Multifunctional zoning optimization of agricultural land use by coupling Bayesian belief network with density peaks clustering – a case study in Guangdong, China

Agricultural sustainable development for food and environmental security relies largely upon our understanding of the functions of an agricultural ecosystem and their relationships. This study aims to define functional zones by identifying the relationships among the individual functions of the Guangdong Province’s agricultural land resources for sustainable development. Therefore, a specific Bayesian belief network (BBN) was built to identify the trade-off/synergy relationships between functions, and the density peaks clustering (DPC) algorithm was used to perform function-oriented zoning. As a result, significant synergies of food production function are identified with other functions, while a trade-off exists with food cleanliness. The 1 km grid scale set as the zoning unit can be more appropriate for a land use plan to consider functional relationships and specific management options. Ultimately, the Guangdong agricultural land is zoned into five functional zones (green agriculture, tropical agriculture, productive agriculture, ecological agriculture, and urban agriculture) with specific options and stakeholders’ policies that are suggested to weaken existing and potential trade-offs accordingly. Evidently, coupling BBN with DPC demonstrates an enhanced capability to optimize an agricultural land use plan by balancing multifunctional synergies/trade-offs for advancing sustainable agricultural development.