测绘通报 ›› 2021, Vol. 0 ›› Issue (10): 150-153.doi: 10.13474/j.cnki.11-2246.2021.324

• 技术交流 • 上一篇    下一篇

空间特性约束的县域耕地生产效益评价——以紫云苗族布依族自治县为例

李辉, 吴启明, 申朝永, 刘芳, 李婷, 张峥   

  1. 贵州省第三测绘院, 贵州 贵阳 550004
  • 收稿日期:2020-10-22 修回日期:2021-03-05 出版日期:2021-10-25 发布日期:2021-11-13
  • 通讯作者: 吴启明。E-mail:657820206@qq.com
  • 作者简介:李辉(1986-),男,硕士,高级工程师,主要从事时空大数据研究。E-mail:lh406833325@126.com

Production benefits evaluation of cultivated land in counties based on spatial characteristics: take the Miao-Bouyei autonomous county of Ziyun as an example

LI Hui, WU Qiming, SHEN Chaoyong, LIU Fang, LI Ting, ZHANG Zheng   

  1. The Third Institution of Surveying and Mapping of Guizhou Province, Guiyang 550004, China
  • Received:2020-10-22 Revised:2021-03-05 Online:2021-10-25 Published:2021-11-13

摘要: 本文以紫云苗族布依族自治县为试点,首先利用地理信息空间分析技术,分析评价耕地空间特征、农业物资运输配套设施空间分布、农业灌溉资源空间分布等因子对耕地生产效益的影响。然后从地理国情普查、耕地地球化学质量调查等成果数据中甄选影响因子,构建影响因子样本集,提出梯度距离概念,计算等级耕地经济效益权重,并构建偏最小二乘回归模型,评价各因子对耕地生产效益的影响。最后从地理空间角度提出了提升县域农业生产效益的改良措施。

关键词: 空间分析, 空间特征, 梯度距离, 耕地生产效益, 偏最小二乘回归模型

Abstract: This study takes the Miao-Bouyei autonomous county of Ziyun as a pilot site and uses geographic information spatial analysis technology to analyze and evaluate the impact of spatial factors- such as the characteristics of farmland, the distribution of agricultural material transportation facilities, and the distribution of agricultural irrigation resources, on the productivity of cultivated land. This study selects influential factors from the results of the census of geographical conditions and geochemical surveys, constructs a sample set of influential factors, proposes the concept of gradient distance, simulates the weight of economic benefits of graded farmland, and builds a partial least squares regression model to evaluate the impact of each factor on farmland production. Finally, from the perspective of geographic space, measures to improve county agricultural production are proposed.

Key words: space analysis, spatial characteristics, gradient distance, cropland production benefit, partial least squares regression model

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