Seed Yield Stability of Autumn Sowing Chickpea Genotypes Using Nonparametric Methods

Chickpea is one of the most important legumes in Iran and accounts for almost 84% of dietary legumes. Chickpea seed yield is strongly influenced by environments, and breeders often determine the stability of high-yielding genotypes across different environments before being introduced as a cultivar. Accurate study of the nature of genotype × environment allows breeders to identify stable and compatible genotypes and has always been one of the important issues in the production and release of new stable and high-yielding cultivars in breeding programs. . Adaptation of chickpea genotypes to environmental conditions is important for crop production stability in different years and places. Existence of the interaction of genotype and environment affects the value of genotypes in different places. The present study was conducted to investigate the effect of genotype by environment interaction on grain yield of chickpea genotypes and cultivars in four environments and to identify stable and high-yielding genotypes under rainfed conditions as autumn planting.

In this study, twelve cultivars and advanced genotype of chickpea were planted during two cropping years (2016-2018) in a randomized complete block design with three replications in semi-warm (Kuhdasht) and temperate (Khorramabad) areas of Lorestan province. Various nonparametric methods such as non-parametric statistics of Si (1), Si (2), Si (3) and Si (6), Thennarasus statistics of NPi (1), NPi (2), NPi (3) and NPi (4), mean rank stability statistics (R), stability statistics of Ketata et al (σr, σmy), Kang stability statistics (Ysi), Fox stability statistics (TOP, MID and LOW) and Genotype Stability Index (GSI) were used for estimating the stability of genotypes. the principal component analysis method was used to better understand the relationships between different statistics..

The results of combined analysis of variance showed that the main effects of environment (including location, year and location ×year) and genotype ×environment were significant at the 1% probability level and the main effects of genotype, location × genotype and year × genotype were significant at the 5% probability level. The effects of genotype, environment and the genotype by environment interaction accounted for 6.48, 77.4 and 13.03% of the total squares, respectively.The biplot of the first principal component (PC1) versus the second principal component (PC2) classified the studied nonparametric stability statistics into three groups.Based on the statistics of NPi (1), NPi (2), NPi (3) and NPi (4), the genotypes with the lowest values are considered as stable genotypes. According to NPi (1) statistics, G1, G6 and G9 genotypes were identified as the most stable and G3 and G5 genotypes as the most unstable genotypes. Based on NPi (2) and NPi (4) parameters, G1, G10 and G9 genotypes were the most stable and G5, G12 and G4 genotypes were the most unstable. Based on the results, the first two principal components explained 68% (42% and 26%, respectively), of the variance of the main variables). Cluster analysis using mean seed yield and non-parametric statistics, placed chickpea genotypes in two main groups. The first cluster included mean seed yield, TOP, MID, σr and σmy. The second cluster consisted of four sub-clusters .

Based on Si (1), Si (2), Si (3) and Si (6) statistics, G1, G10 and G9 genotypes with the lowest values were identified as the most stable genotypes. G1 and G9 genotypes with the lowest GSI were recognized as the best genotypes in terms of grain yield and stability. Based on the parameters with the dynamics concept of stability, genotypes G1, G10 and G9 were identified as stable genotypes with high yield.