A nonlinear mathematical model integrated with the CROPWAT decision support tool to optimize cropping patterns under different climate conditions

A nonlinear programming model was developed for optimizing cropping patterns with two objectives: maximizing farming revenue and minimizing irrigation water consumption. After determination of dominant crops in the study area, the water requirement of each crop was calculated by CROPWAT. Next, the volume of water used by farmers to irrigate the crops was computed. Based on the costs of agricultural inputs and farming operations and the price of products, the profit earned by farmers was calculated. The calculations were carried out for current and future climate conditions. For the future periods, input data to CROPWAT was obtained by simulating the climate parameters in the general circulation model (Hadley Centre Coupled Model version 3) under three emission scenarios, namely, A2, B1, and A1B. Then, the Long Ashton Research Station Weather Generator statistical model was used for the downscaling of data from the general circulation model. Finally, an NLP optimization model was developed in LINGO-20 to optimize the cropping pattern. The results indicated that by optimizing the cropping pattern, the farming revenue increased up to 65% on average compared with the existing (nonoptimal) cropping pattern, while at the same time, the agricultural water consumption was reduced by 5%.