Farm-based cropping pattern optimization and conjunctive use planning using piece-wise genetic algorithm (PWGA): a case study

Abstract

So far, several agro-economic optimization models have been developed in order to deal with agricultural water resources planning and management, among which the cropping pattern and water allocation optimization models are of significant importance. However, these models usually consider the land as a plain entirety and fail to account for a number of important contributing factors, including the private ownership of agricultural lands and the unique characteristics of each land parcel. Hence, the solutions provided by these models are not fully applicable in real-world and are only meant to help the decision-maker in analyses. In the present study, a new model is developed in order to incorporate farm-level data, and to propose optimal cropping pattern and conjunctive use decisions at farm level. Furthermore, a modified genetic algorithm has been developed by piecewise reorganization of chromosome structure, namely piecewise genetic algorithm (PWGA), and is presented in order to tackle the nonlinearity and the high number of variables involved in the model. A URM-based groundwater model is also coupled with PWGA in order to improve the computational efficiency of the framework. The proposed model is then run for various scenarios and the results are compared with those of the general approaches. The results of this study demonstrate that the general plain-level models tend to overestimate the expected net benefits. Moreover, the proposed model is able to optimize the cropping pattern and water allocation rules over the area while supporting each land parcel with appropriate decisions.