Benchmarking farmers’ irrigation decisions using farm competition data and a crop growth model

Abstract

Rising irrigation demand and declining groundwater resources threaten the sustainability of irrigated maize production in Nebraska. Benchmarking farmers’ applied irrigation against optimal amounts offers insight into management performance and highlights opportunities for improvement. However, the few benchmarking studies conducted in Nebraska have often relied on simplified modeling assumptions to estimate optimal irrigation, potentially overestimating the magnitude of differences from actual practices and overlooking factors that influence farmers’ decisions. This study applied the DSSAT CERES-Maize model to determine optimal irrigation using multi-year data from the University of Nebraska-Lincoln’s Testing Ag Performance Solutions (UNL-TAPS) program. The model, calibrated for cultivar traits and site-specific conditions, demonstrated strong agreement between simulated and observed phenology and yield during validation (NRMSE 0.80). The validated model was used to benchmark farmer teams’ irrigation decisions and quantify deviations from optimal irrigation. Seasonal differences between applied and optimal irrigation ranged from − 175 to 306 mm, with a mean of 4 mm. Fifty-four percent of the teams applied more water than optimal, with a mean over-irrigation of 58 mm, less than the extent and magnitude of over-irrigation reported in previous studies. The variability in optimal irrigation (CV = 21%) was lower than actual irrigation (CV = 44%), indicating that behavioral factors played an important role in irrigation decisions made by farmer teams. These findings provide evidence to support improved on-farm water management and the development of policies that encourage sustainable irrigation practices.

Bibliographic Information