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确定中国耕地碎片化的驱动力

Tianci Gu¹, Wanxu Chen^2,3, Jiale Liang⁴

¹Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.

Environmental science and pollution research international

|September 14, 2023

概括

中国的耕地碎片化正在加剧,影响农业效率和粮食安全. 土地使用强度是主要的驱动因素,但区域差异需要针对可持续农业量身定制的管理策略.

科学领域:

农业科学农业科学
环境科学环境科学
地理空间分析的研究.

背景情况:

耕地碎片化 (CLF) 对中国的农业生产效率,现代化和粮食安全构成重大挑战.
了解CLF的时空动态和驱动因素对于有效的农业政策和可持续的土地管理至关重要.
现有研究需要进一步阐明在中国多样化的农业景观中,CLF驱动力的区域差异.

研究的目的:

分析2000年至2020年中国耕地碎片化的时空进化模式.
确定和量化CLF在不同农业地区的空间差异化背后的关键驱动力.
为中国的可持续土地资源利用和CLF管理战略提供科学见解.

主要方法:

利用遥感数据,使用2000年,2010年和2020年的景观模式指标量化CLF.
采用地理探测器模型来分析CLF在不同农业地区的驱动力及其空间变化.
评估了研究期间耕地面积的变化以及碎片化趋势.

主要成果:

在中国各地,耕地碎片化加剧,尽管耕地面积总体略有下降.
在CLF模式中观察到显著的空间自相关性,在2000-2010年和2010-2020年之间,土地面积变化的明显趋势.
在大多数农业地区,土地使用强度成为推动CLF的主要因素,其他因素的影响在区域内有显著差异.

结论:

该研究强调了耕地碎片化的日益增长的趋势及其与土地面积变化的复杂关系.
土地使用强度是一个关键的,尽管在区域范围内变化的,CLF的驱动因素,需要特定区域的政策干预.
这些发现为制定有针对性的政策提供了至关重要的科学支持,以保护耕地和管理中国可持续农业发展的碎片化.

关键词:

中国中国中国中国.耕地碎片化耕地碎片化驱动机制的驱动机制地质探测器模型景观模式指标景观模式指标空间自相关性空间自相关性

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