Abundant resources compensate for the uneven distribution of ungulates in desert grassland.

Introduction: Strategically managing livestock grazing in arid regions optimizes land use and reduces the damage caused by overgrazing. Controlled grazing preserves the grassland ecosystem and fosters sustainability despite resource limitations. However, uneven resource distribution can lead to diverse grazing patterns and land degradation, particularly in undulating terrains.

Assessment of the vegetation sensitivity index in alpine meadows with a high coverage and toxic weed invasion under grazing disturbance.

Maintaining healthy ecosystems is essential to ensure sustainable socio-economic development. Studies combining remote sensing data with grassland health assessments, extensively performed at different scales, are important for monitoring grassland health from a spatiotemporal perspective to enable scientific grazing management. However, most studies only use quantitative grassland degradation indices, such as grassland cover; this is done despite the fact that some degraded grasslands maintain a high level of cover solely by virtue of the proliferation of toxic weeds.

Seasonal dynamics of cattle grazing behaviors on contrasting landforms of a fenced ranch in northern China.

The number of livestock per unit area is commonly used as a proxy of grazing pressure in both experimental studies and grassland management. However, this practice ignores the impact of landform heterogeneity on the spatial distribution of grazing pressure, leading to localized patches of degraded grassland. The spatial distribution of actual grazing density thus needs to be examined.

Method for Classifying Behavior of Livestock on Fenced Temperate Rangeland in Northern China.

Different livestock behaviors have distinct effects on grassland degradation. However, because direct observation of livestock behavior is time- and labor-intensive, an automated methodology to classify livestock behavior according to animal position and posture is necessary. We applied the Random Forest algorithm to predict livestock behaviors in the Horqin Sand Land by using Global Positioning System (GPS) and tri-axis accelerometer data and then confirmed the results through field observations.