Search Results (333)

Burned-area totals are useful for national monitoring, but they do not reveal how, when or under what social and ecological conditions a landscape burns. We developed an event-based fire-regime and stewardship framework for Eswatini, a topographically compressed southern African country where protected areas, communal rangelands, cropland margins, plantation landscapes and peri-urban interfaces occur in close proximity. Global Fire Atlas event histories for 2001–2025 were organised by fire year and intersected with approximately 10 km² hexagonal units. The burned-area rate, event frequency, recurrence, seasonality, large-fire dominance, pyrodiversity and trend were used to classify fire-regime types independently of socio-ecological predictors. An XGBoost regression model, evaluated on a 20% held-out test set, was interpreted using exact TreeSHAP diagnostics. Fire activity was strongly seasonal: July–September accounted for 78.2% of the burned area, with August alone accounting for 34.2%. Eight fire-regime types were identified, ranging from low-information and episodic units to frequent small-fire mosaics, large-fire-dominated areas and emerging burned-area intensification regimes. The burned-area-rate model performed well on held-out data (R2 = 0.71; Spearman rho = 0.75). Human modification, goat density, elevation, forest probability, fuelwood dependence and precipitation seasonality ranked among the most influential predictors, but their fitted effects were non-linear and often bidirectional. The combined diagnostics supported six adaptive management zones covering protected-area stewardship, conservation-sensitive management, settlement–livelihood interfaces, late-season risk reduction, monitoring and integrated landscape management. Although the Eswatini results are context-specific, the workflow offers a transferable way to connect fire histories, socio-ecological contexts and place-based stewardship in African mosaic landscapes. Full article

Soil health refers to the combined physical, chemical, and biological properties of soils that allow them to function as a living system that sustains life. While numerous reviews summarize several methods to measure soil health, this review critically evaluates how the Soil Management Assessment Framework (SMAF) performs across diverse ecosystems, including croplands, agroforestry, coastal mangrove, and rangelands. We explore the evolutionary shift from single indicators to integrated assessment frameworks, tracing how targeted management practices, such as conservation tillage, crop rotation, cover cropping, organic amendments, and water management, alter physical, chemical, and biological indicator performance. The SMAF framework and scoring system are outlined using examples from around the world. This synthesis concludes that while SMAF provides an exceptionally rigorous, non-linear platform for quantitative Soil Quality Index (SQI), its practical execution remains deeply constrained by data-intensive requirements, selection of appropriate indicators, and adaptation to local contexts. To close this gap, we outline critical future directions, integrating the framework with AI and machine learning, real-time Internet of Things (IoT) field sensors, and dynamic digital twins. Ultimately, this work aims to shift soil monitoring from descriptive reporting to predictive, automated intelligence to provide the exact data needed for sustainable land management decisions. Full article

Solonetz and Solonetzic rangelands are widespread in arid regions of Central Kazakhstan, where pasture degradation is often difficult to assess because surface vegetation patterns do not always reflect subsurface soil constraints. This study aimed to evaluate degradation patterns in Solonetz pasture ecosystems of the Ulytau region by integrating field soil-profile descriptions, laboratory analyses, vegetation observations, forage productivity data and Sentinel-2A-derived MSAVI. Ten monitoring soil profiles were examined for particle-size distribution, soluble salts, ionic composition, exchangeable cations, available N, P and K, vegetation cover and forage yield. USDA textural classification, salt-distribution analysis, Pearson correlation, PCA, RDA and MSAVI-based mapping were used to link soil-profile properties with vegetation and spectral response. The first two PCA axes explained 65.5% of the total variance, while selected soil profile constrains accounted for 58% of the variation in vegetation cover, forage yield and MSAVI in the RDA analyses. The results showed strong profile heterogeneity, with clay enrichment, subsurface salt accumulation, alkalinity and Na- or Mg-related exchange–complex imbalance associated with several degradation pathways. Surface horizons were often weakly saline, whereas deeper layers contained stronger chemical and physical limitations. MSAVI values were low across the monitoring sites and reflected vegetation–soil surface conditions rather than salinity or sodicity directly. MSAVI ranged from 0.0888 to 0.2148, with a mean value of 0.1248. Combining soil-profile diagnostics with Sentinel-2A MSAVI improved the reliability of interpreting spatial degradation patterns and provides a practical framework for monitoring spatially heterogeneous Solonetz rangelands, supporting sustainable rangeland management under arid conditions. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

This study addresses the issue of accurate and rapid aboveground biomass estimation in rangeland ecosystems, as traditional grazing methods are labor-intensive, while modern remote sensing techniques often require expensive equipment and controlled conditions. The goal of this work is to develop an efficient and accessible approach for biomass estimation of natural pastures based on ground-level RGB images captured with smartphones. For this purpose, a dataset consisting of 1196 field images and corresponding biomass values collected from 40 districts in southern Kazakhstan was used, and a wide + tiles architecture based on the DINOv3 model of Vision Transformer was proposed. The model utilized attention pooling and feature fusion mechanisms to integrate both global and local features, and various preprocessing and augmentation strategies were comparatively examined. Experimental results demonstrated that the proposed method exhibits high accuracy (with the best result being R² = 0.733, MAE ≈ 0.779 c/ha), where the DINOv3 model showed clear advantages over ConvNeXtV2. Furthermore, the impact of preprocessing strategies was minimal, and the importance of high-resolution images was clearly established. The obtained results show that the proposed method performs consistently under heterogeneous field conditions and allows for reliable biomass estimation without the need for specialized equipment. This makes it a practical tool for monitoring pastures, planning forage supply, and supporting agronomic decision-making. Full article

Accelerometer sensors and artificial intelligence (AI) are reshaping automated behavior monitoring in precision livestock management, yet their joint deployment on extensive rangelands is constrained by energy and bandwidth budgets. Low-Power Long-Range Wide-Area Network (LoRaWAN) collars address these constraints by compressing the raw tri-axial signal on the device into a single scalar per reporting interval, the Motion Index (MI). This onboard compression preserves enough signal to separate active behaviors but discards the per-axis and frequency content that fine-grained classification typically relies on. On a dataset of 9222 labeled observations from 24 cows across four breeds, MI distinguishes walking from grazing reliably but fails to separate ruminating from resting; both correspond to a stationary animal and yield near-zero, statistically indistinguishable distributions. Earlier MI-only models reached only about 65% four-class accuracy, and ruminating was commonly merged into resting. We show that much of this loss can be recovered by treating the MI stream as a time series. Session-aware lag features, rolling statistics, and an autoregressive previous-behavior feature lift four-class macro-F1 from 0.647 to 0.94, with per-class F1 of 0.95 for ruminating and 0.92 for resting (and at least 0.92 for every behavior). In autonomous deployment the previous behavior must be predicted rather than observed; for this setting we add a Viterbi sequence-decoding step that combines the classifier’s per-step outputs with a learned behavior-transition model, recovering a substantial part of the ruminating signal from the activity stream alone while keeping walking and grazing reliable. The gain is consistent across seven classifiers and four genetically distinct breeds, indicating that it is driven by the features rather than by a specific model. Full article

This study quantified the extent of cropland abandonment in relation to bush/shrub encroachment and natural rangeland in Sencherere village, Limpopo Province, from 1994 to 2024. Landsat 5, 7, 8, and 9 images were used to classify three land-cover categories using a Random Forest algorithm, with overall accuracies ranging from 80% to 85% and Kappa coefficients between 0.73 and 0.80. Results show that cropland abandonment followed a non-linear trend, decreasing from 498 ha (37.7%) in 1994 to 200 ha (15.14%) in 2014, suggesting a period of recovery or re-cultivation during this interval. However, this trend reversed thereafter, with abandonment increasing again to 473 ha (35.81%) in 2024, indicating renewed abandonment of cultivated areas. This pattern suggests that cropland use in the study area is not a progressive one-directional abandonment process, but rather a cyclical interaction between abandonment and reclamation influenced by changing environmental and socio-economic conditions over time. Bush or shrub cover expanded substantially over the 30 years, increasing from 51 ha (3.86%) in 1994 to 354 ha (26.8%) in 2024, indicating a strong shift toward woody vegetation dominance. Natural rangeland cover fluctuated considerably from 195 ha in 1994 to 385 ha in 2004, declining to 65 ha in 2014 before partially recovering to 115 ha in 2024. Rainfall variability showed no clear long-term trend, suggesting that climatic patterns alone do not explain the observed land-cover changes; therefore, other drivers may have influenced this. The study highlights dynamic local trends of cropland abandonment and woody vegetation expansion, underscoring the need for continued monitoring and targeted investigation into the socio-economic and ecological drivers shaping these changes to support effective land-use planning and rangeland management in semi-arid communal systems. Full article

Degraded rangelands in semi-arid pastoral systems are widely associated with declining vegetation, soil carbon loss, and worsening household livelihoods. However, the mechanisms linking rangeland degradation to household income remain poorly understood, particularly in a panel-data context. This study examines how rangeland condition, vegetation dynamics, and livestock by-product underutilization are related to household income in Öndörshireet Soum, Töv Aimag, Mongolia. The analysis is based on a multi-source panel dataset covering 2018 to 2024, combining Sentinel-2 NDVI time series, soil organic carbon measurements from 120 permanent plots, and a five-wave survey of 114 households. The results indicate widespread and persistent degradation. Nearly 90 percent of monitored plots are at least moderately degraded; NDVI shows a steady decline over time; and average soil carbon levels remain well below those observed at a managed reference site. Over the same period, real household income declined despite a gradual increase in herd size. Econometric estimates show that vegetation condition is positively associated with income, whereas higher levels of by-product waste are associated with lower income, even after accounting for precipitation variability. The interaction results further suggest that the benefits of herd expansion weaken when production losses remains high. Taken together, these findings indicate that ecological decline and low value capture from livestock operate simultaneously to constrain pastoral livelihoods. Improvements in pasture condition alone appear insufficient to offset these pressures when a substantial share of livestock value is not recovered. While the results offer useful insights for rangeland policy, further evidence from multiple sites would be needed to assess causality and the extent to which these patterns apply beyond a single soum. Full article

Rangelands occupy over half of the Earth’s terrestrial surface and play an important role in supporting biodiversity and livelihoods. However, widespread degradation—particularly in arid and semi-arid regions—has compromised their ecological function. Traditional monitoring approaches that rely on vegetation cover metrics from optical satellite imagery fail to capture the three-dimensional structure of vegetation, which is critical for assessing ecosystem condition and guiding restoration and management efforts. This study demonstrates the application of high-density airborne LiDAR (ALS) data (~15–20 points/m²) to identify and map vegetation structural types across 370,000 hectares of semi-arid rangelands in Western Australia. Using an unsupervised fuzzy c-means clustering algorithm on seven minimally correlated ALS-derived structural metrics, we identified eight statistically distinct vegetation structural classes. The resulting structural map revealed spatial heterogeneity in vegetation structure, including in areas with similar vegetation cover, with high confidence in structural attribution in 74.5% of the study area. The rangeland-specific structural classes developed in this study, which incorporate measures of classification certainty, offer a robust framework for vegetation structural mapping in field data-scarce environments. This framework can support ecological condition assessments and provide a basis for rangeland management and restoration planning. Full article

Wildland fire scientists have made substantial advances in measuring fire behavior, but properly collecting data is often beyond the capacity of prescribed fire managers and by definition all but impossible for wildfire events. While a method for the immediate assessment of burn severity has been developed around multispectral imagery from space-based Earth observation systems, there has been little comparison of these post hoc metrics to actual fire behavior. Meanwhile, the application of research results from experimental prescribed burns to rangeland affected by wildfire can be impeded by a lack of understanding of how immediate burn severity differs between wildfires and prescribed burns, especially in rangelands. Overall, much of what is known about wildland fire behavior, severity, and effects comes from forests, whereas rangelands are characterized by having lower fuel loads comprised of fine vegetation that promotes high rates of spread and brief residence time. This paper provides rangeland-specific information on the relationships between direct field-based fire behavior measurements and a space-based index of burn severity (differenced Normalized Burn Ratio, ΔNBR, from Sentinel-2 imagery), and uses those data to compare burn severity across 54 prescribed burns in North Dakota, USA, and 28 nearby wildfires in the US Northern Great Plains. In prescribed burns, remotely sensed burn severity increased with rate of spread and flame temperature 15 cm above the ground, but had no statistically significant relationship with soil surface temperature. In the semi-arid western zone of the Northern Great Plains, wildfires and prescribed burns had similar, low–moderate severity; wildfires in the eastern zone tended to be of moderately high severity and thus greater than the low severity of the experimental prescribed burns. By describing meaningful gradients in surface fire behavior in rangelands with ΔNBR, even those without the capacity to measure fire behavior in the field can monitor prescribed fire effectiveness and incorporate burn severity in adaptive management plans. Understanding the relationship between burn severity across wildfires and prescribed burns is a critical step in applying knowledge gained from research on prescribed fires to areas impacted by wildfire. Resistance to prescribed burning might be overcome by increasing livestock managers’ experience with post-fire forage resources through grazing areas burned in unintentional wildfires, but current practice and policy discourage or outright prevent ranchers from doing so. Future research ought to connect burn severity with ecosystem recovery metrics to ensure post-fire grazing does not impair rangeland sustainability. Full article

Globally, land use and land cover (LULC) change is a major driver of soil organic carbon (SOC) dynamics in mountainous ecosystems, where steep slopes, shallow soils, and strong climatic gradients amplify land use impacts. This review systematically synthesises empirical evidence regarding how LULC transitions influence SOC dynamics in mountainous landscapes, with particular emphasis on dominant trends, underlying mechanisms, methodological bottlenecks, and future research perspectives. Following PRISMA guidelines, we evaluated 30 carefully screened peer-reviewed studies that explicitly link temporal LULC change to carbon dynamics in mountainous environments. The results show SOC losses across most LULC transitions, especially following forest and rangeland conversion to cropland and built-up land. In contrast, SOC recovery is time-lagged, partial, and often decoupled from rapid aboveground biomass recovery. Methodologically, while static carbon models (e.g., InVEST) are demonstrated to systematically underrepresent lateral erosion-driven SOC losses, they have been highly adopted in the synthesised studies, highlighting their practical scalability in data-scarce and complex mountain terrains. Finally, the synthesis reveals a strong geographic bias in the literature, with most studies emerging from Asia, highlighting substantial knowledge gaps in other regions. Prioritising empirical multidecadal SOC monitoring in highly vulnerable and underrepresented regions, such as African mountainous systems, where socioeconomic pressures are expected to intensify, is critical for developing integrative, evidence-based strategies for sustainable land management under accelerating LULC change. Full article

Wearable sensors and remote sensing technologies are rapidly increasing opportunities to measure grazing animal behavior, energetics, and performance in extensive rangeland systems. However, despite significant advances in device capabilities, the livestock sector lacks an ecological framework that connects sensor data to the metabolic processes driving animal growth and efficiency. In this paper, we apply the movement ecology paradigm to grazing beef cattle as a demonstration of how metabolic theory, animal behavior, and landscape heterogeneity interact to influence energy budgets. We first describe the mechanistic relationships among basal metabolism, thermoregulation, activity, and forage intake, highlighting how movement patterns reflect underlying metabolic states. Next, we review key variables measurable through modern sensors, including GPS, accelerometers, rumen temperature boluses, and remote sensing of forage quantity and quality and explain how these data can be integrated into an information system to estimate energy expenditure, resource selection, and physiological stress. Finally, we show how combining movement, behavioral, and landscape data can yield meaningful indicators of performance and health, paving the way for precision livestock management grounded in ecological principles. Integrating metabolic and movement ecology with emerging technologies offers a strong framework for enhancing efficiency, welfare, and sustainability in grazing beef systems. Full article

The livestock sector underpins food security, employment, and rural livelihoods across the Southern African Development Community (SADC), contributing up to 50% of agricultural GDP and supporting more than 60% of rural households. Yet climate change poses escalating threats through heat stress, declining pasture productivity, water scarcity, and vector-borne diseases that compromise productivity and economic resilience. This review identifies and locates effective climate change mitigation strategies along the livestock value chain, spanning production, processing, transport, and consumption, to promote sustainable, low-emission, and inclusive growth in the SADC region. A broad review of 46 peer-reviewed and institutional sources (2000–2024) was undertaken, focusing on livestock-related mitigation within SADC and comparable agro-ecological systems. Strategies were thematically categorized by value-chain stage and assessed for their emission-reduction and livelihood-enhancement potential. Local strategies include genetic improvement for low-methane and heat-tolerant breeds, adaptive rangeland and feed management, renewable-energy adoption in processing, climate-resilient transport infrastructure, and consumer awareness of low-emission products. Evidence suggests potential GHG-emission reductions of 18–30%, coupled with productivity gains and improved smallholder incomes. Coordinated implementation through the SADC Regional Agricultural Investment Plan (2021–2030) and national policies can transform the livestock sector into a climate-resilient driver of inclusive growth. Further research should quantify the socioeconomic feasibility and scaling potential of these strategies across production systems. Successful integration of climate change mitigation imperatives must be tailored to local biophysical conditions (e.g., rainfall, soil type) and socioeconomic contexts (e.g., market access, cultural practices). Full article

Understanding how land management influences soil carbon (C) and nitrogen (N) dynamics is critical for improving ecosystem resilience and carbon sequestration potential in semiarid rangelands. This study used classical field- and laboratory-based methods to assess soil organic carbon (SOC), organic matter (OM), and N content at 13 sites across four ecological provinces in eastern Oregon, USA. Treated sites—where traditional rangeland restoration and management practices had been applied to them (i.e., juniper removal, sagebrush removal, post-fire grass seeding, and land conversion to pasture)—were paired with adjacent untreated control sites. Soil samples were collected at two depths, 0 to 10 cm and 15 to 25 cm and analyzed for C, N, OM, bulk density (BD), soil volumetric water content (SVWC), porosity, and texture. Soil C and N stocks were calculated on an area basis (t ha⁻¹), and statistical analyses were conducted using one-way ANOVA and correlation tests. Treated sites generally exhibited higher soil C, N, and OM content compared to untreated sites, particularly in the upper 10 cm of soil. Data obtained from the two soil depths (0 to 10 cm and 15 to 25 cm) were averaged and assumed to represent the top 30 cm of the soil profile, corresponding to the effective rooting zone at each field. The site where sagebrush removal was followed by grass seeding exhibited the highest soil C and N stocks (115.8 t C ha⁻¹ and 9.2 t N ha⁻¹, respectively). This site also had the highest OM content (9.53%), which was observed in the topsoil layer (0 to 10 cm) across all sites and depths. Strong positive correlations between C and N were detected across all sites (mean r = 0.92), while negative correlations were observed between soil C and bulk density at several locations. Results suggest that vegetation management practices such as woody plant removal and grass establishment can enhance soil C storage and nutrient retention in semiarid rangeland ecosystems. These findings provide baseline data to inform land management strategies aimed at improving soil health and carbon sequestration potential in the Pacific Northwest region in the USA. Full article

Pasture ecosystems in the arid regions of Kazakhstan are highly vulnerable to the combined effects of climatic variability and increasing grazing pressure, while long-term spatial assessments of degradation remain limited. This study develops an integrative remote sensing-based framework for assessing pasture degradation in Atyrau Oblast by combining long-term NDVI time series (2000–2023) with grazing pressure indicators ($K_{sust}$ and LIPS), field observations, and climatic data. The results show that 49.3% of pasturelands are degraded, with statistically significant negative NDVI trends observed across most administrative districts. Areas experiencing pasture overload ($K_{sust}$ > 1.2) spatially coincide with persistent vegetation decline, and significant negative relationships between NDVI and livestock numbers are identified in several districts. The analysis also reveals spatial heterogeneity and lagged responses of vegetation dynamics to grazing pressure under varying climatic conditions. The proposed approach provides a novel integrative framework that links spectral vegetation indicators with climate-adjusted grazing metrics, enabling the identification of degradation hotspots and supporting spatially differentiated pasture management. This framework can be applied in regional land monitoring systems to improve decision-making for sustainable rangeland use under climate change. Full article