Ghazy Mona M F, Yehia Waleed M B, El-Hashash Essam F, Hatab Safwat H, El-Absy Karima Mohamed
Forage Crops Research Department, Agriculture Research Center, Field Crops Research Institute, Giza, 12619, Egypt.
Cotton Breeding and Genetic Department, Cotton Research Institute, Agriculture Research Center, Giza, 12619, Egypt.
Sci Rep. 2025 Sep 12;15(1):32478. doi: 10.1038/s41598-025-18797-y.
The current study aims to assess the ten cowpea genotypes that are stable and adaptive under three locations during the growth seasons of 2021, 2022, and 2023 using multivariate stability statistics. The fresh cowpea yield (t/ha) using combined ANOVA and AMMI analysis revealed that the environment is the most important factor and that variations between genotypes, environment, and genotype by environment interaction (GEI) were significant (p < 0.01). Eight main component axes (PCs) were obtained from the sum of squares of the GEI component. The mean squares for the first five PCs were significant (p < 0.05 or 0.01). The Sids location boosted the fresh cowpea yield (t/ha) of every genotype studied throughout all growing seasons, with Sakha and Ismailia locations coming in second and third, respectively. In terms of fresh cowpea yield, the G3 genotype produced the highest mean response, but with moderate stability in the nine environments conditions. G7 was the most stable genotype, with the least amount of yield variation across nine conditions, and was identified by AMMI-based stability parameters. Additionally, according to AMMI and GGE biplot analysis, G7 and G2 genotypes showed excellent stability. Most pairs of AMMI-based stability metrics under study had significant rank correlation coefficients (P < 0.05 or 0.01) in a positive direction. Using a GGE biplot polygon of "which-won-where", the environments were separated into two mega-environments. Using the AMMI model and GGE biplot analysis, the environments E4, E5, and E9 had the longest vectors and the highest fresh cowpea yield (t/ha). To guarantee sustainable progress in cowpea production systems, the study emphasizes the necessity of using a multidimensional strategy for genotype evaluation. This will allow breeders to make well-informed decisions for resilience and productivity under a variety of environmental situations. This raises the prospect of concurrent indirect selection of these traits to take advantage of GEI and increase Egypt's production of fresh cowpeas.
本研究旨在利用多变量稳定性统计方法，评估2021年、2022年和2023年生长季期间在三个地点稳定且适应性良好的10个豇豆基因型。通过联合方差分析和AMMI分析得出的鲜豇豆产量（吨/公顷）表明，环境是最重要的因素，基因型、环境以及基因型与环境互作（GEI）之间的差异均具有显著性（p < 0.01）。从GEI成分的平方和中获得了8个主成分轴（PCs）。前五个PCs的均方具有显著性（p < 0.05或0.01）。在所有生长季中，Sids地点提高了所研究的每个基因型的鲜豇豆产量（吨/公顷），Sakha和伊斯梅利亚地点分别位居第二和第三。就鲜豇豆产量而言，G3基因型产生了最高的平均响应，但在九种环境条件下稳定性适中。G7是最稳定的基因型，在九种条件下产量变化最小，并通过基于AMMI的稳定性参数得以确定。此外，根据AMMI和GGE双标图分析，G7和G2基因型表现出优异的稳定性。所研究的大多数基于AMMI的稳定性指标对在正方向上具有显著的秩相关系数（P < 0.05或0.01）。利用“哪个获胜于何处”的GGE双标图多边形，将环境分为两个大环境。使用AMMI模型和GGE双标图分析，环境E4、E5和E9具有最长的向量和最高的鲜豇豆产量（吨/公顷）。为确保豇豆生产系统的可持续发展，该研究强调了采用多维策略进行基因型评估的必要性。这将使育种者能够在各种环境情况下，就恢复力和生产力做出明智的决策。这增加了同时间接选择这些性状以利用GEI并提高埃及鲜豇豆产量的前景。
