Understanding genotype by environment interaction (GEI) represents a challenge in Kersting's groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet] breeding for selecting high-performing and stable lines across environments. Here, we investigated GEI and stability in Kersting's groundnut using factor analytic (FA) based linear mixed models and environmental covariates. A total of 375 accessions were evaluated across 3 years (2017, 2018, and 2019) and two locations (Sékou and Savè) in Benin, generating five environments (E1, E2, E3, E4, and E5). The traits measured included days to 50% flowering (DFF), grain yield (YLD), and 100-seed weight (HSW). The study generated multi-environment values for grain yield and its components in Kersting's groundnut. The genetic correlations between pairs of environments ranged from −0.71 to 0.99. The genetic correlations between YLD and HSW indicated positive and moderate to high correlations in all environments. The FA analysis revealed that FA₂ structure accounted for 93.9% of the genetic variability in DFF with factor 1 accounting for more than 90% of the environments variations. Two factors explained 87% of the genetic variance in grain yield, and 70% of the environments variability were clustered by factor 1. For HSW, two factors explained 85% of the genetic variance of the environments, and factor 1 accounted for 72.7%. Combining environmental covariates to FA models revealed that precipitation, temperature, and growth cycle duration were highly correlated to the environmental loadings of factor 1. Relative humidity and solar radiation showed moderate to high correlations with factor 2 loadings. Those covariates explained the high GEI among environments clustered by a given factor. Precipitations and temperatures affected the variations in grain yield. Finally, based on latent regression analysis, the accessions AF202, AF221, AF223, AF225, and AF256 were identified as accessions combining best performance for grain yield, early flowering, and 100-seed weight, showing adaptability across environments and stability to some environments.

中文翻译：

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使用因子分析模型和环境协变量对 Kersting 花生（Macrotyloma geocarpum）的三个农艺性状进行环境相互作用的基因型和稳定性分析


通过环境相互作用 (GEI) 了解基因型是 Kersting 花生 [Macrotyloma geocarpum (Harms) Maréchal 和 Baudet] 育种中选择跨环境高性能且稳定品系的挑战。在这里，我们使用基于因子分析 (FA) 的线性混合模型和环境协变量研究了 Kersting 花生的 GEI 和稳定性。 3 年（2017 年、2018 年和 2019 年）和贝宁的两个地点（塞库和萨维）总共评估了 375 个种质，生成了 5 个环境（E1、E2、E3、E4 和 E5）。测量的性状包括 50% 开花天数 (DFF)、籽粒产量 (YLD) 和 100 粒种子重量 (HSW)。该研究得出了 Kersting 花生中谷物产量及其组成部分的多环境值。环境对之间的遗传相关性范围为-0.71 至0.99。 YLD 和 HSW 之间的遗传相关性表明在所有环境中都存在正相关性和中度至高度相关性。 FA分析显示，FA ₂ 结构占DFF遗传变异的93.9%，其中因子1占环境变异的90%以上。两个因素解释了 87% 的粮食产量遗传变异，70% 的环境变异由因子 1 聚类。对于 HSW，两个因素解释了 85% 的环境遗传变异，因子 1 占 72.7%。将环境协变量与 FA 模型相结合表明，降水、温度和生长周期持续时间与因子 1 的环境负荷高度相关。相对湿度和太阳辐射与因子 2 负荷显示出中等到高度的相关性。这些协变量解释了由给定因素聚集的环境中的高 GEI。 降水和气温影响粮食产量的变化。最后，基于潜在回归分析，将材料AF202、AF221、AF223、AF225和AF256确定为产量、早花和百粒重最佳性能的材料，显示出跨环境的适应性和对某些环境的稳定性。