Sunflower yield modeling with XAI: Historical weather impacts and forecasting

Abstract

This study applies explainable artificial intelligence (XAI) to analyze the impact of inter-year variation in weather conditions on oilseed sunflower yields across the United States. By integrating historical yield data from 1976 to 2022 with meteorological data, we identified key weather predictors influencing sunflower yields at national and state levels along with critical yield-sensitive threshold temperature and precipitation values that predict reduced yield. Using machine learning algorithms, we developed predictive models to forecast future yields under different Shared Socioeconomic Pathways (SSPs) from 2021 to 2080. Our findings reveal significant yield declines due to increased temperatures and altered precipitation patterns, but with regional variability in the magnitude of these impacts. The most critical climate variables identified include maximum temperatures and total precipitation during summer months. Our XAI approach enhances model transparency, offering valuable insights for farmers and policymakers to develop adaptive strategies for sunflower cultivation under climate change. Future research incorporating additional factors like soil characteristics and agricultural practices can further refine yield predictions.