资源科学 ›› 2022, Vol. 44 ›› Issue (3): 480-493.doi: 10.18402/resci.2022.03.05

• 土地资源 • 上一篇    下一篇

广东省耕地非粮化的时空分异及其驱动机制

张颖诗(), 冯艳芬(), 王芳, 陈子龙, 李晓航   

  1. 广州大学地理科学与遥感学院,广州 510006
  • 收稿日期:2021-07-30 修回日期:2021-11-14 出版日期:2022-03-25 发布日期:2022-05-25
  • 通讯作者: 冯艳芬,女,广东清远人,副教授,从事乡村地理与土地资源利用研究。E-mail: fengyanfen@gzhu.edu.cn
  • 作者简介:张颖诗,女,广东广州人,硕士研究生,从事乡村地理与土地资源利用研究。E-mail: yig2112@163.com
  • 基金资助:
    国家自然科学基金项目(42071262);广州市哲学社会科学发展“十三五”规划项目(2020GZYB90);广州大学科研立项项目(YK2020016)

Spatiotemporal differentiation and driving mechanism of cultivated land non-grain conversion in Guangdong Province

ZHANG Yingshi(), FENG Yanfen(), WANG Fang, CHEN Zilong, LI Xiaohang   

  1. School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
  • Received:2021-07-30 Revised:2021-11-14 Online:2022-03-25 Published:2022-05-25

摘要:

粮食安全是保障国家经济发展和社会稳定的基石,日益严峻的耕地非粮化问题加剧了粮食安全风险,直接影响中国社会经济的可持续发展。本文从食物性生产的非粮化角度,选取广东省为研究区域,以县域为评价单元,运用社会经济统计数据及地理空间数据,集成Theil指数、空间自相关模型、随机森林模型、主成分分析和聚类分析等方法,从全省四大区域角度探讨了广东省2005和2019年耕地非粮化的时空演变特征、影响因素及其驱动类型。结果表明:①广东省耕地非粮化率由2005年的48.47%上升至2019年的54.65%,耕地非粮化程度由低度为主转向中度为主,且耕地非粮化利用空间差异呈现缩小态势,以珠三角的演变差异最为显著。②耕地非粮化的空间分布保持稳定的集聚状态,并以珠三角的高高集聚和东翼、西翼、山区的低低集聚为主。③路网密度、地均GDP对耕地非粮化存在显著的推动作用,农村第一产业劳动力占比、各县到省会城市距离是抑制耕地非粮化的重要因素。④耕地非粮化驱动类型以经济驱动型为主(44.63%),主要分布在珠三角;其次是农业支持型(33.06%),大多分布在山区;资源依赖型占比最少(22.31%),集聚在东、西两翼。研究结果有利于精准识别广东省耕地非粮化的时空特征,提升当地耕地非粮化的风险管控水平,对于农业可持续发展以及保证地区的粮食安全具有重要的理论与现实意义。

关键词: 耕地非粮化, 驱动机制, Theil指数, 随机森林, 广东省

Abstract:

Food security is the cornerstone of national economic development and social stability. The increasingly serious problem of cultivated land non-grain conversion intensifies the risk of food insecurity and directly affects the sustainable development of China's social and economic systems. From the perspective of non-grain production of food, this study selected Guangdong Province as the research area and county as the evaluation unit and based on socioeconomic statistics and geospatial data, using Theil index, spatial autocorrelation model, random forest model, principal component analysis, and cluster analysis to examine the spatiotemporal characteristics, influencing factors, and driving types of cultivated land non-grain conversion in 2005 and 2019. The results show that: (1) The non-grain rate of cultivated land in Guangdong Province increased from 48.47% in 2005 to 54.65% in 2019. The non-grain degree of cultivated land had changed from low to moderate, and the spatial differentiation of non-grain rate of cultivated land showed a decreasing trend, especially in the Pearl River Delta area. (2) The spatial distribution of non-grain cultivated land maintained a stable agglomeration state and showed high concentration in the Pearl River Delta area and low concentration in the east wing, west wing, and mountainous sub-regions of the province. (3) Road network density and GDP per square kilometer had significant promoting effects on cultivated land non-grain conversion, while the proportion of labor force in rural primary industry and distance between counties and provincial capital cities were important factors to restrain the cultivated land non-grain conversion. (4) The main driving type of cultivated land non-grain conversion was economy driven (44.63%), mainly distributed in the Pearl River Delta area. This was followed by agriculture supporting type (33.06%), mostly distributed in the mountainous areas. The resource dependency type was the least dominant (22.31%) and was concentrated in the east and west wing sub-regions. This research result is conducive to accurately identify the spatiotemporal characteristics of cultivated land non-grain conversion in Guangdong Province, improve the risk control level of local cultivated land non-grain conversion, and has important theoretical and practical significance for sustainable agricultural development and ensuring regional food security.

Key words: cultivated land non-grain conversion, driving mechanism, Theil index, Random Forest, Guangdong Province