Herbivore Types as a Key Driver of Spatial Variations in Aboveground Biomass Trends in Alpine Grasslands

Abstract Managing grazing regimes is a key strategy for promoting the sustainable use of grasslands. The Qinghai‐Xizang Plateau, dominated by alpine grasslands, is subjected to extensive grazing by livestock. Apart from some control experiments, the regional impact of livestock species on alpine grassland has been overlooked, mostly due to the spatial mismatch between statistical livestock data and grassland biomass data. Using cross‐scale feature extraction and random forest models, we developed a 1 km × 1 km gridded long‐term grazing intensity data set distinguishing between herbivore types between cattle and sheep from 2000 to 2019. We used the propensity score matching method to identify the impact of grazing intensity and herbivore types on aboveground biomass on the Qinghai‐Xizang Plateau. The data set demonstrated strong spatio‐temporal consistency with county‐level statistics, with R 2 values above 0.9 and Nash‐Sutcliffe efficiency (NSE) ranging from 0.93 to 0.98. The interannual trends of sheep (mainly Tibetan sheep) and cattle (mainly yaks) were largely opposite, with a significant decline in the sheep‐to‐cattle ratio in 42% of areas. At high grazing intensity, the aboveground biomass increase rate was slower, and aboveground biomass decreased more rapidly with an increasing sheep‐to‐cattle ratio. These findings advance our understanding of the spatio‐temporal dynamics of grazing intensity on the Qinghai‐Xizang Plateau and provide new insights into how grazing intensity and herbivore composition jointly shape alpine grassland productivity at the landscape scale.

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Publication Details

Journal
Earth s Future
Published
2026-02-01
DOI
https://doi.org/10.1029/2025ef006956
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Herbivore Types as a Key Driver of Spatial Variations in Aboveground Biomass Trends in Alpine Grasslands

yijia Wang, Yanxu Liu, Jiaxi Song, yingchun Fu et al.
Earth s Future
Remote Sensing in Agriculture
article

Herbivore Types as a Key Driver of Spatial Variations in Aboveground Biomass Trends in Alpine Grasslands

yijia Wang, Yanxu Liu, Jiaxi Song, yingchun Fu, Yu Han, Bojie Fu, Songjun Xu, Zhiyun Jiang, Peng Chen
article en

Abstract

Abstract Managing grazing regimes is a key strategy for promoting the sustainable use of grasslands. The Qinghai‐Xizang Plateau, dominated by alpine grasslands, is subjected to extensive grazing by livestock. Apart from some control experiments, the regional impact of livestock species on alpine grassland has been overlooked, mostly due to the spatial mismatch between statistical livestock data and grassland biomass data. Using cross‐scale feature extraction and random forest models, we developed a 1 km × 1 km gridded long‐term grazing intensity data set distinguishing between herbivore types between cattle and sheep from 2000 to 2019. We used the propensity score matching method to identify the impact of grazing intensity and herbivore types on aboveground biomass on the Qinghai‐Xizang Plateau. The data set demonstrated strong spatio‐temporal consistency with county‐level statistics, with R 2 values above 0.9 and Nash‐Sutcliffe efficiency (NSE) ranging from 0.93 to 0.98. The interannual trends of sheep (mainly Tibetan sheep) and cattle (mainly yaks) were largely opposite, with a significant decline in the sheep‐to‐cattle ratio in 42% of areas. At high grazing intensity, the aboveground biomass increase rate was slower, and aboveground biomass decreased more rapidly with an increasing sheep‐to‐cattle ratio. These findings advance our understanding of the spatio‐temporal dynamics of grazing intensity on the Qinghai‐Xizang Plateau and provide new insights into how grazing intensity and herbivore composition jointly shape alpine grassland productivity at the landscape scale.

Earth s FutureVol. 14(2)
Southwest University (CN), South China Normal University (CN), Chinese Academy of Sciences (CN), Beijing Normal University (CN), Research Center for Eco-Environmental Sciences (CN), Beijing University of Civil Engineering and Architecture (CN)
National Science Foundation, National Natural Science Foundation of China, South China Normal University
Openalex Percentile: Top 58%
Remote Sensing in Agriculture
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