The Effect of Soil-moisture Uncertainty on Irrigation Water Use and Farm Profits

Abstract:

In many areas worldwide, farmers’ determine when to irrigate crops based on indirect proxies for soil-moisture status such as the feel or visual appearance of the soil. Development of 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 to what extent uncertainty in soil-moisture estimates are a key driver of irrigation inefficiencies and economic losses has not been explicitly studied. Here, 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 water use and farm profits. We apply this modelling framework to a case study of an irrigated maize crop 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 knowledge of soil texture and measurement of daily soil water flux inflows and outflows. Even for unrealistically large errors in both soil-texture and water-flux measurements, impacts on per-hectare water use and profits was marginal (10mm increase and $28 decrease respectively). In contrast, the farmers’ choice of irrigation strategy was shown to have a much larger impact on water use and profits than uncertainty in soil-moisture information used to implement that strategy. Our findings therefore suggest that near-optimal irrigation decisions can be made without perfect soil-moisture information. This conclusion suggests that providing farmers with improved irrigation scheduling recommendations – for example utilizing crop-water models and optimization techniques – would have a larger impact on water-use efficiency than simply providing farmers with more accurate information.