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 Optimum cropping pattern increases productivity where input resources are limited. An optimized cropping pattern was developed for a region in Moghan Plain, located in the northwestern Iran, to help water supplier in pre-season decision making on water and land allocation. AquaCrop simulation model was calibrated and executed for yield predictions for 11 different crops and 13 diverse soil types. Evaluation of AquaCrop model showed great robustness for a broad range of crops, even for the crops like canola and alfalfa that were undefined for the model. The precise generated crop water functions revealed the ideal conditions for water allocation by considering the impact of the existing limitation in monthly water availability on optimum cropping pattern without imposing any manipulation. Optimum cropping pattern based on water productivity (OCPWP) was identified by LINGO software. Integrating AquaCrop model and LINGO optimization problem solver created a Decision Support System (DSS) for technical analysis at the regional level. The created DSS is able to support the OCPWP in terms of the complex regional crop-mixture acreage. The ecological considerations introduced diverse winter crops to benefit from autumn precipitations. This strategy decreases irrigation requirement and saves some water for spring/summer high water-demanding crops like alfalfa and cotton, which generally enhances the system resiliency. The generated DSS revealed that 8,762 m³ water ha^-1 was required for optimum cropping pattern, which is 8% lower than the maximum and 3% more than the average available water.