Investigation of deficit irrigation strategies combining SVAT-modeling, optimization and experiments

Irrigation farming is the greatest consumer of the Earth’s freshwater resources. In the light of an increasing water demand caused by growing population, effective methods are required that use the available water resources efficiently and increase the overall productivity of irrigation systems. In this contribution, a combined approach of simulation–optimization and experiments was applied to investigate and evaluate two different irrigation strategies and their parameters for maize with the objective to achieve high water productivity (WP) with high reliability. Thereby, a soil–vegetation–atmosphere transfer (SVAT) model was used to simulate crop growth and soil water transport, together with task-specific optimization algorithms to determine optimal parameters for irrigation schedules and sensor-based full and deficit irrigation controls. An intensively monitored 3-year irrigation experiment was conducted for testing different irrigation designs and verifying the simulation–optimization approach. A new sensor for measuring soil water potentials from pF 0 to pF 7 allowed for applying optimized irrigation thresholds greater than 1,000 hPa. Attained $$\mathrm{WP}_{\mathrm{ET}}$$WPET from the irrigation experiments were generally high and ranged from 1.8 to $$2.3\hbox { kg m}^{-3}$$2.3kgm-3. The impact of irrigation system parameters on WP, such as irrigation interval, sensor depth, number of irrigation thresholds, and the soil’s initial water content were evaluated and discussed. Results indicate that thresholds beyond the measurement range of commonly used tensiometers are feasible. Furthermore, the combination of SVAT-modeling and optimization has the potential to systematically investigate and improve irrigation systems as well as to reduce the number of required field experiments.