A new scheme to optimize irrigation depth using a numerical model of crop response to irrigation and quantitative weather forecasts

Abstract

Irrigation management can be improved by utilizing advances in numerical models of water flow in soils that can consider future rainfall by utilizing data from weather forecasts. Toward this end, we developed a numerical scheme to determine optimal irrigation depth on scheduled irrigation days based on a concept of virtual net income as a function of cumulative transpiration over each irrigation interval; this scheme combines a numerical model of crop response to irrigation and quantitative weather forecasts. To evaluate benefits, we compared crop growth and net income of this proposed scheme to those of an automated irrigation method using soil water sensors. Sweet potato (Ipomoea batatas (L.), cv. Kintoki) was grown in 2016 in a sandy field of the Arid Land Research Center, Tottori University, Japan under either a non-optimized automated irrigation or the proposed scheme. Under the proposed scheme, 18% less water was applied, yield increased by 19%, and net income was increased by 25% compared with the results of the automated irrigation system. In addition, soil water content simulated by the proposed scheme was in fair agreement with observed values. Thus, it was shown that the proposed scheme may enhance net income and be a viable alternative for determining irrigation depths.