LAUSR

Abstract This study presents an optimization model for the allocation of agricultural water and land resources under uncertainty. The model incorporates intuitionistic fuzzy numbers, fuzzy credibility-constrained programming, mixed-integer non-linear programming, and multi-objective programming into a general framework. The model is capable of (1) balancing the trade-off among economic, environmental, and social considerations in an irrigated agricultural system; (2) optimally allocating limited agricultural water and land resources simultaneously; and (3) dealing with the complexities of non-linearity and fuzzy uncertainties concurrently occurring in both parameters and constraints to objectively reflect practical issues in agricultural water and land resources allocations. The developed model is applied to a real case study in northeast China. The net system benefits, global warming potential, water pollution, resource allocation equity, and agricultural water and land allocation schemes among different subareas in different crop growth periods are obtained under various scenarios. The performance of the model is assessed with alternative schemes. The model can help decision makers realize how much confidence one can have in the optimal solutions and manage agricultural water and land resources in a more efficient and environment-friendly way.