Relationship Studies in Cowpea (Vigna unguiculata L. Walp) Landraces Grown under Humid Lowland Condition
Establishing the extent of relationship, identifying the cause and measuring the relative importance of such associations to yield is very crucial for varietal selection, breeding and subsequent improvement of crops, especially cowpea landraces. This research x-rays correlation coefficients and path coefficients of yield and yield-influencing traits in cowpea landraces. Seeds of four varieties of locally grown cowpea were sown in a Randomized Complete Block Design (RCBD) in ten replications. The field study was carried out at the University of Calabar Experimental Farm, University of Calabar, Nigeria, during the 2010-2011 growing season. Correlation coefficients and path coefficients were computed on yield and yield-contributing traits. Results obtained revealed that significant relationships between yield and yield-contributing traits existed which could be indices for selection. Genotypic correlations coefficients were high and more significant than the phenotypic and environmental correlation coefficients. Path coefficient analysis shows that number of pod per plant had the highest direct effects to cowpea yield (0.588). This was followed by number of flowers (0.454), number of seeds per pod (0.366), leaf area at 5 weeks (0.366) and pod length and 100 seed weight (0.316), respectively. Other morphological traits had negative direct effects on seed yield such as vein length at 10 weeks (-0.627), number of leaves at 5 weeks (-0.215), number of leaves at 10 weeks (-0.033), leaf area at 10 weeks -1.124, days to 50% flowering (-0.083) and days to 50% maturity (-0.066). Succinctly, it therefore implies that number of pods per plant, number of leaves per plant, leaf area, number of flowers per plant, pod length and number of seeds per pod are good selection indices for a high yielding variety of cowpea, especially the landraces. This then can be exploited in hybridization programmes involving cowpea.
August 04, 2011; Accepted: November 07, 2011;
Published: January 03, 2012
The fact that pulses have high adaptability potentials and high nutritive values
especially the landraces, should be enough incentives for their exploration
and exploitation if food security is anything to accord priority in Nigeria
(Udensi et al., 2011a, b).
Cowpea (Vigna unguiculata L. Walp) is one of the sixth major cultivated
crop species of the family Leguminosae distributed throughout the tropics
(Pasquet, 2001). The leaves, stems and seeds have been
reported to have antimicrobial properties.
For two decades now, scientists in the field of genetic transformation of cowpea
have made great strides with little success to develop reliable transformation
systems for this crop that is very important as a good source of protein and
energy for people in developing countries of Africa and Asia (Obembe,
2009). Grain legumes generally have been regarded as recalcitrant to transformation
because of poor regeneration ability (especially via callus) and in vitro
regeneration being genotypic-specific. Worst still, most cowpea cultivars are
infrequently amenable to regeneration. There is also the problem of compatible
gene delivery system (Somers et al., 2003; Chandra
and Pental, 2003; Popelka et al., 2004).
Other researchers in this field such as Sahoo et al.
(2000) and Ikea et al. (2003) were however,
not able to provide molecular evidence of stable transformation with Mendelian
transmission of the transgenes to progeny. According to Popelka
et al. (2006), Chaudhury et al. (2007),
Ivo et al. (2008) and Solleti
et al. (2008a, b) there are seeming roads
at the end of the tunnel as regarding cowpea transformation and improvement
though with little percentage transformation efficiency. These recent successes
in cowpea genetic transformation have therefore paved way for the introduction
of more agronomic traits to cowpea, thereby enhancing the genetic diversity
of the crop and consequently complementing existing breeding programmes (Obembe,
2009). Additionally, molecular (DNA) or markers-assisted selection and improvement
through Quantitative Trait Loci (QTLs) mapping has advanced successfully, the
need to complement these approaches should not be overemphasized as more often
than not local farmers and breeders are deprived of these high yielding genotypes
(Udensi et al., 2010). Regrettably, since the
Sub-Saharan Africa, indeed Nigeria is bedeviled with institutional and infrastructural
problems coupled with the whooping sums of money involved in biotechnological
processes, resting all our shoulders on these modern techniques might spell
doom to future food security which is topical in vision 2020 agenda of the government
in all tiers.
The study of relationships among quantitative traits is important for assessing
the feasibility of joint selection of two or more traits and hence for evaluating
the effect of selection for secondary traits on genetic gain for the primary
traits under consideration (Umar et al., 2010).
While correlation measures the mutual association between two variables (traits),
path coefficient analysis on the other hand, identifies the cause and measures
the relative importance of the association. Additionally, path coefficient analysis
provides an effective means of partitioning correlation coefficients into unidirectional
pathways and alternate pathways thus permitting a critical examination of specific
factors that produce a given correlation which can be successfully employed
in formulating an effective selection programme (Salahuddin
et al., 2010). Importantly, path coefficient is synonymous to standardized
coefficient of regression after a multiple regression analysis using data which
are independent of original units of measurement (Cramer
and Wehner, 2000b). The technique of path coefficient analysis has been
extensively used by Azeem and Azhar (2006), Afiah
and Ghoneim (2000), Cramer and Wehner (2000b) and
Akinyele and Osekita (2006). Additionally, Iqbal
et al. (2003) has used this method in soybean; Yadev
et al. (2001), Arshad et al. (2003),
Ghafoor et al. (2003) and Arshad
et al. (2004) have used the technique in other legumes.
This presented study however, is aimed at selecting superior genotype(s) of cowpea landraces through correlation studies which could be exploited in hybridization programmes to develop and select hybrids with superior traits.
MATERIALS AND METHODS
Four landraces of cowpea-Akidi, Olaudi, Ileje ajaka and Ileje were obtained
from dealers in Enugu and Kogi States, Nigeria, respectively. A plot of land
measuring 10x10 m was manually cleared in the University of Calabar Experimental
Farm, Calabar. Five beds were made with a spacing of 2 m between beds. Three
seeds were sown in a hole of 4 cm deep per variety (Center
for New Crops and Plants Products, 2002). The 4 varieties were randomized
on each bed with 8 replications per variety using Randomized Complete Block
Design (RCBD). Spacing was 50x75 cm. After seedling emergence, each stand of
individual variety was thinned down to 2 stands. Weeding was done 3 and 5 weeks
after planting while staking was done 4 weeks after planting. Days to seedling
emergence was noted while data on number of leaves, leaf area and vein length
were collected 5 and 10 weeks after planting. Days to 50% flowering and days
to 50% maturity were also recorded while number of flowers per plant; pod lengths,
inter-node length per plant, number of seeds per pod, number of pods per plant,
seed yield per plants and 100 seed weight were obtained at maturity.
Statistical analyses: Data obtained were subjected to correlation and
path coefficient analysis using statistical software PASW 18. Path coefficient
was taken as the standardized coefficient of regression (direct effect) while
the indirect effect was computed by multiplying the path coefficient of individual
traits with their corresponding correlation coefficients (Cramer
and Wehner, 2000a). The residual effect was estimated while genotypic, phenotypic
and environmental correlations were computed according the method of Singh
and Chaudhury (1985).
Simple pearson correlation: Our result in Table 1
presents the Pearson correlation coefficients. It shows that there were significant
positive correlations between days to seedling emergence and number of leaves
at 10 weeks (0.684*), vein length at 5 weeks and days to 50% flowering (0.684*)
and pod length per plant and seed yield per plant (0.93***). Conversely, there
were also negative significant correlation between leaf area at 5 weeks and
pod length (-0.651*) and seed yield per plant (-0.725*); days to 50% flowering
and number of seed per plant (-0.918***). However, other morphological traits
studied had high correlations but were not significant.
Phenotypic correlation: Phenotypic correlation results revealed that there were positive significant association between number of leaves at 5 weeks and inter-node length (0.665*); leaf area at 5 weeks and number of pods per plant (0.665*); number of flowers and pod length (0.905***); inter-node length and seed yield (0.6300*) while negative significant relationship existed between days to seedling emergence and number of seeds per pod (-0.8473**) (Table 2).
Genotypic correlation: Furthermore, the genotypic correlation of yield
and yield-related traits showed that there were generally high significant associations
among most of the characters studied. The result shows that days to seedling
emergence significantly correlated positively with vein length at 10 weeks (0.896**),
leaf area at 5 weeks (0.84**), leaf area at 10 weeks (0.62*) and negatively
with inter- node length (-0.858**), days to 50% flowering (-0.835**) and number
of seeds per pod (-0.919***). It was also observed that vein length at 10 weeks
significantly correlated positively with number of pods per plant (0.801**);
leaf area at 5 and 10 weeks and days to 50% maturity with number of pods per
plant (0.633*, 0.764**, 0.688*), respectively. There were positive significant
association between number of leaves at 10 weeks, number of flowers with number
of seeds per pod (0.739**, 0.979***). Days to seedling emergence, vein length
at 5 weeks correlated negatively with number of seeds per pod (-0.919***, -0.646*)
||Pearson correlation on yield and yield-related traits in landraces
of cowpea (Vigna unguiculata L. Walp)
|*p = 0.05, **p = 0.01, ***p = 0.001
||Phenotypic correlation of yield and yield-contributing traits
in cowpea (Vigna unguiculata L. Walp) landraces
|*p = 0.05, **p = 0.01, ***p = 0.001
||Genotypic correlation of yield and yield-contributing traits
in cowpea (Vigna unguiculata L. Walp) landraces
|*p = 0.05, **p = 0.01, ***p = 0.001
Environmental correlation: Result obtained on environmental correlation revealed that there were positive significant associations between vein length at 10 weeks and days to 50% maturity (0.63*) and seed yield per plant (0.63*) but negatively with 100 seed weight (-0.998***). Positive significant relationship existed between number of leaves and leaf area at 5 weeks (0.859**) and pod length (0.681*) while correlating negatively with number of pod per plant (-0.648*) and 100 seed weight (-0.984***). It was also observed that number of pod per plant correlated positively with seed yield per plant (0.899**). However, other morphological traits had high correlation coefficients (Table 4).
Path coefficients: Direct (path coefficients) and indirect effects of
yield-contributing traits to the yield of cowpea are as presented on Table
5. It shows that number of pod per plant had the highest direct effects
to cowpea yield (0.588). This was followed by number of flowers (0.454), number
of seeds per pod (0.366), leaf area at 5 weeks (0.366) and pod length and 100
seed weight (0.316), respectively. Other morphological traits had negative direct
effects on seed yield such as vein length at 10 weeks (-0.627), number of leaves
at 5 weeks (-0.215), number of leaves at 10 weeks (-0.033), leaf area at 10
weeks -1.124, days to 50% flowering (-0.083) and days to 50% maturity (-0.066).
Some traits exhibited high total genotypic correlation coefficients.
It is undoubtedly obvious that genetic dissection and mapping of a Quantitative
Traits Loci (QTLs) of any crop to decipher the exact locus of the chromosome
where a particular trait could be found for onward improvement is a herculean
task and thus needs preliminary work such as relationship studies which will
guide selection. According to Umar et al. (2010),
relationships study among quantitative traits is important for assessing the
feasibility of joint selection of two or more traits. Yield (seed yield) as
a complex polygenic trait influenced by many genes and factors working in synergy
(Ezeaku and Mohammed, 2006) is the focal point in cowpea
breeding and improvement.
According to Akinyele and Osekita (2006), mutual association
of traits is often expressed by the phenotypic, genotypic and environmental
correlations. Phenotypic correlation is directly proportional to the genotypic
and environmental correlations. Present result shows that there were positive
significant phenotypic correlation between number of leaves at 5 weeks and inter-node
length; leaf area at 5 weeks and number of pod per plant; inter-node length
and seed yield and number of flowers per plant and pod length. The broader the
leaves, the more leaf surface that will be exposed to photosynthetic activity
and the more pod that will be produced. This might be due to the higher photosynthetic
efficiency of the varieties (Fagwalawa, 2000; Akinyele
and Osekita, 2006). This however, might mean more flowers which might affect
the pod lengths and ultimately seed yield. Their facilitatory role contributes
significantly to the final yield and thus should be considered during selection
to improve yield in cowpea breeding. Conversely, the negative significant phenotypic
correlation observed between days to seedling emergence and number of seeds
per pod indicates that the time taken for cowpea variety to germinate and emerge
into seedling could affect the number of seeds produced per pod. It might suggest
that selection of cowpea genotypes should take into cognizance early germination
capacity which could lead to higher seed production.
||Environmental correlation of yield and yield-contributing
traits in cowpea (Vigna unguiculata L. Walp) landraces
|*p= 0.05, **p= 0.01, ***p= 0.001
||Direct (bold) and indirect effects of yield-contributing traits
in cowpea landraces (Vigna unguiculata L. Walp)
A positive genetic correlation between two desirable traits makes selection
easy for improving both traits simultaneously while the reverse is the case
for negative correlation. Results obtained show that the genotypic correlation
coefficients were very high as compared to the phenotypic and environmental
effects. This indicates greater contribution of genotypic factor in the growth
and development of these trait associations. It also show that vein length at
10 weeks significantly correlated positively with number of pods per plant leaf
area at 5 and 10 weeks and days to 50% maturity with number of pods per plant,
respectively. There were also positive significant associations between numbers
of leaves at 10 weeks, number of flowers with number of seeds per pod. This
corroborates the report of Cramer and Wehner (2000a,
b). High and significant genotypic correlation coefficients
are an indication that selection of cowpea landraces for future breeding programmes
should be fundamentally based on the superiority of the genotypes alongside
with their phenotypic expression. These according to Akinyele
and Osekita (2006) are very important in determining the yield of the crop.
This implies that these traits should be given high priority during selection.
Traits with negative genotypic correlation imply that a lot of breeding programmes
are needed to develop such traits. The significant relationship between days
to 50% flowering and days to 50% maturity is suggestive that there is a strong
relationship between the stage of plant growth at which flowering is initiated
and the time taken to complete the crops life cycle (Akinyele
and Osekita, 2006).
The implication of significant positive relationship of environmental correlation
is that environmental factors favour the performance of cowpea and thus should
be selected for as a component of yield. Interestingly, our result revealed
positive significant environmental correlations between number of leaves at
10 weeks and leaf area at 5 weeks; leaf area at 10 weeks and number of flowers
per plant; number of leaves at 10 weeks and pod length; nein length at 10 weeks
and seed yield; number of pod per plant and seed yield and number of seeds per
pod and 100 seed weight. This means that environmental factors prevailing during
the growing season of these cowpea genotypes favoured the production of longer
vein, leaf production and broader leaf surface area which might have culminated
to higher flower numbers, longer pods and most importantly the seed yield. However,
other traits with negative but significant correlation coefficients imply that
environmental factors could have exerted great impediments to the growth and
development of the traits. This obviously will be detrimental to the crop productivity.
Additionally, although some of these characters that exercise negative correlations
with one another will be difficult to select for in characterization of desired
traits, those with negative association but non-significant correlation will
be disregarded in selection for crop or variety improvement (Henry
and Krishna, 1990; Akinyele and Osekita, 2006).
Accordingly to Cramer and Wehner (2000a), a large path
coefficient indicates that the change will result in a proportional or inversely
proportional change in another correlated trait, whereas a weak coefficient
indicates that the change will have little effect on the second trait. Using
(Cramer and Wehner, 2000a) statistical test for the
importance of path coefficient (strong = 0.7 to 1.0 or -0.7 to -1.0; weak =
-0.69 to 0.69), our result fall below their mark. This notwithstanding, other
authors in the field reported path coefficients lesser than Cramer
and Wehner (2000a) hypothesis but asserting their strong effects on yield
(Vange and Moses, 2009; Akinyele
and Osekita, 2006). The trait-contributing the highest direct effect was
number of pods per plant which also corroborates with the earlier reports of
Khan et al. (2000), Singh
and Yadava (2000) and Shrivastava et al. (2001)
in soybean. The high positive direct effects of number of pod per plant, number
of flowers, number of seeds per pod, pod length, days to seedling emergence,
leaf area and 100 seed weight on seed yield suggests that with other variables
held constant, an increase in the above-mentioned morphological traits might
increase seed yield per plant. This was also the position of Akinyele
and Osekita (2006) in their report on Abelmoschus esculentus. The
positive direct effects of 100 seed weight on seed yield corroborates reports
of Yadev et al. (2001) in wheat; Ashraf
et al. (2002) in urdbean and Arshad et al.
(2004) in chicken pea.
Furthermore, according to Singh and Chaudhury (1985),
if the total genotypic correlation coefficient is positive with negative or
negligible direct effect, the indirect effects might be the causal factor of
correlation. Critically, the seed yield of cowpea landraces might not be attributed
directly by the morphological traits investigated rather via their alternative
pathways (indirect routes) (Table 5). This according to Salahuddin
et al. (2010) should be considered simultaneously for during selection.
Succinctly, it therefore implies that number of pods per plant, number of leaves per plant, leaf area, number of flowers per plant, pod length and number of seeds per pod are good selection indices for a high yielding variety of cowpea, especially the landraces. This then can be exploited in hybridization programmes involving cowpea.
Afiah, S.A.N. and E.M. Ghoneim, 2000. Correlation, stepwise and path coefficient analysis in Egyptian cotton under saline condition. Arab Univ. Agric. Sci., 8: 607-618.
Akinyele, B.O. and O.S. Osekita, 2006. Correlation and path coefficient analyses of seed yield attributes in okra (Abelmoschus esculentus (L.) Moench). Afr. J. Biotechnol., 5: 1330-1336.
Direct Link |
Arshad, M., A. Bakhsh and A. Ghafoor, 2004. Path coefficient analysis in chickpea (Cicer arietinum L.) under rainfed conditions. Pak. J. Bot., 36: 75-81.
Direct Link |
Arshad, M., A. Bakhsh, M. Zubair and A. Ghafoor, 2003. Genetic variability and correlation studies in chickpea (Cicer arietinum L.). Pak. J. Bot., 40: 637-647.
Ashraf, M., A. Ghafoor, N.A. Khan and M. Yousaf, 2002. Path coefficient in wheat under rainfed conditions. Pak. J. Agric. Res., 17: 1-6.
Direct Link |
Azeem, I.K. and S.F.M. Azhar, 2006. Estimates of heritability and pattern of association among different characters of Gossypium hirsutum (L.). Pak. J. Agric. Sci., 37: 1-2.
Center for New Crops and Plants Products, 2002. Cajanus cajan (L.) Millsp. Purdue University, USA.
Chandra, A. and D. Pental., 2003. Regeneration and genetic transformation of grain legumes: An overview. Curr. Sci., 84: 381-387.
Direct Link |
Chaudhury, D., S. Madanpotra, R. Jaiwal, R. Saini, P.A. Kumar and P.K. Jaiwal, 2007. Agrobacterium tumefaciens-mediated high frequency genetic transformation of an Indian cowpea (Vigna unguiculata L. Walp.) cultivar and transmission of transgenes into progeny. Plant Sci., 172: 692-700.
Cramer, C.S. and T.C. Wehner, 2000. Path analysis of the correlation between fruit number and plant traits of cucumber populations. HortScience, 35: 708-711.
Direct Link |
Cramer, C.S. and T.C. Wehner, 2000. Fruit yield and yield component correlations of four picking cucumber populations. Cucurbit Genet. Coop. Rep., 23: 12-15.
Ezeaku, I.E. and S.G. Mohammed, 2006. Character association and path analysis in grain sorghum. Afr. J. Biotech., 5: 1337-1340.
Direct Link |
Fagwalawa, L.D., 2000. Agro-physiological characterization of some early, medium and late maturing varieties of cowpea under sole and intercropping systems. Ph.D. Thesis, Department of Biological Sciences, Bayero University, Kano.
Ghafoor, A., F.N. Gulbaaz, M. Afzal and M. Arshad, 2003. Inter-relationship between SDS-Page markers and agronomic characters in chickpea (Cicer arietinum L.). Pak. J. Bot., 35: 613-624.
Henry, A. and G.V. Krishna, 1990. Correlation and path coefficient analysis in pigeon pea. Madras Agric. J., 77: 443-446.
Ikea, J., I. Ingelbrecht, A. Uwaifo and G. Thottappilly, 2003. Stable gene transformation in cowpea (Vigna aconitifolia L. walp) using particle gun method. Afr. J. Biotechnol., 2: 211-218.
Iqbal, S., T. Mahmood, M. Tahira, M. Ali, M. Anwar and M. Sarwar, 2003. Path analysis in mash (Vigna mungo L.). Pak. J. Bot., 22: 160-167.
Ivo, N.L., C.P. Nascimento, L.S. Vieira, F.A. Campos and F.J. Aragao, 2008. Biolistic-mediated genetic transformation of cowpea (Vigna unguiculata) and stable Mendelian inheritance of transgenes. Plant Cell Reports, 27: 1475-1483.
PubMed | Direct Link |
Khan, A., M. Hatam and A. Khan, 2000. Heritability and interrelationship among yield determining components of soybean varieties. Pak. J. Agric. Res., 116: 5-8.
Obembe, O.O., 2009. Exciting times for cowpea genetic transformation research. Aust. J. Basic Applied Sci., 3: 1083-1086.
Direct Link |
Pasquet, R., 2001. Vigna savi. In: Flora Zambesiaca: Phaseoleae, In: Mackinder, B., R. Pasquet, R. Polhill and B. Verdcourt (Eds.). Vol. 3 Part 5, Royal Botanic Gardens, Kew, UK., pp: 121-156.
Popelka, J.C., N. Terryn and T.H.V. Higgins, 2004. Gene technology for grain legumes: Can it contribute to the food challenge in developing countries?. Plant Sci., 167: 195-206.
Popelka, J.C., S. Gollasch, A. Moore, L. Molvig and T.J.V. Higgins, 2006. Genetic transformation of cowpea (Vigna unguiculata L.) and stable transmission of the transgenes to progeny. Plant Cell Rep., 25: 304-312.
Direct Link |
Sahoo, L., T. Sushma-Sugla, N.D. Singh and P.K. Jaiwal, 2000. In vitro plant regeneration and recovery of cowpea (Vigna unguiculata) transformants via Agrobacterium-mediated transformation. Plant Cell Biotechnol. Mol. Biol., 1: 47-54.
Salahuddin, S., S. Abro, M.M. Kandhro, L. Salahuddin and S. Laghari, 2010. Correlation and path coefficient analysis of yield components of upland cotton (Gossypium hirsutum L.) sympodial. World Applied Sci. J., 8: 71-75.
Shrivastava, M.K., R.S. Shukla and P.K. Jain, 2001. Path coefficient analysis in diverse genotype of soybean (Glycine max L.). Adv. Plant Sci., 4: 47-51.
Singh, J. and H.S. Yadava, 2000. Factors determining seed yield in early generation of soybean. Crop Res. Hisar, 20: 239-243.
Singh, R.K. and B.D. Chaudhury, 1985. Biometrical Methods in Quantitative Analysis. Kalyani Publishers, New Dehli, India pp: 318.
Solleti, S.K., S. Bakshi and L. Sahoo, 2008. Additional virulence genes in conjunction with efficient selection scheme, and compatible culture regime enhance recovery of stable transgenic plants in cowpea via Agrobacterium tumefaciens-mediated transformation. J. Biotechnol., 135: 97-104.
Solleti, S.K., S. Bakshi, J. Purkayastha, S.K. Panda and L. Sahoo, 2008. Transgenic cowpea (Vigna unguiculata) seeds expressing a bean α-amylase inhibitor 1 confer resistance to storage pests, bruchid beetles. Plant Cell Rep., 27: 1841-1850.
Somers, D.A., D.A. Samac and P.M. Olhoft, 2003. Recent advances in legume transformation. Plant Physiol., 131: 892-899.
Direct Link |
Udensi, O., E.A. Edu, U.J. Umana and E.V. Ikpeme, 2011. Estimation of genetic variability in locally grown pulses (Cajans cajan (L.) Millsp and Vigna unguiculata (L.) Walp): A panacea for sourcing superior genotypes. Pak. J. Biol. Sci., 14: 404-407.
CrossRef | Direct Link |
Udensi, O., E.J. Umana, E.A. Edu and E.V. Ikpeme, 2011. Screening locally grown pulses for proximate, anti-nutritive and mineral compositions: Indices for conservation and improvement. Int. J. Agric. Res., 6: 504-510.
CrossRef | Direct Link |
Udensi, O., E.V. Ikpeme, A.A. Markson, E.A.B. Edu, U.J. Umana and I.S. Urua, 2010. Selection of soybean genotypes using morphological markers. Int. J. Curr. Res., 7: 5-8.
Direct Link |
Umar, M.L., M.G. Sanusi and F.D. Lawan, 2010. Relationships between some quantitative characters in selected cowpea germplasm (Vigna unguiculata [L] Walp). Notulae Sci. Biol., 2: 125-128.
Vange, T. and O.E. Moses, 2009. Studies on genetic characteristics of pigeon pea germplasm at Otobi, Benue State of Nigeria. World J. Agric. Sci., 583: 714-719.
Yadev, G.C., PK. Singh, BB. Singh and R. Verma, 2001. Genetic variability and path coefficients in Urdbean. Indian J. Pulses Res., 14: 143-144.