhya Pradesh). He was born
on 4 April, 1935 in Naulana, Gautampura, Indore (MP). Dr Singh is survived by
a son and a daughter. He completed his B Sc from Gwalior and M Sc (Ag) from
Agriculture College, Kanpur. He also received PhD from a US university. Dr
Singh worked as Professor in JNKVV, Jabalpur before joining as Project Director,
Directorate of Pulses Research, (Presently IIPR), Kanpur. He was life member of
ISPRD. He also served as consultant in the Caribbean Agricultural Research &
Development Institute (CARDI), Trinidad and later on served ICRISAT, Hyderabad as Principal Pigeonpea
Breeder (1986 - 1997). He also lived in the US but surrendered his green card and returned to India and
started welfare work for the society. Dr Singh had a great sense of humor and was compassionate person.
As per his last wish, his eyes and body were donated for medical research to MGM Medical College,
Indore (MP). May almighty give the bereaved family the strength and courage to face this loss.
Journal
of Food Legumes
25(4), 2012
Instructions
to Authors
Journal of Food Legumes (formerly Indian Journal of Pulses
Research) publishes original papers, short communications
and review articles by renowned scientists, covering all areas
of food legumes research. The paper should not have been
published or communicated elsewhere. Authors will be solely
responsible for the factual accuracy of their contribution.
Language of publication is English (British).
Please send your manuscript to following address:
Secre tary
ISPRD
Indian Institute of Pulses Research
Kalyanpur, Kanpur 208 024, India
Email: secretary.isprd@gmail.com
Manuscript must be submitted through e-mail. You should
also submit a hard copy of your manuscript for our official
record. Besides author(s) is required to submit a certificate
that the paper is exclusive for Journal of Food Legumes.
Manuscripts must conform to the Journal style (see the latest
issue). Correct language is the responsibility of the author.
After having received your contribution (date of submission),
there will be a review process before the editorial board takes
decision regarding acceptance for publication. One copy of
the revision together with the original manuscript must be
returned to the subject editor or Secretary. The submitted
paper must be one complete word document file comprising a
title page, abstract, text, references, tables, figure legends and
figures. When preparing your text file, please use only Times
New Roman for text (12 point, double spacing) and Symbol
font for Gr eek letters to a void ina dver tent cha racter
substitutions.
Format
Every original paper should be divided into the following five
sec tions: ABSTRACT, Key words, INTRODUCTION,
MATERIALS AND METHODS, RESULTS AND
DISCUSSION, and REFERENCES. The manuscript should be
typed on one side of the paper only, double spaced, and with
4-cm margins with page and line numbers. The main title must
be capital bold. Subheading must be bold italic and Sub-sub
heading normal italic.
At the head of the manuscript, the following information
should be given: the title of the paper, the name(s) of the
author(s), the institute where the research was carried out,
the present addresses of the authors (foot note) and of the
corresponding author (if different from above Institute).
Authors are required to provide running title of the paper.
You must supply an E-mail address for the corresponding
author.
The abstract should contain at least one sentence on each of
the following: objective of investigation (hypothesis, purpose,
aim), experimental material, method of investigation, data
collection, result and conclusions. Maximum length of abstract
is 175 words. Up to 10 key words should be added at the end
of the abstract and separated by comma. Key words must be
arranged alphabatically (e.g., EMS, Gamma ray, Mungbean,
Mutations, Path coefficient, ......).
Each figure, table, and bibliographic entry must have a
reference in the text. Any correction requested by the reviewer
should also be integrated into the file.
Manuscript file including tables must be in MS Word and
Windows-compatible and must not contain any files other
than those for the current manuscript. Please do not import
the figures into the text file. The text should be prepared using
standard software (Microsoft Word); do not use automated
or manual hyphenation.
xi
Length
Manuscripts should not exceed a final length of 15 printed
pages, i.e., 5,000 words, including spaces required for figures,
ta bles and lis t of references. Manuscr ipts for short
communications should not exceed 3000 words (3 printed
pages, with not more than a total of 2 figures or tables).
Units, abbreviations and nomenclature
For physical units, unit names and symbols, the SI-system
should be employed. Biological names should be given
according to the latest international nomenclature. Botanical
and zoological names, gene designations and gene symbols
are italicised. Yield data should be reported in kg/ha. The name
of varieties or genotypes must start and end with single
inverted comma (e.g., ‘Priya’, ‘IPA 204’, ......).
Tables and Figures
Tables and figures should be limited to the necessary minimum.
Please submit reproducible artwork. For printing of coloured
photogr aph, aut hors wil l be charged Rs. 4000/- per
photograph. It is essential that figures are submitted as highresolution scans.
References
The list of references should only include publications cited
in the text. They should be cited in alphabetical order under
the first author ’s name, listing all author s, the ye ar of
publication and the complete title, according to the following
examples:
Becker HC, Lin SC and Leon J. 1988. Stability analysis in plant
breeding. Plant Breeding 101: 1-23.
Sokal RR and Rholf FJ. 1981. Biometry, 2nd Ed. Freeman, San
Francisco.
Tandon HLS. 1993. Methods of Analysis of Soils, Plants, Water
and Fertilizers (ed). Fertilizer Development and Consultation
Organization, New Delhi, India. 143 pp.
Singh DP. 1989. Mutation breeding in blackgram. In: SA Farook
and IA Khan (Eds ), Breedi ng Food Legumes. Premier
Publishing House, Hyderabad, India. Pp 103-109.
Takkar PN and Randhawa NS. 1980. Zinc deficiency in Indian
soils and plants. In: Proceedings of Seminar on Zinc Wastes
and their Utilization, 15-16 October 1980, Indian Lead-Zinc
Information Centre, Fertilizer Association of India, New Delhi,
India. Pp 13-15.
Satyanarayan Y. 1953. Photosociological studies on calcarious
plants of Bombay. Ph.D. Thesis, Bombay University, Mumbai,
India.
In the text, the bibliographical reference is made by giving the
name of the author(s) with the year of publication. If there are
two references, then it should be separated by placing ‘comma’
(e.g., Becker et al. 1988, Tandon 1993). If references are of the
same year, arrange them in alphabatic order, otherwise arrange
them in ascending order of the years.
While preparing manuscripts, authors are requested to go
through the late st issue of the journal. Authors are also
required to send the names & E-mail address of at least 3-4
reviewers appropriate to their articles.
16. Dissemination of pulse production technologies in Uttar Pradesh : A micro-level analysis
Rajesh Kumar, S.K. Singh, Purushottam and Uma Sah
17. Pigeonpea (Cajanus cajan L.) price movement across major markets of India
D.J. Chaudhari and A.S. Tingre
SHORT COMMUNICATIONS
18. Genetic variability, character association and path coefficient analysis in faba bean
B.K. Chaubey, C.B. Yadav, K. Kumar and R.K. Srivastava
19. A comparative study of hybrid and inbred cultivars for germination and other related traits of pigeonpea
M. Bharathi and K.B. Saxena
20. Screening of chickpea (Cicer arientinum L.) genotypes for identification of source of resistance to
Botrytis grey mould
Lajja Vati, K.P.S. Kushwaha and Abhijeet Ghata
21. Management of Fusarium wilt of lentil using antagonistic microorganisms in Tarai region of Uttarakhand
Ankita Garkoti and H.S. Tripathi
22. Effect of phosphorus and zinc on yield and economics of mothbean under semiarid conditions
L.R. Yadav, Poonam Choudhary, Santosh, O.P. Sharma and Meenu Choudhary
Reviewer Index (2012)
Author Index (2012)
Subject Index (2012)
ISPRD Fellowship awards, 2012
Obituary
340
344
348
351
355
358
361
i
ii
iv
vi
viii
x
ISSN
0970-6380
Online ISSN
0976-2434
Volume 25
I SPR D
1987
Number 4
December 2012
Contents
RESEARCH PAPERS
1 Status, scope and strategies of arid legumes research in India- A review
D. Kumar and A.B. Rodge
2. Transferability of cowpea and azuki bean derived SSR markers to other Vigna species
Ravindra Bansal, Sudhir Kumar Gupta and T. Gopalakrishna
3. Genetic diversity studies in blackgram (Vigna mungo L. Hepper)
M. Srimathy, M. Sathya and P. Jayamani
4. Stability analysis for seed yield and its component traits in rice bean (Vigna umbellata)
Wungsem Rungsung and S.A.P.U. Changkija
5. Sequence comparison of coat protein gene of Mungbean yellow mosaic India virus isolates infecting
mungbean and urdbean crops
Naimuddin and M. Akram
6. Bio-efficacy of some insecticides against H. armigera (Hubner) on chickpea (Cicer arietinum L.)
P.S. Singh, R.K. Shukla and N.K. Yadav
7. Comparison of nodC and 16S rDNA gene analysis of rhizobia associated with legumes of arid and
semi-arid regions of Haryana
Rajesh Gera, Ranjana Bhatia and Varun Kumar
8. Phenology, dry matter distribution and yield attributes under normal and drought stress conditions
in Lentil (Lens culinaris Medik.)
Vijay Laxmi
9. Efficacy of post emergence herbicides on weed control and seed yield of rajmash (Phaseolus vulgaris L.)
Baldev Ram, S.S. Punia, D.S. Meena and J.P. Tetarwal
10. Enhancing water use efficiency and production potential of chickpea and fieldpea through seed bed
configurations and irrigation regimes in North Indian Plains
J.P. Mishra, C.S. Praharaj and K.K. Singh
11. Variability in the nutrients, antinutrients and other bioactive compounds in soybean
Reeti Goyal, Sucheta Sharma and B.S. Gill
12. Effect of presoak treatment on cooking characteristics and nutritional functionality of rice bean
V.D. Pawar , M.K. Akkena, P.M. Kotecha, S.S. Thorat and V.V. Bansode
13. Factors associated with economic motivation of legume growers in desert area of Rajasthan
Subhash Chandra, P.Singh and J.P. Lakhera
14. Farmers participatory approach in seed multiplication of pulses in Bundelkhand region - A case study
Purushottam, S.K. Singh, C.S. Praharaj and Lakhan Singh
15. Tropical Legumes 2 pigeonpea seed system in India: An analysis
M.E. Holmesheoran, M.G. Mula, C.V.S. Kumar, R.P. Mula and K.B. Saxena
Published by Dr AK Choudhary, Secretary on behalf of
The Indian Society of Pulses Research and Development (www.isprd.in) from
Indian Institute of Pulses Research, Kanpur-208 024
Phone : 09019870914
E-mail: secretary.isprd@gmail.com, akiipr23@yahoo.com
at Army Printing Press, 33, Nehru Road, Sadar Cantt. Lucknow-2 Ph.: 0522-2481164
For free download of JFL articles, please also visit: www.indianjournals.com
255
273
279
282
286
291
294
300
306
310
314
321
326
330
334