Grazing is one of the predominant human activities taking place today inside protected areas, with both direct and indirect effects on the vegetation community. We analyzed the effects of grazing intensity on grass composition during four grazing seasons containing 78 plant species belonging to eight plant functional groups, which include perennial tall grass (6 species), perennial short grass (6 species), shrubs (3 species), legumes (9 species), Liliaceae herb (8 species), annual/biennial plants (11 species), perennial short forbs (16 species) and perennial tall forbs (18 species). We estimated grazing intensity at four levels, control, light, moderate and heavy grazing intensity corresponding to 0.00, 0.23, 0.46 and 0.92 animal units ha⁻¹, respectively. We found that each plant functional group showed a different response to grazing intensity. Perennial tall grasses that were dominated by high palatable mesophyte and mesoxerophyte grass showed a significant decrease with grazing intensity, while the medium palatable xerophyte and widespread grasses that were the predominant short perennial increases with grazing intensity. The perennial tall forbs that were dominated by the mesophyte grass also decreased, but the decrease was statistically insignificant. The influence of grazing density on species is also related to soil factors (soil nutrient, soil moisture and soil temperature and soil bulk density). Some functional groups such as tall fescue and Liliaceae herbs, remained stable—which may be related to the changes in the soil environment caused by grazing activities. The findings of this study could provide a standpoint for assessing the current grazing management scenarios and conducting timely adaptive practices to maintain the long-term ability of grassland systems to perform their ecological functions.

Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model’s generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model’s generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP_@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep’s daily behaviour for precision livestock management, promoting modern husbandry development.

Soil microorganisms promote material transformation and energy flow in the entire ecological environment and play a key role in the stability and development of grassland ecosystems. Studies on the impacts of grazing on the soil microbial community and the establishment of a reasonable grazing intensity are crucial to improve our knowledge of the mechanisms underlying grassland degradation and to accurately assess the influence of grazing management on grassland functions and the nutrient cycle. Based on the grassland grazing control experimental platform, we compared the structure and diversity characteristics of soil microbial communities under six grazing intensities (0.00, 0.23, 0.34, 0.46, 0.69, and 0.92 AU ha⁻¹) (1 AU = 500 kg of adult cattle) on the Hulunbuir Leymus chinensis meadow steppe. The results showed that soil microbial biomass carbon (MBC) and nitrogen (MBN) decreased with increasing soil depth. The 0–10 cm soil layer of G0.34 had the highest MBC and MBN, and the G0.92 treatment had the lowest MBC and MBN. Heavy grazing significantly decreased the MBC and MBN contents in the soil surface layer. The soil bacterial diversity under light grazing treatment (0.23 AU ha⁻¹) was higher than that under heavy grazing, and the fungal diversity under the no-grazing treatment was higher than that under the grazing treatment. Overgrazing reduced the bacterial species in the soil. The plant belowground biomass significantly (p = 0.039) influenced the bacterial community structure, and the soil pH (p = 0.032), total nitrogen (p = 0.011), and litter (p = 0.007) significantly influenced the fungal community. The effects of grazing on microbial communities were primarily driven by vegetation productivity, litter mass, and soil geophysical and chemical characteristics. This study deepened our understanding of the impacts of grazing practices on soil microbial communities on the meadow steppe, suggesting that moderate-disturbance grazing can promote the sustainable development of grassland vegetation-soil microorganisms.