Search Results (3,306)

Transitioning from traditional to organic production is gaining popularity worldwide with significant challenges including weed management. We evaluated how legumes sown as cover crops in a synchronous intercropping (SI) system with organic oat (Avena sativa) as the main crop impacted weed communities. A split-plot design was set up on a farm in Poularies (Quebec, Canada) to compare Melilotus officinalis, Trifolium incarnatum, Trifolium repens and a control without legumes for two years (2019–2020). We determined the botanical composition, calculated diversity indices, and measured plant functional traits. Species richness was similar (S = 5.5 ± 0.4) across treatments in 2019, but higher in the control (S = 12.2 ± 2.6) and lower (S = 6.0 ± 1.2) under T. incarnatum in 2020. Shannon diversity was lower in 2019 (H′ = 1.49 ± 0.07) than in 2020 (H′ = 1.99 ± 0.04), and higher under the control (H′ = 1.87 ± 0.05) than under T. incarnatum (H′ = 1.46 ± 0.04). Weeds under T. incarnatum had a high specific leaf area and a resource-acquisition strategy, while those in the control had a higher leaf dry matter content and a resource-conservation strategy. Our study brings novel results on the use of legumes in SI systems to control weeds. Using T. incarnatum in a SI system with oat had the greatest capacity to cover the ground, control weeds and reduce their diversity, but this species and the acquisitive weeds in this treatment could compete with the main crop. Future research should evaluate the quantity and quality of yields to complete this ecological study and give appropriate agronomic recommendations. Our results could provide agronomists and farmers with indications on the level of competition weeds exert on the cropping system depending on the SI treatment. Full article

Despite recent advances in artificial intelligence for agriculture, reliable crop recommendation remains constrained by limited access to soil diagnostics, insufficient integration of environmental context, and the absence of transparent, quantitative evaluation frameworks. This study addresses the research question: How can we integrate multiple indicators to generate accurate, explainable, and context-sensitive crop recommendations? To this end, we propose a multimodal decision-support framework that combines image-based soil texture classification with geospatial, and climatic information. A convolutional neural network was trained on a curated dataset of 3250 soil images aggregated from four publicly available sources, covering four primary soil texture classes, alongside tabular soil and nutrient data. The model was evaluated using 5-fold stratified cross-validation, achieving an average classification accuracy of 99.30% (standard deviation ≈ 0.66), and was further validated on an independent hold-out test set to assess generalization performance. To enhance practical applicability, the framework incorporates elevation, rainfall, temperature, and major soil nutrients, and employs a large language model to generate user-oriented, interpretable justifications for each recommendation. Crop recommendations were quantitatively evaluated using a novel Agronomic Suitability Score (ASS), which measures alignment across soil compatibility, climatic suitability, seasonal alignment, and elevation tolerance. Across six geographically diverse case studies, the framework achieved mean ASS values ranging from 3.76 to 4.96, with five regions exceeding 4.45, demonstrating strong agronomic validity, robustness, and scalability. A Streamlit-based application further illustrates the system’s ability to deliver accessible, location-aware, and explainable agronomic guidance. The results indicate that the proposed approach constitutes a scalable decision-support tool with significant potential for sustainable agriculture and food security initiatives. Full article

Chieng Hac in northern Vietnam is expanding maize cultivation, intensifying water competition and soil erosion. This study mapped regional water balance and erosion using remote sensing and GISs by coupling the Thornthwaite–Mather (TM) water balance model with the Revised Universal Soil Loss Equation (RUSLE) at 12.5 m resolution. Land cover was classified into maize, tree crops, paddy, forest, and other types using Random Forest. The TM model used 2021 precipitation and temperature measurements to estimate evapotranspiration, surplus, and deficit, while the RUSLE quantified soil loss. Two scenarios were evaluated: a baseline reflecting existing land use and an adjusted case applying strip cropping on 10–20° maize slopes and converting maize to tree crops on slopes > 20°. Tree crop conversion increased evapotranspiration and prolonged seasonal deficits relative to maize, increasing water deficit from 1013.6 to 1022.2 mm/year. In contrast, the interventions reduced mean soil loss from 15.52 to 11.51 t/ha/year, with the largest decline in the 5–25 t/ha/year class. Residual hotspots persisted on steep slopes and near drainage lines. The integrated framework highlights trade-offs between erosion control and seasonal water availability, supporting slope-based land-use planning in upland agricultural systems. These findings offer guidance for slope-based land-use planning by indicating that intervention priorities should vary depending on slope conditions and local water availability. 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

Soil is essential for human survival, with approximately 95% of global food production originating from land. However, over the past century, overexploitation has led to soil degradation and biodiversity loss, with significant impacts on agroecosystems. Portuguese agriculture faces diverse challenges, particularly in the horticultural sector, which occupies substantial territory and supports key economic chains. Consequently, indicators for assessing soil quality are crucial, with mesofauna serving as sensitive bioindicators due to their ecosystemic roles. Among sustainable practices, cover crops are believed to mitigate soil issues by enhancing the biotic functionalities. This study aimed to evaluate the impact of cover crops on soil biological quality in horticultural systems in Portugal. From 2022 to 2025, six horticultural fields in the Alentejo, Ribatejo, and Oeste regions were assessed, introducing cover-crops before main crops and comparing them to controls. Soil samples were collected during cover and main crop presence; mesofauna was extracted via Berlese-Tullgren funnels and classified under the QBS-ar methodology. Results showed enhanced soil biological quality (p < 0.001) in cover crop plots compared to controls, with no significant differences across regions (p = 0.66) or crop types (p = 0.37), indicating the implementation of cover crops as the primary driver for enhanced soil health. Full article

White mold caused by Sclerotinia sclerotiorum (Lib.) de Bary continues to threaten yield and quality and remains a stubborn, sometimes unpredictable constraint in many cropping systems. The pathogen’s broad host range and its capacity to persist for years as sclerotia mean that fields can carry risk long after visible symptoms fade. Disease development is often driven by short windows of favorable temperature and moisture that promote germination and ascospore release and dispersal, while myceliogenic infection from soil-borne sclerotia can also initiate disease directly. Yet dependable control is still undermined by durable inoculum, limited stable host resistance, variable biocontrol performance, and shrinking chemical options together with fungicide resistance risk. Here we consolidate current understanding and ongoing uncertainties around sclerotial formation and germination cues, the environmental drivers that shape epidemic onset, and the processes governing host colonization, including the roles of cell wall-degrading enzymes, oxalic acid, and redox regulation, as well as the continuing debate over necrotrophic versus hemibiotrophic phases. Management is considered from a practical perspective, covering cultural risk reduction, forecasting-guided fungicide programmes supported by resistance-management principles, and biological control strategies targeting sclerotia. Across systems, the evidence points to the same lesson: single tactics rarely remain reliable under field variability, whereas integrated packages that reduce soil inoculum and align interventions with risk are more durable. Future priorities include resolving early infection events, improving prediction of carpogenic germination under changing climates, increasing the consistency of biocontrol, and accelerating resistance breeding supported by genomic resources. Full article

Mining and metallurgical activities are among the main sources of heavy metal (HM) contamination of terrestrial ecosystems and the creation of persistent technogenic pollution hotspots. This study aimed to provide a comprehensive assessment of the accumulation of zinc (Zn), cooper (Cu), cadmium (Cd) and lead (Pb) in soils and vegetation under conditions of long-term industrial impact in Ridder, East Kazakhstan Region. A total of 52 soil samples were collected from 0–5 cm and 5–20 cm depths at 26 sites, and 44 species of natural vegetation, as well as three dominant agricultural crops, were examined. Soil concentrations of Zn (4415 mg·kg⁻¹), Cu (1177 mg·kg⁻¹), Cd (179 mg·kg⁻¹), and Pb (1996 mg·kg⁻¹) were classified as extremely high. Cadmium contributed most to the potential ecological risk (Cd > Pb > Zn > Cu). The industrial zone’s vegetation cover was predominantly formed by stress-tolerant and ruderal species, including Artemisia vulgaris, Calamagrostis epigeios, Bunias orientalis, Dactylis glomerata, Convolvulus arvensis, and Urtica dioica. The agricultural crops (Helianthus annuus, Avena sativa, and Triticum aestivum) mainly accumulated HMs in their root systems, with limited translocation to their aboveground organs (TF < 1). This indicates the predominance of phytostabilisation mechanisms, and highlights the potential of locally adapted plants for managing contaminated areas. Full article

The synergistic impacts of land use/land cover (LULC) transformations and weather pattern variabilities (WPV) represent a primary driver of hydro-geological instability, threatening agricultural productivity, soil conservation, and water quality. Disentangling the discrete contributions of these stressors to runoff and sediment yield (SY) remains a significant challenge, particularly in complex, confluence-proximal watersheds lacking major hydraulic regulations. This study investigates the Tirumakudalu Narasipura watershed in Karnataka, India, an agriculturally intensive system undergoing rapid peri-urbanization. Leveraging the process-based geospatial interface of the Water Erosion Prediction Project (GeoWEPP), we analyzed hydrological responses over a 24-year period (2000–2023) and projected future trajectories through 2030. To overcome the traditional constraints of GeoWEPP, which was developed for small-scale watersheds (<260 ha), we present a novel upscaling framework utilizing a multi-site multivariate temporal calibration of hydrological response variables to exploit its process-based precision in capturing distributed soil erosion and landscape heterogeneity. This approach is further reinforced by an ancillary data validation to minimize error propagation while model-upscaling. Our findings reveal projected increases in runoff and SY of 14.69% and 49.23%, respectively, between 2000 and 2030. Notably, the sub-decadal acceleration from 2023 to 2030 (17.32% for runoff and 18.51% for SY) underscores a shifting dominance where LULC-driven surface modifications now outweigh climatic variance in forcing hydrologic change. Furthermore, the study quantifies how anthropogenic interventions such as strategic crop selection, tillage intensity, and irrigation regimes act as critical determinants of topsoil preservation. These results provide a scalable, economically feasible framework for precision land stewardship and sustainable watershed management in rapidly developing tropical landscapes. Full article

Crop residue open burning is a major source of atmospheric pollutants that degrade regional air quality, enhance climate forcing, and threaten public health through emissions of particulate matter, greenhouse gases, and toxic species. In southern China, satellite-based emission estimates are often underestimated because frequent cloud cover and limited spatiotemporal resolution hinder the detection of agricultural fires. In this study, crop residue open burning emissions in Guangxi province from 2017 to 2023 were quantified using a statistical approach. The open burning proportion (OBP) was updated on an annual basis using the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and recently reported emission factors (EF_S) were adopted to enhance estimation accuracy. Annual emissions of pollutants were then spatially distributed to 0.05° × 0.05° grid cells based on satellite-detected fire counts and land cover information. The results indicated the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO₂), nitric oxide (NO_X), carbon monoxide (CO), carbon dioxide (CO₂), fine particles (PM_2.5), coarse particles (PM₁₀), ammonia (NH₃), methane (CH₄) and non-methane volatile organic compound (NMVOC) in Guangxi province during 2017–2023 were 58.90, 230.48, 37.90, 213.95, 4234.41, 108,775.48, 583.09, 667.70, 46.36, 322.74 and 710.20 Gg, respectively. Sugarcane residue burning was identified as the dominant contributor, accounting for 41.26–64.38% of total emissions, followed by rice (20.66–43.06%), corn (5.11–17.25%), and cassava (4.33–6.45%). Emissions exhibited clear interannual variability, declining from 2017 to 2020 under strict control measures and increasing again from 2021 to 2023 as enforcement weakened. Incorporating annually updated VIIRS-derived OBP_S into the statistical inventory improves the temporal representation and reliability of multi-year emission estimates for agricultural burning. Full article

Traditional soil management in vineyards and olive groves of semi-arid regions relies on repeated tillage, which accelerates soil organic matter (SOM) oxidation and limits long-term carbon storage. In the context of carbon-neutral agricultural strategies, understanding how alternative practices influence SOM stocks, redistribution, and stabilization is essential. We sampled six paired sites in central Spain (three vineyards and three olive groves), each comprising adjacent plots under conventional tillage or continuous cover cropping, at 0–10 and 10–30 cm depths. We analyzed water-extractable organic carbon (WEOC), optical properties of water-extractable organic matter (WEOM; specific UV absorbance at 254 nm (SUVA₂₅₄) and the absorbance ratio E4/E6), β-glucosidase activity, and the SOC/clay ratio as a proxy for mineral-associated SOC stabilization. Depth was the main factor structuring SOC and biological activity, with higher values in the topsoil. Management effects on bulk SOC were limited although cover cropping increased aboveground biomass and influenced WEOC dynamics. Vertical contrasts (30–10 cm) showed a positive association between WEOC and SOC/clay, suggesting that increased WEOC at depth co-varies with stabilization potential. Partial least squares analysis for 10–30 cm showed that SOC/clay was associated with WEOC, E4/E6, and β-glucosidase activity. These results suggest that subsoil carbon stabilization in semi-arid conditions may be linked to DOC availability and microbial processing rather than directly to surface biomass inputs. Full article

Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients for temperate fruit trees, including hazelnut, and transpiration-based models have been proposed, while several studies have successfully linked Vegetation Indices and thermal metrics to single and basal crop coefficients in vineyards, orchards and field crops. However, no information is available on the use of UAV-derived spectral and thermal indices to estimate crop coefficients in high-density hazelnut orchards. This study compares crop coefficients obtained from traditional approaches (the FAO56 single crop coefficient, a transpiration-based coefficient, and ground cover reduction factors) with coefficients estimated from UAV-derived Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI) in a subsurface-drip-irrigated hazelnut orchard (cv. Tonda Francescana^®) with two planting densities (625 and 1250 trees ha⁻¹) in central Italy. Multispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI, while a local weather station provided reference evapotranspiration. Empirical relationships were calibrated between crop coefficients and ground cover, NDWI, and CWSI, and mid-season coefficients were applied to estimate daily crop evapotranspiration, which was then compared with the irrigation volumes supplied during the 2024 season. The standard FAO56 crop coefficient (Kc = 0.9) overestimated evapotranspiration, especially at the lower planting density, whereas ground cover-based reduction factors recalibrated for hazelnut and the transpiration-based coefficient provided estimates more consistent with the applied irrigation. UAV-based NDWI- and CWSI-derived crop coefficients produced mid-season values close to those obtained with the transpiration-based method for both planting densities, confirming that spectral and thermal information can effectively capture the combined effects of canopy development and water status. These results indicate that combining traditional methods with UAV-derived indices offers a flexible framework to refine crop coefficients in high-density hazelnut orchards and support more accurate and spatially explicit irrigation scheduling. Full article

Understanding the intricate interrelationships among ecosystem services (ESs) is fundamental to advancing sustainable ecological management. This study focuses on the Taihu Basin and examines five representative ESs, including water yield (WY), carbon sequestration (CS), soil retention (SR), habitat quality (HQ), and crop production (CP), for the years 2000, 2010, and 2020. Spatial distribution characteristics and spatiotemporal dynamics were quantified through the combined application of the InVEST model, a food production model, and ArcGIS. Spearman correlation analysis and K-means clustering were then applied to characterize trade-offs and synergies among ESs and to delineate ecosystem service bundles at multiple spatial scales, including 1 km × 1 km grids, 10 km × 10 km grids, and the county level, while GeoDetector was used to identify the associated driving mechanisms. The results indicated that (1) between 2000 and 2020, the spatial distribution pattern of the ESs in the Taihu Basin underwent significant changes, with WY and SR increasing by 48.97% and 51.89%, respectively, while HQ, CS, and CP decreased by 17.2%, 15.5%, and 47.6%. (2) From an overall perspective of trade-offs and synergies, the interactions among ESs shifted from trade-offs (r < 0) to synergies (r > 0) as the scale increased. From the perspective of the spatial characteristics of trade-offs and synergies, the intensity of these interactions varied significantly with increasing scale, but the trend remained relatively stable. (3) The Taihu Basin can be categorized into six ES bundles (ESBs). ESB 1, ESB 3, ESB 4, and ESB 5 have relatively stable ES structures, whereas ESBs 2 and 6 display significant variations. (4) The primary factors influencing ESs vary significantly across different spatial scales, with land use/land cover (LULC) and the proportions of arable land, forestland, and buildings exhibiting strong explanatory power. This highlights the critical role of coupled natural and anthropogenic processes in shaping the spatial patterns of ESs. This study considers the spatiotemporal variation and scale dependence of ecosystem services, providing management recommendations tailored to different regions and spatial scales, and offering a scientific basis for regional ecological planning and watershed governance. Full article

Over the past few decades, legume-based intercropping has emerged as a strategic agronomic practice to enhance the sustainability and resilience of agro-ecosystems, thanks to its ability to perform biological nitrogen fixation and store soil organic carbon. The present study, given the growing recognition of agroecological practices, aims to analyze through a global bibliometric analysis the research conducted between 1986 and 2025 on legume–non-legume intercropping, with particular emphasis on its ecological and agronomic benefits. The investigation, carried out according to the PRISMA protocol on the Scopus database, selected 167 original English-language articles, excluding reviews, conference proceedings, modeling studies, and meta-analyses. China and India are identified as the most productive countries. Co-occurrence and bibliographic coupling analyses highlight thematic clusters centered on soil fertility, microbial communities, productivity, and the mitigation of environmental impact. Furthermore, management practices such as integrated rotations, cover crops, and agroforestry systems amplify the benefits in terms of carbon accumulation and resilience to adverse climate conditions. The distribution of publications by journal highlights the centrality of journals such as Agriculture, Ecosystems & Environment and Plant and Soil. Overall, the data confirm the crucial role of intercropping as a pillar of the agroecological transition, underscoring the need for policies and research programs capable of amplifying its global adoption. The findings of this study may guide future interdisciplinary research and evidence-based policy decisions aimed at optimizing the design of resilient intercropping systems, tailored to address the challenges posed by climate change and the growing demands of global food security. Full article

Management of legume cover crops to reduce their cost by using no-tillage and reducing seed rate could increase their adoption. Despite the growing interest in cover crops, no information exists simultaneously regarding the potential of different species and how the sowing method and seed rate affect nitrogen (N) contribution and the yield of the subsequent maize crop. During a four-year field trial, under irrigated conditions in the Ebro valley (NE Spain), three leguminous cover crop species (pea, common vetch and hairy vetch), two cover crop seeding methods (conventional tillage and no-tillage) and two seeding rates (normal and 25% reduced) were tested and compared with a control treatment without a cover crop. The aboveground cover crop biomass and the N derived from biological fixation (BNF); aboveground biomass and total N in weeds; soil mineral nitrogen; and the effect on maize grain yield and N content were evaluated. Pea and common vetch produced more biomass (+76%) and had a higher N uptake (+50 to 60%) compared to hairy vetch. The sowing of the cover crops after no-tillage combined with a reduced sowing rate reduced biomass production by 14%. The percentage of nitrogen derived from the atmosphere (Ndfa) was above 60% for all species and the differences in total N derived from biological fixation (BNF) among treatments were related to the aboveground biomass. The introduction of cover crops reduced weed growth compared to the control especially in the no-tillage treatment. Cover crops increased maize grain yield by 12% and N uptake by 17% compared to the control treatment without a cover crop. Full article

Drone-mounted ground-penetrating radar (GPR) systems offer new opportunities for integrating subsurface characterization into remote sensing workflows. However, the interaction between flight parameters, surface conditions, and vegetation characteristics remains poorly understood. This study investigates the impact of flight altitude, surface topography, crop presence, and canopy water content on the stability and interpretability of GPR signals collected using a drone. Field experiments were conducted under controlled conditions using agricultural plots with variable canopy cover and soil moisture regimes. Radargrams were processed to evaluate signal amplitude, reflection continuity, and attenuation patterns in relation to terrain slope and vegetation structure derived from co-registered RGB drone imagery. The results reveal that lower flight altitudes and smoother surfaces yield higher signal coherence and greater subsurface penetration, while increased canopy water content and biomass reduce signal strength and clarity. Integrating drone-based GPR observations with surface spectral and thermal data improved discrimination between soil and vegetation-induced signal distortions. The findings highlight the potential of drone–GPR systems as a complementary layer in a multi-sensor remote sensing framework for precision agriculture, environmental monitoring, and 3D soil mapping. Full article