乌兰布和沙地新开发人工绿洲土地优化结构评价

采用灰色系统关联分析方法对开发区进行土地优化结构评价，结果表明：①在林—农生产系统中，农作物的选择是提高土地资源转化率和经济效益的基本条件。瓜类作物收益高于粮食作物，瓜类作物平均收益为５０３２．９４元／（ｈｍ２·ａ）；粮食作物平均为２４８０．４０元／（ｈｍ２·ａ），前者是后者的２．０３倍。种植番茄和甜菜可获得较高的经济效益，其收益分别为５０７０．００元／（ｈｍ２·ａ）和４０９５．００元／（ｈｍ２·ａ）。②小麦套种玉米收益比单种小麦和玉米分别提高３７．０％和２２．２％；１粮＋复种结构的经济效益比套种结构平均提高７３．７５％。③试区各类用地不尽合理，防护林、经济林、人工草场、农地所占比例分别是：４９．５％、１１．９％、３．５％、３５．１％。根据该地区的总体规划和当地的约束条件，各类用地比例应分别调整为２８．５２％、１２．１１％、３３．０６％、２６．３２％。调整后人均年产值可达９２０８．９４元。

An Uncertain Programming Model for Land Use Structure Optimization to Promote Effectiveness of Land Use Planning

Land use structure optimization(LUSO) is an important issue for land use planning. In order for land use planning to have reasonable flexibility, uncertain optimization should be applied for LUSO. In this paper, the researcher first expounded the uncertainties of LUSO. Based on this, an interval programming model was developed, of which interval variables were to hold land use uncertainties. To solve the model, a heuristics based on Genetic Algorithm was designed according to Pareto Optimum principle with a confidence interval under given significance level to represent LUSO result. Proposed method was applied to a real case of Yangzhou, an eastern city in China. The following conclusions were reached. 1) Different forms of uncertainties ranged from certainty to indeterminacy lay in the five steps of LUSO, indicating necessary need of comprehensive approach to quantify them. 2) With regards to trade-offs of conflicted objectives and preferences to uncertainties, our proposed model displayed good ability of making planning decision process transparent, therefore providing an effective tool for flexible land use planning compiling. 3) Under uncertain conditions, land use planning effectiveness can be primarily enhanced by flexible management with reserved space to percept and hold uncertainties in advance.

Optimal Land Use Structure for Sustainable Agricultural Development——A Case Study in Changsha County,South Central China

Environmental and social problems caused by overfertilization,excessive pesticides,and encroachment on farmland are increasingly serious in agricultural settings,especially in suburban agricultural areas and highly intensive agricultural areas.Hence,modern agriculture not only pursues economic benefits,but it also pays more attention to ecological functions and social stability.This paper proposes a set of methods which are designed to realize optimal agricultural benefits and sustainable development by scientifically adjusting the land use structure.Taking Changsha County in South Central China as a case study,this paper first built an index system and adopted the information entropy-TOPSIS method to assess the economic,social,and ecological benefits of agricultural land use.Next,a coupled coordination model and an obstacle model were chosen to diagnose those factors that remained as obstacles to achieving the sustainable and coordinated development of the benefits of agricultural land use.Finally,based on the analysis of the changes in the benefits and obstacles over time,socio-economic and ecological constraints were established,and the multi-objective linear programming method(MOLP)was used to determine the comprehensive benefits and optimal land use structure.The results indicate that:(1)The agricultural benefits were stably increasing from 0.20 in 1996 to 0.79 in 2016.(2)The economic benefit index is no longer the main obstacle,while the social benefit index,which includes components such as the food security index,has become the principal influencing factor.(3)The optimal land use structure and comprehensive benefits were presented by taking into consideration the economic development,environmental protection,and social needs.This study emphasizes economic development,but it also seeks coordinated development with comprehensive benefits.The results of the study could provide scientific recommendations for optimizing the agricultural land use spatial patterns and sustainable land use.

Rural land use spatial allocation in the semiarid loess hilly area in China:Using a Particle Swarm Optimization model equipped with multi-objective optimization techniques

Semiarid loess hilly areas in China are enduring a series of environmental conflicts between urban expansion,cultivated land conservation,soil erosion and water shortage,and require land use allocation to reconcile these environmental conflicts.We argue that the optimized spatial allocation of rural land use can be achieved by a Particle Swarm Optimization (PSO) model in conjunction with multi-objective optimization techniques.Our study focuses on Yuzhong County of Gangsu Province in China,a typical catchment on the Loess Plateau,and proposes a land use spatial optimization model.The model maximizes land use suitability and spatial compactness based on a variety of constraints,e.g.optimal land use structure and restrictive areas,and employs an improved PSO algorithm equipped with a determinant initialization method and a dynamic weighted aggregation (DWA) method to obtain the optimized land use spatial pattern.The results suggest that (1) approximately 4% of land use should be reallocated and these changes would alleviate the environmental conflicts in the study area;(2) the major reshuffling is slope farmland and newly added construction and cultivated land,whereas the unchanged areas are largely forests and basic farmland;and (3) the PSO is capable of optimizing rural land use allocation,and the determinant initialization method and DWA can improve the performance of the PSO.