The study utilizes explainable artificial intelligence (XAI) combined with machine learning to assess how inter‑annual weather variability influences oilseed sunflower yields across the United States from 1976 to 2022. Key climate predictors, especially summer maximum temperature and total precipitation, were identified, and predictive models were projected under various Shared Socioeconomic Pathways to 2080, revealing region‑specific yield declines.

The study performed ionomic profiling and genome-wide association studies on a diverse panel of pearl millet infield across two seasons to uncover genetic factors controlling nutrient acquisition. Soil analyses revealed stable depth-dependent patterns for phosphorus and zinc, while leaf ion concentrations showed high heritability and associations with root and agronomic traits. Integrating GWAS with gene expression data identified candidate ion transport/homeostasis genes for breeding nutrient-efficient, climate-resilient millet.