Assessment of uncertainty effects on crop planning and irrigation water supply using a Monte Carlo simulation based dual-interval stochastic programming method

In this study, a Monte Carlo simulation based dual-interval stochastic programming (MC-DSP) method is developed for assessment of uncertainty effects on crop planning and irrigation water supply associated with multiple uncertainties expressed as dual intervals and probability distributions. MC-DSP can permit in-depth analyses of various policies that are associated with different levels of economic consequences (due to uncertain water inflow) when the pre-regulated irrigation targets are violated. The developed method is applied to crop planning and water allocation for the Zhangweinan River Basin in China. Solutions of crop planning and irrigation-water allocation under different probability distributions and plausibility degrees are generated. Results reveal that surface water availabilities associated with different probability distributions can lead to changed system benefits and irrigation shortages. Moreover, water is insufficient to satisfy the requirement for wheat due to its high requirement for irrigation, which may lead to the risk of food supply. Each subarea of farmland would suffer water deficit under all scenarios (particularly for subareas of Daming county and Neihuang county) when inflow level range from very-low to high. The conflicts between economic development and agricultural sustainability would be a challenged issue that would enforce the local authority to adjust the current food security policy.