Landraces of chickpea (Cicer arietinum L.) in Iran have not been adequately characterized for their agronomic and morphological traits. Such characterization would be helpful in the development of improved cultivars, so in this study 362 chickpea accessions, collected from the major chickpea growing areas of Iran, were evaluated to determine their phenotypic diversity. High coefficients of variation (CVs) were recorded in pods/branch, seeds/pod, yield/plant, seeds/plant, pods/plant and branches/plant. Using principal component (PC) analysis, the first four PCs with eigenvalues more than 1 contributed 84.10% of the variability among accessions, whereas PC 5 to PC 10 were less than unity. PC 1 was positively related to days to first maturity, days to 50% flowering and days to 50% maturity. The characters with the greatest weight on PC 2 were seeds/plant and yield/plant, whereas PC 3 was mainly related to pods/plant, seeds/pod and 100-seed weight, and PC 4 was positively related to pods/branch and negatively to branches/plant. The germplasm was grouped into four clusters using cluster analysis. Each cluster had some specific characteristics of its own and the cluster I was clearly separated from clusters II, III and IV. These accessions are an important resource for the establishment of a core collection of chickpeas in the world. Key words: chickpea (Cicer arietinum L.); cluster analysis; diversity; principal component analysis.Chickpea (Cicer arietinum L.) is a major food legume and an important source of protein in many countries. It is cultivated mainly in Algeria, Ethiopia,

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A gronomy J our n al • Volu me 10 0 , I s sue 4 • 2 0 0 8 ABSTRACT Crop growth is a multifactorial nonlinear process and diff erent kinds of models have been developed to predict crop yield. In recent years, crop growth models have become increasingly important as major components of agriculture-related decision-support systems. Moreover, clustering is a multivariate analysis technique widely adopted in agricultural studies. Using this method, diff erent genotypes (accessions) of crops can be classifi ed and characterized. Th is paper discusses the use of soft computing techniques such as artifi cial neural networks (ANN) and fuzzy logic based approaches in regression and clustering problems. Th e ANN technology was used for modeling the correlation between crop yield and 10 yield components of chickpea (Cicer arietinum L.). Also, the fuzzy c-means (FCM) clustering technique was used for the classifi cation of 362 chickpea genotypes based on their agronomic and morphological traits. Th e ANN performed very well. Among the various ANN structures, a model of good performance was produced by 10-14-3-1 structure with a training algorithm of back-propagation and hyperbolic tangent transfer function in the hidden and output layers. Th e model was able to predict the chickpea yield data of 0.32 to 14.38 g plant -1 with a RMSE and T value of 0.0195 g plant -1 and 0.988, respectively. T statistics measures the scattering around line (1:1). When T is close to 1.0, the fi tting is desirable. Th e mean absolute error, relative error, and coeffi cient of determination between actual and predicted data were 0.0109 g plant -1 , -1.07%, and 0.991, respectively. Th e ANN model predicted 90.3% of the yield data with relative errors ranging between ±5%. Th e consequent reduction in the number of training data from 250 to 50, decreased the training RMSE, but increased the prediction error. It was found that even with a few number of patterns in the training dataset (50 patterns), the prediction error of the ANN model was in the range of acceptance for yield modeling. Obviously, with 25 × 10 3 iterations, the ANN models with 5 and 10 input variables gave almost the same estimation of the chickpea yield. Th e results of clustering showed that the FCM clustering technique can be successfully applied to classify chickpea genotypes in terms of agronomic and morphological traits.

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