Complex and Non-Linear Modeling of Genotype × Environment Interaction via Tree-Based Learning Algorithms and Shapley Decomposition

Evaluating G × E interactions is key to plant breeding, but models like AMMI struggle with complex non-linear dynamics. This study combines Machine Learning (ML) algorithms with local additive SHAP decomposition to model the G × E interaction. A maize Grain Yield (GY) dataset from CIMMYT was used, containing nine genotypes evaluated across 20 environments. Mean GY ranged from 2652.00 kg ha−1 (Environment 19) to 7516.00 kg ha−1 (Environment 12), with Genotype 6 (G6) being the highest GY (5330.17 kg ha−1). According to the AMMI Stability Value (ASV), G7 and G3 were identified as the most stable genotypes despite their relatively low grain yield. In contrast, G6, the highest-yielding genotype, was ranked only fourth by ASV but achieved the top position in both the SHAP SSI and SHAP Effect metrics. These findings indicate that the environmental variation reflects a predictable non-linear responsiveness rather than stochastic instability. A high Spearman correlation was captured between GY per se and the SHAP Effect (ρ^=0.98, p < 0.0001), while the SHAP Stability metric showed a weak association with traditional linear measures (ρ^=0.48 with ASV, p = 0.2216). The ML-SHAP approach presents itself as an interesting and complementary tool in breeding, allowing one to better understand the complex and non-linear G × E interaction.