Search Results (61,624)

This study presents a unified semantic segmentation framework for UAV-based multispectral crop stress mapping, focusing on the integration of water stress and rust disease conditions within a common label space. Unlike conventional approaches that address individual stress factors independently, the proposed framework harmonizes heterogeneous datasets with different annotation schemes into a single multi-class segmentation problem. To achieve this, UAV multispectral orthomosaics are processed using a patch-based strategy and a multi-head UNet++ architecture incorporating segmentation, edge-aware, and Signed Distance Transform (SDT) branches. In addition, a physics-informed output-space refinement module based on fractional partial differential equations (FPDE) is introduced to enhance spatial coherence and boundary preservation in the predicted maps. Experimental results demonstrate the effectiveness of the proposed framework within the evaluated dataset setting, particularly in terms of boundary delineation, spatial consistency, and minority-class detection. The study highlights the feasibility of integrating heterogeneous stress conditions into a unified segmentation framework and provides a foundation for future research on scalable multi-source agricultural monitoring systems. Full article

This study explores the relationships among climate change, food security, economic growth, and migration in the nine countries with the lowest rankings on the Notre Dame Global Adaptation Initiative (ND-GAIN) Index. It identifies the most vulnerable countries to climate change and the least prepared, using panel data from 1999 to 2022. The results show a bidirectional causal relationship between climate change and food security. Climate change worsens food insecurity by reducing agricultural productivity, which in turn drives up food prices. Conversely, agricultural policies aimed at increasing production can contribute to climate change if implemented unsustainably. A bidirectional causal relationship has been identified between climate change, food security, and migration. Finally, a bidirectional causal relationship has also been determined between economic growth, climate change, and migration. Changes in economic growth affect sectors, the labor market, and overall well-being, which in turn influence migration decisions. All these findings provide policymakers with valuable guidance for developing sustainable strategies that consider climate change, effectively manage migration, and prioritize food security. The findings indicate that climate change, food security, economic growth, and migration cannot be addressed in isolation; therefore, a holistic policy approach should be adopted. Full article

Invasive species, whose geographic distribution is expanding, seeing introduction and establishment in previously pest-free areas, have major environmental and economic impacts. The problem of invasive pests is multidimensional and complex and can only be tackled through strong integration and the use of various approaches [1]. Climate change, intense human mobility, and increased international and transcontinental trading have brought biological invasions to the forefront of the list of threats to agricultural production worldwide. Full article

The steno-endemic Verbascum rupicola faces a precarious future due to its extreme habitat specialization on tectonically active hydrothermal quartz veins. This study presents a long-term assessment based on periodic population censuses spanning 18 years (2007, 2016, and 2025) to assess the demographic and spatial trends of its global population in the Tahtalı Dam basin, Türkiye. Field surveys, GIS-based habitat mapping, and controlled pollination experiments were integrated with seed germination kinetics and ex situ cultivation trials. Results reveal a precipitous 69.12% global population decline, primarily driven by a 33.41% habitat loss from agricultural expansion in 2011 and the total extirpation of three sub-populations by a major wildfire in 2017. Furthermore, a “reproductive squeeze” was identified, where climate-induced reductions in flower production (18.87%) are compounded by intensifying floral predation by Pieris rapae. Reproductive analysis revealed random monomorphic enantiostyly—reported for the first time in the genus—which functions as a flexible mating system prioritizing outcrossing while providing reproductive assurance. Despite high intrinsic seed viability (69.12%), ex situ cultivation largely failed (3.5% survival; 1 out of 28 transplanted individuals), underscoring the species’ obligate chasmophytic nature. Consequently, V. rupicola meets the criteria for Critically Endangered (CR) status, necessitating urgent “micro-reserve” protection of its remaining habitat and in situ restoration efforts. Full article

Reliable short-term agroclimatic forecasting is essential for precision agriculture, irrigation planning, disease-risk assessment, and microclimatic decision support. However, field-deployed sensor systems often operate under degraded data conditions, including missing measurements, noise, temporal interruptions, and limited local computational resources. These constraints make it necessary to evaluate not only forecasting accuracy under clean data, but also model robustness under realistic sensor-data degradation. The objective of this study is to compare machine-learning models for one-step-ahead agroclimatic time-series forecasting under degraded sensor-data conditions. Using a real meteorological dataset collected by a field weather station in the Dnipro region of Ukraine, twelve regression models were evaluated: Ridge Regression, Random Forest, Extra Trees, Gradient Boosting, HistGradientBoosting, Support Vector Regression, Linear SVR, KNN, PLSRegression, ElasticNet, Lasso, and MultiTaskElasticNet. The models were tested under five controlled scenarios: baseline data, missing values, additive noise, reduced training history, and combined noise–missingness degradation. Quantitatively, Ridge Regression achieved the strongest baseline temperature-forecasting performance, with MAE = 0.318 and R² ≈ 0.98 under clean data. It also maintained R² > 0.90 when trained on only 50% of the available history. Under Gaussian noise with σ = 0.05–0.10, Ridge Regression and HistGradientBoosting maintained R² values in the range of 0.95–0.97, whereas under combined degradation with σ = 0.10 and 20% missing data, HistGradientBoosting retained R² > 0.85. These findings indicate that machine-learning models differ substantially in their sensitivity to sensor-data degradation and that robustness-oriented benchmarking is necessary before selecting models for agroclimatic forecasting systems. Full article

Accurate detection and efficient loss assessment serve as critical technical foundations for disaster evaluation by agricultural insurance providers. However, existing detection methods often face limitations such as low detection accuracy and high missed detection rates when dealing with small-scale sunburn lesions in the trellis cultivation environment of kiwifruit, due to unclear texture features and severe canopy obstruction. This study proposes the YOLO-ST-OD model, an improved version of YOLOv11s, for detecting sunburned kiwifruit fruits with small targets in different complex environments. By dynamically adjusting the receptive field via the LSKNet module to achieve the precise detection of key features and suppression of background noise, and by coordinating with the multi-branch spatial and channel enhancement mechanism of the MCSEAM module to effectively compensate for feature loss caused by overlapping leaves or fruits, the system utilizes the RFAMPS module to fuse sub-pixel convolution with dynamic attention for high-fidelity spatial reconstruction. The experimental results show that the precision rate (P) of the YOLO-ST-OD model reaches 0.862, the mean average precision (mAP) reaches 0.837, and the recall rate reaches 0.818. Compared with mainstream models such as YOLOv5s, YOLOv7, YOLOv8s, YOLOv9, YOLOv10s and Faster CNN, it has better comprehensive performance in terms of precision, mAP and floating-point computation. Compared with the baseline model YOLOv11s, which achieved accuracy of 0.813, mAP of 0.752 and recall of 0.792, the YOLO-ST-OD model saw improvements of 6.03%, 8.78% and 5.68% in average accuracy, recall and mAP, respectively. The experimental results also demonstrated the robust performance of YOLO-ST-OD across varying levels of occlusion, fruit densities and imaging altitudes. This research can provide technical support for the rapid assessment of sunscald damage to kiwifruit, enabling faster post-disaster assessments and reducing costs for insurers. Full article

This study investigated the effects of different straw return practices—no-tillage with straw mulching (SM), shallow tillage with straw incorporation (SS), and deep tillage with straw incorporation (DS)—on the content and structural characteristics of soil water-soluble organic carbon (WSOC) under a maize–soybean rotation in the black soil region in the Northeast of China. Compared with SM, SS and DS increased WSOC content by 39.0% and 28.8% in the 0~20 cm layer (p < 0.05), and by 28.4% and 8.5% in the 20–40 cm layer, respectively. Deep tillage combined with straw return reduced the WSOC/SOC ratio. The DS treatment exhibited the highest levels under maize straw incorporation, while SM treatment showed the highest levels under soybean straw incorporation. Spectral indices in both maize and soybean seasons—including the fluorescence index (FI, ranging from 1.53 to 1.57 in the maize season and from 1.53 to 1.67 in the soybean season), biological index (BIX, ranging from 0.84 to 1.79 in the maize season and from 0.61 to 0.74 in the soybean season), and humification index (HIX, ranging from 0.51 to 0.79 in the maize season and from 0.84 to 0.97 in the soybean season)—collectively indicated that WSOC predominantly consisted of microbially processed organic matter with a low degree of humification. PARAFAC modeling resolved two fluorescent components in maize season: C1 (humic acid-like substances, accounting for 34.8–54.9%) and C2 (Tryptophan-like substance, accounting for 45.1–65.2%), and two components in the soybean season: C1 (humic-like substances, 51.0–53.7%), and C2 (Fulvic acid-like substance 46.3–49.0%). Overall, deep straw return promotes soil humification but increases the structural complexity of WSOC. This systematic investigation provides mechanistic insights into how straw return practices regulate the quantity and quality of labile carbon pools in agricultural ecosystems over time. Full article

Land-use planning in ecologically sensitive landscapes requires balancing biodiversity conservation, ecosystem service provision, agricultural production, settlement expansion, and infrastructure demand within a single spatial system. This challenge is particularly significant in Mediterranean environments, where long-term land transformations and increasing development pressures intensify conflicts among competing land-use priorities. Accordingly, the present study develops an integrated spatial zoning and decision-support framework for Osmaniye Province, southern Türkiye. The framework integrates fuzzy multi-criteria evaluation, CatBoost-based machine learning, SHAP-based interpretability, and NSGA-II multi-objective optimization. The workflow followed a sequential decision process in which an expert-derived zoning surface was first established through fuzzy evaluation, reconstructed from continuous spatial predictors using CatBoost, interpreted through SHAP, and refined through NSGA-II under explicit spatial constraints. By using the expert-derived zoning surface as the learning target, the CatBoost stage aimed to evaluate the internal consistency and spatial learnability of the planning logic within a present-day zoning context. The results indicated that the integrated framework distinguished conservation, controlled-use, and development priorities while identifying the key environmental and anthropogenic drivers shaping class-specific zoning outcomes. The final zoning structure allocated 37.9% of the study area to conservation, 43.6% to controlled use, and 18.5% to development. The study shows that by including a transitional zone with varying proportions of conservation, controlled use, and development, a more balanced distribution among the three goals can be achieved compared to a fixed partition into these three zones. The findings further demonstrate that this approach is more effective than current zoning, which does not accommodate such trade-offs. Full article

Glyphosate [N-(phosphonomethyl)glycine] is the most widely used herbicide worldwide, and its environmental persistence has prompted increasing interest in microbial processes that may contribute to its dissipation. This study evaluated a collection of 15 soil-derived actinobacterial strains for plant growth-promoting traits, extracellular enzymatic activities, glyphosate tolerance, and glyphosate removal under nutrient-sufficient and phosphate-starved conditions. Herbicide tolerance evaluated on agar plates was widespread across the collection, with all strains sustaining growth at 10 and 50 g L⁻¹ of glyphosate. Under nutrient-sufficient conditions glyphosate removal remained limited, with maximum values of 16.15 ± 2.08% (Streptomyces sp. Con7.16) and 15.34 ± 2.89% (Streptomyces sp. Z38). In contrast, prior phosphate starvation markedly enhanced removal efficiency, reaching 42.21 ± 3.59% in Streptomyces sp. Z38 and 39.46 ± 1.94% in Streptomyces sp. Con7.16. Transmission electron microscopy coupled with X-ray microanalysis in the selected Streptomyces sp. Z38 revealed starvation-associated depletion of intracellular polyphosphate granules, followed by partial replenishment when glyphosate was supplied as the sole phosphorus source, consistent with indirect evidence of glyphosate-derived phosphorus acquisition. Genome mining of Streptomyces sp. Z38 identified candidate genes potentially consistent with a non-canonical, C-P lyase-independent phosphonate utilization route; however, these assignments are based exclusively on bioinformatic evidence and require experimental validation. Collectively, these findings indicate that phosphate limitation enhances glyphosate removal in the selected actinobacteria, and the physiological and genomic data are consistent with a starvation-triggered shift toward alternative phosphorus scavenging strategies. Because this strain is intended for future phytoremediation applications in glyphosate-contaminated agricultural soils, elucidating the underlying phosphorus dynamics is essential for anticipating its functional behavior and environmental relevance. Full article

This scoping review maps 224 empirical studies (205 from a structured Scopus search, 2020–2026, plus 19 from a targeted Central Asia supplement) across four digital technology domains for crop production: IoT and sensor-based systems, UAVs and remote sensing, machine learning and AI, and nanostructured agrochemicals. The review follows the PRISMA-ScR framework and pursues three research questions concerning documented effects and validation limitations (RQ1); cross-cutting barriers in human capital, data governance, and infrastructure (RQ2); and the state of empirical evidence from Central Asia and Kazakhstan relative to international findings (RQ3). Across all four domains, the strongest reported effects occur where the data-to-decision-to-action loop is closed and sustained over multiple seasons, yet most published metrics rest on single-season, single-site, or controlled-environment validation that overstates likely field portability. IoT and selected UAV and ML workflows are closest to operational readiness where maintenance, calibration, and advisory support are sustained. Nanostructured materials remain the least mature domain in agronomic terms. For Central Asia, foundational monitoring and salinity-oriented remote sensing are the most immediately transferable elements; intervention-grade ML and integrated digital systems require local calibration, extension infrastructure, and multi-season field validation that are largely still absent. The review identifies the digital skills gap, incomplete data governance, and underreported total cost of ownership as the principal institutional barriers to scaling. Policy priorities include shifting from technical pilots to multi-season agronomic proof, building intermediary service capacity, and establishing transparent data-governance frameworks before large-scale procurement. Full article

Green and blue-green infrastructures are key for reducing the effects of urban heat islands driven by rapid city expansion. However, the spatial relationship between land-cover patterns and air-temperature distribution, plus the combined cooling effects of green and blue spaces, remains insufficiently explored. This study applies the InVEST Urban Cooling Model to analyze the spatiotemporal changes in land use and their impact on the heat-mitigation service provided by green and blue spaces in the city of Arequipa, Peru, between 2006 and 2024. Furthermore, land-use change is projected for 2030 using the CA-Markov model and the InVEST Scenario Generator tool. These projections enabled the evaluation of two heat-mitigation scenarios by modifying the spatial distribution of green, blue-green, and urbanized areas. The findings indicate that urbanized areas doubled over the measurement period. The greatest loss of agricultural land and tree-covered areas occurred between 2020 and 2024, with a decline of up to 5%. Correspondingly, the percentage of low heat mitigation index areas (0.1–0.2 and ≤0.1) increased by 3.8%, reaching a total increase of up to 6.7%. Scenario simulations showed that reducing both green and blue-green infrastructure had similar impacts on the heat-mitigation index, providing valuable insights for urban planning and environmental management. Full article

As climate change worsens, irrigation modernization has become critical for better water distribution and maintaining rice production in the face of increasing water constraints. However, there remains a gap in quantification regarding the environmental trade-offs between pump-managed and gravity-based irrigation systems, especially in integrated assessments that relate economic performance, carbon emissions, and water use. This study used an integrated framework of water productivity (WP), consumptive water footprint (WF), carbon footprint, and eco-efficiency to compare gravity-based and pump-managed systems in the Don Chedi Operation and Maintenance Project, Thailand, from 2021 to 2023. The results showed no significant differences in WP and WF between systems. WP averaged 0.39 kg m⁻³ during the wet seasons and 0.54 kg m⁻³ during the dry seasons, while the WF averaged 2517 m³ t⁻¹ and 1854 m³ t⁻¹, respectively. These findings indicate that pump-managed irrigation enhanced operational flexibility and yield stability but did not substantially improve water use efficiency. However, compared with the gravity-based system, the pump-managed system produced much greater carbon emissions, with total carbon footprints ranging from 1.252 to 1.333 tCO₂eq t⁻¹, or five times higher in the irrigation process. Eco-efficiency metrics rose by up to 8.11% despite this environmental burden, indicating enhanced economic resilience amid fluctuating water conditions. These results show a recurring trade-off between low-carbon agricultural development and irrigation modernization. The study therefore emphasizes the importance of integrating renewable energy and low-carbon technologies into pump-based irrigation systems to support climate-resilient and sustainable agricultural transitions. Full article

This article examines the context and aims of We Feed the UK: a multi-site series of arts projects and exhibitions, organised by the Gaia Foundation, that were exhibited at venues across the United Kingdom from February 2024 to June 2025. These aims were to celebrate and advocate for diverse regenerative food production businesses and community initiatives through poetry and photography. The featured enterprises combine food production with objectives such as biodiversity renewal, community development, mental health support and social justice, and the article proposes that this combination of environmental advocacy and affective social issues appeals to a wide and diverse audience. It supports this proposal through an examination of the first photography project in the series: Johannes Pretorius’s Intervention and Renewal, that engaged with a Cumbrian dairy farm that successfully combines biodiversity regeneration, organic agriculture and educational initiatives. Drawing upon Actor–Network Theory and notions of time as they pertain to the photograph, this examination reveals a project that offers both familiar imagery of British pastoral tropes, and the contemporary realities of the British food production system. As such it offers multiple points of engagement for audiences, and an effective entry point for the We Feed the UK programme. Full article

Cotton is one of the world’s important cash crops and occupies a significant position in agricultural production and the national economy. However, insect pests severely affect the growth, yield and quality of cotton. To ensure high and stable cotton yields, the cultivation of insect-resistant transgenic cotton via transgenic technology can not only effectively reduce the impact of chemical pesticides on crops but also exert excellent control effects against pests such as cotton bollworms. In this study, the plant expression vector pC2300-VEC harboring the target genes epsps, cry1Ac and vip3A was introduced into the genome of the recipient cotton cultivar CCRI 24 via Agrobacterium-mediated transformation. The obtained transgenic cotton plants were subjected to the identification of target genes and target traits, and the insect-resistant transgenic cotton line BrsC35 was ultimately obtained. PacBio sequencing combined with conventional molecular characterization methods was used to analyze its insertion site, copy number and other characteristics, providing a new germplasm for insect-resistant transgenic cotton. Full article

As one of the key water systems in Bosnia and Herzegovina, the Bosna River Basin plays a vital role in sustaining agricultural production, industrial development, and water supply for municipalities. Accurate streamflow forecasting is fundamental to optimising water resource planning. This study explores streamflow forecasting using long-term data (1961–2020) from five meteorological stations and one hydrological station distributed across various sections of the basin. For precise streamflow forecasting, the study employs several machine-learning models: Random Forest, LSTM, and XGBoost. Model performance is evaluated using widely used metrics, including mean absolute error, root mean square error, Nash–Sutcliffe efficiency (NSE), and Kling–Gupta efficiency (KGE). Among the tested models, Random Forest proved to be the most accurate for streamflow forecasting, confirming its effectiveness in capturing the complex dynamics of hydrological processes. During the testing phase, the Random Forest model achieved an NSE of 0.591 and a KGE of 0.591, demonstrating good generalisation and reliable predictions. The results demonstrate the strength of Random Forest in capturing nonlinear hydrological patterns and supporting reliable streamflow forecasting for national water management. Moreover, as a novel approach, explainable AI was applied using SHAP analysis to go beyond the regular predictions of the models, thereby providing a deeper understanding of the model’s performance described by the magnitude and direction of influence of each problem feature. Full article