Study on the Impact of Grazing Density on Seasonal Pasture NPP in the Northern Slope of the Tianshan Mountains in Xinjiang: A Case Study of Hutubi County

Grazing pressure (GP) was a key factor influencing net primary productivity (NPP) in pasturelands and was characterized by two indicators: grazing intensity (GI) and grazing density (GD). However, current research has not yet clarified whether the mechanisms linking GP to NPP varied by season, or whether seasonal thresholds of grazing pressure existed. This study employed the Carnegie–Ames–Stanford Approach (CASA) model to estimate NPP over eight time periods between 2010 and 2024 for three seasonal pastures (spring–autumn, summer, and winter) in the study area. Estimation accuracy was evaluated by comparing our NPP estimates with existing NPP products. Trends in NPP and their significance were analyzed using the Sen–MK method, followed by further examination of spatiotemporal variations in NPP across the three seasonal pastures. Subsequently, by comparing two grazing pressure indicators (GI and GD), we identified the optimal metric to represent GP and, on this basis, analyzed the spatiotemporal variations and threshold dynamics of pasture NPP across three seasons under the influence of GP. Results indicated that the CASA model achieved R2 > 0.90 for multi-year NPP estimation, with RMSE ranging from 27 to 45 g C m−2 y−1. Spring–autumn and winter pastures exhibited pronounced slope changes and intense spatiotemporal NPP variations, whereas summer pastures showed insignificant slope changes and stable spatiotemporal NPP patterns. Of the two GP indicators, the GD metric developed herein more effectively characterized grazing pressure across the study area. Across the three seasonal pastures, a consistent negative feedback between GD and NPP was evident; however, its strength differed markedly, with spring–autumn and winter pastures exhibiting greater NPP sensitivity to GD. The GD thresholds for spring–autumn, summer, and winter pastures in the study area were approximately 900, 700, and 5000 sheep km−2, respectively. Exceeding these thresholds led to degradation, while falling below them promoted recovery. The study revealed a threshold-mediated negative feedback between GD and NPP across seasonal pastures, quantified season-specific upper bounds of carrying capacity, and provided an evidence base for zoned rest/rotational grazing and GD regulation along the northern slope of the Tianshan Mountains.

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Journal
Agriculture
Published
2025-11-23
DOI
https://doi.org/10.3390/agriculture15232413
Primary Topic
Remote Sensing in Agriculture
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article
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article

Study on the Impact of Grazing Density on Seasonal Pasture NPP in the Northern Slope of the Tianshan Mountains in Xinjiang: A Case Study of Hutubi County

Hang Zhou, Changhui Ma, Xu Ma, Chenhui Zhu et al.
Agriculture
Remote Sensing in Agriculture
article

Study on the Impact of Grazing Density on Seasonal Pasture NPP in the Northern Slope of the Tianshan Mountains in Xinjiang: A Case Study of Hutubi County

Hang Zhou, Changhui Ma, Xu Ma, Chenhui Zhu, Xiaolin Wang, Qiang Luo, Teng Jiao
article en

Abstract

Grazing pressure (GP) was a key factor influencing net primary productivity (NPP) in pasturelands and was characterized by two indicators: grazing intensity (GI) and grazing density (GD). However, current research has not yet clarified whether the mechanisms linking GP to NPP varied by season, or whether seasonal thresholds of grazing pressure existed. This study employed the Carnegie–Ames–Stanford Approach (CASA) model to estimate NPP over eight time periods between 2010 and 2024 for three seasonal pastures (spring–autumn, summer, and winter) in the study area. Estimation accuracy was evaluated by comparing our NPP estimates with existing NPP products. Trends in NPP and their significance were analyzed using the Sen–MK method, followed by further examination of spatiotemporal variations in NPP across the three seasonal pastures. Subsequently, by comparing two grazing pressure indicators (GI and GD), we identified the optimal metric to represent GP and, on this basis, analyzed the spatiotemporal variations and threshold dynamics of pasture NPP across three seasons under the influence of GP. Results indicated that the CASA model achieved R2 > 0.90 for multi-year NPP estimation, with RMSE ranging from 27 to 45 g C m−2 y−1. Spring–autumn and winter pastures exhibited pronounced slope changes and intense spatiotemporal NPP variations, whereas summer pastures showed insignificant slope changes and stable spatiotemporal NPP patterns. Of the two GP indicators, the GD metric developed herein more effectively characterized grazing pressure across the study area. Across the three seasonal pastures, a consistent negative feedback between GD and NPP was evident; however, its strength differed markedly, with spring–autumn and winter pastures exhibiting greater NPP sensitivity to GD. The GD thresholds for spring–autumn, summer, and winter pastures in the study area were approximately 900, 700, and 5000 sheep km−2, respectively. Exceeding these thresholds led to degradation, while falling below them promoted recovery. The study revealed a threshold-mediated negative feedback between GD and NPP across seasonal pastures, quantified season-specific upper bounds of carrying capacity, and provided an evidence base for zoned rest/rotational grazing and GD regulation along the northern slope of the Tianshan Mountains.

AgricultureVol. 15(23)
Zhejiang Normal University (CN), Institute of Agricultural Resources and Regional Planning (CN), Chinese Academy of Agricultural Sciences (CN), Xinjiang University (CN)
Openalex Percentile: Top 65%
Remote Sensing in Agriculture
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