Optimizing Chickpea Yield: GGE Biplot Analysis of Sowing Seasons

Abstract

Maximizing chickpea production in varying environmental conditions is crucial for ensuring food security and agricultural sustainability. This study addresses this challenge by evaluating sixty-six chickpea genotypes across winter and spring sowing seasons over two years (2016-17 and 2017-18). Utilizing an Augmented Trial Design with three replications and six check varieties, the study reveals significant effects of genotype, sowing season, and their interaction on all examined traits (p≤0.01). Notably, winter sowing season led to a substantial increase in yield, demonstrating up to a 57.1% improvement compared to spring sowing season. The first two components of principal component analysis accounted for 67% of the total variation of chickpea genotypes. In the examined traits, the lowest variation was observed in 100 seed weight, followed by seed yield. Biplot analysis, Pearson’s correlation, and heatmap analysis indicated a positive association of seed yield with pod number of plant and seed number per plant, yet a negative association for plant height, first pod height and 100 seed weight. SMN56, SMN57, and SMN51 genotypes consistently performed well across both sowing and growing seasons for desired traits. Furthermore, genotypes SMN13, SMN20, SMN37, SMN38, and SMN54 excelled during spring sowing season, while Gülümser, İnci, Aydın 92, SMN14, SMN20, and SMN39 were superior in winter. This study not only underscores the advantage of winter sowing for improved yield and other traits but also demonstrates the effectiveness of GGE biplot analysis in genotype selection, offering valuable implications for future chickpea breeding efforts.