Optimal irrigation scheduling under water quantity and quality constraints accounting for the stochastic character of regional weather patterns

In arid countries both water scarcity and salinity represent the key factors which drastically limit crop yield in irrigated agriculture. In addition, relatively poor management practices with pretty low water productivity (WP) seriously aggravate the situation. In order to get “more crop per drop', i.e., to substantially improve water use efficiency, this thesis proposes the novel strategy NEMO (Nested Experimental, Modeling, and Optimization Strategy) for reliably evaluating an optimal irrigation schedule. The proposed methodology relies upon a close interaction between in-depth field investigations and physically based process modeling. It is tailored specifically to fit the requirements in resource-restricted regions. Comprehensive field experiments, on site measurements as well as various laboratory analyses provide a representative database for characterizing the relevant environmental parameters as e.g. the soil properties at the considered location and the prevailing climate. A substantial part of the data obtained from the field experiments provided the input for the internationally recognized SVAT software DAISY1 or APSIM2, both physically based irrigation models which have already been successfully applied in arid regions. APSIM - which is used in the advanced parts of the study - includes not only a process based model for soil moisture transport but also a plant physiological model which describes the plant behavior under specific irrigation scenarios for a selected crop throughout a growing season. The adaption of the irrigation model to local conditions and its preliminary parameterization firstly follows available guidelines and data for areas with similar climate and soil conditions. Reference data and deterministic weather data served to build up DAISY’s basic model files. DAISY is then used within the framework of the custom made and problem oriented optimization software GET-OPTIS for evaluating the corresponding optimal irrigation schedule for a first preliminary series of experiments (IrrEx1). A second series of field experiments (IrrEx2) was accompanied by transient soil moisture measurements, which served for evaluating the soil hydraulic parameters, while the obtained yield was used for calibrating the plant physiological model of APSIM. Taking still into account the stochastic nature of weather phenomena, a stochastic optimization with GET-OPTIS was then applied not only for the traditional full irrigation but also for the most important deficit irrigation and the irrigation with saline water. The obtained optimal irrigation schedules are subsequently used for a final series of rigorous irrigation experiments (IrrEx3) which specifically focused on: (1) full irrigation for high yields with most economic water application, (2) deficit irrigation aiming at a maximum yield with only a limited amount of irrigation water, and (3) full irrigation with saline irrigation water for maximum yield. At the harvesting time, the observed crop yield and the water productivity were compared - together with other plant characteristics - with the corresponding calculated values. The agreement between calculated and measured crop data was excellent. All the field experiments have been performed following a parallel use of the common traditional FAO class A-Pan method and the novel NEMO technology. Based on the outcome of the field experiments, the NEMO applications demonstrated a striking superiority throughout all scenarios as compared to the FAO method as regards economic efficiency and sustainable use of irrigation water in both aspects water quantity and salt accumulation. Contrary to common practice, the optimal NEMO irrigation schedule - which relies on stochastic weather data - has an extended validity. Together with the use of physical data and adequate process models, the developed methodology features a highly promising potential for generalizing the experimental findings for other, environmentally similar, regions. NEMO thus opens wide possibilities for a cost effective and sustainable long-term application to other arid or semi-arid areas