Using a conceptual Bayesian network to investigate environmental management of vegetable production in the Lake Taihu region of China

Vegetable farms are one of many nitrogen (N) sources adversely affecting Lake Taihu in eastern China. Given the lack of quantitative "cause and effect" relationships and data relating to these systems, we developed a conceptual Bayesian network to investigate and demonstrate causal relationships and the effects of different mitigation strategies on N exports from vegetable farms in the Lake Taihu region. Structurally, the network comprised one primary transport factor, one primary source factor and three post-mobilisation strategies, and three output factors.In general the network suggests that N exports are more sensitive to transport factors (i.e. runoff volumes) than source factors (i.e. fertiliser application rates) although the cumulative effects of excessive fertiliser were not considered. Post-mobilisation mitigations such as wetlands and ecoditches appear to be particularly effective in decreasing N exports however their implementation on a regional scale may be limited by land availability. While optimising N inputs would be prudent, the network suggests that better irrigation practice, including improved irrigation scheduling, using less imported water and optimising rainfall utilisation would be more effective in achieving environmental goals than simply limiting N supply. There is little quantitative data describing N exports from Chinese vegetable farms.Conceptual equations were used to develop a semi-quantitative Bayesian Network.The Conceptual Bayesian Network is used to compare a range of mitigation strategies.

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