Multiobjective land–water allocation model for sustainable agriculture with predictive stochastic yield response

Consider an agricultural land–water resource allocation problem in which yields are spatial dependent and stochastically correlated. To achieve sustainability, we formulate a multiobjective (MO) optimization problem, the decision maker determines cultivation areas supplemental irrigation water levels at different locations, with social, economic, environmental goals mind. For social goal, minimize root mean squared difference of incomes among locations. economic production risk. We show that minimizing risk is equivalent to maximizing service level, when demand normally distributed. utilization. Assume yield vector locations follows multivariate normal distribution. MO using weight global criterion method, provide sufficient condition for convex quadratic programming. demonstrate applicability our proposed framework case study sugarcane Thailand. capture response water, propose several models including linear nonlinear regressions, obtain closed-form expression probit models. The numerical experiment reveals solution significantly improves goals, compared current policy. Finally, illustrate how apply model quantify monetary value from reducing variability, could be resulted smart or precision agriculture.