Evaluation of Crop Models for Simulating and Optimizing Deficit Irrigation Systems in Arid and Semi-arid Countries Under Climate Variability

Abstract

The variability of fresh water availability in arid and semi-arid countries poses a serious challenge to farmers to cope with when depending on irrigation for crop growing. This has shifted the focus onto improving irrigation management and water productivity (WP) through controlled deficit irrigation (DI). DI can be conceived as a strategy to deal with these challenges but more knowledge on risks and chances of this strategy is urgently needed. The availability of simulation models that can reliably predict crop yield under the influence of soil, atmosphere, irrigation, and agricultural management practices is a prerequisite for deriving reliable and effective deficit irrigation strategies. In this context, this article discusses the performance of the crop models CropWat, PILOTE, Daisy, and APSIM when being part of a stochastic simulation-based approach to improve WP by focusing primarily on the impact of climate variability. The stochastic framework consists of: (i) a weather generator for simulating regional impacts of climate variability; (ii) a tailor-made evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply; and (iii) the above mentioned models for simulating water transport and crop growth in a sound manner. The results present stochastic crop water production functions (SCWPFs) that can be used as basic tools for assessing the impact on the risk for the potential yield due to water stress and climate variability. Example simulations from India, Malawi, France and Oman are presented and the suitability of these crop models to be employed in a framework for optimizing WP is evaluated.