Evaluation of Grain Yield Stability of Lentil Genotypes using Non-Parametric Statistics and AMMI Analysis - Amin Namdari; Payam Pezeshkpoor; Mohammad Barzali; Asghar Mehraban; Amir Mirzaie; Mostafa Haghpanah (پژوهش‌های حبوبات ایران, 2025)
IntroductionTo identify and introduce cultivars with high yield and stability, evaluating the genotype-by-environment interaction in multi-environment trials is of paramount importance. One of the most significant challenges for lentil breeders is selecting genotypes with high yield across diverse environments. Assessing genotypes in different environments can lead to the identification of genotype(s) with high yield potential and yield stability. Genotypes exhibiting lower responses to environmental effects typically have lower genotype × environment interactions and are desirable selections for breeders, especially if they demonstrate higher yield potential compared to control. Various univariate and multivariate methods exist for evaluating genotype-environment interactions. This study evaluated the grain yield stability of advanced lentil genotypes in tropical climates using two methods: univariate (non-parametric statistics) and multivariate (additive main effects and multiplicative interaction, AMMI). Materials and Methods Fifteen selected genotypes along with two check cultivars, Gachsaran and Sephar, were evaluated in a randomized complete block design with three replications across eight environments (four warm and semi-warm regions: Gachsaran, Khorramabad, Moghan, and Ilam over two years). Each experimental plot consisted of four rows, 6 meters long, with a plant density of 200 plants per square meter. Grain yield was measured after harvest. To analyze the data, after ensuring the homogeneity of error variances across environments using Bartlett's test, a combined analysis of variance was performed for the eight studied environments. The METAN package in the Rver4.3.2 statistical software was used for AMMI analysis, calculation of non-parametric statistics, and plotting AMMI1 and AMMI2 biplots. Results and DiscussionThe results of the combined analysis of variance for grain yield (based on the AMMI model) showed that the effects of environment, genotype and the interaction of genotype × environment were significant at the statistical probability level of 1%. Considering the significance of the genotype × environment interaction, it is possible to perform stability analyze on data in order to interpret this interaction. Stability analysis using the non-parametric TOP statistic, identified genotypes 12 and 10 as the most stable genotypes. The Kang statistic also ranked genotypes 12 and 13 as the best genotypes. Among the non-parametric statistics, TOP and Kang placed high-yielding genotypes at the top of the yield stability rankings, whereas the rankings based on Thennarasu , Nassar and Huehn statistics favored genotypes with lower mean yields. According to the Thennarasu statistic, genotypes 5, 6, and 2 were identified as the most stable, but all three had low mean yields. Similarly, genotypes with high yield stability based on the Nassar and Huehn statistics, had low mean yields. By calculating the SIIG statistic and considering all non-parametric statistics, genotype 12 was found to have the best combination of yield and yield stability. In addition to this genotype, genotypes 13, 1, and 11 could also be considered desirable, considering both yield and stability. One of the most widely used multivariate methods for stability analysis is the additive main effects and multiplicative interaction (AMMI) model. The results of the combined analysis of variance for grain yield based on the AMMI model showed that the first three principal components of the genotype × environment interaction were significant, explaining approximately 40.4%, 26.5%, and 19.8% (a total of 86.7% for the first three components and 67.1% for the first two components) of the genotype × environment interaction, respectively. The AMMI1 biplot, considering mean grain yield and the value of the first principal component, identified genotypes 10, 11, and 12 as superior genotypes. The AMMI2 biplot, considering the values of the first and second principal components, also identified genotype 12 as the most stable in terms of grain yield. The two check cultivars, Gachsaran and Sephar, showed lower yield stability than the superior genotypes based on both biplots. Genotypes positioned at the center of the biplot exhibit general stability and are recommended for cultivation across most of the investigated environments. In contrast, genotypes located farther from the center and closer to specific environments demonstrate stability exclusive to those environments. In this regard, genotype 3 exhibited specific adaptation to environment 4, while genotypes 13 and 14 showed specific adaptation to environments 2 and 1, respectively. ConclusionsBased on the obtained results, genotype 12 stands out as the best performer in terms of both high yield and stability. Therefore, it can be considered a promising candidate for release as a new cultivar in warm and semi-warm regions.