Pixel scale spatiotemporal patterns of agricultural water and land resources carrying capacity guide precision resource management
文献类型： 外文期刊
作者： Du, Shengnan 1 ; Gao, Maofang 1 ; Li, Qiang 1 ; Guo, Aiqing 2 ;
作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arable Land North, Beijing 100081, Peoples R China
2.Hebei GEO Univ, Sch Land Sci & Spatial Planning, Shijiazhuang 050031, Peoples R China
关键词： Projection pursuit model; Pixel scale; Spatiotemporal dynamic evolution law; Scenario simulation; Resources management
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）
ISSN： 0168-1699
年卷期： 2025 年 236 卷
页码：
收录情况： SCI
摘要： Water and land resource carrying capacity (WLRCC) critically influence food security, economic stability, and social sustainability. This study develops an innovative ESPADIP (endowment, support, pressure, advancement, destruction, impact, protection) framework, incorporating a genetic algorithm-optimized projection pursuit model (GA-PPM), ordered weighted averaging, kernel density estimation, and spatial autocorrelation analysis. We systematically investigated the spatiotemporal evolution, driving mechanisms, and future scenarios of WLRCC in Heilongjiang Province at pixel-scale resolution. The results exhibited a cyclical N-shaped trajectory of WLRCC over time, with pivotal transitions in 2010 (-2.72 % decline) and 2015 (+2.50 % recovery) driven by policy shifts in the Grain-for-Green Program and Water-land Conservation Planning. Spatial analysis revealed high-capacity clusters (WLRCC > 0.75) in agriculturally intensive south-central regions and the Sanjiang Plain, in contrast to low-capacity zones (WLRCC < 0.35) in Daxing' Bostanling's forested north, where soil erosion diminishes arable land suitability. Scenario projections revealed that prioritizing development over conservation would expand medium-low carrying capacity areas from 25.3 % to 81.1 %, accelerating farmland degradation. By integrating high-resolution ESPADIP diagnostics with dynamic monitoring systems, this study provides a scalable framework for balancing resource utilization and ecological resilience, offering critical decision support for sustainable land-water governance under global-change scenarios.
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