Novel simulation-based algorithms for optimal open-loop and closed-loop scheduling of deficit irrigation systems

The scarcity of water compared with the abundance of land constitutes the main drawback within agricultural production. Besides the improvement of irrigation techniques a task of primary importance is solving the problem of intra-seasonal irrigation scheduling under limited seasonal water supply. An efficient scheduling algorithm has to take into account the crops' response to water stress at different stages throughout the growing season. Furthermore, for large-scale planning tools compact presentations of the relationship between irrigation practices and grain yield, such as crop water production functions, are often used which also rely on an optimal scheduling of the considered irrigation systems. In this study, two new optimization algorithms for single-crop intra-seasonal scheduling of deficit irrigation systems are introduced which are able to operate with general crop growth simulation models. First, a tailored evolutionary optimization technique (EA) searches for optimal schedules over a whole growing season within an open-loop optimization framework. Second, a neuro-dynamic programming technique (NDP) is used for determining optimal irrigation policy. In this paper, different management schemes are considered and crop-yield functions generated with both the EA and the NDP optimization algorithms compared.