Pre-Season Precipitation and Temperature Have a Larger Influence on Vegetation Productivity than That of the Growing Season in the Agro-Pastoral Ecotone in Northern China
文献类型： 外文期刊
作者： Zhang, Yuanyuan 1 ; Wang, Qingtao 1 ; Zhang, Xueyuan 3 ; Guo, Zecheng 5 ; Guo, Xiaonan 6 ; Ma, Changhui 2 ; Wei, Baocheng 7 ; He, Lei 2 ;
作者机构： 1.Hebei Univ Engn, Sch Landscape & Ecol Engn, Handan 056000, Peoples R China
2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China
3.Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China
4.Curtin Univ, Sch Design & Built Environm, Bentley 6102, Australia
5.Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China
6.Hebei GEO Univ, Hebei Int Joint Res Ctr Remote Sensing Agr Drought, Sch Land Sci & Space Planning, Shijiazhuang 050031, Peoples R China
7.Inner Mongolia Normal Univ, Coll Geog Sci, Hohhot 010022, Peoples R China
关键词： vegetation productivity; climate change; land cover change; precipitation patterns; pre-season climate; APENC
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）
ISSN：
年卷期： 2025 年 15 卷 2 期
页码：
收录情况： SCI
摘要： Climate change and human activities are reshaping the structure and function of terrestrial ecosystems, particularly in vulnerable regions such as agro-pastoral ecotones. However, the extent to which climate change impacts vegetation growth in these areas remains poorly understood, largely due to the modifying effects of human-induced land cover changes on vegetation sensitivity to climatic variations. This study utilizes satellite-derived vegetation indices, land cover datasets, and climate data to investigate the influence of both land cover and climate changes on vegetation growth in the agro-pastoral ecotone of northern China (APENC) from 2001 to 2022. The results reveal that the sensitivity of vegetation productivity, as indicated by the kernel Normalized Difference Vegetation Index (kNDVI), varies depending on the land cover type to climate change in the APENC. Moreover, ridge regression modeling shows that pre-season climate conditions (i.e., pre-season precipitation and temperature) have a stronger positive impact on growing-season vegetation productivity than growing season precipitation and temperature, while the effect of vapor pressure deficit (VPD) is negative. Notably, the kNDVI exhibits significant positive sensitivity (p < 0.05) to precipitation in 34.12% of the region and significant negative sensitivity (p < 0.05) to VPD in 38.80%. The ridge regression model explained 89.10% of the total variation (R-2 = 0.891). These findings not only emphasize the critical role of both historical and contemporary climate conditions in shaping vegetation growth but also provide valuable insights into how to adjust agricultural and animal husbandry management strategies to improve regional climate adaptation based on climate information from previous seasons in fragile regions.
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