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The purpose of irrigation scheduling is to coordinate the timing and amount of water used to irrigate crops in a way that maximizes profits. Using a case study on corn production in the Havana Lowlands region, Illinois, this paper examines the extent to which the incorporation of different types of weather forecasts into irrigation scheduling can increase the profitability of irrigated agriculture. A soil water atmosphere plant (SWAP) model is employed to simulate crop yields using weather forecasts from the growing seasons during a period running from 2002 to 2006. The net profits from the modeling analysis based on the modeled soil moisture and weather forecasts are compared to that from an irrigation schedule that the farmers roughly followed in 2002. The results show that if farmers just use the real-time soil moisture information and the empirical rules set with the SWAP model, crop profits may increase by 16%; the inclusion of 7-day forecasts in a rule-based simulation model boosts the net profit by 21%. Over the 5 testing years, it is found that the proper use of the 7-day forecast can save irrigation water and increase crop yield in dry years. Next, these results are also compared to ones derived using deterministic optimization techniques that assume perfect 2-week and seasonal forecasts to assess the extent to which net profits could increase with further weather forecasting improvements. On average over the 5 testing years, the perfect 2-week and seasonal forecasts yield profits of 42% and 48%, respectively, greater than the scenario based on the 7-day weather forecasts. Furthermore we conduct a stochastic optimization analysis using the monthly probabilistic climate prediction provided by the Climate Prediction Center of the National Oceanic and Atmospheric Administration. A review of the retrieved irrigation schedule shows that the farmers at the study site sometimes applied more water than necessary and, at other times, less than the model based on a 7-day weather forecast and soil moisture simulation suggested. CE Database subject headings: Irrigation scheduling; Optimization; Weather forecasting; Agriculture; Illinois.

This paper presents an approach to calibrating a holistic water resources-economic model, which involves essential water resources and economic components in a consistent model. The model is formulated as an optimization model, with the objective of maximizing economic welfares/profits from water uses. When we apply the model to a baseline scenario against real-world conditions, the economic outputs are often expected to match the observations at the base level, since a wide divergence between model outcomes and actual results is not appropriate for analyzing policy options starting from the baseline. Following the concept of 'positive mathematical programming,' the holistic water resources-economic model is calibrated to a baseline level using programming constraints and 'positive' inferences from baseline observations. The calibration task is complicated by the fact that a large number of interdependent parameters should be calibrated simultaneously. A numerical approach based on hybrid genetic algorithms is used to implement the calibration procedure. CE Database subject headings: Calibration: Optimization: Genetic algorithms: Economic models; Water resources management.