Basic farmland zoning and protection under spatial constraints with a particle swarm optimisation multiobjective decision model: a case study of Yicheng, China.

The rapid development of the Chinese economy has led to an increasing fraction of agricultural land being converted to nonagricultural uses. The zoning and protection of farmland with the best agricultural quality (basic farmland) is extremely intensive and complicated work. In this paper we establish a remote sensing, geographic information system, and particle swarm optimisation (PSO) multiobjective decision model (MODM) to calculate the optimum solution for basic farmland protection. Furthermore, a new particle evolution rule combined with a genetic algorithm is introduced to improve the solution performance. The PSO-based zoning model is then utilised in the case study of Yicheng, Hubei Province, China, to demonstrate that our MODM framework excels in providing an optimum solution for balancing the three objectives of basic farmland zoning and protection: maximising farmland spatial compactness, maximising farmland soil fertility, and minimising transportation cost. In particular, the model compares alternative Pareto-optimal scenarios in which several objectives can be achieved without compromising the other objectives to obtain a real and practical blueprint for action. Our model enables urban planners to test and compare the different scenarios under various particle swarm conditions. In addition, the PSO-based zoning model constitutes a true guide for real-world planners, and this model can be extended to specify basic farmland protection optimisations in other regions of China. [ABSTRACT FROM AUTHOR]