Search Results (4,280)

Cadmium (Cd) is a pervasive heavy metal pollutant in agricultural ecosystems, posing toxic threats to crop productivity, food safety, and the efficacy of biological control programs. The Asian corn borer (ACB), Ostrinia furnacalis is a destructive lepidopteran pest of maize in East Asia, while Trichogramma ostriniae serves as the most widely applied egg parasitoid for the sustainable management of this pest in integrated pest management (IPM) programs. However, the cascading effects of sublethal Cd stress transmitted from herbivorous hosts to egg parasitoids remain poorly understood. Here, we investigated the sublethal effects of dietary Cd exposure (5.0 mg/kg) on the development and Cd bioaccumulation of ACB, and subsequently evaluated the parasitism performance of the egg parasitoid T. ostriniae reared on Cd-contaminated host eggs. Results showed that chronic Cd exposure did not significantly affect larval weight, male pupal weight, or female pupal weight of ACB, but significantly increased female adult body weight, indicating stage-specific stimulatory effects. Cd concentrations in larvae, female pupae, and female adults of ACB were markedly elevated following dietary exposure, with the highest Cd loads detected in female pupae (41.23 mg/kg). However, maternal Cd transfer to eggs did not result in a statistically significant increase in egg Cd concentration. Notably, parasitism by T. ostriniae on Cd-contaminated ACB eggs significantly increased the number of eggs parasitized, although offspring emergence rate and female progeny ratio remained unaffected. This study fills the research gap regarding the trophic transfer of Cd between ACB and T. ostriniae, clarifies the complex bottom-up effects of Cd pollution on host–parasitoid interactions, and provides empirical references for the rational deployment of T. ostriniae as a biocontrol agent in Cd-contaminated maize fields. Full article

Reliable determination of macro- and micronutrients in cereal plant tissues is essential for agronomic management, environmental monitoring, and interlaboratory data comparability. However, most existing plant certified reference materials (CRMs) are derived from temperate-region matrices and do not adequately represent cereals cultivated under semi-arid conditions, where differences in mineral composition may lead to matrix-related analytical bias. In this study, a new multi-element plant reference material, Cereal Plant Powder 2023 (CPP-23), was developed from composite wheat (Triticum aestivum and T. durum) samples collected across major Moroccan agro-ecological zones. The material was processed, homogenized, and evaluated for homogeneity and stability in accordance with ISO 33405:2024, with no significant short- or long-term variability observed. Elemental characterization was performed using microwave-assisted acid digestion followed by ICP-OES for major and trace elements, while total nitrogen was determined using the Kjeldahl method. Method validation demonstrated satisfactory linearity (R² > 0.995), precision, and trueness against established reference materials. Certified values were assigned through an interlaboratory comparison involving eight ISO/IEC 17025-accredited laboratories using robust statistical estimators (ISO 13528:2022). Expanded uncertainties (k = 2) were below 15% for all analytes. While these results indicate acceptable internal consistency, the relatively limited number of participating laboratories and the absence of independent analytical validation techniques represent important constraints. CPP-23 provides a matrix-representative material suitable for quality control and method validation in semi-arid agricultural systems. Nevertheless, its ability to reduce analytical bias relative to existing CRMs and its applicability to specific use cases require further experimental validation. Full article

The agricultural sector is increasingly exposed to a complex set of climatic, economic, and institutional pressures, indicating that resilience assessment alone is insufficient; investments must also be strategically directed toward strengthening resilience. In response, this study proposes an integrated framework for assessing the resilience of crop farms and supporting the strategic screening and relative prioritization of investments at the regional level. The framework is applied to Romania, which is administratively organized into eight NUTS-2 (Nomenclature of Territorial Units for Statistics) development regions. The framework combines a Farm Resilience Composite Index (ICRF)—structured around five interconnected pillars (productive, economic, technological, ecological, and organizational)—with a Strategic Resilience Investment Score (SRIS), designed to translate resilience diagnostic outcomes into strategic screening and relative investment priorities. The methodology integrates normalized regional sub-indicators, expert-based weighting, and catch-up gap measures relative to the observed regional benchmark. It further incorporates complementarity and substitution relationships among sub-indicators, thereby capturing the systemic nature of resilience. Robustness is ensured through sensitivity analysis, Monte Carlo simulations, and a leave-one-region-out jackknife procedure. The results indicate that investment priorities are not driven solely by the magnitude of regional deficits. Instead, they emerge at the intersection of catch-up needs, systemic relevance, and the stability of outcomes under uncertainty. Across all regions, production stability, access to the agricultural knowledge and innovation system (AKIS), and farm-level digitalization consistently emerge as cross-regional priorities. Moreover, at least two of the three initial priorities are retained in 55 of the 56 leave-one-region-out comparisons, indicating a high degree of robustness. In contrast, ecological, economic, and organizational indicators exhibit greater territorial specificity. Overall, the ICRF–SRIS framework provides a transparent and analytically grounded tool for supporting place-based agricultural policy and guiding investment allocation in the context of post-2027 Common Agricultural Policy (CAP). Full article

Accurate monitoring of atmospheric ammonia (NH₃) is important for air-quality assessment and sustainable agriculture, but available datasets differ in spatial resolution, temporal coverage, units, and physical meaning. This study presents a feature-based machine-learning framework with train–test leakage-controlled preprocessing to evaluate relative NH₃ classification consistency across three datasets over China: CAMS GEI, CAMS EAC4, and MEIC. Statistical descriptors were extracted for four spatial–temporal cases: Province–Year, Zone–Year, Province–Season–Year, and Zone–Season–Year. Six classifiers were evaluated using chronological testing and cross-validation. CAMS GEI provided the broadest spatial–temporal coverage, while MEIC showed comparatively stable classifier behavior. The best-performing ensemble models commonly achieved accuracies between 0.97 and 0.99 in the main province-level cases. Mean Absolute Value (MAV) was the leading feature in several province-level analyses, with a maximum reported contribution of 64.26%. These scores describe the separability of threshold-derived reference classes and should not be interpreted as an independent physical prediction of NH₃ from external atmospheric drivers. Full article

Land-use conflicts in non-urbanized areas are fundamentally governance problems rather than purely environmental ones: sustainability outcomes increasingly depend on integrated, cross-sectoral decision-making reconciling forestry, agriculture, tourism, infrastructure, and urbanization within a finite land resource. This study examines whether statutory land acquisitions by a public forest administration can be understood as such a governance instrument rather than simply forest-area expansion, using an original transaction-level database of 911 land purchases by the Polish State Forests (Lasy Panstwowe) through statutory pre-emption rights between 2022 and mid-2026. The database covers 2806.1 hectares and, for the 903 transactions with a determinable price, approximately 114.2 million PLN. Using descriptive statistics, concentration indices (Gini, Herfindahl–Hirschman), an exploratory hedonic-style log–log regression of unit price on parcel area with location and year fixed effects, and a spatial-autocorrelation analysis (Moran’s I), the study examines spatial concentration, price differentiation, and parcel-size effects. Results show pronounced sub-regional concentration (county-level Gini = 0.615, more than double the voivodeship-level value of 0.284), a systematic price premium for parcels below 0.5 ha, and a dominant role of location over parcel size in explaining price variation (R-squared rising from 0.042 to 0.198 with location fixed effects); these are descriptive associations rather than causal estimates, given the absence of parcel-level quality covariates and a fully specified spatial–econometric model. The findings support interpreting statutory pre-emption purchases as a market-based institutional mechanism contributing to boundary rationalization, ownership consolidation, and mediation of competing land-use pressures, with implications for county-level monitoring and cross-sectoral coordination with spatial planning. Full article

Soil salinization and alkalization caused by global climate change have become major constraints on crop production worldwide. Quinoa (Chenopodium quinoa Willd.), owing to its exceptional adaptability to adverse environmental conditions, is considered a promising crop for saline–alkali agriculture. Successful screening and evaluation of saline–alkali-tolerant germplasm at the germination stage is essential for breeding quinoa varieties with enhanced tolerance to compound saline–alkali stress. In this study, 23 quinoa accessions originating from different regions were evaluated for saline–alkali tolerance by exposing seeds to compound saline–alkali solutions at different concentrations in a Petri dish germination assay. During the germination stage, relative vigor index, germination rate, germination energy, shoot length, root length, and fresh weight were determined, and saline–alkali tolerance was assessed using multivariate statistical analyses. The germination ability and seedling growth potential of quinoa germplasm gradually declined with increasing saline–alkali stress. Based on the comprehensive saline–alkali tolerance index at the germination stage, eight accessions were identified as highly tolerant and four accessions as highly sensitive to saline–alkali stress. Among them, LL1 and Z27 exhibited outstanding tolerance and were identified as elite saline–alkali-tolerant germplasm resources. The results indicated that vigor index and fresh weight at the germination stage can serve as key parameters for evaluating saline–alkali tolerance in quinoa germplasm. Overall, this study provides valuable germplasm resources and a practical basis for breeding new quinoa varieties with enhanced saline–alkali tolerance, as well as for the identification and utilization of stress-resistance-related genes in quinoa. Full article

Heavy-metal contamination in alpine agricultural watersheds reflects interacting geological, environmental, and anthropogenic controls. In this study, arsenic (As), copper (Cu), lead (Pb), zinc (Zn), and chromium (Cr) were investigated in 213 farmland soil and sediment samples collected from the Nyangqu River Basin during 2019–2021 and 2024–2025. Pollution status and ecological risks were evaluated using the Nemerow Integrated Pollution Index (Pn) and Håkanson Potential Ecological Risk Index (RI), while potential factors associated with spatial variation were explored using GeoDetector and an explainable machine-learning framework integrating XGBoost, SHAP, and LIME. Mean As, Cu, Zn, and Cr concentrations exceeded Tibetan soil background values, whereas mean Pb remained below background. Farmland soils exhibited higher concentrations of As, Pb, and Zn than sediments, whereas Cu displayed comparable levels between the two media. Among the investigated metals, As showed persistent enrichment, whereas Cr exhibited the greatest spatial variability and strongest local anomalies. Overall, slight pollution dominated the study area (69.0%; median Pn = 1.73), although several hotspots increased the mean Pn to 2.10, indicating moderate pollution at the regional scale. After applying coefficient-adjusted thresholds (23/133), the ecological risk index (RI) ranged from 16.45 to 54.24 (mean = 32.70), with 5.6%, 93.9%, and 0.5% of samples categorized as low, moderate, and considerable risk, respectively, and no samples exhibiting high risk. The associated environmental factors showed element-specific patterns, involving soil physicochemical conditions, geological background, and localized anthropogenic indicators. Notably, factor combinations generally showed greater explanatory power than individual covariates, suggesting stronger joint statistical associations with heavy-metal spatial differentiation. Full article

This study evaluated the technical viability and thermodynamic performance of an open-loop upcycled heat pump dryer, repurposed from a decommissioned window-type air conditioner, for thin-layer red dragon fruit (Selenicereus costaricensis) drying. Experiments were executed at 50 °C under controlled air velocities (0.5, 1.0, and 1.5 m/s), using open-sun drying as a control. Mathematical modeling revealed that the Wang and Singh model best described the drying process ($R^2$: 0.996368–0.999539). Accounting for volumetric shrinkage via equivalent average thickness (L_eq = 0.75 L₀), corrected effective moisture diffusivity (D_eff) ranged from 1.194 × 10⁻⁹ to 3.138 × 10⁻⁹ m²/s, while convective mass transfer coefficients ($h_m$) ranged from 5.241 × 10⁻⁷ to 1.880 × 10⁻⁶ m/s, both peaking at 1.5 m/s due to thinned concentration boundary layers. Thermodynamic assessments showed a maximum COP_hp of 3.41 at 1.0 m/s and a maximum SMER of 0.231 kg/kWh at 1.5 m/s. Individual statistical analysis of L*, a*, and b* parameters confirmed no statistically significant differences (p > 0.05) between heat-pump-dried and fresh samples. Concurrently, a remarkably low descriptive total color difference (ΔE = 1.24) was obtained, compared to open-sun drying (ΔE = 16.22), confirming high color retention. These findings highlight e-waste upcycling as an efficient and sustainable agricultural solution. Full article

Investigating the dynamic interplay between population shrinkage and water resource carrying capacity holds critical implications for safeguarding national food security and ecological resilience. This study systematically examines the underlying mechanisms and spatiotemporal evolution of their coupling relationship in Northeast China, thereby advancing the theoretical framework of human–land systems and delivering empirically grounded insights for sustainable regional development. Drawing on balanced panel data from 34 prefecture-level cities in Northeast China over the period 2010–2023, this study develops a population shrinkage index grounded in registered population dynamics. Building upon the DPSIR (driving forces–pressures–state–impacts–responses) conceptual framework, we construct a comprehensive evaluation system for agricultural water resource carrying capacity, explicitly operationalizing each of its five dimensions. Employing a spatial Durbin model (SDM), we rigorously estimate the direct effect of population shrinkage on carrying capacity, while simultaneously testing the mediating pathways through human capital accumulation and fiscal policy interventions. The empirical analysis reveals that (1) over the study period (2010–2023), population shrinkage in Northeast China intensified progressively, expanding spatially from initially localized pockets to widespread, regionally contiguous areas. Concurrently, agricultural water resource carrying capacity displayed pronounced spatial heterogeneity and statistically significant spatial clustering. (2) Overall, the degree of population decline significantly negatively affects agricultural water resource carrying capacity. Further heterogeneity tests showed that this negative effect exhibits significant variation across different population-contracted regions and provinces. Among the control variables, urbanization rate, per capita GDP, and total water resources availability exhibit statistically significant positive associations with agricultural water resource carrying capacity (3) Over the study period, higher human capital endowment and greater fiscal intervention intensity significantly attenuated the adverse effect of population shrinkage on agricultural water resource carrying capacity. Full article

The sustainability of biomass utilization as a fuel is commonly assessed through thermodynamic and ecological methods—energy, exergy, and emergy analyses—applied in isolation, each with only partial scope. Their integration through multicriteria analysis has been proposed for the sugar industry, but has not yet been formalized as a reproducible, auditable, and generalizable protocol: neither the logical sequence linking balances and decision-making, nor an explicit sustainability rule, nor the treatment of the incommensurability between thermodynamic and ecological accounting has been established. This work formalizes such a protocol in three stages: (i) definition of fuel alternatives and screening of criteria through the Delphi method; (ii) characterization of each alternative through three coupled balances—energy (quantity), exergy (quality), and emergy (environmental cost); and (iii) integration through the Analytic Hierarchy Process (AHP) into a single sustainability ranking with an explicit decision rule, supported by a robustness layer based on Monte Carlo simulation and multi-method comparison. The protocol is demonstrated in the Cuban sugar industry using two steam generators (G.V. VU-40 and Retal-type steam generator) and variants of bagasse, agricultural harvest residues (AHR), and marabou (Dichrostachys cinerea). In the demonstration, AHP weighting ranked the emergy criterion above the exergy and energy criteria (priority vectors 0.539, 0.297, and 0.164, respectively; consistency ratio 0.008), and the bagasse alternative emerged as the most sustainable in both technologies despite not being the most efficient. The robustness analysis confirmed that this verdict is stable: bagasse Pareto-dominates the independent emergy indicators and remains the best alternative in more than 95% of the weight space. The contribution of the work is methodological—the formalization and generalization of the protocol—while the case study illustrates its operation and does not constitute a statistical validation. Full article

Regenerative agriculture is increasingly promoted as a pathway towards sustainable food production, climate resilience, and the restoration of soil ecosystem functions. However, evidence on the actual uptake of regenerative practices and the factors influencing their adoption remains limited. This study compared the self-reported prevalence of 17 soil-health-oriented regenerative practices among farmers in Poland, Germany, and Belarus using a questionnaire survey conducted in 2025 (N = 150). The survey also examined farmers’ motivations, perceived barriers, knowledge sources, and definitions of regenerative agriculture. Adoption frequencies were assessed using a five-point Likert scale and analysed using non-parametric statistical methods. Several practices, including crop rotation and soil pH management, were widely implemented across all three countries and showed only slight variation. In contrast, more complex, system-based practices, such as agroforestry, biological soil monitoring, and crop–livestock integration, showed lower and more variable levels of adoption. Additional subgroup analyses were conducted to assess the robustness of the observed cross-country patterns. Although some associations weakened after stratification, many significant differences persisted. Across all countries, improving soil health was the primary motivation for adopting regenerative agriculture, whereas financial constraints and limited equipment access were the main barriers. Digital media served as the primary source of knowledge about regenerative agriculture across the surveyed countries, although in Belarus, peers and neighbours also represented a highly important source of information. Farmers in all three countries expressed a preference for online communication channels for further learning about regenerative agriculture; however, Polish and Belarusian farmers prefer social media, whereas German farmers preferred webinars and dedicated websites. In-person training sessions also attracted considerable interest among Polish and Belarusian farmers, but were the least preferred information source among German respondents. On the basis of these results, targeted investment support and direct financial incentives appear to be key priorities for promoting the further uptake of regenerative agriculture across all surveyed countries. However, communication and knowledge-transfer strategies are likely to require greater adaptation to country-specific preferences, although digital media are likely to represent the most effective primary channel for disseminating information on regenerative agriculture. Full article

High-resolution satellite imagery has substantially improved the monitoring of vegetation dynamics; however, the influence of spatial resolution and pixel aggregation on NDVI time series consistency remains insufficiently quantified, particularly across multiple native resolutions within a single sensor platform. This study evaluates how Sentinel-2 spatial resolutions (10 m, 20 m, and 60 m) and two pixel aggregation methods (pure-pixel and centroid) affect NDVI time series in 14,031 grassland plots across mainland Spain over the period 2018–2023. High-quality NDVI time series were selected using the Interpolation Efficiency Indicator (IEI), and discrepancies relative to a 10 m pure-pixel baseline were quantified through the Time Series Angle Distance (TSAD) and Root Mean Square Error (RMSE). A sensitivity check confirmed that the radiometric differences between Band 8 (10 m) and Band 8A (20/60 m) introduce negligible bias compared with genuine spatial-resolution effects. Formal non-parametric statistical testing—omnibus Kruskal–Wallis with epsilon-squared (${\epsilon }^2$) effect sizes and pairwise Cliff’s Delta comparisons—was applied to assess the magnitude and practical significance of the observed differences across plot area categories and Köppen climate groups (B, Cs, Cf). Results show that coarser resolutions (60 m) substantially reduce NDVI reliability, excluding more than half of the plots under the pure-pixel criterion and smoothing temporal variability, whereas 10 m and 20 m resolutions preserve most spectral and temporal information. The 20 m resolution introduces moderate but non-severe phenological distortion (median TSAD ≈ 0.05 rad, RMSE ≈ 0.026) with a 78% reduction in data volume and 72% reduction in processing time. The choice between pure-pixel and centroid sampling has negligible impact at 10–20 m but becomes relevant at 60 m, where pure-pixel selection reduces errors from spectral mixing at the cost of severe sample attrition. Parcel area strongly conditions the error metrics, with large effect sizes (${\epsilon }^2=0.273$) in the smallest plots, while Köppen climate classification decisively shapes TSAD (up to ${\epsilon }^2=0.447$), indicating that spatial degradation distorts phenological patterns differently across climate classes. These findings support a multi-scale monitoring strategy: 10 m for fragmented, heterogeneous grasslands (<3 ha), 20 m as a computationally efficient alternative for homogeneous areas (>10 ha), and outline potential implications for policy frameworks such as the Common Agricultural Policy (CAP). Full article

Band selection is a crucial step in hyperspectral imaging to reduce spectral redundancy and processing costs whilst retaining information useful for classification. Most existing approaches require the number of bands to be retained to be set manually or rely on parameters that are difficult to adjust. This work proposes the Clustering-Unified Schur complement for Diversity with Hierarchical Clustering (CUSD-HC). This fully unsupervised band selection method combines Ward’s hierarchical clustering with a greedy selection based on the Schur complement. Bands are grouped by spectral similarity, and then a representative band is chosen from each group to preserve diversity and informational content. The number of bands is determined automatically using a multi-detector k-fold criterion combined with an intrinsic dimension threshold estimated via PCA. Evaluated on six benchmark datasets using four classifiers (SVM-RBF, Random Forest, XGBoost, LightGBM), CUSD-HC achieves an average rank of between 2.7 and 3.3 among nine compared methods, placing it consistently among the leading group. The Nemenyi test shows no statistically significant difference between CUSD-HC and the top-ranked competitors, while CUSD-HC significantly outperforms the weakest baselines (p < 0.05); unlike the best-ranked alternatives, it reaches this level of performance without any manual selection of the number of bands, which is determined automatically from the data. An inter-scene transferability experiment on the WHU-Hi datasets shows a maximum degradation of 3.9 points in overall accuracy (OA), and the transferred bands even outperform the native selection in three cases out of six. Furthermore, the selected bands naturally cover the main spectral regions (visible, near-infrared, and SWIR), which facilitates the interpretation of results for applications such as precision agriculture and environmental monitoring. Full article

Global climate change has intensified drought risk, threatening water resources, agriculture, and ecosystems. This study evaluated four Moderate Resolution Imaging Spectroradiometer (MODIS)-based drought indices in Hebei Province: the evapotranspiration-to-potential-evapotranspiration-based Crop Water Stress Index (ET/PET-based CWSI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Temperature Vegetation Dryness Index (TVDI). During 2001–2020, the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the 1 km gridded soil-moisture product SMCI1.0, Taylor statistics, classification metrics, and typical drought events were used for multi-source consistency evaluation. Although the classification metrics for CWSI indicated moderate drought discrimination and relatively high false-alarm rates, the multi-source consistency evaluation showed that CWSI had the most stable overall performance among the four tested indices. Sen’s slope, the Mann–Kendall test, the Optimal Parameters-based Geographical Detector (OPGD), and random forest were used to examine trends and spatially associated factors. CWSI-derived drought intensity generally decreased, with severe and extreme drought areas decreasing and mild and moderate drought areas increasing. Severe-and-above drought was most prominent in spring. Temperature showed the strongest association with CWSI spatial differentiation, followed by elevation and socioeconomic variables. The interaction between temperature and soil type had the highest explanatory power. These results provide a reference for regional drought monitoring and water resource management. Full article

Dispersal mechanisms of phytopathogenic fungi and oomycetes are critical components of forest disease dynamics. While wind and water are well-studied pathways, the role of large forest herbivores as passive vectors remains significantly overlooked. This study quantifies and compares the pathogen loads carried on the hooves and hair of wild red deer (Cervus elaphus) to evaluate their epidemiological potential. Swab samples were collected from the hooves and hair of harvested deer in the Czerwony Bór Forest District, Poland. Quantitative PCR (qPCR) assays targeting the ITS1 region were deployed to detect total fungal DNA, Alternaria alternata, Fusarium avenaceum/F. tricinctum, and several Phytophthora species. A linear mixed-effects model was implemented to statistically evaluate variations in pathogen loads across anatomical sampling locations while controlling for individual animal variability. Fungal DNA was detected in 87.5% of hoof samples, showing significantly lower Ct values (13.85–18.54) compared to fur samples (17.02–29.56), which exhibited a more patchy distribution (p = 0.016). Similarly, A. alternata transfer was highly favored by hooves (p < 0.001). Conversely, F. avenaceum was more frequently detected on hair. Among oomycetes, Phytophthora pseudosyringae was detected in all sampled animals, whereas Phytophthora cactorum occurred rarely, and other tested Phytophthora species were not detected. Wild deer carry DNA of multiple fungal and oomycete pathogens and may act as potential passive carriers within forest ecosystems. Hooves constitute the primary vector for soil-borne pathogens due to sustained contact with topsoil, whereas hair facilitates the movement of specific canopy or airborne taxa. These findings suggest that wildlife movements should be considered in future forest biosecurity assessments for comprehensive forest health management and for understanding pathogen exchange between forest and agricultural ecosystems. Full article