This ongoing project demonstrates how integrated precision irrigation technologies can improve on-farm irrigation scheduling, crop water productivity, and water quality outcomes. The project focuses on variable rate irrigation (VRI), multi-mode sensor systems, electrical conductivity mapping, high-resolution satellite imagery, evapotranspiration estimates, and artificial intelligence to develop practical irrigation management strategies for producers. A multi-state team from the University of Florida, University of Georgia, and University of Minnesota is evaluating these technologies across major irrigated crop rotations. The project compares static VRI prescriptions based on field variability with dynamic VRI prescriptions informed by satellite-based crop conditions, while also comparing both approaches with conventional uniform irrigation. The goal is to generate research-based guidance that helps growers decide where VRI adds value, how to create useful management zones, and how sensor and remote sensing data can be converted into actionable irrigation decisions. Findings from this work will support the development of grower-focused tools, outreach materials, and decision-support resources to improve irrigation efficiency, reduce unnecessary water application, and lower environmental risks associated with nitrate leaching and runoff.
Study Period: Ongoing
Project team at UMN: Marcel Fodjo Kamdem and Vasudha Sharma
Funding: USDA-NRCS-CIG