RESUMEN

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. Methods: In this study, we developed a herbivore foraging algorithm based on a resource selection function model to analyze foraging distribution patterns, predict the probability of foraging, and identify the determinants of foraging probability in cattle. The study area was a complex desert landscape encompassing dunes and interdunes. Data on cattle movements and resource distribution were collected and analyzed to model and predict foraging behavior. Results: Our findings revealed that cattle prefer areas with abundant vegetation in proximity to water sources and avoid higher elevations. However, abundant resource availability mitigated these impacts and enhanced the role of water points, particularly during late grazing periods. The analysis showed that available resources primarily determine foraging distribution patterns and lessen the effects of landforms and water distance on patch foraging. Discussion: The results suggest that thoughtful water source placement and the subdivision of pastures into areas with varied terrain are crucial for sustainable grazing management. By strategically managing these factors, land degradation can be minimized, and the ecological balance of grassland ecosystems can be maintained. Further research is needed to refine the model and explore its applicability in other arid regions.

RESUMEN

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. Thus, seeking indices that are a more accurate representation of the health status of grassland vegetation is of utmost importance. Therefore, in order to accurately characterize the ecological integrity of grasslands (i.e., while limiting the impact of confounding variables such as weeds), we chose the grassland health comprehensive evaluation index VOR (vigor, organization, and resilience) to assess the health of grasslands on the Tibetan Plateau. We applied the VOR evaluation indices to two rangelands with different grazing intensity on the Tibetan Plateau, and extracted 11 commonly used vegetation indices based on remote sensing images of rangelands,then modeled them with the data from field surveys. Our results show that the FVC, PS, and VOR were higher in lightly grazed pastures than in heavily grazed pastures in the 2017 and 2018 growing seasons. At the beginning of the sampling period, Poaceae accounted for a greater proportion in the HG pasture. However, by August 2018, the proportion of Poaceae in the LG pasture exceeded that in the HG pasture. the proportion of Forbs in the HG pasture was significantly greater than that in the LG pasture. This indicates that vegetation response to grazing disturbance is not only a volume reduction but also a vegetation composition change. The ratio vegetation index was the most sensitive to the vegetation health response, enabling the quantification and prediction of regional vegetation health and objectively reflecting the actual condition of the grassland ecosystem. According to a multiple regression analysis, the main climatic limiting factor in the region is precipitation, which positively correlated with VOR; whereas, grazing disturbance is an important driving factor, and it is inversely correlated with VOR.

RESUMEN

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. Owing to the corresponding changes in resource availability and energy consumption as livestock move across an elevation gradient, we predict that livestock will preferentially use low-land and that different temporal patterns of grazing pressure will occur in the contrasting landforms. GPS location data and a machine learning technique were used to identify the seasonal pattern and the factors driving grazing pressure on a fenced ranch. Over both low-land and sand-dune landforms, the proportion of time that livestock spent on foraging increased from 63% in July to 67% in August and 69% in September, and non-foraging behavior decreased correspondingly. In low-land, the log-transformed average foraging density significantly increased from 0.61 (i.e., total foraging behaviors in 5 days measured at 50-s intervals per 10 × 10 m grid) in July to 0.66 in August and 0.88 in September, whereas there was no significant change on sand-dunes. From July to September, the relative area of low-land foraged by cattle accounted for 31%, 35%, and 36%, respectively, and in sand-dunes the proportions increased from 45% to 47% to 51%. In low-land, the foraging density was negatively correlated with biomass (P = .07), total digestible nutrients (P < .05), and crude protein (P = .06) and positively correlated with acid detergent fiber (P < .05), whereas no such relationships were observed in sand-dunes. Our results indicate that topographic features should be considered when managing livestock, especially during periods with adverse conditions of herbage quality and quantity.

Asunto(s)

RESUMEN

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. The overall accuracy of GPS models was 85% to 90% when the time interval was greater than 300-800 s, which was approximated to the tri-axis model (96%) and GPS-tri models (96%). In the GPS model, the linear backward or forward distance were the most important determinants of behavior classification, and nongrazing was less than 30% when livestock travelled more than 30-50 m over a 5-min interval. For the tri-axis accelerometer model, the anteroposterior acceleration (-3 m/s2) of neck movement was the most accurate determinant of livestock behavior classification. Using instantaneous acceleration of livestock body movement more precisely classified livestock behaviors than did GPS location-based distance metrics. When a tri-axis model is unavailable, GPS models will yield sufficiently reliable classification accuracy when an appropriate time interval is defined.

Asunto(s)