Stochastic model-based optimization of irrigation scheduling

This paper presents a scheme for applying two-stage explicit stochastic optimization to seasonal irrigation scheduling. It is assumed that an ensemble of Ns weather forecasts (scenarios) is available. At each decision point during the season up to Ns Nsmulti-objective optimization problems are solved by assuming a specific scenario for the immediate decision period and all possibleNs scenarios for the subsequent periods. The irrigation schedule selected for implementation during the immediate decision period is the one that produces the highest worst-case yield, which mimics the traditional risk-adverse farmers' strategy. The procedure is illustrated for a maize crop at Davis, CA, modeled with DSSAT. The optimization was performed for ten years, using as forecasts the weather recorded on the previous 15 years. The proposed approach yielded consistently results that were very close to truly optimal, i.e. results that could have been obtained if perfect weather forecasts were available at the beginning of the season. These results were better than those obtained with a deterministic approach that relied on the same data and decision rules but used only a single forecast that consisted of the average weather of the 15 previous years. However, these improved results came at the expense of a significant increase of the computation burden. In addition to the overall improved performance in terms of yield, a main advantage of the stochastic approach is that, since the solution for implementation is selected from an ensemble of solutions, it is possible to develop a selection strategy that mimics farmers' traditional selection strategy. This could prove a key factor toward the adoption of decision support tools that involve model-based optimization.