Search Results (126)

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

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

In June 2023, a larva of grassland caterpillar Gynaephora qinghaiensis naturally infected by an entomopathogenic fungus was collected from an alpine rangeland in Gangcha County, Haibei Tibetan Autonomous Prefecture, Qinghai Province. After laboratory isolation and cultivation, the pathogen was identified as Beauveria bassiana (designated as GC23620) based on morphological characteristics and ITS-rDNA sequence similarity analysis. The larvicidal efficacy of B. bassiana GC23620 against fourth-instar larvae of G. qinghaiensis were assessed using two inoculation methods in laboratory conditions. The infection process and pathogenicity were analyzed by simulation and parameter estimation using the Time–Dose–Mortality (TDM) model. The estimated parameters for the concentration effect of strain GC23620 (β) were 0.56 (leaf dipping method) and 0.30 (insect immersion method), respectively. After treatment with conidial suspensions (1.05 × 10⁵ to 1.05 × 10⁹ conidia/mL), the cumulative corrected mortalities were 72.73–100.00% (leaf dipping method) and 42.42–90.91% (insect immersion method) at 8 days after inoculation (DAI), and the median lethal doses (LD₅₀) decreased to 1.74 × 10³ conidia/mL (leaf dipping method) and 1.85 × 10⁴ conidia/mL (insect immersion method), respectively, during the same post-inoculation period. After inoculation with conidial suspension under a concentration of 1.05 × 10⁶ conidia/mL, the median lethal times (LT₅₀) were 2.40 (leaf dipping method) and 4.51 days (insect immersion method). A control efficacy of 84.27% was obtained for G. qinghaiensis larvae on grassland at 21 days post-treatment after spraying the fermentation solution with a low dose of 1.05 × 10⁵ conidia/mL. In conclusion, B. bassiana strain GC23620 exhibited high pathogenic activity against G. qinghaiensis larvae and has strong potential for the green control of grassland pests. Full article

Plant–animal relationships, such as endozoochory, are crucial for ecosystem dynamics as they improve seed dispersal as well as enhance plant reproductive success. This study investigated the ecological role of camels in seed dispersal and in breaking physical dormancy of three wild perennial Fabaceae species (Retama raetam, Vachellia tortilis subsp. raddiana, and Genista saharae) dominant in Algerian Saharan rangelands. Feeding trials were conducted with four adult female camels (Camelus dromedarius). Each animal received 1500 manually extracted seeds per species in three doses mixed with the regular ration, and feces were collected for 24 days to recover seeds. Untreated control seeds were kept under identical environmental conditions. The results showed that camels contribute to seed dispersal and influence germination differently among species. Seeds of Retama raetam were rapidly excreted, favoring local dispersal and increasing germination from 8% to 32%. Genista saharae seeds showed early excretion and longer retention, promoting wider dispersal and increasing germination from 16% to 52%. Vachellia tortilis subsp. raddiana showed an intermediate pattern, with germination rising from 39% to 70%. Gut passage affected seed size in Genista saharae and Vachellia tortilis subsp. raddiana, while no change occurred in Retama raetam. These findings highlight the camel’s ecological role as a biological vector improving seed viability and supporting regeneration. 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

Quantifying the extent of rangeland and pastureland (collectively termed grazing lands herein) in the US is a critical first step in many grazing lands assessments. This research presents a model-assisted framework to estimate grazing land acreage within arbitrary geographic boundaries by integrating high quality survey data with satellite-based raster geospatial data. Leveraging the image photo interpretation data from the USDA Natural Resources Conservation Service (NRCS) National Resources Inventory (NRI) survey as a reference dataset, we use machine learning to fuse NRI point level data with auxiliary data from the satellite-based Cropland Data Layer (CDL) to enhance the precision of acreage estimates of grazing lands. The methodology includes three steps: (1) modeling the relationship between NRI rangeland and pastureland indicators and CDL variables; (2) generating a high-resolution rangeland and pastureland probabilities map across the contiguous US; and (3) summarizing these probabilities to calculate the acreage of rangeland and pastureland for specific areas of interest. This approach provides researchers and land managers with a scalable tool to define grazing land extents within a self-selected study area, ensuring that subsequent resource characteristics or condition assessments are representative and spatially accurate. Full article

This study investigates the typology of the pastoral farming systems in the arid region of southern Tunisia, with a particular focus on the governorate of Tataouine. A field survey was conducted among 111 livestock farmers distributed across different agro-ecological zones. The typology of breeding systems was established using a Factor Analysis of Mixed Data (FAMD), which identified eleven dimensions explaining 69.74% of the total data variance. The first three dimensions accounted for 15.91%, 8.79%, and 7.67% of the variability, respectively, and were defined by herd composition, resource availability, and management strategies, including variables such as the number of goats, sheep, and camels, distance to water sources, infrastructure, reproductive practices, and workforce availability. Hierarchical clustering revealed three distinct systems: System 1, regrouping “Small Urban Farmers”, defined by small-scale operations relying on family labor, localized feed resources, and market-driven production targeting urban consumers; System 2, representing large livestock, composed of professionalized operations with improved infrastructure, hired labor, and transhumance practices to optimize resource use and productivity; and System 3, for herds with camels, characterized by extensive systems utilizing collective rangelands and camels to adapt to arid conditions and ensure ecological resilience. The results emphasize how ecological constraints, infrastructure, and spatial organization shape the diversity of these systems. This typology provides critical insights into the challenges and potential of livestock farming in arid environments and offers a foundation for designing targeted interventions to support the sustainability of pastoral systems under increasing environmental and economic pressures. Full article

Weather conditions can drastically alter the state of crops and rangelands and, in turn, impact the incomes and food security of individuals worldwide. Satellite-based remote sensing offers an effective way to monitor vegetation and climate variables on regional and global scales. The annual peak normalized difference vegetation index (NDVI), derived from satellite observations, is closely associated with crop development, rangeland biomass, and vegetation growth. Although various machine learning methods have been developed to forecast NDVI over short time ranges, such as one-month-ahead predictions, long-term forecasting approaches, such as one-year-ahead predictions of vegetation conditions, are not yet available. To fill this gap, we develop a two-phase machine learning model to forecast the one-year-ahead peak NDVI over high-resolution grids, using the Four Corners region of the southwestern United States as a testbed. In phase one, we identify informative climate attributes, including precipitation and maximum vapor pressure deficit, and develop the generalized parallel Gaussian process that captures the relationship between climate attributes and NDVI. In phase two, we forecast these climate attributes using historical data at least one year before the NDVI prediction month, which then serve as inputs to forecast the peak NDVI at each spatial grid. We develop open-source data and tools that outperform alternative methods for both gross NDVI and grid-based NDVI one-year forecasts, providing information that can help farmers and ranchers make actionable plans a year in advance. Full article

Wildfire escalation is increasingly threatening ecosystems and communities in Khyber Pakhtunkhwa (KP), Pakistan, particularly in forest and rangeland landscapes where ecological flammability interacts with human activity. While environmental and climatic drivers are well studied, governance factors remain underexplored despite their decisive role in shaping how ecological risk translates into disasters. Regional forests show considerable ecological diversity, including chir pine-dominated stands, mixed temperate conifer forests, broadleaved oak-associated systems, and shrub rangeland mosaics, each differing in fuel structure and fire behavior. Dependence on fuelwood collection, grazing, and forest access further influences ignition probability and fire spread. This study examines how governance failures influence wildfire risk and severity through a Governance-Fire Risk Framework. Governance is treated as a determining institutional condition affecting prevention capacity, regulation of hazardous land use, fuel management, and emergency response effectiveness. A cross-sectional survey of 540 stakeholders from rural (Dir Lower, Dir Upper) and peri-urban districts (Swat, Mansehra, Abbottabad) was analyzed using SPSS (version 26) and AMOS (version 24) (CFA and SEM). Governance failure significantly escalates wildfire risk through delayed emergency response, regulatory non-compliance, political interference, and weak institutional coordination. Institutional preparedness and response capacity reduce risks, whereas corruption intensifies them. Corruption functions through illegal land conversion, diversion of fire management resources, procurement irregularities, nepotistic staffing, and selective enforcement, increasing ignition sources, fuel accumulation, and response delays. Rural districts show stronger governance-fire linkages. Wildfire escalation in KP is governance-driven in interaction with ecological conditions and community dependence on forest resources. Effective mitigation requires anti-corruption measures, rapid response systems, stronger enforcement, and improved preparedness. The study offers a transferable governance-focused framework for wildfire management in fire-prone developing regions. Full article

Dromedary camels possess unique anatomical, physiological, and metabolic adaptations that enable survival in arid environments; however, these same adaptations make them highly sensitive to nutritional imbalance under modern feeding conditions. This review synthesizes current knowledge on nutritional pathologies and metabolic disorders in camels, emphasizing the links between diet composition, foregut fermentation, mineral status, and systemic health. Imbalances in energy and carbohydrates predispose camels to subacute and acute acidosis, negative energy balance, and ketosis-like syndromes, particularly when rapidly fermentable feeds are introduced without adequate fiber or water. Protein and nitrogen disorders, including ammonia toxicity and impaired urea recycling, arise from mismatches between degradable protein, fermentable energy, hydration, and mineral availability. Widespread deficiencies of phosphorus, copper, cobalt, zinc, selenium, and vitamins A and E remain major constraints, leading to pica, poor microbial fermentation, oxidative stress, immunosuppression, reproductive failure, and skeletal disorders. Nutritional disturbances frequently extend beyond the gastrointestinal tract, forming a gut–liver–kidney metabolic axis characterized by hepatic dysfunction, renal compromise, and systemic oxidative stress. The review also addresses gastrointestinal impaction, foreign-body ingestion, toxic plant consumption, and feeding on human food waste as emerging nutritional challenges, particularly in peri-urban systems. Advances in diagnostic ultrasonography, feed evaluation techniques, probiotics, mineral–vitamin supplementation, and omics-based approaches are discussed as tools for improving early diagnosis and precision nutrition. Despite growing research interest, the lack of camel-specific feeding standards and reliance on cattle-based recommendations remain critical gaps. This review highlights the need for species-specific nutrient requirement models, sustainable rangeland management, and integrative research to support the health, resilience, and productivity of camels under changing environmental and production systems. Full article

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions. Full article

Extensive goat farming is the dominant livestock system in the Mediterranean region, where woody rangelands represent essential forage resources for goats. Understanding how goats move and select vegetation within these heterogeneous landscapes–and how these patterns are shaped by herding decisions-is critical for improving grazing management. This study investigated the spatio-temporal movement behavior of a goat flock in a complex woody rangeland using GPS tracking combined with GIS-based vegetation and land morphology mapping. The influence of seasonal changes in forage availability and the shepherd’s management on movement trajectories and vegetation selection was specifically examined over two consecutive years. Goat movement paths, activity ranges, and speed differed among seasons and years, reflecting changes in resource distribution, physiological stage, and herding decisions. Dense oak woodland and moderate shrubland were consistently the most selected vegetation types, confirming goats’ preference for woody species. The shepherd’s management—particularly decisions on grazing duration, route planning, and provision or withdrawal of supplementary feed—strongly affected movement characteristics and habitat use. Flexibility in adjusting grazing strategies under shifting economic conditions played a crucial role in shaping spatial behavior. The combined use of GPS devices, GIS software, vegetation maps, and direct observation proved to be an effective approach for assessing movement behavior, forage selection and grazing pressure. Such integration of technological and classical methods provides valuable insights into diet composition and resource use and offers strong potential for future applications in precision livestock management. Real-time monitoring and decision support tools based on this approach could help farmers optimize grazing strategies, improve forage utilization, and support sustainable rangeland management. Full article

Accurately quantifying grazing intensity (GI) is crucial for assessing grassland utilization and supporting sustainable management. Traditional livestock-based approaches cannot capture the spatial heterogeneity of grazing or its dynamic response to climate variability. The objective of this study was to develop a remote sensing-based quantitative framework for estimating GI across the Inner Mongolian grasslands. The framework integrates MODIS vegetation indices, ERA5-Land climate variables, topographic factors, and field-measured data and GI was quantified as the proportional difference between potential and satellite-derived aboveground biomass (AGB), providing a spatially explicit measure of forage utilization. In this framework, potential AGB (AGBp) represents the climate-driven growth capacity under ungrazed conditions reconstructed using machine learning models, whereas satellite-derived AGB (AGBs) denotes the standing AGB remaining under current grazing pressure. Validation using 324 paired grazed–ungrazed plots demonstrated strong agreement between modeled and observed GI (R² = 0.65, RMSE = 0.18). This AGB-difference-based approach provides an effective and scalable tool for large-scale rangeland monitoring, offering quantitative insights into grass–livestock balance, ecological restoration, and adaptive management in arid and semi-arid regions. Full article