Agricultural land use optimal allocation system in developing area: Application to Yili watershed, Xinjiang Region

Abstract

Abstract:In developing countries, land productivity involves little market, where the agricultural land use is mainly determined by the food demands as well as the land suitability. The land use pattern will not ensure everywhere enough land for certain cropping if spatial allocation just according to land use suitability. To solve this problem, a subzone and a pre-allocation for each land use are added in spatial allocation module, and land use suitability and area optimization module are incorporated to constitute a whole agricultural land use optimal allocation (ALUOA) system. The system is developed on the platform .Net 2005 using ArcGIS Engine (version 9.2) and C# language, and is tested and validated in Yili watershed of Xinjiang Region on the newly reclaimed area. In the case study, with the help of soil data obtained from 69 points sampled in the fieldwork in 2008, main river data supplied by the Department of Water Resources of Xinjiang Uygur Autonomous Region in China, and temperature data provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences, land use suitability on eight common crops are evaluated one by one using linear weighted summation method in the land use suitability model. The linear programming (LP) model in area optimization model succeeds to give out land area target of each crop under three scenarios. At last, the land use targets are allotted in space both with a six subzone file and without a subzone file. The results show that the land use maps with a subzone not only ensure every part has enough land for every crop, but also gives a more fragmental land use pattern, with about 87.99% and 135.92% more patches than the one without, while at the expense of loss between 15.30% and 19.53% in the overall suitability at the same time.

Integrating a Spatially Explicit Tradeoff Analysis for Sustainable Land Use Optimal Allocation

“…Application of this approach, based on a raster, is mainly in three stages: land use spatial allocation, opportunity costs calculation, and tradeoff analysis. The agricultural land use optimal allocation (ALUOA) system [20] was applied in the first stage. The spatial opportunity costs calculation is the intermediate stage that integrates the tradeoff analysis with the ALUOA system.…”

“…It consists of three steps: land suitability evaluation (LSE), land use area optimization, and land use spatial allocation [20]. Interested readers can find content details in paper [20].…”

“…It consists of three steps: land suitability evaluation (LSE), land use area optimization, and land use spatial allocation [20]. Interested readers can find content details in paper [20]. The LSE assesses the degree of satisfaction of the crop requirement by using a weighted linear method for summing up different evaluation criteria, which include soil qualities, terrain factors, water supply conditions, climatic data, and socially locational factors (Table 1).…”

Development and Application of a Planning Support System for Regional Spatial Functional Zoning Based on GIS

“…In comparison, PSS research in China is still in its infancy, but is developing rapidly. The existing research mainly covers: studying the urban master planning layout in the light of foreign PSS [16]; introducing and analyzing basic issues in PSS including concepts, theories, and methods [17,18]; developing and applying PSS to several newly-exploited areas, such as strategic environmental decision assessment systems concerning urbanization [19], land use planning support systems for urban and rural areas [20], planning support systems related to urban growth control [21], and agricultural land optimization systems [22].…”

A Hybrid Inexact Optimization Method for Land-Use Allocation in Association with Environmental/Ecological Requirements at a Watershed Level

“…developed a coupled Cellular Automata (CA) and Radial Basis Function Neural (RBFN) Network model, which combines Geographic Information Systems (GIS) to contribute to the understanding of the complex land use/cover change process [22]. Zhang et al (2012) used a GIS-based spatial optimization model and a linear programming model for regional LUA [23]. Haque and Asami (2014) used a multi-objective function and genetic algorithm for LUA [24].…”