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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.

Intensive crop production on grassland-derived Mollisols has liberated massive amounts of carbon (C) to the atmosphere. Whether minimizing soil disturbance, diversifying crop rotations, or re-establishing perennial grasslands and integrating livestock can slow or reverse this trend remains highly uncertain. We investigated how these management practices affected soil organic carbon (SOC) accrual and distribution between particulate (POM) and mineral-associated (MAOM) organic matter in a 29-y-old field experiment in the North Central United States and assessed how soil microbial traits were related to these changes. Compared to conventional continuous maize monocropping with annual tillage, systems with reduced tillage, diversified crop rotations with cover crops and legumes, or manure addition did not increase total SOC storage or MAOM-C, whereas perennial pastures managed with rotational grazing accumulated more SOC and MAOM-C (18 to 29% higher) than all annual cropping systems after 29 y of management. These results align with a meta-analysis of data from published studies comparing the efficacy of soil health management practices in annual cropping systems on Mollisols worldwide. Incorporating legumes and manure into annual cropping systems enhanced POM-C, microbial biomass, and microbial C-use efficiency but did not significantly increase microbial necromass accumulation, MAOM-C, or total SOC storage. Diverse, rotationally grazed pasture management has the potential to increase persistent soil C on Mollisols, highlighting the key role of well-managed grasslands in climate-smart agriculture.

Abstract Precision Livestock Farming (PLF) represents a significant evolution in the livestock sector, promising to transform farms management improving production efficiency and sustainability, products quality, working conditions and animal welfare. While PLF is increasingly utilised under controlled condition typically linked to intensive livestock systems, its implementation in extensive livestock farms - where animals are primarily raised outdoor - is a challenge. Extensive systems, which cover approximately 67% of global agricultural land, have significant socio-economic importance worldwide, both in terms of food supply and the provision and maintenance of ecosystem services. At the farm level, Precision Livestock Extensive Farming (PLEF) - including the use of wearable sensors, environmental monitoring equipment, and remote sensing - can be widely employed in production monitoring, solving management problems, addressing logistical challenges and improving efficiency in resource management and finally in decision-making processes. Through text mining of 710 scientific articles published between 1980 and September 2024, this review identifies key trends, technologies, and gaps in current research and management approaches. Major findings highlight the prevalence of sensor technologies for monitoring animal behaviour and pasture quality, principally from Australia and United States. The review underscores the potential of PLF to enhance sustainable production in extensive systems, but also calls for more research on the integration of advanced data analytics and remote sensing technologies, including edge computing. The findings aim to guide future research and practical implementations, fostering the development of sustainable livestock farming practices globally with a view to multispecies approaches tacking into account also wildlife-livestock interactions. HIGHLIGHTS PLEF shifts livestock system management from manual to (semi-) automated. Text mining shows the evolution and spread of PLEF literature over the years. PLEF tools help address environmental and livestock system challenges.

ContextHere we develop a practical framework (Mosaico) and report a real-world example of early implementation of a Fire-Smart Territory (FST) in Sierra de Gata-Las Hurdes region of central Spain.ObjectivesWe aimed to assess the impact of landscape changes induced by Local Land Managers (LLM; indirect prevention) on simulated fire spread under different governance scenarios developed in 2016–2021.MethodsFollowing a participatory process in the region, we received 250 proposals for intervention (49.6% from agriculturalists, 22.8% from forest producers-mainly resin tappers-, and 27.6% from shepherds). From the 94 (37.6%) proposals implemented by the end of the study, we quantified changes in fuel models over the whole territory (Scenario 1, S1). Then, we simulated fires in 20 ignition points to estimate area burned in S1 and three other governance scenarios.ResultsTo date, the sole intervention of LLMs results in a low to moderate impact (current mean 10.5; median 1.8), which can be explained by the high frequency of small-scale interventions (agriculture) and the comparatively modest impact on fuel reduction of large-scale interventions (livestock grazing). A combination of LLM and public actions is needed to reach a more substantial reduction of burned area (S2-S3, mean % impact 14.1–18.9; median 6.9–10.8). Relaxing legal/administrative constraints to allow large private intervention would result in the greatest attainable impact on burned area (S4, mean 25.0; median 17.8). Adaptive management of Mosaico approach must be focussed on improving LLM capacity to modify larger portions of the territory and prioritizing critical areas such as fire propagation nodes.

This study aimed to identify the status, determining factors, and challenges in adopting climate smart livestock production practices by farmers. Three-staged sampling techniques were used to select the research sites and 233 sample farmer household respondents. Data were collected mainly using a pre-tested structured questionnaire. Key informant interviews and focus group discussions were also conducted to complement the household survey data. Descriptive statistics and an ordered logistic regression model were applied to analyze the quantitative data. The result revealed that the most adopted practices were composting (85.41%) and manure management (70.39%) while the least adopted technologies were biogas generation (3.86%) and rotation grazing (22.32%). The adoption status of the sampled farmers was also categorized into low (19.74%), medium (67.81%), and high adopter (12.45%). The high cost of improved breed, use of manure for fuel, free grazing, lack of information and awareness were the major constraints to adopting the climate smart livestock production technologies. The result also revealed that education, grazing land, total livestock holding, and extension agent contact contributed significantly and positively to the adoption of smart livestock production technology, while the distance from the water source had an insignificant and negative effect on the adoption status of climate smart livestock production practices. The study suggests the relevance of the cooperation of stakeholders and strengthening extension services for the maximum benefits of climate smart livestock production.

African mixed crop–livestock systems are vulnerable to climate change and need to adapt in order to improve productivity and sustain people’s livelihoods. These smallholder systems are characterized by high greenhouse gas emission rates, but could play a role in their mitigation. Although the impact of climate change is projected to be large, many uncertainties persist, in particular with respect to impacts on livestock and grazing components, whole-farm dynamics and heterogeneous farm populations. We summarize the current understanding on impacts and vulnerability and highlight key knowledge gaps for the separate system components and the mixed farming systems as a whole. Numerous adaptation and mitigation options exist for crop–livestock systems. We provide an overview by distinguishing risk management, diversification and sustainable intensification strategies, and by focusing on the contribution to the three pillars of climate-smart agriculture. Despite the potential solutions, smallholders face major constraints at various scales, including small farm sizes, the lack of response to the proposed measures and the multi-functionality of the livestock herd. Major institutional barriers include poor access to markets and relevant knowledge, land tenure insecurity and the common property status of most grazing resources. These limit the adoption potential and hence the potential impact on resilience and mitigation. In order to effectively inform decision-making, we therefore call for integrated, system-oriented impact assessments and a realistic consideration of the adoption constraints in smallholder systems. Building on agricultural system model development, integrated impact assessments and scenario analyses can inform the co-design and implementation of adaptation and mitigation strategies.F

Virtual fencing (VF) offers a promising alternative to conventional or electrified fences for managing livestock grazing distribution. This study evaluated the behavioral responses of 25 Rarámuri Criollo cows fitted with Nofence® collars in Pine Valley, CA, USA. The VF system was deployed in chaparral rangeland pastures. The study included a 14-day training phase followed by an 18-day testing phase. The collar-recorded variables, including audio warnings and electric pulses, animal movement, and daily typical behavior patterns of cows classified into a High or Low virtual fence response group, were compared using repeated-measure analyses with mixed models. During training, High-response cows (i.e., resistant responders) received more audio warnings and electric pulses, while Low-response cows (i.e., active responders) had fewer audio warnings and electric pulses, explored smaller areas, and exhibited lower mobility. Despite these differences, both groups showed a time-dependent decrease in the pulse-to-warning ratio, indicating increased reliance on audio cues and reduced need for electrical stimulation to achieve similar containment rates. In the testing phase, both groups maintained high containment with minimal reinforcement. The study found that Rarámuri Criollo cows can effectively adapt to virtual fencing technology, achieving over 99% containment rate while displaying typical diurnal patterns for grazing, resting, or traveling behavior. These findings support the technical feasibility of using virtual fencing in chaparral rangelands and underscore the importance of accounting for individual behavioral variability in behavior-based containment systems.

The main scope of the present study is to assess the environmental and economic outcomes of applying distinct Climate-Smart Agricultural (CSA) practices in apple cultivation. Thus, four different CSA practices, including organic farming, cover crops, floral bands, and grazing, were selected, and their environmental and economic performance was evaluated and compared to that of a conventional apple orchard system (baseline). Specifically, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methodologies were applied to assess the environmental and economic sustainability of the studied systems, respectively. Among the studied practices, grazing exhibited the best environmental performance among the modeled scenarios (approximately 25% decrease in greenhouse gas emissions compared to the baseline under the assumed conditions), followed by organic farming that significantly decreased eutrophication- and ecotoxicity-related impacts. Similarly, organic farming and grazing exhibited the best economic performance in the concept of the present study, with the total profit per hectare rising to approximately 5300 € and 4300 €, respectively, compared to the value of 3700 € of the conventional apple orchard. The results suggest that the implementation of CSA practices has the potential to improve the environmental and economic performance of apple orchards under the modeled conditions.

Identifying where and how grazing animals are active is crucial for informed decision-making in livestock and conservation management. Virtual fencing systems, which use animal-mounted location tracking sensors to automatically monitor and manage the movement and space-use of livestock, are increasingly being used to control grazing as part of Precision Livestock Farming (PLF) approaches. The sensors used in virtual fencing systems are often able to capture additional information beyond animal location, including activity levels and environmental information such as temperature, but this additional data is not always made available to the end user in an interpretable form. In this study we demonstrate how a commercial virtual fencing system (Nofence®) can be used to map the spatiotemporal distribution of livestock activity levels in the context of grazing. We first demonstrate how Nofence® activity index measurements correlate strongly with direct in-situ observations of grazing intensity by individual cattle. Using methods adapted from movement ecology for analysis of home range, we subsequently demonstrate how space-use and cumulative and average activity levels of grazing cattle can be spatially mapped and analyzed over time using two different approaches: a simple but computationally efficient cell-count method and a novel adapted version of a more complex Brownian Bridge Movement Model. We further highlight how the same sensors can also be used to map spatiotemporal variations in temperature. This study highlights how data generated from virtual fencing systems could provide valuable additional insights for livestock managers, potentially leading to improved production efficiencies or conservation outcomes.

Fully maintenance-free smart collars for range cattle, sheep and deer must survive years of uncontrolled grazing under highly variable shade and motion conditions. This paper presents an ultra-low-power buck converter governed by a fast integral terminal sliding mode controller (FITSMC) with a fixed-time observer. A new reaching law retains the initial sliding manifold and a negative-power term maintains the constant switching gain to preserve robustness near the surface while attenuating chattering without widening the bandwidth. The fixed-time observer estimates the irradiance and load changes and provides a feed-forward correction, tightening the output regulation regardless of initial conditions. Load step tests with moderate resistance swings showed the proposed method recovers noticeably faster and exhibits slightly lower overshoot than a recent method based on a two-phase power reaching law, while visible inductor current spikes are also suppressed. Simulations under daily grazing profiles confirmed tight output regulation adequate for microwatt data logging and periodic long-range (LoRa) bursts. The sleep mode quiescent current remained in the 9 microamps range, eliminating the need for manual recharge across multi-season field deployments. By integrating robust power electronics with collar-grade solar harvesting, the circuit offers a truly maintenance-free energy path for untethered livestock wearables and supports sustainable precision agriculture.