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Unregulated livestock breeding and grazing can degrade grasslands and damage the ecological environment. The combination of remote sensing and artificial intelligence techniques is a more convenient and powerful means to acquire livestock information in a large area than traditional manual ground investigation. As a mainstream remote sensing platform, unmanned aerial vehicles (UAVs) can obtain high-resolution optical images to detect grazing livestock in grassland. However, grazing livestock objects in UAV images usually occupy very few pixels and tend to gather together, which makes them difficult to detect and count automatically. This paper proposes the GLDM (grazing livestock detection model), a lightweight and high-accuracy deep-learning model, for detecting grazing livestock in UAV images. The enhanced CSPDarknet (ECSP) and weighted aggregate feature re-extraction pyramid modules (WAFR) are constructed to improve the performance based on the YOLOX-nano network scheme. The dataset of different grazing livestock (12,901 instances) for deep learning was made from UAV images in the Hadatu Pasture of Hulunbuir, Inner Mongolia, China. The results show that the proposed method achieves a higher comprehensive detection precision than mainstream object detection models and has an advantage in model size. The mAP of the proposed method is 86.47%, with the model parameter 5.7 M. The average recall and average precision can be above 85% at the same time. The counting accuracy of grazing livestock in the testing dataset, when converted to a unified sheep unit, reached 99%. The scale applicability of the model is also discussed, and the GLDM could perform well with the image resolution varying from 2.5 to 10 cm. The proposed method, the GLDM, was better for detecting grassland grazing livestock in UAV images, combining remote sensing, AI, and grassland ecological applications with broad application prospects.

Humans have grazed on the Qinghai–Tibetan Plateau (QTP) for many thousands of years. In recent decades, the intensity of grazing has increased and several new management strategies have been put into place to address the resulting changes in rangeland condition. Effective management of grazing activities in this region requires understanding the impact of livestock grazing across the diverse array of alpine grassland ecosystems present in the QTP, but recent studies have identified a number of critical uncertainties in the ecological science that underlies these management principles. To address these uncertainties, we carried out a synthesis analysis of the effect of livestock grazing on 26 indicators of ecosystem structure and function based on 61 studies from 88 independent research sites within the QTP. Our synthesis results indicate that livestock grazing exerts complex controls on ecosystem structure and function, which vary according to local landscape characteristics. We found that grazing contributes to greater plant species diversity (Shannon–Wiener index, Simpson dominance index, and Pielou evenness index significantly increased 0.18, 0.05, and 0.03, respectively, due to grazing), but decreased aboveground biomass (47.15%), soil organic carbon (12.41%), soil total nitrogen (12.75%), and microbial biomass carbon (9.42%). Further, ecosystem function is controlled by interactions between grazing and other landscape characteristics such as elevation and mean annual temperature. The management regime currently in place in the QTP, which involves complete exclusion of grazing in some areas, can have variable effects on grassland health. Therefore, the complexity of these responses is an indication that livestock and grassland management may benefit from a more nuanced management regime than is currently utilized in the QTP.

Increasing plant diversity can increase ecosystem functioning, stability, and services in both natural and managed grasslands, but the effects of herbivore diversity, and especially of livestock diversity, remain underexplored. Given that managed grazing is the most extensive land use worldwide, and that land managers can readily change livestock diversity, we experimentally tested how livestock diversification (sheep, cattle, or both) influenced multidiversity (the diversity of plants, insects, soil microbes, and nematodes) and ecosystem multifunctionality (including plant biomass production, plant leaf N and P, above-ground insect abundance, nutrient cycling, soil C stocks, water regulation, and plant–microbe symbiosis) in the world’s largest remaining grassland. We also considered the potential dependence of ecosystem multifunctionality on multidiversity. We found that livestock diversification substantially increased ecosystem multifunctionality by increasing multidiversity. The link between multidiversity and ecosystem multifunctionality was always stronger than the link between single diversity components and functions. Our work provides insights into the importance of multitrophic diversity to maintain multifunctionality in managed ecosystems and suggests that diversifying livestock could promote both multidiversity and ecosystem multifunctionality in an increasingly managed world.

Livestock grazing has been shown to alter the structure and functions of grassland ecosystems. It is well acknowledged that grazing pressure is one of the strongest drivers of ecosystem‐level effects of grazing, but few studies have assessed how grazing pressure impacts grassland biodiversity and ecosystem multifunctionality (EMF). Here, we assessed how different metrics of biodiversity (i.e., plants and soil microbes) and EMF responded to seven different grazing treatments based on an 11‐year field experiment in semi‐arid Inner Mongolian steppe. We found that soil organic carbon, plant‐available nitrogen and plant functional diversity all decreased even at low grazing pressure, while above‐ground primary production and bacterial abundance decreased only at high levels of grazing pressure. Structural equation models revealed that EMF was driven by direct effects of grazing, rather than the effects of grazing on plant or microbial community composition. Grazing effects on plant functional diversity and soil microbial abundance did have moderate effects on EMF, while plant richness did not. Synthesis. Our results showed ecosystem functions differ in their sensitivity to grazing pressure, requiring a low grazing threshold to achieve multiple goals in the Eurasian steppe. A plain language summary is available for this article. Plain Language Summary

Grazing by domestic herbivores is the most widespread land use on the planet, and also a major global change driver in grasslands. Yet, experimental evidence on the long-term impacts of livestock grazing on biodiversity and function is largely lacking. Here, we report results from a network of 10 experimental sites from paired grazed and ungrazed grasslands across an aridity gradient, including some of the largest remaining native grasslands on the planet. We show that aridity partly explains the responses of biodiversity and multifunctionality to long-term livestock grazing. Grazing greatly reduced biodiversity and multifunctionality in steppes with higher aridity, while had no effects in steppes with relatively lower aridity. Moreover, we found that long-term grazing further changed the capacity of above- and below-ground biodiversity to explain multifunctionality. Thus, while plant diversity was positively correlated with multifunctionality across grasslands with excluded livestock, soil biodiversity was positively correlated with multifunctionality across grazed grasslands. Together, our cross-site experiment reveals that the impacts of long-term grazing on biodiversity and function depend on aridity levels, with the more arid sites experiencing more negative impacts on biodiversity and ecosystem multifunctionality. We also highlight the fundamental importance of conserving soil biodiversity for protecting multifunctionality in widespread grazed grasslands. Experimental evidence on the long-term impacts of livestock grazing on biodiversity and function is limited. Here, the authors show that grazing impacts on biodiversity and ecosystem functions are aggravated with aridity using experimental sites across an aridity gradient.

Grassland degradation caused by increases in livestock grazing threatens a variety of ecosystem services. Understanding changes in plant community assembly during the process of grassland degradation in the presence of grazing is important to help restore degraded grasslands worldwide but has received little attention thus far. The grassland degradation process is typified by heterogeneous degradation, that is, gradual formation of degraded patches (hereafter “patchy degradation”). Here, we experimentally examined the effects of herbivore grazing and patchy degradation on plant community assembly using nine pairs of non-degraded (intact) and patch-degraded (fragmented) grasslands subject to grazing by different-sized herbivores (i.e., NG, no grazing; SG, sheep grazing; CG, cattle grazing) over 4 years. Using a null-model approach, we estimated the relative magnitude of deterministic processes of community assembly by comparing the observed and expected β-diversity. We found that in the absence of herbivore grazing, deterministic processes played a greater role in community assembly, regardless of whether patchy degradation had occurred. However, the deterministic processes resulted in plant communities being more spatially similar in non-degraded grasslands while being more dissimilar in patchy degraded grasslands. Compared with non-degraded grasslands, species with strong competitive abilities (i.e., Leymus chinensis) were less dominant in patchy degraded grasslands, indicating relaxed competition and a reduced role of species interactions over plant communities. Instead, patchy degradation added the role of environmental variables over plant communities. SG consistently promoted more stochastic plant community assembly in both non-degraded and patch-degraded grasslands, while CG promoted more stochastic plant community assembly only in the non-degraded state, having no effect in the patch-degraded state. Our study offers important insights into changes in plant community assembly during ongoing patch-degradation of grasslands, indicating the role of increased environmental filtering of soil and reduced species interactions in driving plant community dynamics with increasing grassland patchy degradation. We also uncovered an herbivore species-specific effect on plant community assembly during the process of grassland degradation, which will better inform and improve future grassland restoration planning efforts.

Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO 2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change. Livestock grazing may drive grassland degradation. Here, the authors use process-based modelling validated with empirical data to define a stocking rate threshold across grassland on the Qinghai-Tibetan Plateau, identify vulnerable areas and predict threshold shifts under future climate scenarios.

Grasslands cover one third of the earth’s terrestrial surface and are mainly used for livestock production. The usage type, use intensity and condition of grasslands are often unclear. Remote sensing enables the analysis of grassland production and management on large spatial scales and with high temporal resolution. Despite growing numbers of studies in the field, remote sensing applications in grassland biomes are underrepresented in literature and less streamlined compared to other vegetation types. By reviewing articles within research on satellite-based remote sensing of grassland production traits and management, we describe and evaluate methods and results and reveal spatial and temporal patterns of existing work. In addition, we highlight research gaps and suggest research opportunities. The focus is on managed grasslands and pastures and special emphasize is given to the assessment of studies on grazing intensity and mowing detection based on earth observation data. Grazing and mowing highly influence the production and ecology of grassland and are major grassland management types. In total, 253 research articles were reviewed. The majority of these studies focused on grassland production traits and only 80 articles were about grassland management and use intensity. While the remote sensing-based analysis of grassland production heavily relied on empirical relationships between ground-truth and satellite data or radiation transfer models, the used methods to detect and investigate grassland management differed. In addition, this review identified that studies on grassland production traits with satellite data often lacked including spatial management information into the analyses. Studies focusing on grassland management and use intensity mostly investigated rather small study areas with homogeneous intensity levels among the grassland parcels. Combining grassland production estimations with management information, while accounting for the variability among grasslands, is recommended to facilitate the development of large-scale continuous monitoring and remote sensing grassland products, which have been rare thus far.

Rational utilization of natural resources is crucial in arid and semi-arid areas due to their vulnerable ecosystems and low resource resilience. Achieving a balance between grassland production and livestock grazing, known as the pasture-livestock balance, is essential for the sustainable development of grassland resources on the Mongolian Plateau (MP). This study focuses on the grassland regions of 8 provinces in eastern Mongolia (MNG) and 7 leagues in Inner Mongolia (IMNG), China, during the period from 2018 to 2022. Machine learning methods were employed for land cover classification and above-ground biomass (AGB) estimation. The grassland carrying capacity was assessed using the grassland carrying capacity index (GCC). The results indicate that: (1) The grassland classification accuracy on the MP exceeds 95%, with grassland area accounting for approximately 47% of the total.(2)The AGB of the grasslands exhibits a clear spatial heterogeneity, increasing from southwest to northeast. Additionally, nearly 80% of the grassland productivity is of high quality, reaching up to 250 g/m 2 .(3) Between 2018 and 2022, the MP exhibited a relatively high grassland carrying capacity, with an average of 1.8 SU/ha. However, the overall grassland carrying condition has gradually deteriorated, primarily due to factors such as grassland fires and an increase in livestock numbers. Based on the varying degrees of grassland degradation, different policy recommendations have been proposed. This study approach, findings and policy suggestions are significant for the development of livestock farming and grassland management on the MP.

With increasing affluence in many developing countries, the demand for livestock products is rising and the increasing feed requirement contributes to pressure on land resources for food and energy production. However, there is currently a knowledge gap in our ability to assess the extent and intensity of the utilization of land by livestock, which is the single largest land use in the world. We developed a spatial model that combines fine-scale livestock numbers with their associated energy requirements to distribute livestock grazing demand onto a map of energy supply, with the aim of estimating where and to what degree pasture is being utilized. We applied our model to Kazakhstan, which contains large grassland areas that historically have been used for extensive livestock production but for which the current extent, and thus the potential for increasing livestock production, is unknown. We measured the grazing demand of Kazakh livestock in 2015 at 286 Petajoules, which was 25% of the estimated maximum sustainable energy supply that is available to livestock for grazing. The model resulted in a grazed area of 1.22 million km², or 48% of the area theoretically available for grazing in Kazakhstan, with most utilized land grazed at low intensities (average off-take rate was 13% of total biomass energy production). Under a conservative scenario, our estimations showed a production potential of 0.13 million tons of beef additional to 2015 production (31% increase), and much more with utilization of distant pastures. This model is an important step forward in evaluating pasture use and available land resources, and can be adapted at any spatial scale for any region in the world.