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Smart grazing is a relatively difficult field of digital agriculture. Restricted by the geographical conditions of pastures, poor network infrastructure and low economic output, conventional IoT systems are difficult to apply in the field of grazing. In this paper, we propose the Space-Air-Ground integrated Grazing IoT(SAG-GIoT) system based on the background of yak grazing production in the Qinghai-Tibet Plateau, and define three smart grazing management application scenarios: (1) daily grazing supervision, (2) UAV grazing, (3) searching for yaks. To this end, we have designed the three-tier technical architecture of SAG-GIoT, and developed collar, base station and grazing management system. We designed the all-terrain network service scheme with the BeiDou Satellite-Base Station Sender(BDS-BSS) and Small Base Stations(SBSs), and verified the daily grazing supervision test in long-term. UAV grazing test was carried out in pasture, and a flexible communication networking was realized through the UAV Based Sation(UAV-BS). With the guidance of UAV searching and APP positioning, taking Handled Base Stations(HBSs) in hand, we quickly and accurately find the lost yaks. SAG-GIoT system is characterized as low cost, flexible deployment and global service, and has broad application prospects. Article highlights SAG-GIoT system boosts yak herding efficiency on the Tibetan Plateau, which reduces the risk of livestock loss. Drones and satellite networks solve the problem of signal coverage in vast pastoral areas, which is expected be a universal network for wild. This system is efficient and feasible, with further cost reduction, it is expected to be widely applied and promoted in pastoral areas.

Accurate approaches to aboveground biomass (AGB) estimation are required to support appraisal of the effectiveness of land use measures, which seek to protect grazing-adapted grasslands atop the Qinghai-Tibet Plateau (QTP). This methodological study assesses the effectiveness of one commonly used visible band vegetation index, Red Green Blue Vegetation Index (RGBVI), obtained from unmanned aerial vehicle (UAV), in estimating AGB timely and accurately at the local scale, seeking to improve the estimation accuracy by taking into account in situ collected information on disturbed grassland. Particular emphasis is placed upon the mapping and quantification of areas disturbed by grazing (simulated via mowing) and plateau pika (Ochotona curzoniae) that have led to the emergence of bare ground. The initial model involving only RGBVI performed poorly in AGB estimation by underestimating high AGB by around 10% and overestimating low AGB by about 10%. The estimation model was modified by the mowing intensity ratio and bare ground metrics. The former almost doubled the estimation accuracy from R2 = 0.44 to 0.81. However, this modification caused the bare ground AGB to be overestimated by about 38 and 19 g m−2 for 2018 and 2019, respectively. Although further modification of the model by bare ground metrics improved the accuracy slightly to 0.88, it markedly reduced the overestimation of low AGB values. It is recommended that grazing intensity be incorporated into the micro-scale estimation of AGB, together with the bare ground modification metrics, especially for severely disturbed meadows with a sizable portion of bare ground.

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.

Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine learning models to predict above-ground canopy features in Brachiaria pasture from ground truth data (GTD) and remote sensing at larger (satellite data on the cloud) and smaller (unmanned aerial vehicles (UAV)) scales. First, we used above-ground biomass (AGB) data obtained from Brachiaria to evaluate the relationship between vegetation indices (VIs) with the dry matter (DM). Next, the performance of machine learning algorithms was used for predicting AGB based on VIs obtained from ground truth and satellite and UAV imagery. When comparing more than twenty-five machine learning models using an Auto Machine Learning Python API, the results show that the best algorithms were the Huber with R2 = 0.60, Linear with R2 = 0.54, and Extra Trees with R2 = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R2 of 0.76, Extra Trees with an R2 of 0.75, and Bayesian Ridge with an R2 of 0.70, demonstrating a high potential to predict AGB and DM. This study is the first prediction model approach that assesses the rotational grazing system and pasture above-ground canopy features to predict the quality and quantity of cattle feed to support pasture management in Colombia.

The existing precision grazing technology helps to improve the utilization rate of livestock to pasture, but it is still at the level of “collectivization” and cannot provide more accurate grazing management and control. (1) Background: In recent years, with the rapid development of agent-related technologies such as deep learning, visual navigation and tracking, more and more lightweight edge computing cell target detection algorithms have been proposed. (2) Methods: In this study, the improved YOLOv5 detector combined with the extended dataset realized the accurate identification and location of domestic cattle; with the help of the kernel correlation filter (KCF) automatic tracking framework, the long-term cyclic convolution network (LRCN) was used to analyze the texture characteristics of animal fur and effectively distinguish the individual cattle. (3) Results: The intelligent UAV equipped with an AGX Xavier high-performance computing unit ran the above algorithm through edge computing and effectively realized the individual identification and positioning of cattle during the actual flight. (4) Conclusion: The UAV platform based on airborne depth reasoning is expected to help the development of smart ecological animal husbandry and provide better precision services for herdsmen.

Grazing is the most important and lowest cost means of livestock breeding. Because of the sharp contradiction between the grassland ecosystem and livestock, the grassland ecosystem has tended to degrade in past decades in China; therefore, the ecological balance of the grassland has been seriously damaged. The implementation of grazing prohibition, rotational grazing and the development of a large-scale breeding industry have not only ensured the supply of animal husbandry products, but also promoted the restoration of the grassland ecosystem. For the large-scale breeding industry, the animal welfare of livestock cannot be guaranteed due to the narrow and crowded space, thus, the production of the breeding industry usually has lower competitiveness than grazing. Disorderly grazing leads to grassland ecological crises; however, intelligent grazing can not only ensure animal welfare, but also fully improve the competitiveness of livestock husbandry products. Under the development of urbanization, the workforce engaged in grazing and breeding in pastoral areas is gradually lost. Intelligent grazing breeding methods need to be developed and popularized. This paper focuses on intelligent grazing, reviews grass remote sensing and aerial seeding, wearable monitoring equipment of livestock, UAV monitoring and intelligent grazing robots, and summarizes the development of intelligent grazing elements, exploring the new development direction of automatic grazing management with the grazing robot at this stage.

Grazing significantly affects the distribution, growth, and productivity of shrubs. In this study, we evaluated the effects of grazing disturbance on the spatial distribution patterns and interspecific relationships of two desert shrubs, Ammopiptanthus mongolicus and Sarcozygium xanthoxylon . Three types of grazing conditions were considered, including enclosed area (EA), seasonal rotational grazing area (SRGA), and grazing area (GA) (100 m × 100 m), in the West Ordos Nature Reserve of Inner Mongolia, China. The results showed that A. mongolicus and S. xanthoxylon populations were uniformly distributed at a small scale, and the distribution in EA and SRGA became gradually random. In GA, A. mongolicus population showed aggregated distribution but S. xanthoxylon population showed random distribution at a small scale. Moreover, both A. mongolicus and S. xanthoxylon populations at the 5–7 m scale showed random distribution. At the small and intermediate scales, the two species showed positive interspecific relationships of GA. However, no interspecific relationship was noted between the two species in EA and SRGA. A significant positive relationship ( P  < 0.01) was noted between the two species at 2–9 m and a negative relationship ( P  < 0.01) at 13–17 m scales in GA. Positive relationship ( P  < 0.01) was noted between the two species at 6–13 m scales and a significant negative relationship ( P  < 0.01) at 14–24 m scales in SRGA. The two species of desert shrubs showed positive interspecific relationships at the small scale, and they showed negative relationships as the interspecific competition intensified in the presence of grazing disturbance. When the grazing intensity exceeds a certain threshold, the interspecific relationships become weak. Therefore, moderate grazing would facilitate interspecific competition and species succession, whereas excessive grazing would disrupt natural competition causing desertification ultimately.

Bare patches in alpine meadow are the main manifestation of its degradation. The change of bare patches in an alpine meadow in the Yellow River Source Zone during 2018–2019 was studied in relation to the disturbances caused by plateau pika (Ochotona curzoniae) population and simulated grazing via artificial mowing both independently and interactively. The disturbance was set at three levels of high, medium and no disturbance (control group). Bare patches were mapped by from unmanned aerial vehicle (UAV) images with fine resolution of 1 cm obtained in August 2018 and August 2019 in ArcGIS. The results showed that the total area of bare patches decreased by 112.05 m2 in sub-plots devoid of pika disturbance but increased by 126.37 m2 in other sub-plots. The highest rate of increase is 89.02%. The individual effect of pika exceeds the joint effect of pika and mowing disturbances. The sole effect of mowing is lower than the joint effect of pika disturbance and intensive mowing, but higher than the joint influence of pika disturbance and moderate mowing. Strong pika disturbance (14 per sub-plot) caused the influence of mowing from moderate to intensive to increase by five-fold. The area of bare patches treated with moderate mowing and no pika disturbance decreased at the highest pace (−37.22%). Intensive mowing and medium density pikas (100 pikas/ha) are considered the thresholds at which the bare patches start to expand. Even if the meadow is mowed at the medium and high intensity, the area of bare patches can be significantly reduced if plateau pika population is controlled to a low level. ANOVA analysis and long-term macro-scale satellite-derived results reveal that pika disturbance is more important in causing the bare patches to change than simulated grazing. Therefore, it is more important to control the number of pikas than to reduce grazing intensity to prevent the expansion of bare patches in the degraded alpine meadow in the study area.

Accurate estimation of aboveground biomass (AGB) is essential for monitoring forage availability and guiding sustainable management in high-altitude pastures, where grazing sustains livelihoods but also drives ecological degradation. Although remote sensing has advanced biomass modeling in rangelands, applications in Andean–Amazonian ecosystems remain limited, particularly using UAV-based structural and spectral data. This study evaluated the potential of UAV LiDAR and multispectral imagery to estimate fresh and dry AGB in ryegrass (Lolium multiflorum Lam.) pastures of Amazonas, Peru. Field data were collected from subplots within 13 plots across two sites (Atuen and Molinopampa) and modeled using Random Forest (RF), Support Vector Machines, and Elastic Net. AGB maps were generated at 0.2 m and 1 m resolutions. Results revealed clear site- and month-specific contrasts, with Atuen yielding higher AGB than Molinopampa, linked to differences in climate, topography, and grazing intensity. RF achieved the best accuracy, with chlorophyll-sensitive indices dominating fresh biomass estimation, while LiDAR-derived height metrics contributed more to dry biomass prediction. Predicted maps captured grazing-induced heterogeneity at fine scales, while aggregated products retained broader gradients. Overall, this study shows the feasibility of UAV-based multi-sensor integration for biomass monitoring and supports adaptive grazing strategies for sustainable management in Andean–Amazonian ecosystems.

Grazing intensity (GI) is an important indicator for grazing situations in pastoral areas. However, it has been difficult to be observed directly in the field, due to the randomness and dynamics of the grazing behavior of livestock. Consequently, the lack of actual GI information has become a common issue in studies on quantitatively estimating GI. In this paper, a novel quantitative estimation method is proposed based on the Space-Air-Ground integrated monitoring technology. It systematically integrates GPS tracking technology, Unmanned Aerial Vehicle (UAV) observation technology, and satellite remote sensing technology. Taking Xiangdong Village on the Zoige Plateau as a study area, the trajectory data and UAV images were acquired by the GPS tracking experiments and UAV observation experiments, respectively. The GI at paddock scale (PGI) was then generated with the Kernel Density Estimation (KDE) algorithm and the above data. Taking the generated PGI as training data, an estimation model of GI at region scale (RGI) was constructed by using the time-series satellite remote sensing images and random forest regression algorithm. Finally, the time-series RGI data with a spatial resolution of 10 m in Xiangdong Village were produced by the above model. The accuracy assessment demonstrated that the generated time-series RGI data could reflect the spatial-temporal heterogeneity of actual GI, with a mean absolute error of 0.9301 and r2 of 0. 8573. The proposed method provides a new idea for generating the actual GI on the ground and the time-series RGI data. This study also highlights the feasibility and potential of using the Space-Air-Ground integrated monitoring technology to generate time-series RGI data with high spatial resolution. The generated time-series RGI data would provide data support for the formulation of policies and plans related to the sustainable development of animal husbandry.