The effect of soil-moisture uncertainty on irrigation water use and farm profits

Abstract Technologies that increase the accuracy of soil-moisture monitoring, such as in-situ sensors, have been proposed as a key solution for increasing agricultural water productivity. However, quantifying how uncertainty in soil-moisture estimates lead to irrigation inefficiencies or economic losses has not been explicitly studied. We develop a framework that combines a crop simulation model with a rule-based irrigation decision-making algorithm to assess the impact of soil-moisture uncertainty on irrigation use and farm profits. We apply this modelling framework to a case study of irrigated maize production in Nebraska, United States, a region where improvements in agricultural water productivity are at the forefront of water-policy debates. We consider two main sources of uncertainty that result in a divergence between the farmers’ perception of soil-water content and the true water status, namely errors in the knowledge of soil texture and measurement of daily soil-water flux inflows and outflows. Even for very large errors in both soil-texture and water-flux measurements, impacts on water use and profits were marginal (11 ha-mm increase and $27 ha–1 decrease, respectively). In contrast, farmers’ choice of irrigation strategy had a much larger impact on water use and profits than uncertainty in soil-moisture information used to implement that strategy. Our findings show that near-optimal irrigation decisions can be made without perfect soil-moisture information. This conclusion suggests that providing farmers with improved irrigation scheduling recommendations – utilizing crop-water models and optimization techniques – would have a larger impact on water-use efficiency than simply providing farmers with technologies to more accurately monitor soil-moisture conditions.