Project Number: 2020-13660-009-004-R
Project Type: Reimbursable Cooperative Agreement
Start Date: Oct 1, 2021
End Date: Sep 30, 2026
Integrate remote, proximal, and in-situ sensing data, simulation modeling results, and modern irrigation control systems to improve water management for Southwest cropping systems.
Field experiments will be conducted to develop novel methods for automated, data-driven irrigation management of field crops, including cotton, durum wheat, alfalfa, oilseeds, or guayule. Remote sensing images will be collected weekly over experimental plots via small unmanned aircraft systems (i.e., drones), providing RGB, multispectral and thermal image data at spatial resolutions less than 0.05 m. In-situ soil water content sensors, including neutron meters and time-domain reflectometry (TDR) sensors will be used to collect soil water content data at 0.1-cm increments to a depth of 2 m on a weekly basis. Techniques for data integration with common simulation models, including the FAO-56 water balance method, AquaCrop, the Decision Support System for Technology Transfer (DSSAT), and popular cellular phone irrigation scheduling applications, will be developed. Methods for making irrigation management decisions using these tools will be compared. Irrigation rates delivered to field trials will be precisely controlled using a modern overhead sprinkler system with flow monitoring devices, geopositioning equipment, and site-specific irrigation control technology, which is now located on a 15-acre field in Maricopa, AZ. The feasibility of such techniques has been demonstrated by efforts at the field site since 2014. While the field techniques are proven, the rationale for continued research involves the need for data-driven irrigation management technologies, where in-season crop and soil measurements are integrated with algorithms for optimizing irrigation decisions to produce high-yielding, high-quality crops while minimizing resource requirements and environmental impact. Seasonal agronomic results, including crop yield and quality metrics and irrigation amounts, will be compared among treatments (i.e., different methods for determining irrigation rates). Weekly drone-based images will provide further information on spatial variation in crop growth and water stress. Causal relationships among irrigation amounts, spatiotemporal canopy temperature responses, soil water content status, and resulting crop yield will be identified. Importantly, efforts will be made to identify reasons for yield responses to irrigation management recommendations and whether the data and tools used for those recommendations are accurately encoding the mechanisms that lead to yield response variation. In this way, data collection techniques and algorithms that support irrigation decisions will be evaluated and improved.