基于数据挖掘方法的玉米和油菜土地适宜性分析——以贵州省遵义市为例

张晨; 林艳; 周华

doi:10.13961/j.cnki.stbctb.20220518.002

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基于数据挖掘方法的玉米和油菜土地适宜性分析——以贵州省遵义市为例

试验研究

- 基于数据挖掘方法的玉米和油菜土地适宜性分析——以贵州省遵义市为例
- Analysis on Land Suitability for Maize and Rapeseed Production Based on Data Mining Method—A Case Study at Zunyi City, Guizhou Province
- 水土保持通报 2022年42卷第3期页码：188-198
- 作者机构：
  
  1. 贵州省自然资源勘测规划研究院,贵州,贵阳,550004
  2. 贵州大学生命科学学院,贵州,贵阳,550025
  3. 自然资源部土地利用重点实验室贵州科研基地,贵州,贵阳,550004
- 作者简介：
- 基金信息：
  
  自然资源部碳中和与国土空间优化重点实验室开放资助项目“喀斯特生态脆弱区碳源/汇时空变化特征及低碳目标下国土空间格局优化研究：以贵州省为例”（2021CNTO2004）
- DOI：10.13961/j.cnki.stbctb.20220518.002
  中图分类号： P968;TP391
- 纸质出版：2022
- 稿件说明：
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张晨, 林艳, 周华. 基于数据挖掘方法的玉米和油菜土地适宜性分析——以贵州省遵义市为例[J]. 水土保持通报, 2022,42(3):188-198.

Zhang Chen, Lin Yan, Zhou Hua. Analysis on Land Suitability for Maize and Rapeseed Production Based on Data Mining Method—A Case Study at Zunyi City, Guizhou Province[J]. Bulletin of Soiland Water Conservation, 2022, 42(3): 188-198.
张晨, 林艳, 周华. 基于数据挖掘方法的玉米和油菜土地适宜性分析——以贵州省遵义市为例[J]. 水土保持通报, 2022,42(3):188-198. DOI： 10.13961/j.cnki.stbctb.20220518.002.

Zhang Chen, Lin Yan, Zhou Hua. Analysis on Land Suitability for Maize and Rapeseed Production Based on Data Mining Method—A Case Study at Zunyi City, Guizhou Province[J]. Bulletin of Soiland Water Conservation, 2022, 42(3): 188-198. DOI： 10.13961/j.cnki.stbctb.20220518.002.

摘要

[目的] 开展多作物土地适宜性分析，为农业生产的作物规划、土地利用规划和管理提供一种准确有效的方法。[方法] 基于数据挖掘方法，综合运用随机森林算法、综合指数评价法、空间约束多元聚类和空间统计等多种方法相结合，定量刻画分析了多作物种植土地综合适宜性。[结果] ①随机森林算法能定量精准实现多空间要素数据驱动下作物种植空间布局，且能识别出对玉米和油菜种植土地适宜区选择具有关键影响的因子；②研究区内玉米和油菜种植适宜区具有显著的空间异质性，玉米适宜区占种植土地总面积的91.23%，但油菜适宜区仅占69.64%；同时适宜于二者种植的主要区域占13.08%，主要分布在凤冈县中部及北部、湄潭县北部和余庆县西北部。[结论] 数据挖掘能够为种植土地利用方式选择提供最优方案的可能性，这一方法用于种植土地适宜性评价具有很好的通用性。

Abstract

[Objective] Conducting multi-crop land suitability analysis was carried out in order to provide an accurate and effective method for crop planning

land use planning

and management of agricultural production.[Methods] Based on data mining method

the comprehensive suitability of multi-crop land was quantitatively characterized by a random forest algorithm

a comprehensive index evaluation method

spatially constrained multivariate clustering

and spatial statistics.[Results] ① A random forest algorithm quantitatively and accurately produced the spatial layout of crop planting driven by multi-spatial elements

and identified factors that had a key influence on the selection of suitable planting land for maize and rapeseed. ② The suitable areas for maize and rapeseed planting exhibited significant spatial heterogeneity in the study area. The suitable areas for maize accounted for 91.23% of the total planting land area

but the suitable areas for rapeseed accounted for only 69.64%. The areas suitable for the planting of both crops was 13.08% of the total area

and were mainly located in the central and north parts of Fenggang County

the north part of Meitan County

and the northwest part of Yuqing County.[Conclusion] Data mining provides the possibility of selecting the optimal planting land use pattern. The method used in this study has good potential for evaluating the suitability of land in many locations for the production of various crops.

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