In the vast pastoral landscapes of Xinjiang’s Tianshan Mountains, a delicate dance between grazing pressure and pasture productivity is unfolding. A recent study published in *Agriculture* has shed new light on this intricate relationship, offering valuable insights for the agriculture sector and promising to reshape grazing management practices.
The research, led by Qun Luo from the College of Geography and Remote Sensing Sciences at Xinjiang University, focused on the northern slope of the Tianshan Mountains in Hutubi County. The study aimed to understand how grazing pressure (GP), characterized by grazing intensity (GI) and grazing density (GD), impacts net primary productivity (NPP) in pasturelands across different seasons.
Using the Carnegie–Ames–Stanford Approach (CASA) model, the team estimated NPP over eight time periods between 2010 and 2024 for three seasonal pastures: spring–autumn, summer, and winter. The accuracy of these estimates was validated by comparing them with existing NPP products. “The CASA model proved to be highly reliable, with R² values exceeding 0.90 for multi-year NPP estimation,” Luo explained.
The study revealed that spring–autumn and winter pastures exhibited significant slope changes and intense spatiotemporal NPP variations, while summer pastures showed insignificant slope changes and stable spatiotemporal NPP patterns. This seasonal variability is crucial for farmers and ranchers to understand, as it directly impacts their grazing strategies and overall productivity.
One of the key findings was the identification of grazing density (GD) as the optimal metric to represent grazing pressure. The study found a consistent negative feedback between GD and NPP across the three seasonal pastures, with spring–autumn and winter pastures exhibiting greater NPP sensitivity to GD. “This negative feedback indicates that as grazing density increases, the productivity of the pasture decreases,” Luo noted.
The research also established specific GD thresholds for each seasonal pasture: approximately 900 sheep km⁻² for spring–autumn pastures, 700 sheep km⁻² for summer pastures, and 5000 sheep km⁻² for winter pastures. Exceeding these thresholds leads to pasture degradation, while falling below them promotes recovery. These findings provide a scientific basis for zoned rest/rotational grazing and GD regulation, which can help prevent overgrazing and ensure sustainable pasture management.
The commercial implications of this research are significant. By understanding the optimal grazing density for different seasons, farmers can maximize pasture productivity and avoid the economic losses associated with overgrazing. This knowledge can also guide policy decisions and grazing regulations, ensuring the long-term health of pasturelands and the economic viability of the agriculture sector.
Looking ahead, this research could shape future developments in grazing management and agritech. The use of advanced models like CASA to estimate NPP and the identification of seasonal thresholds for grazing pressure offer a blueprint for similar studies in other regions. As Luo put it, “This study provides a framework for understanding the complex interactions between grazing pressure and pasture productivity, which can be applied to other pastoral landscapes around the world.”
In conclusion, the study by Luo and his team not only advances our scientific understanding of grazing pressure and NPP but also offers practical solutions for sustainable pasture management. By leveraging these insights, the agriculture sector can enhance productivity, prevent degradation, and ensure the long-term viability of pastoral lands.