Search Results (69,763)

The behavior of phosphorus (P) fertilizers in soil is governed not only by fertilizer solubility, but also by P mobility and vertical redistribution within the soil profile under contrasting water regime. This study aimed to investigate the combined effects of water regime, fertilizer type, and soil properties on the vertical redistribution of ammonium acetate–lactate extractable phosphorus (P-AL) in the surface soil layer under controlled pot conditions. Experiments were conducted using three soils with contrasting chemical properties: AC-LO (acidic loam, pH 5.9), NE-CL (neutral clay loam, pH 6.8), and AL-SL (alkaline sandy loam, pH 8.0). Four simulated rainfall regimes were applied at a constant rate of 25 mm day⁻¹, corresponding to cumulative water inputs of 0 mm (W0), 50 mm (W50), 100 mm (W100), and 150 mm (W150). Fertilizer treatments included an unfertilized control (NF), a liquid NP 4–18 fertilizer applied at a low dose (L1), a liquid NP 4–18 fertilizer applied at a high dose (L2), and a solid NPK 15–15–15 fertilizer (S). Water regime exerted the strongest control on P mobility, with P-AL increasing by approximately 40–60% from W0 to W150, depending on soil type. In AC-LO, strong P fixation under low moisture minimized differences among fertilizer treatments, whereas under higher moisture (W100–W150), liquid fertilizers—particularly L2—resulted in P-AL levels approximately 10–30% higher than those of the solid fertilizer. In NE-CL, P mobility was moderate and, under W100–W150, L2 produced P-AL values approximately 10–15% higher than the solid fertilizer, promoting a more uniform P redistribution within the 2–8 cm layer. In AL-SL, the response under wet conditions depended on the water regime: at W100, L2 generated P-AL values comparable to the solid fertilizer, whereas at W150, L2 increased P-AL by approximately 11% relative to the solid form. Overall, the results indicate that soil chemical properties primarily regulate the extent of phosphorus redistribution, while water regime controls its intensity and fertilizer form influences the initial spatial configuration of P within the surface soil layer. The findings provide mechanistic insight into short-range phosphorus transport in soil, without allowing direct inferences regarding agronomic efficiency or crop response. Full article

Rhododendron arboreum Sm., an ecologically and culturally important Himalayan tree species and a key species in Himalayan forests, is increasingly threatened by forest degradation, climate change, and habitat fragmentation. However, previous studies have mainly focused on predicting climatic suitability, with limited integration of field-based population ecology and future climate projections, particularly in the western Himalayas. Therefore, the current investigation integrates population ecology and species distribution modeling (MaxEnt model) under CMIP6 climate scenarios (2070 and 2090) to identify climatically suitable and ecologically viable habitats for long-term species persistence across Himachal Pradesh, using 95 occurrence points and seven environmental predictors. Field data confirmed R. arboreum as a dominant species, strongly associated with Quercus leucotrichophora and Cedrus deodara. Habitat suitability was primarily driven by temperature seasonality (58.6%) and precipitation seasonality (14.8%), with 4508 km² currently suitable. Future projections forecast a distinct upshift but with high uncertainty regarding total area; projections ranged from potential habitat expansion under optimistic models (BCC-CSM2-MR) to significant contraction under pessimistic models (IPSL-CM6A-LR). Overall, findings prioritize climatically stable refugia (Kalatop-Khajjiar, Chail, and Churdhar wildlife sanctuary) not only for ecological monitoring but also as critical areas for developing socio-ecological management strategies to support community-based conservation and livelihood adaptation. Full article

Drought and soil nutrient deficiency are critical constraints on plant growth and ecological restoration in desert steppes; however, the interactive mechanisms between arbuscular mycorrhizal fungi (AMF) and phosphorus fertilization remain poorly elucidated. To investigate the regulatory mechanisms governing root system architecture (RSA) remodeling and regrowth capability in Agropyron under drought stress, a controlled experiment was conducted using two genotypes: Inner Mongolia (NM) and Xinjiang (XJ). The experimental design comprised three water regimes (70%, 50%, and 30% field capacity [FC]), two P levels (P0, P1), and two inoculation treatments (A0, A1). The results indicated the following: (1) Although drought significantly inhibited Agropyron growth, the combined application of AMF and P (A1P1) induced a highly significant synergistic effect, augmenting total aboveground biomass by 66.08–160.58% compared to the control. This synergy exhibited distinct “environmental dependency,” being most pronounced under moderate drought conditions (50% FC). (2) Mechanistic analysis revealed that A1P1 optimized RSA by significantly increasing total root length, root surface area, and root volume (e.g., total root length increased by 281.4–375.1% under severe stress), thereby enhancing water and nutrient acquisition. (3) The A1P1 treatment significantly mitigated the decline in regrowth potential induced by successive clipping, sustaining a higher tiller number (increasing by up to 1.8-fold in the 3rd clipping). (4) The XJ genotype was characterized by higher basal biomass and root investment “high-yield phenotype”, whereas the NM genotype demonstrated greater sensitivity to AMF-P regulation “highly responsive phenotype”. In conclusion, the synergistic interaction between AMF and P mitigates drought stress by reshaping RSA and enhancing regrowth capability, providing a theoretical basis for the efficient management of arid grasslands. Full article

Peach tree (Prunus persica L. Batsch) is a fruit species of great economic importance worldwide. Thousands of chemical leaf analyses are performed on a yearly basis to support decision-making about fertilizer application. However, traditional methods to determine nutrient content in plant tissue require a mix of strong acids, besides being time-consuming and generating polluting waste. Visible (Vis) and near-infrared (NIR) spectroscopy combined with multivariate techniques emerges as a potential solution to overcome limitations of traditional chemical analyses. The aim of the present study is to combine Vis-NIR spectral data and multivariate techniques to test strategies for the development of models to estimate nutrient content in peach leaves. The study estimated N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn content in the leaves of peach trees grown in two locations, namely: Pelotas and Pinto Bandeira, in Southern Brazil. Therefore, local and regional scale prediction models were developed by combining preprocessed Vis-NIR spectral data to both Savitzky–Golay first-derivative (SGD1d) and partial least squares regression (PLSR) multivariate technique. Most of the proposed prediction models showed average accuracy (R² ≥ 0.50 and <0.75, RPIQ ≥ 1.9 and <3.0). The local-1 ‘PB’ model showed higher nutrient prediction accuracy than the regional ‘PB + Pelotas’ model and the local-2 ‘Pelotas’ model. Estimates on nutrient content in peach tree leaves subjected to local, local-1 ‘PB’ and local-2 ‘Pelotas’ models fed with data collected in the same site showed better performance than calculations based on data from other sites and/or regions. Finally, the current study allowed making updates in the refinement of more sustainable techniques to set nutrient content. Full article

Soil salinization is a widespread form of land degradation that severely constrains agricultural productivity and ecosystem stability. Efficient and transferable monitoring methods are therefore essential for large-scale salinization assessment. Remote sensing provides timely and synoptic observations, while the integration of multi-source datasets offers complementary spectral and spatial information. In this study, we developed a cross-platform spectral index specifically for soda saline–alkali (carbonate/bicarbonate-dominated) soils by integrating laboratory spectra and hyperspectral satellite observations through a collaborative, cross-dataset spectral feature selection framework. Dual-band spectral indices were constructed from transformed reflectance spectra, and a stepwise coupled correlation analysis was applied to identify representative candidates that consistently exhibited strong associations with log-transformed soil electrical conductivity (logEC) across datasets. An optimal central-wavelength analysis was then performed to determine a stable and transferable band pair. The study was conducted in the Songnen Plain of Northeast China using laboratory-measured soil spectra and Ziyuan-1 02D Advanced Hyperspectral Imager data, and the proposed index was further validated using Landsat-8 and Sentinel-2 Multispectral data. Results show that the proposed Difference Index based on Square Root Reflectance at 520 nm and 900 nm (DISRR520900) exhibited consistent relationships with logEC (R = 0.60 for hyperspectral satellite data and R = 0.82 for laboratory spectral data), outperforming commonly used salinity indices in terms of cross-sensor stability. The spatial distribution of soil salinization derived from DISRR520900 is highly consistent with true-color imagery, and multi-source data fusion further improves mapping continuity and spatial coverage. It should be noted that the proposed index is primarily applicable to bare or sparsely vegetated soil surfaces in soda saline–alkali regions. Under dense vegetation cover, substantial crop residue, or wet surface conditions, additional masking or correction may be required. These results demonstrate that DISRR520900 provides a stable cross-sensor solution for large-scale soil salinization mapping within comparable soil chemical contexts. Full article

The modern development of the energy and metallurgy industries is accompanied by the increasing use of coal in the form of fuel and raw material. However, at the same time, urgent issues are arising concerning assessments of its radiological and environmental safety. Coal and ashes accumulate natural radionuclides (such as thorium, uranium, and potassium-40), and toxic and rare earth elements (REEs) that are capable of migrating into the environment during the processes of production, burning and ash disposal. Special attention has recently been paid to rare earth elements that are of economic value as critical metals for sophisticated technologies, but these can pose environmental risks. Their presence in coal is becoming an increasingly relevant issue for cross-disciplinary research, at the intersection of geochemistry, radioecology and the sustainable use of natural resources. Moreover, issues regarding the radiological safety of coal deposits and their derivative products are especially crucial for Kazakhstan, Russia, China and other countries with developed coal production industries. Studies demonstrate that ash and slag of thermal power plants can comprise increased concentrations of natural radionuclides that can accumulate in soil, water and the environment. Therefore, the study of rare earth, toxic and radioactive element contents in coal using nuclear analytical methods is of high practical and environmental significance, especially in terms of assessing radiation load on the environment, designing control measures and ash disposal, and the prospect of the selective extraction of REEs from the coals. Full article

Model tests are effective for studying the entire deformation and evolution process of reservoir landslides. The sensitivity of similar materials to seepage effects is crucial to the accuracy of landslide model testing. Based on a fuzzy evaluation of in situ sliding zone soil, this study compared three similar materials, using shear tests and microscopic SEM to assess the similarity. The optimal similar material (sliding zone soil: bentonite: standard sand = 50%: 20%: 30%) with a water content of 13.5% and a permeability coefficient of 3.8 × 10⁻⁶ cm/s was identified, simultaneously matching physical–mechanical properties and seepage effects. When the proportion of in situ sliding zone soil exceeds that of bentonite, the in situ sliding zone soil dominates the strength. Cohesion depends on interparticle cementation force and water film viscosity. Bentonite modifies these forces in stages, leading to a trend where cohesion (c′) first increases and then decreases with rising water content, while the internal friction angle (φ’) decreases continuously. Model test results indicate the failure mode of reservoir landslides is a three-stage traction-braking failure, evolving from initial shallow deformation to deep progressive failure and finally to overall large-scale instability. The proposed similar material exhibits reliable physical–mechanical and seepage similarity and can be directly applied in physical model tests of reservoir-induced landslides to reproduce the hydro-mechanical coupling behavior of sliding zones. Full article

Red soils suffer from nutrient imbalances and low-phosphorus availability. Rational intercropping plays an important role for increasing crop yield and improving nutrient use efficiency, while its long-term effects on biogeochemical cycles and ecological stoichiometric stability are poorly understood. Based on a 7-year continuous field experiment in low-phosphorus red soil, the soil enzyme activity, soil carbon (C), nitrogen (N), phosphorus (P) and C:N:P content, soil microbial biomass (MBC, MBN, MBP), and their ecological stoichiometric characteristics in maize monoculture (MM) and maize//soybean intercropping (MI) under four phosphate fertilization gradients (0, 60, 90, 120 kg P₂O₅ hm⁻²) were investigated. The impacts of continuous MI on soil CNP ecological stoichiometric stability in red soil were studied. The results showed that intercropping significantly elevated the content of soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and microbial biomass (MBC, MBN, MBP). Compared to maize monoculture, the contents of SOC, TN, and TP in intercropping soils increased by an average of 26.01%, 12.08%, and 7.58%, respectively, and soil MBC, MBN, and MBP increased by an average of 40.87%, 29.50%, and 38.34%, respectively, across different phosphate application gradients. Intercropping also significantly enhanced the activities of key C-, N-, and P-cycling enzymes (β-glucosidase, urease, acid phosphatase), increased by an average of 33.47%, 14.69%, and 60.15%, respectively. Most importantly, intercropping substantially improved the stoichiometric homeostasis of the microbial biomass and decreased the homeostasis index 1/H of MBC, MBN, MBP. Continuous intercropping shifted MBN from a sensitive to a strongly homeostatic state, MBP to homeostatic and the MBC/MBP ratio from weakly to strongly homeostatic in red soil. In conclusion, continuous MI in low-P red soil demonstrably increases soil nutrient content, enhances soil enzyme activity, and promotes ecological stoichiometric stability. This system represents one of the optimized cropping models for the synergistic enhancing of soil ecological stability in red soil regions. Full article

Replacing conventional chemical binders with natural polymers in geotechnically treated soil allows for the creation of more sustainable materials with both valuable ecological and mechanical properties. Xanthan gum and sodium alginate are natural polymers with excellent binding properties and water retention, which can help reduce carbon emissions. However, there is a lack of research on how to achieve optimal performance through the rational formulation of different biopolymers. This study investigates the use of these two natural biopolymers as binders (xanthan gum and sodium alginate) in slope-protection habitats treated with soil optimised using response surface methodology (RSM) within Design-Expert analysis software. The effects of xanthan gum concentration, sodium alginate concentration, and time, as well as their interactions on the properties of treated soil, ryegrass growth, and soil greenhouse gas emissions were evaluated, resulting in an optimized substrate formulation that balances good geotechnical properties with low environmental impact. Pot cultivation trials indicated that cohesion (c) and internal friction angle (φ) increased linearly with rising xanthan gum and sodium alginate concentrations, while the number of ryegrass plants (N_p) and root area ratio (RAR) decreased linearly with increasing binder concentration. Both CO₂ and CH₄ fluxes increased with rising binder concentrations. An analysis of variance (ANOVA) revealed that xanthan gum concentration had a stronger promoting effect on c and φ and a stronger inhibiting effect on N_p and RAR than sodium alginate. In contrast, sodium alginate concentration exhibited a stronger inhibitory effect on CO₂ and CH₄ fluxes. Through comprehensive optimization of geotechnical properties, vegetation growth, and greenhouse gas emissions, the optimal formulation was determined to be 0.885% for xanthan gum and 0.791% for alginate. The optimized composition resulted in increases of 38.6% and 19.1% for c and φ, respectively, while N_p and RAR increased by 7.7% and 15.0%, respectively. CO₂ and CH₄ fluxes decreased by 61.6% and 65.2%, respectively. This study contributes to advancing the sustainability of geotechnical treatments to favour vegetation regrowth. However, these materials will need to be further tested under field conditions to verify their effectiveness and duration. Full article

With increasing worldwide attention to green and sustainable energy, thermal infrared remote sensing technology has gained significant popularity for detecting geothermal anomalies, as it can overcome the limitations of traditional ground surveys. This study explores the potential application of thermal infrared images in geothermal exploration within the Datong Basin. We mainly utilized Landsat-8 images to obtain the actual land surface temperature (LST), hydrothermal alteration, and linear structures of the Datong Basin. Radiative transfer equation algorithm (RTE), principal component analysis (PCA), and interactive interpretation method were applied in this study. The results show that LST retrieval through the RTE method accurately reveals geothermal anomalies in the Datong Basin. Five areas with distinct high-LST values were identified as geothermal anomaly zones based on field investigation, including Xiejiatun, Gushancun, Taipingpu, Shuitongsi, and Wenjiayao–Yuanjialiang. Effective estimation of hydrothermal alteration zones (dominated by clays, OH⁻/H₂O, and carbonates) in the basin was achieved using the PCA method and band combinations. In total, 394 linear structures were obtained through interactive interpretation, including 45 concealed structures. All of these linear structures were associated with deep-seated faults. The basin’s primary controlling structures are the Yunmen Mountain piedmont fault (F1-1) and the northern margin of Xiong’er Mountain faults (F1-2 and F1-3), with F1-1 and F1-3 playing a key role in regional thermal regulation. The high-LST premium geothermal target zones of Shuitongsi and Gushancun were identified based on remote sensing interpretations and geothermal geological conditions. Furthermore, strong consistency was verified between the remote sensing predictions and four deep drilling temperature field measurements. This study confirms that remote sensing is an effective approach for geothermal potential identification, providing a scientific basis for future sustainable resource exploration in other regions. Full article

Soybean cyst nematode (SCN) is the most economically devastating pathogen of soybean in North America. Even at low to moderate infestation levels, SCN can cause 20–30% yield loss without producing any visible aboveground symptoms. In severely infested fields, yield reductions can reach 60–70% and, in extreme cases, exceed 80%. Prior research on identifying SCN infestations has primarily relied on traditional machine-learning methods applied to Unmanned Aerial System (UAS)-based multispectral imagery, with limited success. This study hypothesizes that deep-learning (DL) methods can more effectively capture the subtle spectral and spatial signatures in multispectral images of SCN stress. To address this gap, we evaluate the performance of advanced DL architectures, including Vision Transformer (ViT) and a customized Convolutional Neural Network (CNN), for detecting SCN infestation in soybean fields using multispectral UAS imagery. Spectral analysis of the multispectral imagery revealed that the near-infrared (NIR) band is a strong discriminator between non-detected and SCN-infested areas. The DL models trained and tested across multiple growth stages showed promising results. The four-timestamp ViT model (3 June, 29 July, 19 August, and 2 September) achieved an F1-score of 0.74, while the five-timestamp SCN–CNN model (3 June, 22 July, 29 July, 19 August, and 2 September) achieved an F1-score of 0.75. Although overall performance was comparable, ViT demonstrated more stable performance across varying training and test data distributions. These findings highlight the effectiveness of DL architectures to automatically extract subtle, complex plant features from multispectral imagery throughout the growing season. Compared with manual, time-consuming soil-sampling techniques, the proposed framework enables more precise spatial and temporal monitoring of SCN infestations across fields. Full article

Interactions among plant roots, soil, and microorganisms in the rhizosphere regulate nutrient cycling, plant health, and ecosystem resilience. Recent advances in DNA sequencing and multi-omics are contributing to a shift from primarily descriptive surveys toward more mechanistic and predictive frameworks. This review synthesizes methodological developments and conceptual insights spanning microbial ecology, functional genomics, and agricultural applications. We first summarize DNA-based approaches—marker-gene sequencing, shotgun metagenomics, and quantitative nucleic acid assays—and then complementary omics layers, including metatranscriptomics, metaproteomics, metabolomics, epigenomics, ionomics, and phenomics. We next outline computational advances in data integration, network modeling, and visualization that help represent complex multi-layered datasets as biologically interpretable systems. Applications relevant to climate resilience and sustainable agriculture are discussed, including the design of synthetic microbial communities, the identification of biomarkers for soil health and stress tolerance, and case studies in which rhizosphere multi-omics informs crop breeding and soil management strategies. Overall, these developments underscore the potential of treating microbes as functional and, to some extent, manageable components of the plant holobiont. Looking ahead, we identify key research gaps involving standardized workflows, cross-scale causal inference, and real-time monitoring pipelines that integrate molecular diagnostics with remote sensing and edge–cloud analytics. By linking ecological mechanisms with translational practice, multi-omics frameworks may support the development of more sustainable, data-driven agriculture that better aligns productivity with environmental stewardship. Full article

Accurate estimation of forest aboveground carbon (AGC) is crucial for understanding the carbon cycle and formulating climate policies, yet it remains challenging in complex mountainous regions. This study used machine learning framework to estimate the spatiotemporal dynamics of AGC in the Three Parallel Rivers region of China from 2003 to 2024. By integrating China’s National Forest Continuous Inventory (NFCI) data with multispectral satellite imagery, we employed a two-stage feature selection strategy to identify key predictor variables. Among three ensemble algorithms tested, the Random Forest model achieved the optimal performance (R² = 0.74). The results indicated a net increase of 67.05 Tg in total AGC over the two decades, with a spatial pattern characterized by higher densities in the west and north. Geographical Detector analysis revealed that the driving forces were synergistic, with the interaction between temperature and population density exhibiting the most prominent explanatory capacity. This study provides a high-resolution (30 m) benchmark for AGC in a global biodiversity hotspot and underscores the critical role of ecological protection policies in enhancing carbon sequestration, offering valuable insights for managing similar mountain ecosystems worldwide. Full article

The environmental degradation of plastics results not only in their mechanical fragmentation into microplastics (MPs), but also in polymer main-chain scission processes, causing continuous leaching and/or volatilization of low-molecular-weight species, often characterized by a hazardous profile. In this study, we investigated the hydrophilic photooxidation products (HyPOPs) generated upon UV irradiation of polystyrene (PS) and their transformation catalyzed by the enzyme laccase from the fungus Trametes versicolor. Through a series of enzymatic tests, the enzyme was found to promote coupling and conjugation reactions of HyPOPs into poorly soluble compounds mimicking natural humic acids. The enzymatic activity of laccase was studied under different experimental conditions to simulate those found in environmental matrices. Due to their oligomeric nature, these humic acid-like products of metabolic transformation by the fungal laccase are here nicknamed “mycoplastics” (i.e., polymers from fungi). This enzymatic biodegradation and biotransformation of xenobiotic HyPOPs highlights the role of specific enzymes as biological tools for environmental self-repair of polluted ecosystems. Moreover, it opens new perspectives for remediation strategies targeting elusive micro- and nanoplastics and their continuously generated hazardous molecular degradation by-products. Humic acid-like products resulting from laccase conversion of HyPOPs could contribute to the rehabilitation of contaminated sites by promoting the removal of toxic contaminants from soil and water. Full article