Fuzzy two-stage non-point source pollution management model for agricultural systems—A case study for the Lake Tai Basin, China

Non-point source (NPS) pollution from agricultural lands has aroused widespread concerns throughout the world. In this study, an interval fuzzy two-stage stochastic non-point source pollution mitigation (IFTNS) model was developed for agricultural systems management under uncertainty through land retirement and nutrients discharge trading programs. The developed IFTNS model incorporated fuzzy possibilistic programming (FPP) and interval linear programming (ILP) within a general two-stage stochastic programming (TSP) framework. The IFTNS model could not only deal with uncertainties presented as probability density functions, fuzzy membership functions, and discrete interval numbers, but also provide an effective linkage between the pre-regulated agricultural systems management policies and the associated economic implications. Moreover, the IFTNS model could help to analyze various trading scenarios associated with different plausibility degree levels. The IFTNS model was applied to a case study with a nutrients discharge trading program within a subcatchment of the Lake Tai Basin in China. The results indicated that reasonable solutions under different plausibility degree levels were generated, which could be used for generating decision alternatives and thus help decision makers to identify desired agricultural NPS pollution mitigation policies. The results also indicated that land retirement and nutrients discharge trading programs could effectively achieve agricultural NPS pollution mitigation and maximize net system benefits.