LightGBM consistently achieved R2 values greater than 0.92 for key traits, such as stomatal conductance, panicle width, and grain yield, demonstrating its potential to optimize N management. Gustav performed best under high N, whereas cultivar responses to low N were genotype-specific, captured effectively by the machine learning models. These findings highlight the role of AI models in predicting cultivar performance and supporting sustainable agricultural decisions.
Altaf et al. (Sat,) studied this question.