Search Results (677)

Hungary is experiencing more frequent and severe droughts due to climate change, with 60% of its arable land in the vulnerable Great Hungarian Plain. Drought events in 2012 and 2022 reduced maize yields by more than 50% in some regions. This review synthesizes studies (2000–2025) on remote sensing capabilities, climate change impacts, and farmer adaptation in Hungarian agriculture. Remote sensing technologies (Sentinel, Landsat, MODIS) and indices (NDVI, VCI, LST, TCI) achieve high accuracy (often >80%) in drought detection under validated conditions, yet technical and financial barriers limit uptake among smallholder farmers. Climate projections indicate that a 2 °C temperature rise by 2050 will expand drought-affected areas. Farmer adaptation varies sharply by farm size: large farms (>100 ha) adopt precision agriculture (65% uptake), while smallholders (<10 ha) rely on crop rotation and drought-resistant varieties. Although substantial support is provided through the EU Common Agricultural Policy, institutional fragmentation and weak extension services—which reach only 32% of farmers—undermine its effectiveness. Bridging this gap requires integrating accessible remote sensing tools with targeted smallholder support and reformed extension services. Full article

The spatial distribution and productivity of subsistence resources are central to food security, nutrition, and cultural vitality in circumpolar Indigenous communities. Yet few studies incorporate Indigenous Knowledge in methodology to monitor subsistence plant species. Here, we apply participatory action research to develop a monitoring system for the culturally and nutritionally important Rubus chamaemorus (atsalugpiaq, salmonberry) near the Yup’ik village of Quinhagak in southwest Alaska. With support from community members, two ground-truth surveys assessed berry productivity at nine sites within Quinhagak’s Traditional Land Use Area. Seventeen interviews identified key themes related to subsistence harvest and highlighted winter meteorological factors important for analysis. We compiled a multi-year dataset including PlanetScope eight-band SuperDove imagery (3 m GSD); airborne LiDAR and satellite-derived DEMs; and four meteorological parameters. Linear regression and multiple adaptive regression splines were tested to evaluate relationships among vegetation health, climate, landscape features, and berry productivity. Model outputs identified chlorophyll-related vegetation indices, particularly MTCI, as strong predictors of harvest outcomes, with higher flowering-season MTCI values associated with greater berry abundance. This work establishes a foundational, scalable approach for the long-term monitoring of Arctic subsistence plants in conjunction with Arctic communities and demonstrates the value of multi-layer data integration in regions historically challenging for remote sensing and ground surveys improving outcomes for regional harvest predictions and increased understanding of possible mechanisms controlling berry productivity in Arctic regions. Full article

Given the enormous span of potential strategies to address climate change, it is difficult to build consensus on what to prioritize. In 2021, we conducted 63 semi-structured interviews with climate change experts in the U.S. (N = 33) and India (N = 30). Experts indicated how they would address climate change through mitigation, adaptation, carbon dioxide removal (CDR), and solar geoengineering (SG). Our experts studied climate change from a variety of disciplines and were not necessarily subject matter experts in CDR or SG. Most experts stated that while more research is needed on CDR and SG, there is low appeal to deploying them in responding to climate change. Across our entire sample, we find that 44% of experts supported deploying CDR compared to 3% for SG. We also find that 17% of experts opposed the deployment of CDR, while twice as many (35%) opposed deploying SG. While there is far more support for traditional measures like mitigation and adaptation, most experts were hesitant to support technologies like CDR and SG to limit warming to 1.5 °C or 2 °C to prevent dangerous climate impacts, with statements tending toward a precautionary principle. Deep interdisciplinary engagement by climate change experts on CDR and SG is essential to understanding these technologies’ potential roles in addressing climate change and the perceptions of risk of these technologies held by experts who work on other areas of the climate problem. We highlight the potential for follow-up studies on broader expert opinions of CDR and SG, as well as evaluating whether perceptions and opinions are lagging behind fast-changing developments in the field. Full article

This study presents a high-resolution, multi-proxy palaeoenvironmental reconstruction of the Tövises fen at Pocsaj, Hungary, utilising lithostratigraphical, geochemical, malacological, and palynological analyses supported by radiocarbon dating. The sedimentary sequence documents the transition from a Late Glacial fluvial system (c. 19,000–16,000 cal BP) to a cut-off meander and subsequent oxbow lake, eventually evolving into a peat-forming fen. Malacological and palynological data reveal the co-occurrence of cold-tolerant Late Pleistocene elements and the early appearance of thermomesophilous taxa at the onset of the Holocene. This suggests that the favourable microclimate of the adjacent loess-covered high bank and the humid alluvial plain functioned as a cryptic refugium for temperate broad-leaved trees and associated fauna during the Late Glacial. Anthropogenic impact is traceable from the Mesolithic, characterised by Corylus management, intensifying through Neolithic agriculture to a peak during the Roman Imperial Period. Geochemical markers in the upper peat sequence reflect increased biomass and medieval habitation, while recent malacofaunal shifts indicate progressive desiccation. Despite modern drainage attempts, the Tövises fen remains a biodiversity hotspot of high conservation value, preserving relict wetland communities. Full article

Carbon footprint and C balance are used to understand whether an agroecosystem is a C source or sink. Our objective was to evaluate C inputs and outputs for determining C balance for pulse crops in rotation with spring wheat (Triticum aestivum L.) from 2021 to 2022 to 2024–2025 in the US northern Great Plains. Pulse crops (chickpea [Cicer arietinum L], lentil [Lens culinaris Medik.], and pea [Pisum sativum L.]) were rotated with spring wheat to form four crop rotations (chickpea–spring wheat, lentil–spring wheat, pea–spring wheat, and spring wheat–spring wheat). Straw C was 26–74% lower for pulse crops than spring wheat, but 19–23% greater for pea–spring wheat than chickpea–spring wheat and lentil–spring wheat. Root biomass and rhizodeposit C were 24–31% greater for spring wheat–spring wheat than chickpea–spring wheat and pea–spring wheat. Grain C was 21% greater for pea than chickpea, but 64–97% lower for pulse crops than spring wheat. Cumulative CO₂ flux from May to April was 14–17% greater for spring wheat–spring wheat than chickpea–spring wheat and lentil–spring wheat. Soil C sequestration rate was greater for pea and spring wheat than chickpea and lentil, or greater for pea–spring wheat and spring wheat–spring wheat than other crop rotations. Carbon balance was 5–16% lower for pulse crops than spring wheat, or 9–16% lower for pulse crop–spring wheat rotations than spring wheat–spring wheat. Because of greater C input and C sequestration rate, spring wheat can reduce C loss compared to pulse crops, or continuous spring wheat can reduce the loss compared to pulse crop–spring wheat rotations. 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

Climate-driven heat and water stress are increasingly compromising rainfed maize yields in transition zones, with significant implications for global food security. While continental-scale models of crop suitability exist, they often fail to capture the high-resolution heterogeneity of agricultural landscapes or distinguish between irrigated and rainfed systems in semi-arid regions. This study models the current and future suitability of rainfed maize in Kansas, USA, using a Maximum Entropy (MaxEnt) approach. To accurately isolate biophysical constraints, we employed a novel data-filtering workflow using the USDA Cropland Data Layer (CDL) and Landsat-based Annual Irrigated Datasets (LANID) to train the model exclusively on rainfed occurrences. We projected suitability shifts for the mid- (2041–2070) and end-of-century (2071–2100) periods under two CMIP6 Shared Socioeconomic Pathways (SSP3-7.0 and SSP5-8.5), using high-resolution CHELSA bioclimatic variables. The model, achieving an Area Under the Curve (AUC) of 0.73 and validated against 30 years of historical USDA production records, reveals a distinct spatial contraction of areas climatically suitable for growing maize. Projections indicate a significant decline in suitability across Western and Central Kansas driven by rising temperatures and precipitation variability, with the most highly suitable optimal habitats projected to decline by approximately 90% by mid-century. These findings quantify mounting climate impacts on maize-growing areas of the Great Plains and provide spatially explicit baselines for the development of regional adaptation strategies and groundwater conservation policies. Full article

The late Paleogene White River Group is a post-Laramide sedimentary succession that occurs within Laramide intermontane basins and atop some basement-cored uplifts. Detrital zircon U-Pb dates of tuffaceous sandstones from the uppermost White River Group in eastern Wyoming and western Nebraska yield a maximum depositional age of 23.07 ± 0.46 Ma, which overlaps the emplacement of the Markagunt Gravity Slide in Utah’s Marysvale Volcanic Field at 23.05 + 0.22/− 0.20 Ma. The Marysvale Volcanic Field (~31–18 Ma) lies at the east margin of the Nevadaplano, a longstanding highland in the Sevier Hinterland later dismembered by basin and range extension. Strata both proximal and distal to the Marysvale Volcanic Field show an increase in Marysvale provenance up to the emplacement of the Markagunt Gravity Slide. After emplacement, distal sediment sourcing of Miocene strata in the Great Plains shifted back to older Paleogene volcanic fields farther west in Nevada. This temporal relationship suggests that the collapse of the Marysvale Volcanic Field associated with the emplacement of the Markagunt Gravity Slide forced drainage reorganization and sediment sourcing during the transition from White River to Arikaree Group sedimentation. Full article

To solve the problems of low seeding precision and the poor operational adaptability of traditional no-till seeders under dense planting mode, and meet the agronomic requirements for high maize yield, this study carried out optimization and experimental research on the no-till precision fertilizer-seed co-sowing system for maize with wide-narrow row dense planting, relying on the experimental base of the Science and Technology Courtyard for Super High-Yield Cropping Systems in Qihe, China Agricultural University. Through modular integration and the optimization of key components, precise row spacing adjustment and improved sowing depth consistency in complex plots were achieved. A tractor-implement integrated a kinematic model and a dynamic model of the seed metering tube, which were constructed to quantify the correlation between operational parameters and motion states, providing theoretical support for structural parameter optimization. Field tests showed that all operational quality indicators of the system met the local high-yield requirements for no-till dense planting; the comprehensive performance was optimal at a density of 75,000 plants·ha⁻¹, with the best seeding uniformity (coefficient of variation: 5.65%), seedling emergence and seedling uniformity, which is well adapted to the agronomic characteristics of the wheat–maize rotation areas in the Huang-Huai-Hai Plain. Subsequent optimization by reducing the operating speed and increasing the spring stiffness can further improve the operational quality, realize the deep integration of agronomy and agricultural machinery, provide agricultural machinery support for high-yield and high-quality maize cultivation, and is of great significance for improving agricultural production efficiency and resource utilization. Full article

Cultivated land fragmentation has become a critical constraint on regional agricultural sustainable development. Revealing its spatial patterns and driving mechanisms is of great significance for optimizing the utilization and management of cultivated land resources and enhancing regional agricultural productivity. This study focuses on the main grain-producing areas in the middle and lower reaches of the Yangtze River Basin. It constructs a Cultivated Land Fragmentation Index (CLFI) using an integrated method that combines landscape index analysis with an entropy-weighted approach, based on 2023 land-use data. The optimal analytical grain size and extent were determined before employing geographic detectors to identify dominant factors influencing cultivated land fragmentation. The key findings include the following: (1) The appropriate spatial resolution for fragmentation analysis was identified as 330 m, with an optimal analysis extent of 8910 m. (2) CLFI values ranged from 0.001 to 0.973, exhibiting significant spatial heterogeneity. The central plains and northeastern regions demonstrated low fragmentation levels and better contiguous cultivated land distribution, while the western and peripheral areas showed higher fragmentation. A provincial-scale comparison revealed that Jiangxi Province had the highest fragmentation level (0.255), whereas Jiangsu Province had the lowest (0.146). The topographic gradient analysis indicated a decreasing trend from the Guizhou Plateau (0.503) to the North China Plain (0.125), with plateaus and basins showing significantly higher fragmentation than hilly and plain regions. (3) Dominant controlling factors varied among provinces: In provinces with greater topographic relief (Anhui, Hubei, Hunan, Jiangxi), natural factors like elevation, slope gradient, and NDVI primarily controlled fragmentation patterns; in contrast, socioeconomic factors such as nighttime light intensity dominated in Jiangsu Province, characterized by flat terrain and high urbanization. Multi-factor interactions generally enhanced explanatory power regarding spatial patterns, confirming that cultivated land fragmentation is a result of comprehensive multi-factor interactions. This study reveals the spatial distribution characteristics of cultivated land fragmentation at the pixel scale in the study region, providing theoretical foundations and decision-making references for the efficient utilization of cultivated land resources and rural land system reforms. Full article

As a crucial national ecological barrier, China’s Southern Collective Forest Region (SCFR) plays an essential role in maintaining regional ecological security and promoting sustainable development. Understanding the mechanisms driving the evolution of its ecosystem service value (ESV) is of great significance. Based on county-level data from 2000 to 2023, this study integrated the equivalent factor method, spatial autocorrelation analysis, the XGBoost-SHAP model, geographically and temporally weighted regression (GTWR), and partial least squares structural equation modeling (PLS-SEM) to examine the spatio-temporal evolution patterns and driving mechanisms of ESV in the SCFR. The results showed that ESV in the SCFR exhibited an overall downward trend, with a cumulative loss of 1973.77 × 10⁸ CNY. This was primarily due to marked reductions in hydrological and climate regulation services. The spatial distribution of ESV exhibited a significant heterogeneity—higher in the southwestern and southeastern mountainous regions, and lower in the northern plains and coastal zones, with the center of gravity shifting first to the northeast and then to the southwest. Local spatial autocorrelation revealed relatively stable “High–High” and “Low–Low” clustering characteristics, where high-value clusters were consistently distributed in core forest zones, while low-value clusters overlapped highly with urban agglomerations. Socio-economic factors exerted a significantly stronger influence on ESV than natural factors. Population density (POP), land use intensity (LUI), and gross domestic product (GDP) were identified as the dominant drivers, exhibiting distinct non-linear threshold effects and significant spatio-temporal heterogeneity. PLS-SEM analysis further quantified LUI as the dominant direct inhibitory pathway on ESV, highlighting urbanization’s indirect negative effect mediated through intensified LUI. Meanwhile, terrain effects were confirmed to positively influence ESV indirectly by constraining LUI and modulating local climate. The analytical framework of “threshold identification–spatio-temporal heterogeneity–causal pathway analysis” proposed in this study elucidated the complex driving mechanisms of ESV evolution, providing valuable guidance for ecological restoration evaluation and differentiated environmental governance. Full article

Accurately identifying medium- and low-yield farmlands (MLYF) and diagnosing their constraints are essential for targeted improvement of productivity and national food security. However, traditional evaluation is usually limited by coarse spatial resolution and high labor costs, and a methodological gap remains between large-scale MLYF classification and system constraints diagnosis. To address the current methodological gaps, this study developed a comprehensive framework to determine the spatial distribution of MLYF in northern China and clarify their key constraints. The framework combined the Spatio-Temporal Random Forest (STRF) algorithm with vegetation indices (VIs), climate, and soil data to delineate MLYF and uses interpretable machine learning to diagnose major constraints. The model showed high explanatory power and ensured the reliability of attribution results. The results showed that MLYF exhibited obvious spatial heterogeneity, accounting for 48.66% of the total cultivated land in the study area. These MLYF are primarily concentrated in the northwestern Loess Plateau (LP), the central Along the Great Wall (ATGW) region, and the peripheries of the Huang-Huai-Hai (HHH) Plain. In addition to spatial classification, our analysis revealed significant differences in constraint mechanisms: soil structural, nutrient, and salinization constraints predominantly restrict productivity in the HHH Plain, whereas water stress and soil erosion are the primary drivers of yield gaps in the LP and ATGW regions. These findings provide new data and insights for understanding the spatial heterogeneity of farmland quality in typical dryland agricultural regions in northern China, and offer a scientific basis for targeted land improvement and regional agricultural sustainability. Full article

The hydrocarbon composition of liquefied PM1 aerosol samples collected using the particle into liquid system (PILS) at the Atmospheric Radiation Measurement (ARM) site of the Southern Great Plains (SGP) of the USA was analyzed in terms of organic compound composition. The results indicate that anthropogenic aliphatic compounds contributed significantly to the organic pool of PM1 fine aerosols in the ambient air of the rural area of the Southern Great Plains, with a broad range of aliphatic hydrocarbons (HCs) being the dominant organic component. The molecular markers of hopanes and steranes were generally absent or present in trace amounts in most samples, but a significant number of low-abundance hopanes and steranes were detected in only two samples, while the aromatic compounds were generally insignificant and comprised mainly low molecular weight naphthalene and its methylated derivatives. The overall composition of organic compounds and the back trajectories analysis for the sampling days suggest that the local petroleum refinery and vehicular emissions are the two major sources of the aliphatic and aromatic compounds in the fine aerosols, while plant wax may occasionally contribute a minor portion of organic matter. Furthermore, it was found that the organic composition of PM1 fine aerosol was clearly related to the ambient air temperature and suggests that the temperature is a controlling factor of organic aerosol formation. Full article

Background: Improving grain yield in wheat remains a top priority, requiring integrated breeding and genetic strategies. This complexity poses a major challenge, driven by quantitative polygenic inheritance, environmental influence, and intricate genetic interactions. We investigated genetic factors and their interactions for agronomic and yield traits in two high-yielding winter wheat cultivars adapted to the US Southern Great Plains. Methods: A bi-parental mapping population consisting of 221 F₇ recombinant inbred lines (RIL) derived from ‘TAM 204’ and ‘Iba’ was evaluated for three years in 11 Texas environments. Both parents and RIL population were genotyped on Illumina NovaSeq 6000 and sequences were aligned to IWGSC RefSeq v1.0 using Bowtie2 for SNP calling. For QTL analyses, each trait was analyzed by individual environment, across multiple environments and mega-environments. Results: A total of 86 QTL were mapped for five traits and among them 32 were consistent in more than one environment or analysis. Among consistent QTL, four were pleiotropic to more than one agronomic or yield traits mapped on chromosomes 2B (57.18, 59.47 Mb) and 2D (29.34, 40.64 Mb). The consistent QTL on chromosome 2D (29.34 Mb) was pleiotropic to GYLD, DTH, TW, TKW and explained maximum phenotypic variation for all traits, representing photoperiod gene (Ppd-D1). Another QTL on chromosome 2D (40.64 Mb) was pleiotropic to GYLD and TW and based on the physical position comparisons it likely reflects a unique locus in Iba. The pleiotropic consistent QTL Qgyld.tamu.2B.59 from TAM 204 represents Ppd-B1 gene. Moreover, it is more likely that Qdth.tamu.5B.575 represents the Vrn-B1 gene in Iba. A total of 23 digenic epistatic interactions involved consistent QTL for all traits. Amongst these, epistatic interactions between the consistent QTL on 2B (57.18 Mb) and 2D (29.34 Mb) were observed for GYLD, DTH and TKW. Conclusions: Our findings revealed key allelic diversity and interaction effects in elite wheat cultivars, paving the way for marker development for identified pleiotropic loci and implementation in marker-assisted selection and recombination breeding. Full article

Adverse weather conditions significantly degrade mobility and safety at rural signalized intersections, where high approach speeds and limited driver expectancy amplify operational and crash risks. While autonomous vehicles (AVs) have the potential to improve traffic performance, it takes a significant duration to penetrate. During this period, mixed traffic with human drivers and AVs will dominate. In this mixed traffic, the impacts of AVs at low penetration levels on adverse weather remain insufficiently understood, particularly in rural contexts. This study presents a simulation-based assessment of the effects of low AV penetration on mobility and safety at a rural signalized intersection under varying weather conditions. A calibrated microsimulation model was developed using PTV VISSIM to represent clear, rain, and snow scenarios with autonomous vehicles introduced at low penetration rates within conventional traffic. Mobility performance was evaluated using delay, travel time, and average speed, while safety impacts were assessed through surrogate safety measures extracted using the Surrogate Safety Assessment Model (SSAM), including time-to-collision and post-encroachment time. Results indicate that low levels of AV penetration of 10% can improve overall mobility performance compared with conventional traffic, particularly under adverse weather conditions. Safety outcomes show a reduction in conflict frequency and severity under low AV penetration, with more pronounced benefits observed during degraded weather scenarios. Further AV penetration from 10% to 25% may not significantly improve in a rural environment. The findings suggest that early-stage AV deployment may offer measurable mobility and safety benefits at rural signalized intersections, even before widespread adoption. This study provides practical insights for transportation agencies and policymakers regarding the potential role of low-penetration AV integration in enhancing rural traffic operations and safety under adverse weather conditions. Full article