Comparison of the value of information for the management of deficit irrigation systems in different climate regions

Due to climate change, extreme weather conditions such as droughts may have an increasing impact on the water demand and the productivity of irrigated agriculture. For the adaptation to changing climate conditions, knowledge about adequate irrigation control strategies and information, e.g., about future climate development and soil properties, is of great importance for the optimal operation of irrigation systems. We consider climate and soil variability within one probabilistic simulation-optimization framework for irrigation scheduling based on Monte Carlo simulations to support informed decisions. The framework implements optimizers for full, deficit, and supplemental irrigation strategies. We provide the  Matlab code as the open source Deficit Irrigation Toolbox (DIT). For this analysis, we apply DIT for preliminary test simulations for a global numerical deficit irrigation experiment (GDIE) which allows for the analysis of both the impact of the selected irrigation strategy on water productivity and the value of information about (i) different scheduling methods, (ii) climate development, and (iii) soil hydraulic properties. The first results show a strong dependency on the value of information about climate and soil for sites required for increasing water productivity in different climate regions. Moreover, DIT can enable and support the site-specific transformation of low efficient rainfed and irrigated systems achieving higher water productivity and food insecurity on a local scale.