Adaptability and stability of soybean for grain yield in shaded environments

The aim of this study was to identify, through different methodologies, soybean cultivars with adaptability and stability for grain yield in environments with different levels of light restriction. The grain yield of sixteen cultivars was evaluated in environments with 25% and 48% restriction of photosynthetically active radiation (PAR) in the agricultural years 2019/2020 and 2021/2022. Based on the results, an adaptability and stability analysis was performed using the Eberhart and Russell, ANN (Artificial Neural Network) and GGE (Genotype plus Genotype-Environment interaction) methods. Grain yield varied with the levels of PAR restriction and agricultural years, being higher in environments A3 (2021/2022 25% PAR) and A1 (2019/2020 25% PAR), respectively. Cultivars NS7780, 8579RSF, NS8338 and RK6718 showed higher yield. The adaptability of cultivars AS3680, M7110, and 74177RSF was low, while that of NS8338 and NS7780 was high. Cultivars NS8338, 74177RSF, RK7518, and M6210 showed high phenotypic stability to environments.

Keywords:
Glycine max; artificial neural network; photosynthetically active radiation