Water productivity and irrigation water management practices: A study of the Arkansas Irrigation Yield 'Most Crop per Drop' Contest

This study has two objectives: (1) to identify which Irrigation Water Management (IWM) practices and combinations are associated with higher water productivity (WP) under producer-managed field conditions, and (2) to compare applied irrigation water and yield outcomes between fields managed by producers and those managed in collaborative demonstrations. We use data from the Arkansas Irrigation Yield “Most Crop per Drop” Contest (2018–2023) to analyze six IWM practices, including Computerized Hole Selection (CHS), Surge irrigation, Soil Moisture Sensors (Sensors), Multiple-Inlet Rice Irrigation (MIRI), Alternate Wetting and Drying (AWD), and Furrow-Irrigated Rice (FIR), individually and in combination across corn, soybean, and rice. Descriptive statistics and Seemingly Unrelated Regression (SUR) models show that some IWM practices are associated with higher water productivity (WP). In corn, combinations of CHS, Sensors, and Surge are associated with higher WP, driven primarily by yield gains rather than reductions in applied irrigation water. In soybean, combinations of CHS and Sensors are associated with higher WP through reduced irrigation application without yield penalties. In rice, results are less consistent, although SUR analysis provided some evidence that the MIRI/AWD combination was associated with higher WP. Comparisons with previously published demonstration fields indicate that irrigation outcomes may differ between producer-managed and extension-supervised settings, potentially because of differences in implementation support and management conditions, although these comparisons should be interpreted as descriptive rather than causal. These findings offer practical insights for producers, extension agents, and policymakers seeking to improve irrigation management and water productivity.