By pairing crop models with real-time field data and weather forecasts, this UC Davis and Technion team has shown how growers can save water without sacrificing yield, even as droughts intensify and groundwater shrinks.
ISAYA KISEKKA, UCD, Professor of Agricultural Water Management
RAPHAEL LINKER, Technion, Professor of Agricultural Engineering
Rafi Linker with Isaya Kisekka on the Davis Campus.
Two Young Scientists Trained
The team jointly advised a PhD student Floyid Nicolas, who now works as an Assistant Professor at Oregon State University, Department of Biological & Ecological Engineering. They also oversaw the postdoctoral research of Iael Raij-Hoffman, now at The Southern Arava R&D.
$304,000 Follow-On Funding
Researchers Kisekki and Linker secured a major grant from The BARD US Israel Agricultural Research and Development Fund.
Five Joint Publications
Joint publications in peer-reviewed journals are listed at the bottom of this post.
Farmers in California and Israel both grow much of their food in dry regions where every gallon of water counts. With a collaboration initiated with a seed grant from Calisrael in 2018, Professor Isaya Kisekka (UCD) teamed up with Agricultural Engineer Professor Rafi Linker (Technion) to build computer tools that work like a GPS for irrigation.
Their system runs a crop growth model and keeps updating it with field measurements of how the plants are actually growing. It then works out the best schedule for watering, so growers can save water without losing yield.
The team tested the approach in California processing tomato fields, the crop behind much of the world’s ketchup and pasta sauce. They have since extended it to multi-year planning, helping farmers and water managers decide which crops to plant and how much land to irrigate under limited water over several seasons.
Their work helps farmers in both places keep producing food as droughts become more common and groundwater supplies shrink.
Full Publication List
Kisekka, I., Nicolas, F., & Linker, R. (2025). Field evaluation of data assimilation of LAI and model-based optimization of irrigation scheduling of processing tomatoes. Irrigation Science, 43(6), 1471–1483.
Linker, R., & Kisekka, I. (2025). An optimization framework for multi-year planning of land and water allocation. Agricultural Water Management, 314.
Linker, R., & Kisekka, I. (2023). Model-based simulation-optimization of irrigation scheduling – A field evaluation with processing tomatoes. Smart Agricultural Technology, 4, 100234.
Linker, R., & Kisekka, I. (2022). Concurrent data assimilation and model-based optimization of irrigation scheduling. Agricultural Water Management, 274, 107924.
Linker, R., & Kisekka, I. (2017). Model-based deficit irrigation of maize in Kansas. Transactions of the ASABE, 60(6), 2011–2022.