This individual will leverage large agricultural datasets from connected equipment (planters, sprayers, combines, etc.), weather data, soil maps, satellite and drone imagery, and other sources to build scalable agronomic decision-support models to improve productivity, profitability, and sustainability in corn, soybean, and cotton production systems. Develop modeling approaches for G x E x M interactions to predict the outcomes of various management scenarios (planting date, variety selection, fertility management, crop care, etc.) in corn, soybean and cotton production systems across diverse geographies.