Search Results (1,739)

Effective and sustainable control strategies for pine wilt disease, caused by the pine wood nematode (Bursaphelenchus xylophilus), are urgently needed, as reliance on conventional chemical nematicides faces increasing limitations. In this study, a new kind of integrated approach is proposed. It pairs microbial fermentation filtrates with the green chemicals arecoline and sodium silicate. The filtrates were obtained from bacterial and fungal strains that were had isolated from Pinus massoniana rhizosphere soil. The nematicidal efficacy of individual and combined treatments was evaluated in vitro, while their ability to induce systemic resistance in P. massoniana seedlings was assessed through defense enzyme assays, malondialdehyde (MDA) content measurement, and defense-related gene expression analysis. Results identified several highly effective combinations, particularly arecoline plus CSZ33 and sodium silicate plus CSUFT-F23, which achieved over 72% control efficacy. These formulations not only showed direct toxicity but also significantly enhanced the plant’s antioxidant capacity and upregulated key defense genes. Furthermore, untargeted metabolomics linked these effects to specific bioactive metabolites in the fermentation filtrates, such as D-glutamic acid. This work demonstrates that hybrid bio-chemical formulations can successfully merge immediate pathogen suppression with long-term host resistance priming, offering a promising, sustainable strategy for the integrated management of pine wilt disease. Full article

High temperatures accelerate bolting and shorten the vegetative phase, thereby reducing the marketable yield in lettuce(Lactuca sativa L.). Using the KNOU lettuce core collection (KLC; n = 288), which represents major horticultural types, we integrated genome-wide association studies (GWAS) with genotyping-by-target-sequencing (GBTS), a multiplex target amplicon sequencing approach, to develop compact SNP marker panels for breeding-relevant prediction of reproductive timing. The KLC was genotyped via genotyping-by-sequencing (GBS; 97,528 SNPs) and phenotyped across two spring-to-summer seasons to analyze cumulative temperature to bolting (CTTB) and cumulative temperature to anthesis (CTTA) under protected cultivation conditions, revealing broad variation and high heritability (H = 0.79 and 0.74, respectively). Multi-model GWAS consistently identified a major hotspot on chromosome 7 for both traits, whereas additional loci showed trait- and year-specific effects. A lead SNP on chromosome 7 was validated by KASP, confirming a consistent allelic effect across genetic backgrounds. GWAS-supported loci were converted into compact GBTS panels (CTTB-only, CTTA-only, and pooled), and their ability to predict genomic estimated breeding values (GEBVs) was evaluated via repeated 5-fold cross-validation. The pooled panel achieved the highest predictive performance for CTTB (up to R² = 0.41 with random forest and R² = 0.37 with RR-BLUP), outperforming the CTTB-only panel. In contrast, CTTA prediction was more moderate (up to R² = 0.32). Overall, this GWAS-to-GBTS panel strategy provides a practical basis for low-cost, early selection of reproductive timing in lettuce breeding. Full article

Restoration-oriented forest management is increasingly recognized as an important strategy for enhancing long-term carbon sequestration and rehabilitating degraded peri-urban forest landscapes. This study presents a scenario-based assessment of projected carbon sequestration trajectories under a National Reserve Forest Project implemented in peri-urban Wuhan, central China. Thirteen silvicultural models were grouped into three management pathways: intensive plantation cultivation, transformation of existing degraded stands, and tending of young and middle-aged forests. Carbon sequestration was evaluated over a 40-year assessment period (2024–2063) using a Biomass Expansion Factor-based accounting framework incorporating above- and belowground biomass, harvested wood products, and conservative baseline deductions consistent with national and provincial methodologies. The results indicate a sustained long-term increase in projected carbon sequestration despite periodic short-term declines associated with planned thinning and harvesting cycles. Transformation-oriented pathways contributed the largest cumulative project-scale sequestration and generally exhibited relatively strong area-normalized sequestration performance compared with intensive plantation and tending pathways. Intensive plantation systems displayed greater temporal fluctuation associated with shorter rotation cycles and repeated harvesting events. The analysis also highlights the importance of distinguishing between area-normalized sequestration efficiency and cumulative project-scale contribution, as models with moderate per-hectare performance generated substantial total carbon benefits because of their larger implementation area. The findings suggest that restoration-oriented management of existing degraded stands may provide a relatively stable long-term carbon-sequestration pathway in peri-urban forest systems where land availability for large-scale afforestation is constrained. The study also demonstrates the applicability of conservative scenario-based accounting frameworks for restoration-oriented forest carbon assessment and planning under data-limited conditions. Full article

Non-grain use of cultivated land (NGUCL) in ecologically fragile regions has become a major challenge to food security and land sustainability, yet its spatiotemporal dynamics, spatial spillover effects, and associated factors remain insufficiently understood. Taking Ningxia, China, as a typical semi-arid to arid transition zone, this study developed a phenology-informed framework that combined multi-temporal Landsat imagery, random forest classification, spatial autocorrelation analysis, centroid and standard deviation ellipse models, and a spatial lag model to identify and analyze NGUCL in 2005, 2010, 2015, and 2020. Within the cultivated land boundary, NGUCL was further decomposed into cash crop-cultivated farmland (CCCF) and farmland abandonment (FA). The results show that the classification framework achieved robust performance, with overall accuracies above 85% across the benchmark years. Food-crop mapping reached an OA of 86.38–90.12% and a Kappa of 0.80–0.85, while FA mapping reached an OA of 85.60–86.74% and a Kappa of 0.70–0.72. NGUCL in Ningxia exhibited strong subregional differentiation under the gradients of northern irrigation, central arid, and southern mountainous conditions. CCCF was more closely associated with irrigated and agriculturally productive areas, whereas FA was concentrated in ecologically constrained counties and showed stronger dispersion and migration complexity. Spatial econometric results further indicate significant spatial spillover effects, suggesting that NGUCL-related processes in one county are associated with those in neighboring counties. The effects of natural, socioeconomic, and agricultural production factors also varied by type and period, indicating that NGUCL in ecologically fragile regions is not a homogeneous land-use transition process. By distinguishing CCCF from FA, this study provides a more nuanced interpretation of NGUCL and offers empirical evidence for understanding cultivated land transition and governance in ecologically fragile areas. Full article

Accurate mapping of winter rapeseed cultivation areas is crucial for food security assessment and agricultural resource management, yet remains a persistent challenge in mountainous regions characterized by complex topography and highly fragmented field parcels. To address these challenges, this study develops a progressive deep learning framework using single growing-season data from the Hanzhong Basin. We conducted a structured comparison of remote sensing indices, machine learning, and deep learning approaches for rapeseed identification in heterogeneous landscapes. First, sensitivity analysis of the Flowering Index for Rapeseed was performed to identify the optimal parameterization, yielding high inter-class separability (ND = 0.959) during peak flowering and a threshold-based overall accuracy (OA) of 94.41%. Second, a multidimensional feature space was constructed by integrating Sentinel-2 spectral bands, image texture metrics, and topographic variables; Random Forest-based feature importance selection subsequently enhanced Support Vector Machine classification performance to an OA of 90.70%. Third, we proposed an innovative three-stage progressive UNet++ architecture: Stage1 focuses on binary rapeseed/non-rapeseed classification to establish spatial priors; Stage2 refines discrimination among spectrally similar vegetation classes (rapeseed and other vegetation); and Stage3 achieves comprehensive seven-class semantic segmentation. A weighted focal loss function combined with a weight inheritance mechanism was employed to mitigate class imbalance and facilitate inter-stage knowledge transfer. The final model attained an OA of 98.65% and a mean intersection over union of 95.29%, while effectively suppressing salt-and-pepper noise artifacts in geometrically fragmented parcels. Our findings demonstrate the substantial advantages of progressive deep learning strategies for crop monitoring in topographically constrained environments. Full article

Medicinal plants grown outside their native forest habitat may produce phytochemical profiles that differ from wild-harvested material, yet the ecological mechanisms underlying these differences remain poorly synthesized across disciplines. This review proposes that the forest understory functions as a multi-signal elicitation system in which canopy light filtering, arbuscular mycorrhizal fungi (AMF), and above-ground biotic interactions collectively shape secondary metabolite profiles. AMF-mediated induced systemic resistance and above-ground biotic interactions operate through confirmed jasmonate-mediated pathways. Sunfleck-driven reactive oxygen species signaling is hypothesized but untested, and the red-to-far-red ratio modulated phytochrome B pathway characterized in Arabidopsis remains unconfirmed in shade-tolerant species. Using three saponin-rich medicinal plants (Panax vietnamensis, Panex quinquefolius, and Paris polyphylla) as case studies, we formalize this as a testable chemical terroir hypothesis with three falsifiable predictions. We also translate it into an ecological co-cultivation design principle with three production levels and a two-step operational framework, and identify priority experiments, analytical methods, and implementation challenges needed for validation. These contributions bridge forest ecology and medicinal plant science while identifying critical evidence gaps requiring resolution before field implementation. Full article

Background: The artificial cultivation of Dendropanax dentiger under forest understory conditions offers a sustainable alternative to wild harvesting, yet the metabolic adaptations underlying transplantation stress and recovery remain poorly understood. Objectives: In this study, we performed a comparative metabolomics analysis of different organs (leaves, current-year stems, three-year-old stems, and roots) from wild D. dentiger plants and those transplanted to the understory. Methods and Results: Metabolite annotation and classification revealed that over 60% of the metabolites fell into the categories of lipids and lipid-like molecules, organoheterocyclic compounds, phenylpropanoids, and polyketides. Further differential analysis of metabolites showed that understory transplantation significantly altered the metabolic profiles of all organs, exhibiting organ-specific response patterns. For the metabolite components in the organs of transplanted and wild D. dentiger, these metabolites were mainly classified into eight categories: alkaloids and derivatives; benzenoids; lignans, neolignans and related compounds; lipids and lipid-like molecules; organic acids and derivatives; organoheterocyclic compounds; phenylpropanoids and polyketides; and organic oxygen compounds. Notably, the contents of (-)-asarinin, (Z)-1-(methylthio)-5-phenyl-1-penten-3-yne, and stearidonic acid (SDA, 18:4n-3) were higher in transplanted plants than in wild plants, indicating the potential of understory cultivation for the targeted extraction of these bioactive compounds. Conclusion: These findings provide a metabolomics basis for optimizing the artificial cultivation and quality control of D. dentiger. This study highlights the value of metabolomics in understanding the metabolic composition of D. dentiger and offers a reference for its artificial cultivation. Full article

Bamboo is an ecologically and economically important forest resource in China, and understanding how climate change reshapes bamboo habitat suitability is essential for sustainable cultivation, introduction, and germplasm conservation. Bambusa emeiensis, an accepted bamboo species native to southern China and widely cultivated in southwestern China, has important management and utilization value, yet its future habitat dynamics and the stability of its highly suitable core habitats remain poorly understood. To address this gap, an ensemble species distribution modeling framework based on BIOMOD2 was used to predict the current and future suitable habitats of B. emeiensis under multiple climate scenarios, identify the dominant environmental constraints, and compare shifts between overall suitable habitat and highly suitable core habitat. The ensemble model showed high discrimination capacity under random cross-validation, but its transferability should be interpreted cautiously because occurrence records may be spatially autocorrelated and the projections remain correlative. Annual temperature range, elevation, and precipitation of the warmest quarter emerged as the strongest statistical predictors of distribution. Under the current climate, suitable habitats were concentrated in southwestern China, especially in the transitional zone spanning southern Sichuan, southwestern Chongqing, and northern Guizhou. Across all six future scenarios examined, the total suitable area declined relative to the current climate, with reductions ranging from about 25% under SSP3-7.0–2090s to more than 50% under SSP5-8.5–2050s, and highly suitable core habitat contracted even more strongly (by 41–95% across scenarios). In addition, centroid shifts of overall suitable habitat were not always synchronized with those of highly suitable core habitat, suggesting that climate change may reorganize not only habitat extent, but also the internal spatial arrangement of optimal environments. These findings indicate that the future management of B. emeiensis should prioritize the persistence, connectivity, and managed directional relocation of core habitats rather than relying solely on changes in total suitable area. Full article

Kiwifruit (Actinidia deliciosa cv. Hayward) is a high-demand crop due to its nutritional value. Climate change increasingly challenges its cultivation, particularly under Mediterranean conditions, due to limited water resources. Therefore, the early detection of water stress onset is crucial for optimizing irrigation water use and enhancing kiwi productivity. In this context, advanced sensors capable of continuously monitoring critical hydrodynamic parameters, combined with machine learning approaches, offer a promising solution for reliable prediction of plant water status, supporting irrigation decision-making systems. This study develops and evaluates machine learning (ML) models to predict trunk water potential (Ψtrunk), integrating soil moisture, climatic variables, and plant-based measurements, including sap flow. Various machine learning models were evaluated including Ridge Regression, Lasso Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), using soil moisture, trunk water potential (Ψtrunk), sap flow, and microclimatic variables (relative humidity, wind speed, temperature, solar radiation, vapor pressure deficit, and reference evapotranspiration). Among the tested models, XGBoost demonstrated the best performance, achieving an accuracy of approximately 0.80, followed by Ridge, Lasso and SVM, which showed similar accuracy. Full article

Accurate, high-resolution irrigation-related spatial information is paramount to diverse applications, including water resources management, food security, and agricultural planning. To address this need, our study leveraged machine learning algorithms and integrated multi-source data to extract and analyze land use types and spatiotemporal dynamics of irrigated farmland across provinces in the lower reaches of the Yellow River Basin over the 2008–2022 period. The results indicate that cultivated land remained dominant and largely stable, although localized losses occurred in peri-urban areas due to urban expansion. Construction land increased significantly, particularly in Shandong where it expanded by more than 15%, while forest and grassland areas grew under national ecological programs. The Random Forest (RF) algorithm achieved robust performance in identifying irrigated farmland, with overall accuracy exceeding 85% and regression with statistical irrigation data yielding R² values above 0.9 over the past 15 years at the city level. Spatiotemporal analysis showed strong variability in Henan, with irrigated area declining by 8–12% during drought years and recovering in wetter years, while Shandong experienced relative stability but a gradual 5% decline since 2015, driven by groundwater depletion and stricter regulation. The findings suggest irrigation expansion has reached near-saturation, given stable cultivated land and continuous improvements in water use efficiency. Future strategies should prioritize water use efficiency, water saving technologies, and equitable allocation to ensure sustainable agricultural development. Full article

Understanding how P availability affects root turnover and P redistribution within plants is essential for optimizing fertilization strategies and sustaining forest growth under low-P soils. This study evaluated the effects of P fertilization on root system dynamics, plant growth, and P nutrition of Handroanthus heptaphyllus, a flowering landscape tree, cultivated in a subtropical climate. Plants were grown under two soil P levels (low and high). Plant height, stem diameter, leaf P concentration, soil P availability, total numbers of living and dead fine roots, total fine root surface area, and fine root production rate were measured at 18, 24, 30, and 36 months after planting. Phosphate fertilization increased soil P availability during the first 24 months and resulted in significant gains in plant height, stem diameter, fine root production, total surface area, and the ratio between living and dead fine roots, indicating a higher proportion of living roots relative to dead ones. Under high P availability, the greatest fine root production and surface area of living fine roots occurred in the 0–20 cm soil layer, reflecting localized P application near the plants. High P availability enhanced root system development, promoted greater soil exploration, and improved P uptake. These results indicate that under P supplementation, plants strategically invest in root growth, improving nutrient acquisition efficiency and reducing dependence on external inputs. Increased phosphorus availability enhances root growth and increases fine root production and turnover. Minirhizotron monitoring effectively captured shifts in root system dynamics driven by P availability, including enhanced root growth, increased fine root production and turnover, and improved nutrient uptake under high P, as well as limited root activity under low P conditions, indicating a more conservative strategy with reduced investment in root production. Full article

Short-rotation sugar beet (Beta vulgaris L.) cultivation in the Western Forest-Steppe of Ukraine is often accompanied by increased phytopathogenic pressure and impaired rhizosphere functioning, creating a need for biological tools to stabilize the plant–soil system. This study evaluated the effects of arbuscular mycorrhiza and an antagonistic microbial consortium on pathogen pressure, rhizosphere activity, yield, and technological quality of sugar beet under different crop rotations. Field experiments were conducted in 2023–2025 using a three-factor design that included rotation, mycorrhizal inoculation, and microbial inoculation. The highest phytopathogenic pressure was recorded in the maize–soybean–sugar beet rotation, where the cumulative frequency of dominant pathogens reached 94.0% and the root rot severity index in the control was 28.6%. Arbuscular mycorrhiza reduced disease development by 14.6–16.4%, whereas the antagonistic consortium reduced it by 25.6–27.9% relative to the control. Their combined application was most effective, decreasing root rot severity to 9.6–17.1% and increasing root colonization, available phosphorus, and dehydrogenase activity in the rhizosphere. The highest yield (80.5 t/ha) and sugar content (18.5%) were obtained in the soybean–winter wheat–sugar beet rotation under combined inoculation. AMF can improve phosphorus acquisition and mycorrhiza-induced tolerance, whereas antagonistic fungi can directly suppress soil-borne pathogens through competition, antibiosis, and mycoparasitism, their combined use may provide complementary protection in disease-conducive rotations. Overall, integrating arbuscular mycorrhiza with antagonistic microorganisms is a promising approach for reducing pathogen pressure and improving sugar beet performance in short-rotation systems. Full article

The rapid expansion of ski tourism in Northeast China has triggered extensive land use and land cover change (LULCC), yet the micro-scale spatial mechanisms linking historical land conversion to the accessibility of tourist services remain largely unquantified. This study addresses this gap by integrating annual 30 m CLCD land cover data with GIS network analysis of Points of Interest (POIs) around 30 major ski resorts (2018–2023). Specifically, it makes a novel distinction between the accessibility outcomes of construction-oriented and agriculture-oriented land transitions. Results indicate that while forest-to-construction conversion significantly predicts reduced travel distances to services (e.g., hotels: r = −0.532, p < 0.01), a distinct and previously unreported agri-tourism synergy emerges: forest-to-cropland conversion is positively associated with higher per capita tourist spending (r = 0.366, p < 0.05). This finding challenges the conventional zero-sum view of land use competition and suggests that cultivated landscapes can function as complementary tourism assets. These empirical patterns provide an evidence-based framework for integrated land-transport planning in emerging winter sports destinations. Full article

Cropping systems and agronomic practices play a critical role in regulating soil organic matter dynamics and carbon dioxide (CO₂) emissions, which are key components of the global carbon cycle and climate change mitigation. However, the combined effects of tillage practices and seasonal climatic variability on CO₂ fluxes in chernozem soils (chernozems, WRB classification; highly fertile, humus-rich soils typical of steppe regions) of Northern Kazakhstan remain insufficiently understood. The aim of this study was to quantify soil CO₂ emissions under conventional tillage, no-till, and bare fallow systems during spring wheat cultivation on ordinary chernozems. Field experiments were conducted between 2023 and 2025 in the Kostanay Region (Kazakhstan). Soil CO₂ fluxes were measured using a chamber-based method, while soil temperature, moisture, and microbial community structure were monitored simultaneously. The results revealed pronounced seasonal and interannual variability in CO₂ emissions, ranging from 2 to 27 g CO₂·m⁻²·day⁻¹. Conventional tillage resulted in higher peak emissions due to increased soil aeration and accelerated organic matter mineralization, whereas no-till systems exhibited a more stable seasonal pattern and lower temperature sensitivity of soil respiration (Q₁₀ = 2.40 for no-till and 3.25 for conventional tillage). The application of machine learning techniques (Random Forest) significantly improved the prediction accuracy of CO₂ fluxes (R² = 0.67; RMSE = 3.37 g CO₂·m⁻²·day⁻¹) compared to linear models. These findings provide a scientific basis for the development of climate-smart agricultural practices aimed at improving carbon management in semi-arid steppe agroecosystems. Full article

Understanding the environmental drivers underlying the spatial heterogeneity of soil organic carbon (SOC) in mountainous regions remains a major challenge in digital soil mapping. This study investigated the spatial distribution and driving mechanisms of SOC contents in a typical subtropical mountainous area using an integrated modeling and interpretation framework based on 132 soil samples. The SOC content in Yangshan County ranged from 3.33 to 50.00 g kg⁻¹, with a coefficient of variation of 48.64%, indicating a moderate level of variability across the study area. Six mainstream modeling approaches were compared, including multiple linear regression (MLR), geographically weighted regression (GWR), Cubist, eXtreme Gradient Boosting (XGBoost), random forest (RF), and a hybrid RF-GWR model. The results showed that RF outperformed traditional linear methods and other machine learning approaches, achieving an R² of 0.45 and RMSE of 7.78 g kg⁻¹, while the hybrid model further improved prediction accuracy (R² = 0.48). Then, spatial mapping revealed a clear elevational gradient, with higher SOC values concentrated in forested mountainous areas in the north and lower values distributed across low-elevation cultivated and disturbed zones. SHAP analysis identified intrinsic soil properties, particularly total nitrogen (TN) and cation-exchange capacity (CEC), as dominant controls on SOC contents. When extended to prediction datasets, relative humidity (RH) and mean annual precipitation (MAP) showed greater importance on SOC, suggesting an amplification of climatic factors at the broader scale. Subsequently, hotspot analysis of GeoShapley components further revealed the spatial differentiations in group indicators, with overall contributions ranked as soil physicochemical properties (36.4%) > geographic conditions (21.1%) > climate (17.4%) > organisms (12.9%) > parent material (12.1%). Soil properties formed clustered hotspots overlaid on carbonate-dominated areas, while geographic conditions and climate primarily acted as spatial modulators, generating localized zones of intensified or weakened influence across the landscape. The integrated framework proposed in this study has potential applicability across broader regions. These findings provided a scientific basis for the localized interpretation of environmental drivers of SOC and offered valuable support for region-specific land management and sustainable decision-making. Full article