地理学报

地理学报 ›› 2019, Vol. 74 ›› Issue (3): 411-420.doi: 10.11821/dlxb201903001

• 土地利用与生态系统服务 •    下一篇

1992-2017年基于荟萃分析的中国耕地撂荒时空特征

张学珍1(), 赵彩杉2, 董金玮1, 葛全胜1()   

  1. 1. 中国科学院地理科学与资源研究所 中国科学院陆地表层格局与模拟重点实验室,北京 100101
    2. 哈尔滨师范大学黑龙江省普通高等学校地理环境遥感监测重点实验室,哈尔滨 150025
  • 收稿日期:2018-03-26 修回日期:2018-12-10 出版日期:2019-03-25 发布日期:2019-03-19
  • 作者简介:张学珍(1981-), 男, 博士, 副研究员, 主要从事气候变化研究。E-mail: <email>xzzhang@igsnrr.ac.cn</email>
  • 基金资助:
    国家自然科学基金项目(41471171, 41790424);中国科学院前沿科学重点研究项目(QYZDB-SSW-DQC005);中国科学院青年创新促进会资助(2015038)

Spatio-temporal pattern of cropland abandonment in China from 1992 to 2017: A Meta-analysis

Xuezhen ZHANG1(), Caishan ZHAO2, Jinwei DONG1, Quansheng GE1()   

  1. 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. Key Laboratory of Remote Sensing Monitoring of Geographic Environment, Harbin Normal University, Harbin 150025, China
  • Received:2018-03-26 Revised:2018-12-10 Published:2019-03-25 Online:2019-03-19
  • Supported by:
    National Natural Science Foundation of China, No.41471171, No.41790424;The Key Research Program of Frontier Sciences from CAS, No.QYZDB-SSW-DQC005;Youth Innovation Promotion Association of CAS, No.2015038]

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摘要：

当前耕地撂荒已成为一种日益普遍的土地利用变化现象,对粮食安全和生态环境具有重要影响。为揭示1992-2017年中国耕地撂荒的时空特征及驱动机制,采用集成分析的思路,对县（市）域尺度撂荒的研究成果进行了汇总梳理,从中提取撂荒时间、位置、规模（程度）、成因等信息,并进行了荟萃分析。结果发现：1992-2017年全国范围内有撂荒记录的县（市）共165个,主要分布于南方,集中分布区呈逆时针旋转90°的“T”字型,纵轴自北至南贯穿甘肃东南部、四川东部、重庆境内,直至贵州西部和云南北部;横轴位于长江中下游地区,自西向东贯穿湖北、湖南、安徽、江西。其中,湖南、四川、安徽有撂荒记录的县（市）数量较多,分别为24个、23个、21个,其次是湖北、重庆、福建、甘肃、云南,均超过10个。“T”字型结构的形成经历两个阶段,大致以2010年为界,此前有撂荒记录的县（市）为102个,集中分布于沿长江中下游的东西向带状区域,形成横轴;此后新增63个县（市）,集中分布于自甘肃东南部至贵州西部和云南北部地区的南北向带状区域,形成纵轴。撂荒格局的形成过程与区域经济发展和产业结构调整背景下的农业劳动力析出基本同步。农业收益低和劳动力不足分别是86%和78%的县（市）撂荒形成的共性因素。

关键词: 中国, 撂荒, 荟萃分析, 时空特征

Abstract:

Cropland abandonment has increasingly occurred in China, which potentially affects national food and ecological security. This study collected existing publications focusing on cropland abandonment. Then, we extracted the key characteristics of cropland abandonment events, including time, location, magnitude, and driving force. The meta-analysis was carried out on these items to disclose the spatio-temporal pattern and main drivers of cropland abandonment in China in the last three decades. We found that there were a total of 163 counties in which cropland abandonment occurred according to the existing literature. These counties were mostly located in southern China and, in particular, exhibited a pattern of a T-shape on 90 degree anticlockwise rotation. The vertical axis exhibited a north-south belt across southeastern Gansu Province, eastern Sichuan Province, Chongqing, western Guizhou Province, and northern Yunnan Province. The horizontal axis exhibited a west-east belt along the middle to lower reaches of the Yangtze River across southern Hubei Province, northern Hunan Province,w and central to southern Anhui Province. In the provinces of Hunan, Sichuan, and Anhui, cropland abandonment can be found in 24, 23, and 21 counties, respectively; in Hubei, Chongqing, Fujian, Gansu, and Yunnan provincial-level areas, there are more than 10 counties with cropland abandonment. The formation of the “T” structure underwent two stages. Before 2010, there were 102 counties with cropland abandonment that mainly existed along the middle to lower reaches of the Yangtze River, where the horizontal axis occurred. Since 2010, there have been 63 counties with cropland abandonment, mostly existing in the north-south belt from southeastern Gansu to northern Guizhou, where the vertical axis occurred. The spatio-temporal patterns of cropland abandonment events matched well with the labor emigration from agriculture in the context of regional economic development and industrial restructuring. The low economic efficiency of agriculture and the shortage of agricultural labor are two common drivers leading to cropland abandonment in 86% and 78% of counties, respectively.

Key words: China, cropland abandonment, meta-analysis, spatio-temporal pattern