Search Results (118)

Livestock intensification drives biodiversity loss, making impact quantification essential. Life Cycle Assessment (LCA) can evaluate whether regenerative practices, such as silvopastoral systems, mitigate this loss, but it requires specific characterization factors (CFs). In this pilot study, we applied the countryside Species-Area Relationship (SAR) model to derive the first invertebrate-specific CFs using dung beetles (Scarabaeinae). From field surveys, we calculated intensity-specific CFs for potential species loss (PSL/m²) in pastureland and cropland. We assessed biodiversity impacts per 1 kg calf live weight (LWC) across three livestock regimes: native silvopastoral (NSP, minimal land use), intensive silvopastoral (ISP, light land use), and monoculture (MC, intense land use). Results show high dung beetle affinity for NSP. The CFs distinguished impact intensity levels: MC had the highest PSL per area (6.76 × 10⁻¹⁰ PSL/m²), followed by ISP (5.93 × 10⁻¹⁰ PSL/m²) and NSP (4.99 × 10⁻¹⁰ PSL/m²). However, normalizing by yield reversed this trend: MC showed the lowest impact per 1 kg LWC (7.64 × 10⁻⁸ PSL/kg LWC), ISP was intermediate (1.06 × 10⁻⁷ PSL/kg LWC), and NSP had the highest impact (1.31 × 10⁻⁷ PSL/kg LWC). Incorporating upstream feed production significantly increased the overall biodiversity footprint, underscoring the need for comprehensive system boundaries. Integrating broader biodiversity components and landscape context remains essential to fully capture livestock management effects. Full article

Land use and land cover change (LULCC) is increasingly recognized as a dominant driver of hydrological alteration in tropical watersheds, often exceeding the influence of climatic variability. This study evaluates the spatiotemporal dynamics of LULCC and their implications for hydrological sustainability in the Uberabinha River Basin, southeastern Brazil, between 1990 and 2020. Utilizing MapBiomas data and statistical analysis, the results reveal a marked expansion of mechanized agriculture, particularly soybean cultivation, which grew from 3426 ha to 54,162 ha, and urban areas, which expanded by approximately 89.4%. Conversely, natural vegetation and pasturelands decreased continuously, with pastures showing the sharpest absolute reduction, from 72,248 ha to 34,535 ha. Despite a 10.76% increase in annual precipitation between 1990 and 2020, the hydrological response exhibited a severe decline in streamflow, characterized by a 76.35% drop in minimum flow. Furthermore, the runoff index decreased from 0.0574 in 1990 to 0.0211 in 2020, indicating a critical loss in the basin’s capacity to convert rainfall into streamflow. These findings demonstrate a clear decoupling between precipitation and streamflow driven by LULCC, posing a severe threat to regional water security and highlighting the urgent need for integrated land–water management. Full article

Groundwater is the primary source of livestock watering across the arid pasturelands of western Kazakhstan, yet no systematic field hydrochemical assessment has been published for this region in over 40 years. This study presents the first systematic field-based hydrochemical characterisation of groundwater sources used for pasture livestock watering in the West Kazakhstan Region and Aktobe Region, filling a critical data gap that has persisted since the Soviet era. Specifically, it characterises the hydrochemistry, water quality, and infrastructure condition of groundwater sources, and evaluates the groundwater resource potential against current and projected livestock water demand. A total of 139 groundwater samples were collected along 11,182 km of field routes during May–July 2025, and analysed for 25 physicochemical parameters; hydrochemical classification was performed using AquaChem 11, and spatial analysis was conducted in ArcGIS 10.8. The groundwater chemistry distribution is bimodal: fresh bicarbonate-calcium-magnesium waters (TDS < 3.0 g/L) constitute approximately 80% of samples, while highly mineralised chloride-sulphate-sodium waters (TDS up to 9.91 g/L) occur in salt-dome-influenced discharge zones. Nitrate concentrations exceeded 50 mg/L in 23–36% of samples, with maxima of 635 mg/L, reflecting intensive anthropogenic contamination near livestock facilities. Predictive exploitable fresh groundwater resources exceed current livestock demand by a factor of 162. The principal constraint on pasture water supply is not resource scarcity but the non-operational status of 51–75% of inspected watering infrastructure, a legacy of post-Soviet institutional collapse that requires urgent rehabilitation. 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

Accurate soil moisture assessment is essential for effective agricultural management in the southern US, where water availability has a significant impact on crop productivity. This study evaluates the Soil Moisture Active Passive (SMAP) Level-4 daily soil moisture product using in situ measurements from Natural Resources Conservation Service (NRCS) Soil Climate Analysis Network (SCAN) stations and the US. Climate Reference Network (USCRN) across diverse agroecosystems in Texas from 2016 to 2024. SMAP’s performance was examined across ten climate zones and six major land cover types, including urban regions, pastureland, grassland, rangeland, shrubland, and deciduous forests. Statistical metrics, including the coefficient of determination (R²), Root Mean Square Error (RMSE), Bias, and unbiased RMSE (ubRMSE) were used to evaluate the agreement between SMAP-derived and in situ soil moisture measurements. Results show that SMAP effectively captures seasonal soil moisture dynamics but exhibits spatially variable accuracy. The highest agreement was observed at Panther Junction (R² = 0.57, RMSE = 2.29%), followed by Austin (R² = 0.57, RMSE = 9.95%). While a weaker coefficient of determination was observed at PVAMU (R² = 0.28, RMSE = 11.28%) and Kingsville (R² = 0.11, RMSE = 7.33%), likely due to heterogeneity in land cover, and urbanized landscapes in these stations. Applying the quantile mapping bias correction methods significantly reduced RMSE and improved the accuracy of SMAP soil moisture data at some in situ measurement stations. The results highlight the importance of station-specific calibration and the integration of satellite and ground-based measurements to improve soil moisture monitoring for agriculture and drought management in Texas and similar regions. Full article

Scotch broom (Cytisus scoparius; Fabaceae) is an invasive nitrogen-fixing shrub widespread in New Zealand, where it impacts forestry, pasturelands, and native ecosystems. Although several biological control agents have been released, Scotch broom continues to expand in regions such as the North Island’s Central Plateau. Scotch broom affects the germination and growth of other plants and modifies arthropod communities (including pollinators, herbivores, and predators) within its invaded range. Volatile organic compounds (VOCs) play a key role in mediating plant–plant and plant–arthropod interactions, potentially contributing to this invasive plant’s ecological success. However, Scotch broom’s VOC emissions in its invaded ranges remain poorly understood. We examined VOC emissions from flowering and non-flowering Scotch broom plants in the Central Plateau and assessed links with biotic and abiotic factors. Our aims were to (1) characterise differences in VOCs between phenological stages; (2) explore shifts in arthropod community composition; and (3) evaluate correlations between VOC emissions, arthropod groups and environmental variables. Flowering plants had higher diversity and abundance of VOCs, with blends dominated by monoterpenes, aromatics, and fatty acid esters, whereas non-flowering plants were characterised by green leaf volatiles (GLVs). Flowering stages supported Hemiptera and Thysanoptera (herbivores), which were positively correlated with fatty acid esters. In contrast, GLVs correlated with Araneae (predators) abundance. Temperature was the strongest predictor of VOC emission patterns, showing significant correlation with most compound classes. These results advance understanding of Scotch broom invasion ecology and highlight the need to further explore individual compounds potentially influencing arthropod composition to inform both native arthropods conservation and future biocontrol strategies. Full article

This dataset presents field observations collected in the municipality of Goiatuba, Goiás State, Brazil, a consolidated and representative agricultural frontier of the Brazilian Cerrado biome. The region presents diverse land use dynamics, including annual cropping systems, irrigated fields with up to three harvests per year, and pasturelands. We conducted a field campaign from 3 to 7 November 2025, corresponding to the beginning of the 2025/2026 Brazilian crop season, when crops were at distinct early phenological stages. To ensure representativeness, we delineated 117 reference fields prior to the field campaign, and an additional 463 plots were surveyed during work. Geographic coordinates, crop types, and photographic records were obtained using the GPX Viewer application, a handheld GPS receiver, and the QField 3.7.9 mobile GIS application running on a tablet uploaded with Sentinel-2 true-color imagery and the municipal road network. Plot boundaries were subsequently digitized in QGIS Desktop 3.34.1 software, following a conservative mapping strategy to minimize edge effects and internal heterogeneity associated with trees and water catchment basins. In total, more than 26,000 hectares of agricultural fields were mapped, along with additional land use and land cover polygons representing water bodies, urban areas, and natural vegetation fragments. All reference fields were labeled based on in situ observations and linked to Sentinel-2 mosaics downloaded via the Google Earth Engine platform. This dataset is well-suited for training, testing, and validation of remote sensing classifiers, benchmarking studies, and agricultural mapping initiatives focused on the beginning of the agricultural season in the Brazilian Cerrado. Full article

Grazing pressure (GP) was a key factor influencing net primary productivity (NPP) in pasturelands and was characterized by two indicators: grazing intensity (GI) and grazing density (GD). However, current research has not yet clarified whether the mechanisms linking GP to NPP varied by season, or whether seasonal thresholds of grazing pressure existed. This study employed the Carnegie–Ames–Stanford Approach (CASA) model to estimate NPP over eight time periods between 2010 and 2024 for three seasonal pastures (spring–autumn, summer, and winter) in the study area. Estimation accuracy was evaluated by comparing our NPP estimates with existing NPP products. Trends in NPP and their significance were analyzed using the Sen–MK method, followed by further examination of spatiotemporal variations in NPP across the three seasonal pastures. Subsequently, by comparing two grazing pressure indicators (GI and GD), we identified the optimal metric to represent GP and, on this basis, analyzed the spatiotemporal variations and threshold dynamics of pasture NPP across three seasons under the influence of GP. Results indicated that the CASA model achieved R² > 0.90 for multi-year NPP estimation, with RMSE ranging from 27 to 45 g C m⁻² y⁻¹. Spring–autumn and winter pastures exhibited pronounced slope changes and intense spatiotemporal NPP variations, whereas summer pastures showed insignificant slope changes and stable spatiotemporal NPP patterns. Of the two GP indicators, the GD metric developed herein more effectively characterized grazing pressure across the study area. Across the three seasonal pastures, a consistent negative feedback between GD and NPP was evident; however, its strength differed markedly, with spring–autumn and winter pastures exhibiting greater NPP sensitivity to GD. The GD thresholds for spring–autumn, summer, and winter pastures in the study area were approximately 900, 700, and 5000 sheep km⁻², respectively. Exceeding these thresholds led to degradation, while falling below them promoted recovery. The study revealed a threshold-mediated negative feedback between GD and NPP across seasonal pastures, quantified season-specific upper bounds of carrying capacity, and provided an evidence base for zoned rest/rotational grazing and GD regulation along the northern slope of the Tianshan Mountains. Full article

Land use and land cover (LULC) changes driven by agricultural and livestock expansion pose significant threats to the Brazilian savanna (Cerrado). This study aimed to analyze, map, and simulate LULC changes in the southern mesoregion of Maranhão State by generating geospatial scenarios projected through 2030. LULC changes between 2015 and 2020 were analyzed using Landsat images classified with the Random Forest machine learning algorithm. A spatial model based on cellular automata was employed to simulate land use and land cover scenarios for the year 2030. When comparing the simulated map with the reference map, an overall accuracy of 70.28% and a Kappa index of 0.608 were observed. Results revealed a decrease in native savanna and grassland areas, with a corresponding increase in agricultural and pasturelands, notably in municipalities such as Balsas, Riachão, Tasso Fragoso, Carolina and Porto Franco. The 2030 simulation predicts continued agricultural expansion and a potential reduction of approximately 19% in native Cerrado vegetation cover, highlighting municipalities of Campestre do Maranhão, Porto Franco, São João do Paraíso, Feira Nova, Estreito, Balsas, Tasso Fragoso and Carolina. These findings underscore the value of integrating remote sensing and spatial modeling techniques within the framework of Geomatics to support environmental monitoring and management of land-use dynamics, including expansion, contraction, diversification, and agricultural intensification. This approach provides critical insights into anthropogenic impacts on sensitive ecosystems, informing sustainable planning in tropical savanna regions. Full article

Evaluating the management effectiveness of protected areas (PAs) is critical for refining conservation strategies. One of the key components in the management of PA is the regulation of human disturbance. We evaluated the management effectiveness of the Qilian Mountain National Nature Reserve (QMNNR) in mitigating human disturbance for 2000–2022. Human footprint was used as a key indicator of human disturbance. It integrates eight human disturbance factors: built environments, population density, night-time lights, cropland, pastureland, roads, railways, and navigable waterways. Evaluations are conducted across dual spatial dimensions: (1) constructing an equal-area external buffer zone to compare human footprint dynamics inside versus outside the reserve; and (2) testing the hypothesis that “stricter zonation correlates with improved control of human disturbance” by analyzing management gradients across four functional zones (core, buffer, experimental, and peripheral protection zones). Key findings include the following: (1) The increase in human footprint within the reserve was markedly lower than in surrounding areas, with the internal–external human footprint disparity expanding from 2000 to 2022. (2) Spatial analysis reveals concentrated disturbance hotspots in northern buffer zones, whereas only marginal increases occurred in Sunan County within the reserve. (3) Human footprint growth across functional zones followed a clear ascending order: core zone < buffer zone < experimental zone < peripheral protection zone, validating the efficacy of zoned management. Collectively, these results demonstrate that the QMNNR has effectively curbed human disturbance expansion—particularly in its core area—though vigilance is warranted against emerging “ecological island” risks in the northern peripheral zone. The proposed dual-dimensional human footprint assessment framework further offers a standardized evaluation methodology for large-scale PA in mitigating human disturbance. Full article

Birds are important indicators of ecosystem health and provide a range of benefits to society. It is important, therefore, to understand the impacts of agricultural land use changes on bird populations. The cultivation of energycane (EC)—a sugarcane hybrid—for biofuel production represents one form of agricultural land use change in southern Florida. We used passive acoustic monitoring (PAM) to examine bird community use of experimental EC fields and other agricultural land uses at two study sites in southern Florida. We deployed 16 acoustic recorders in different study plots and used the automatic species identifier BirdNET to identify 40 focal bird species. We found seasonal differences in daily avian species diversity and richness between EC experimental plots and reference agricultural fields (corn fields, orchards, pastureland), and between time periods (pre-planting, post-planting). Daily avian species diversity and richness were lower in the EC experimental plots during Fall and Winter months when plants reached maximum height (>400 cm in some areas). Despite seasonal differences in daily measures of species diversity and richness, we found no differences in cumulative species richness, suggesting that there may be little overall (season-long) effects of EC production. These findings could provide insight to avian seasonal habitat preferences and underscore the potential limitations of PAM in areas experiencing dynamic vegetation changes. More research is needed to better understand if utilization of EC cropping systems results in positive or negative effects on avian populations (e.g., foraging habitat quality, predator–prey dynamics, nest success). Full article

Alfalfa (Medicago sativa L.), a drought-tolerant legume, significantly influences carbon and nitrogen cycling in arid and semi-arid regions. This study investigated carbon and nitrogen storage and sequestration dynamics in alfalfa pastureland cultivated for 2–7 years under temperate continental arid climate conditions (110–190 mm annual precipitation). Overall, the biomass, carbon and nitrogen sequestration in alfalfa pasture, and carbon and nitrogen storage and sequestration in soil exhibited a quadratic pattern with planting years. The above-ground biomass peaked at 19.28 t·hm⁻², with carbon and nitrogen sequestration reaching the highest level at 10.18 t·hm⁻² and 0.511 t·hm⁻², respectively, in year 5. Both annual carbon and nitrogen sequestration of the below-ground vegetation exhibited an increase, reaching a peak before decreasing with planting year, and from Y3 to Y7, the sequestration values were consistently higher than those in Y2. Soil carbon and nitrogen sequestration peaked in year 3. Compared to the adjacent fallow lands, alfalfa pasturelands maintained positive soil carbon sequestration until year 6 but became negative (−8.03 t·hm⁻²) by year 7. From years 2–6, alfalfa pasture fixed carbon and nitrogen at comparable rates but returned disproportionately less carbon than nitrogen to the soil. To optimize sustainability, we recommend (1) rotating alfalfa after 6 years to prevent soil nutrient depletion and (2) applying carbon-rich fertilizers post-year 3 to balance nutrients and prolong productivity in arid climates. Full article

In the current context of the international climate agenda, understanding both the sources of greenhouse gas (GHG) emissions and the mechanisms for their mitigation is a fundamental requirement for low-carbon development strategies. Kazakhstan has pledged to reduce its GHG emissions by 15–25% by 2030, relative to 1990 levels, and to achieve carbon neutrality by 2060. However, there is no unified methodology for comprehensively assessing the national carbon balance, particularly at the regional scale. This study addresses this gap by analyzing GHG emissions and carbon sequestration capacities across Kazakhstan’s regions using a sectoral disaggregation approach and scenario-based modeling aligned with IPCC methods. Emission hotspots were identified in the energy sector (328 MtCO₂-eq), agriculture (118 MtCO₂-eq—primarily from pasturelands), and transport (7 MtCO₂-eq). In contrast, current carbon sinks—mainly forest ecosystems and abandoned pasturelands—account for only 3.97 and 13.9 MtCO₂-eq, respectively. The research evaluates the technical potential for emissions reduction through the best available technologies (BAT), livestock management, partial transition to gas-powered vehicles, and reforestation. A geoengineering scenario combining all measures suggests that Kazakhstan could meet its 2030 climate targets, although full carbon neutrality by 2060 would remain out of reach under current policy trajectories. The Akmola region is examined as a representative case study, demonstrating a possible 35% reduction in net emissions by 2035. This work contributes a regionally nuanced, data-driven framework for integrating ecosystem services into national climate policy and identifies nature-based solutions—especially forest management—as essential components of Kazakhstan’s decarbonization pathway, offering insights for other carbon-intensive economies. Full article

Tall fescue-dominated pasturelands are widespread in the eastern United States and typically lack substantial plant diversity. Establishing native wildflowers into tall fescue pastures has the potential to benefit bee populations and boost pollinator ecosystem services. In this study, tall fescue pastures at five on-farm sites in Virginia, USA, were planted with wildflowers native to North America and paired with sites with conventional tall fescue pastures. Honeybee apiaries were established at the ten locations, and variables related to hive strength were measured over two years. The main study objectives were to: (1) compare metrics of hive strength between diversified and conventional pastures, (2) determine whether honeybees used native-sown wildflowers as a source of pollen, and (3) explore whether native-sown wildflowers were visited more by honeybees and other pollinators compared with nonnative, unsown forbs. Diversified pastures had many more plant species and blooms compared with conventional pastures, but this had little effect on hive parameters. Pollen DNA metabarcoding revealed that honeybee diets were similar regardless of whether hives were associated with diversified or conventional pastures. Honeybees foraged mostly on plants in the surrounding landscape—especially white clover (Trifolium repens) and less so on native wildflowers. Native-sown wildflowers received more visits from native pollinators, however. We hypothesize that the native-sown wildflowers had little impact on hive strength metrics because honeybees had access to abundant, white clover blooms and other flowering species in these landscapes. Native wildflowers that bloom in late summer/early autumn after white clover blooms diminish may be of greater value to honeybees in pasture settings. Full article