Search Results (369)

Soil salinization is an important negative factor that reduces the fertility of irrigated arable land. The fields in southern Kazakhstan are at high risk of salinization due to the dry arid climate. In some cases, even the top layer of soil has a significant degree of salinization. The use of a UAV equipped with a multispectral camera can help in the rapid and highly detailed mapping of salinity in cultivated arable land. This article describes the process of preparing the labeled data for assessing the salinity of the top layer of soil and the comparative results achieved due to using machine learning methods in two different districts. During an expedition to the fields of the Turkestan region of Kazakhstan, fields were surveyed using a multispectral camera mounted on a UAV; simultaneously, the soil samples were collected. The electrical conductivity of the soil samples was then measured in laboratory conditions, and a set of programs was developed to configure machine learning models and to map the obtained results subsequently. A comparative analysis of the results shows that local conditions have a significant impact on the quality of the models in different areas of the region, resulting in differences in the composition and significance of the model input parameters. For the fields of the Zhetisay district, the best result was achieved using the extreme gradient boosting regressor model (linear correlation coefficient Rp = 0.86, coefficient of determination R² = 0.42, mean absolute error MAE = 0.49, mean square error MSE = 0.63). For the fields in the Shardara district, the best results were achieved using the support vector machines model (Rp = 0.82, R² = 0.22, MAE = 0.41, MSE = 0.46). This article presents the results, discusses the limitations of the developed technology for operational salinity mapping, and outlines the tasks for future research. Full article

Recycled bio-wastes such as compost and vermicompost, and bioenergy byproducts such as digestate and biochar are widely acknowledged for their role as soil conditioners capable of preserving soil fertility, maintaining soil health, and acting as a bio-adsorbent of organic soil pollutants (BIOSORs). Moreover, they are attracting increasing attention for use as effective carriers of microbial consortia into arable soils. This study aims to combine selection of bacteria tolerating contaminants of emerging concern (CECs) and their use to fortify BIOSORs. Seventeen bacterial strains isolated from commercial bio-stimulant formulations were studied together with three strains previously isolated and identified as Bacillus subtilis, Bacillus licheniformis, and Serratia plymuthica. All the strains were tested in vitro for their ability to grow under increasing concentrations (0, 0.2, 0.5 and 1 mg L⁻¹) of CECs: bisphenol A, 4-nonylphenol, penconazole, and S-metolachlor. Results highlighted a variability in the tolerance of the bacteria to the tested CECs. The B. subtilis, B. licheniformis, and S. plymuthica were the most promising strains, individually or as consortium, to tolerate individual CECs and their mix. Moreover, they exhibited metabolic activity when inoculated in the BIOSORs. Nevertheless, additional investigations such as quantitative assessment of CECs are needed to validate the methodology. This work contributes to investigate the feasibility of stable and functionally active microbially enriched bio-sorbents (Me-BIOSORs) and provides preliminary evidence supporting the potential to be used in soil–plant systems at the field scale. Full article

Hailstorms represent one of the most damaging convective hazards for agriculture, yet quantifying their impacts at a landscape scale remains challenging due to their localized and short-lived nature. In this study, we combine weather radar parameters and Sentinel-2 multispectral imagery to assess vegetation damage caused by two major hail events in northeastern Romania: Rădăuți (17 July 2016) and Dolhasca (30 July 2020). Radar-derived hail kinetic energy (HKE) was used as a rapid temporal indicator of hail occurrence, with a threshold of 300 J m⁻² applied to delineate potentially affected areas. Sentinel-2 Level-1C imagery, selected under strict temporal and cloud cover criteria, was processed to generate pre- and post-event Normalized Difference Vegetation Index (NDVI) maps, from which NDVI differences (ΔNDVI) were computed. Thresholds of 0.10 and 0.20 were applied to identify moderate and severe vegetation stress, respectively. The results demonstrate strong spatial correspondence between radar-derived HKE cores and Sentinel-2 ΔNDVI reductions. In Rădăuți, where only one post-event image was available, ΔNDVI thresholds identified between 2236 and 5856 ha of affected vegetation within the HKE > 300 J m⁻² zone. In Dolhasca, where three post-event images were available (5, 8, and 15 days), the analysis revealed 6200–9100 ha affected at 5 days, decreasing to 4800–7200 ha at 8 days, and further to 3100–5600 ha at 15 days post-event. This temporal gradient highlights both the recovery of vegetation and the diminishing sensitivity of the ΔNDVI signal with increasing time elapsed since the event. Analysis by land use classes showed arable fields to be the most sensitive, followed by orchards and pastures, while forests exhibited smaller but persistent declines. This study demonstrates the robustness of integrating radar-derived hail kinetic energy with Sentinel-2 NDVI differencing for the spatiotemporal assessment of hail damage. The approach provides both rapid detection and temporally resolved mapping of hail damage, underlining the critical role of time as a determining factor in impact assessments. These findings have strong implications for operational crop monitoring, disaster response, and risk management in hail-prone regions. Full article

Operationalizing Earth Observation (EO)-based Machine Learning (ML) algorithms (or GeoML) for ingestion in environmental Digital Twins remains a challenging task due to the complexities associated with balancing real-time inference with cost, data, and infrastructure requirements. In the field of GHG monitoring, most GeoML models of land use CO₂ fluxes remain at the proof-of-concept stage, limiting their use in policy and land management for net-zero goals. In this study, we develop and demonstrate a Digital Twin-ready framework to operationalize a pre-trained Random Forest model that estimates the Net Ecosystem Exchange of CO₂ (NEE) from drained peatlands into a biweekly, field-scale CO₂ flux monitoring system using EO and weather data. The system achieves an average response time of 6.12 s, retains 98% accuracy of the underlying model, and predicts the NEE of CO₂ with an R² of 0.76 and NRMSE of 8%. It is characterized by hybrid data ingestion (combining non-time-critical and real-time retrieval), automated biweekly data updates, efficient storage, and a user-friendly front-end. The underlying framework, which is part of an operational Digital Twin under the UK Research & Innovation AI for Net Zero project consortium, is built using open source tools for data access and processing (including the Copernicus Data Space Ecosystem OpenEO API and Open-Meteo API), automation (Jenkins), and GUI development (Leaflet, NiceGIU, etc.). The applicability of the system is demonstrated through running real-world use-cases relevant to farmers and policymakers concerned with the management of arable peatlands in England. Overall, the lightweight, modular framework presented here integrates seamlessly into Digital Twins and is easily adaptable to other GeoMLs, providing a practical foundation for operational use in environmental monitoring and decision-making. Full article

Arable field abandonment is a major driver of landscape change in rural areas worldwide. It is defined as the cessation of agricultural activities and the withdrawal of agricultural management on land. This study examined arable land abandonment and subsequent land use and land cover (LULC) changes in Gotyibeni, Manqorholweni, Mawane, and Melani villages over a 20-year period. The aim was to understand these changes and how rural livelihoods and social relationships within and between households were perceived to have transformed following the LULC shifts. Landsat 5, 7, 8, and 9 multispectral imageries with a 30 m spatial resolution were analysed for two periods (i.e., 2000–2010 and 2010–2020). Five land cover classes were mapped: arable fields, grasslands, homestead gardens, residential areas, and shrublands. Post-classification change detection revealed a steady decline in arable fields, largely replaced by grasslands, shrublands, and residential areas. User accuracy was above 80% across all LULC maps, providing confidence in the LULC results. To link these spatial changes with social outcomes, 97 households that had abandoned field cultivation were purposively selected across the four villages. Semi-structured interviews were conducted to capture household experiences. Findings showed that reduced field cultivation was perceived to undermine household economic status, with households increasingly dependent on government social grants amid high unemployment. In addition, weakened social connections and shifts in cultural practices were reported. Overall, the study demonstrated how combining satellite imagery with community perspectives provides a comprehensive understanding of rural arable land abandonment and its consequences. Full article

Afforestation of post-agricultural land is one of the most important challenges of modern forestry, posed by economic demand and climate protection. Unfortunately, stands introduced on such degraded soils are not sustainable and their productive value is limited. The present study tested the effects of two substances—Humac and Alginite—on the community structure of mesostigmatid mites colonizing plots overgrown by Platanus × acerifolia (Aiton) Willd, also comparing them with the mite communities of arable field and 64-year-old stands of Pinus sylvestris L. and Quercus robur L. growing on post-agricultural land. A total of 306 mite individuals were recorded, belonging to 45 taxa and 14 families. The results indicate a moderately positive effect of Humac fertilization on the mite communities studied. A similar impact has not been demonstrated for Alginite. In contrast, all parameters studied (density, species richness and diversity of mite communities) reached the highest values in the P. sylvestris stand. Humac application harmonizes Mesostigmata mite community structures between young and older stands and may be considered a beneficial practice for the afforestation of former agricultural land. Full article

Agricultural land management is a major determinant of terrestrial carbon (C) fluxes and has substantial implications for greenhouse gas (GHG) mitigation strategies. This study evaluated the net ecosystem carbon balance (NECB) of an agricultural field in an organic integrated crop–livestock system (ICLS) with a ley-arable rotation in northern Germany over two years (2021–2023). Carbon dioxide (CO₂) fluxes were measured using the eddy covariance (EC) method to derive net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (RECO). This approach facilitated an assessment of the temporal dynamics of CO₂ exchange, alongside detailed monitoring of field-based C imports, exports, and management activities, of a crop sequence including grass-clover (GC) ley, spring wheat (SW), and a cover crop (CC). The GC ley acted as a consistent C sink (NECB: −1386 kg C ha⁻¹), driven by prolonged photosynthetic activity and moderate biomass removal. In contrast, the SW, despite high GPP, became a net source of C (NECB: 120 kg C ha⁻¹) due to substantial export via harvest. The CC contributed to C uptake during the winter period. However, cumulatively, it acted as a net CO₂ source, likely due to drought conditions following soil cultivation and CC sowing. Soil cultivation events contributed to short-term CO₂ pulses, with their magnitude modulated by soil water content (SWC) and soil temperature (TS). Overall, the site functioned as a net C sink, with an average NECB of −702 kg C ha⁻¹ yr⁻¹. This underscores the climate mitigation potential of management practices such as GC ley systems under moderate grazing, spring soil cultivation, and the application of organic fertilizers. To optimize CC benefits, their use should be combined with reduced soil disturbance during sowing or establishment as an understory. Additionally, C exports via harvests could be offset by retaining greater amounts of harvest residues onsite. Full article

Terpenoids, as a class of natural products with extensive biological activities, hold broad application prospects in the fields of medicine, food, materials, and energy, with the global market scale projected to reach USD 10 billion by 2030. Traditional chemical synthesis and plant extraction methods rely on petroleum and plant resources, suffering from problems such as environmental pollution, cumbersome procedures, low yields from plant sources, enantioselectivity, geographical constraints, and competition for resources. Biocatalytic conversion of biomass feedstocks via microbial cell factories serves as an environmentally friendly alternative for the synthesis of terpenoids, but current production mostly depends on starch-based glucose, which triggers issues of food security and competition for arable land and water resources. This review focuses on the biocatalytic conversion of non-food alternative carbon sources (namely lignocellulose, acetate, glycerol, and waste oils) in the microbial synthesis of terpenoids, systematically summarizing the current research status and cutting-edge advances. These carbon sources exhibit potential for sustainable production due to their low cost, wide availability, and ability to reduce resource competition, but they also face significant technical bottlenecks. We systematically analyze the current problems in the biocatalytic conversion process and put forward some available solutions. It is hoped that this study will provide theoretical and technical suggestions for breaking through the bottlenecks in the biocatalytic conversion of non-food carbon sources and promoting the efficient and sustainable production of terpenoids. Full article

Understanding the long-term impact of agricultural practices on soil parameters is essential for improving soil quality and sustainability. Soil Organic Carbon (SOC) and total Nitrogen (N) are key indicators due to their influence on crop productivity, nutrient cycling, and microbial activity. This study assesses the effects of tillage intensity (inversion vs. non-inversion) and organic amendments (manure vs. no manure) on SOC and total N dynamics in Mediterranean rain-fed arable systems. Data were collected over a ten-year field trial (2011–2020) in Catalonia, under cereal–legume rotation and organic management, focusing on two soil depths (0–10 and 10–20 cm). Fertilization was the main driver of SOC and N changes. Non-inversion tillage promoted topsoil accumulation and microbial colonization, especially during the first period (2011–2015). The combination of manure and reduced tillage led to faster and greater SOC increases. Moreover, initial SOC levels were negatively related to SOC changes in the topsoil. These results revealed the combination of manure and non-inversion tillage as the more suitable management practice to preserve soil quality in organic arable rain-fed systems, emphasizing the importance of understanding the impact of agricultural management in the long-term under Mediterranean conditions. Full article

Global agriculture is confronting unprecedented pressures from population growth, diminishing arable land, and severe rural labor scarcity, necessitating the advancement of intelligent agricultural equipment. As a core component of precision farming, unmanned agricultural tractors demand highly accurate and robust path tracking control. However, conventional methods often fail to cope with unstructured terrain and dynamic wheel slip under real field conditions. This paper proposes an extended state observer (ESO)-based improved Stanley guidance law, which incorporates real-time sideslip angle observation, adaptive preview-based path curvature compensation, and a sliding mode heading controller. The ESO estimates lateral slip caused by varying soil conditions, while the modified Stanley law utilizes look-ahead path information to proactively adjust the desired heading angle during high-curvature turns. Both co-simulation in Matlab-Carsim and field experiments demonstrate that the proposed method significantly reduces lateral tracking error and overshoot, outperforming classical algorithms such as fuzzy Stanley and sliding mode controller, especially in U-turn scenarios and under low-adhesion conditions. Full article

Sustainable nitrogen (N) management in arable crops requires the real-time assessment of crop growth and N uptake, particularly in water-limited environments. In the present study, we conducted two large-scale field experiments with rainfed and irrigated wheat in South-East Turkey to evaluate the effectiveness of drone- and satellite-based spectral indices, in combination with neural network models, for estimating biomass and nitrogen uptake. Four N fertilizer rates in the irrigated fields (N₀: 0, N₆: 60, N₁₂: 120, and N₁₆: 160 kg N ha⁻¹) and five N rates in the rainfed fields (N₀: 0, N₂: 20, N₄: 40, N₅: 50, and N₆: 60 kg N ha⁻¹) were tested. Highest fresh biomass was 57.7 ± 1.1 and 15.9 ± 1.0 t/ha⁻¹ for irrigated and rainfed treatments, respectively, with 2.5-fold higher grain yield in irrigated (8.2 ± 1.2 t/ha⁻¹) compared to rainfed (2.9 ± 0.9 t/ha⁻¹) wheat. Drone-based spectral indices, especially those based on the red-edge region (CL_{Red_edge}), correlated strongly with biomass (R² > 0.9 in irrigated wheat) but failed to explain crop N concentration throughout the vegetation period. This limitation was attributed to the nitrogen dilution effect, where increasing biomass during crop growth leads to a decline in the concentration of nitrogen, complicating its accurate estimation via remote sensing. To address this, we employed a two-layer feed-forward neural network model and used SPAD and plant height values as supplementary input parameters to enhance estimations based on vegetation indices. This approach substantially enhanced the predictions of N uptake (R² up to 0.95), while even simplified model version using only NDVI and plant height parameters achieved significant performance (R² = 0.84). Overall, our results showed that spectral indices are reliable predictors of biomass but insufficient for estimating nitrogen concentration or uptake. Integrating indices with complementary crop traits in nonlinear models provides acceptable estimates of N uptake, supporting more precise fertilizer management and sustainable wheat production under water-limited conditions. Full article

Soil salinity and sodicity are escalating global threats to agricultural productivity, severely limiting crop yield and quality. In the Igdir Plain of Türkiye, high summer temperatures, minimal precipitation, and a shallow groundwater table have intensified salinity-related challenges, currently affecting one-third of the arable land. Despite the substantial impact of salinity stress on eggplant (Solanum melongena L.) production, studies addressing plant tolerance mechanisms under real field conditions remain limited. In this study, eggplant was cultivated in eight distinct soil classes under open-field conditions to evaluate the effects of soil salinity and saline-alkalinity on morphological, physiological, and biochemical traits. Increasing soil exchangeable sodium percentage (ESP) and electrical conductivity (ECe) levels significantly suppressed plant height, root length, stem diameter, and leaf area, along with over 90% reductions in shoot and root biomass. Salinity impaired the uptake of essential nutrients (Ca, K, P, and Fe), while promoting toxic Na⁺ accumulation in leaves. This ionic imbalance induced oxidative stress, as indicated by elevated malondialdehyde (MDA), hydrogen peroxide (H₂O₂), and antioxidant enzyme activities (SOD, CAT, APX), all of which were strongly correlated with proline accumulation. The results highlight a coordinated plant response under salinity stress but also reveal the insufficiency of natural defense mechanisms under high salinity levels. Unless supported by external interventions to improve stress resilience and ensure productivity, growing eggplant in saline–alkaline soils should be avoided. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

This study investigates the spatial heterogeneity of carbon pools in young Betula sp. stands on former arable lands in the southern Moscow region, Russia. The findings could be useful for the current estimates and predictions of the carbon balance in such forest ecosystems. The research focuses on understanding the interactions between plant cover and the environment, i.e., how environmental factors such as stand density, tree diameter and height, light conditions, and soil properties affect ecosystem carbon pools. We also studied how heterogeneity in edaphic conditions affects the formation of plant cover, particularly tree regeneration and the development of ground layer vegetation. Field measurements were conducted on a permanent 50 × 50 m sampling plot divided into 5 × 5 m subplots, in order to capture variability in vegetation and soil characteristics. Key findings reveal significant differences in carbon stocks across subplots with varying stand densities and light conditions. This highlights the role of the spatial heterogeneity of soil properties and vegetation cover in carbon sequestration. The study demonstrates the feasibility of indirect estimation of carbon stocks using stand parameters (density, height, and diameter), with results that closely match direct measurements. The total ecosystem carbon stock was estimated at 80.47 t ha⁻¹, with the soil contribution exceeding that of living biomass and dead organic matter. This research emphasizes the importance of accounting for spatial heterogeneity in carbon assessments of post-agricultural ecosystems, providing a methodological framework for future studies. Full article

Tillage is a major factor influencing soil biological communities, particularly earthworms, which play a key role in soil structure and nutrient cycling. To address soil degradation, less-intensive tillage practices are increasingly being adopted globally and have shown positive effects on earthworm populations when applied consistently over extended periods. However, understanding of the earthworm population dynamics in the period following the implementation of changes in tillage practices remains limited. This three-year field study (2021–2023) investigates earthworm populations during the early transition phase (4–6 years) following the conversion from conventional ploughing to conservation (<8 cm depth, with residue retention) and no-tillage systems in a temperate arable system in central Slovenia. Earthworms were sampled annually in early October from three adjacent fields, each following the same three-year crop rotation (maize—winter cereal + cover crop—soybeans), using a combination of hand-sorting and allyl isothiocyanate (AITC) extraction. Results showed that reduced tillage practices significantly increased both earthworm biomass and abundance compared to conventional ploughing. However, a significant interaction between tillage and year was observed, with a sharp decline in earthworm abundance and mass in 2022, likely driven by a combination of 2022 summer tillage prior to cover crop sowing and extreme drought conditions. Juvenile earthworms were especially affected, with their proportion decreasing from 62% to 34% in ploughed plots and from 63% to 26% in conservation tillage plots. Despite interannual fluctuations, no-till showed the lowest variability in earthworm population. Long-term monitoring is essential to disentangle management and environmental effects and to inform resilient soil management strategies. Full article