Nonlinear and region-specific effects of grassland management practices on net ecosystem productivity in China’s grasslands

Grasslands are important terrestrial carbon sinks and play a critical role in the global carbon cycle and climate change mitigation. China has implemented a range of grassland restoration and management practices in recent decades. However, their relative importance, nonlinear relationships with net ecosystem productivity (NEP), and interactions with environmental factors remain insufficiently understood at the national scale. This study integrates long-term remote sensing, environmental, and grassland management practice datasets and applies the XGBoost-SHAP modeling to investigate the spatiotemporal patterns and drivers of grassland NEP across China. The results indicated that NEP increased across 73.44% of China’s grasslands, with the most pronounced increases occurring in the northeastern grasslands and the eastern Qinghai-Tibet Plateau. Grassland management variables accounted for part of the variation in NEP captured by the model, with grazing intensity (GI) identified as the most important management predictor. GI showed a significant nonlinear association with grassland NEP. Across different ecological regions, GI interacted most strongly with elevation, temperature, vapor pressure deficit, and slope, highlighting marked spatial heterogeneity in the effects of grassland management. These findings underscore the critical role of grassland management in regulating grassland NEP and provide a scientific basis for optimizing region-specific management strategies and safeguarding ecological security.

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Publication Details

Journal
Applied Geography
Published
2026-10-03
DOI
https://doi.org/10.1016/j.apgeog.2026.104205
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Nonlinear and region-specific effects of grassland management practices on net ecosystem productivity in China’s grasslands

Saule Kazhapovna Makenova, Chengcheng Gang, Zheng Weiwei, Qiao Li et al.
Applied Geography
Remote Sensing in Agriculture
article

Nonlinear and region-specific effects of grassland management practices on net ecosystem productivity in China’s grasslands

Saule Kazhapovna Makenova, Chengcheng Gang, Zheng Weiwei, Qiao Li, Aliya Adeli, Huimin Cao, Yanan Wang, Sun Zhang, Haimeng Shi, Wei Chen
article en

Abstract

Grasslands are important terrestrial carbon sinks and play a critical role in the global carbon cycle and climate change mitigation. China has implemented a range of grassland restoration and management practices in recent decades. However, their relative importance, nonlinear relationships with net ecosystem productivity (NEP), and interactions with environmental factors remain insufficiently understood at the national scale. This study integrates long-term remote sensing, environmental, and grassland management practice datasets and applies the XGBoost-SHAP modeling to investigate the spatiotemporal patterns and drivers of grassland NEP across China. The results indicated that NEP increased across 73.44% of China’s grasslands, with the most pronounced increases occurring in the northeastern grasslands and the eastern Qinghai-Tibet Plateau. Grassland management variables accounted for part of the variation in NEP captured by the model, with grazing intensity (GI) identified as the most important management predictor. GI showed a significant nonlinear association with grassland NEP. Across different ecological regions, GI interacted most strongly with elevation, temperature, vapor pressure deficit, and slope, highlighting marked spatial heterogeneity in the effects of grassland management. These findings underscore the critical role of grassland management in regulating grassland NEP and provide a scientific basis for optimizing region-specific management strategies and safeguarding ecological security.

Applied GeographyVol. 197
Shandong University of Technology (CN), Chinese Academy of Sciences (CN), Institute of Soil and Water Conservation (CN), S.Seifullin Kazakh Agro Technical University (KZ), Ministry of Water Resources of the People's Republic of China (CN), Northwest A&F University (CN)
Openalex Percentile: Top 11%
Remote Sensing in Agriculture
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