Search Results (1,035)

Organic manure and grass mulching are widely recognized as modifiers of soil microbial communities and nutrient dynamics; however, the combined effects of these practices on nitrogen fractionation and microbial functionality in orchard ecosystems remain poorly understood. This study conducted a comprehensive evaluation of soil nitrogen fractions, enzymatic activity, microbial diversity and functional traits in walnut orchards under three management practices: organic manure (OM), grass mulching combined with manure (GM), and chemical fertilization (CF) in China’s Loess Plateau. The results revealed that OM and GM significantly enhanced soil nutrient pools, with GM elevating total nitrogen by 1.96-fold, soil organic carbon by 97.79%, ammonium nitrogen by 128%, and nitrate nitrogen by 54.56% relative to CF. Furthermore, the OM significantly increased the contents of total hydrolysable nitrogen, amino sugar nitrogen, amino acid nitrogen, ammonia nitrogen, hydrolysable unidentified nitrogen, non-acid-hydrolyzable nitrogen compared to the CF and GM treatments. Meanwhile, ASN and AN had significant effects on mineral and total nitrogen. The OM and GM had higher activities of leucine aminopeptidase enzymes (LAP), α-glucosidase enzyme, β-glucosidase enzyme (βG), and N-acetyl-β-D-glucosidase enzyme (NAG). Microbial community analysis revealed distinct responses to different treatments: OM and GM enhanced bacterial Shannon index, while suppressing fungal diversity, promoting the relative abundance of copiotrophic bacterial phyla such as Proteobacteria and Chloroflexi. Moreover, GM favored the enrichment of lignocellulose-degrading Ascomycota fungi. Functional annotation indicated that Chemoheterotrophy (43.54%) and Aerobic chemoheterotrophy (42.09%) were the dominant bacterial metabolic pathways. The OM significantly enhanced the abundance of fermentation-related genes. Additionally, fungal communities under the OM and GM showed an increased relative abundance of saprotrophic taxa, and a decrease in the relative abundances of potential animal and plant pathogenic taxa. The Random forest model further confirmed that βG, LAP, and NAG, as well as Basidiomycota, Mortierellomycota, and Ascomycota served as pivotal mediators of soil organic nitrogen fraction. Our findings demonstrated that combined organic amendments and grass mulching can enhance soil N retention capacity, microbial functional redundancy, and ecosystem stability in semi-arid orchards. These insights support the implementation of integrated organic management as a sustainable approach to enhance nutrient cycling and minimize environmental trade-offs in perennial fruit production systems. Full article

Cultivated land quality is critical for soil productivity and scientific fertilization. This study analyzed its evolution and impact on soil productivity across three ecological regions (southern, central, and northern Shanxi) in Shanxi Province, China, from 1998 to 2021). Using data from 8 long-term experimental sites (1998–2021) and 50 monitoring stations (2016–2021), we employed random forest analysis to evaluate temporal trends in key soil indicators. The results show the following: (1) Northern Shanxi exhibited the greatest improvement in soil fertility, with organic matter increasing by 98.2%, total nitrogen by 57.2%, available phosphorus by 131.7%, and available potassium by 17.1%. (2) Nitrogen fertilizer application increased across all regions, while phosphorus and potassium inputs generally declined. (3) Crop yields improved substantially—southern Shanxi wheat and maize increased by 15.3% and 20.9%, respectively, while central and northern Shanxi maize yields rose by 30.9% and 75.4%. Random forest models identified regional characteristics (40%), nitrogen fertilization (20%), and available phosphorus (18%) as primary influencing factors. Although cultivated land quality improved overall, soil fertility remained medium to low. Region-specific management strategies are recommended: rational nitrogen use in all regions; nitrogen control with phosphorus supplementation in the south; focused improvement of available phosphorus and potassium in the center; and increased organic fertilizer in the north. These measures support scientific nutrient management and sustainable agricultural production. Full article

Long-term greenhouse operations face a critical challenge in the form of soil quality degradation, yet the key intervention periods and underlying mechanisms of this process remain unclear. This study aims to quantify the effects of greenhouse lifespan on the evolution of soil quality and to identify critical periods for intervention. We conducted a systematic survey of greenhouse operations in a representative area of Yunnan Province, Southwest China, and adopted a space-for-time substitution design. Using open-field cultivation (OF) as the control, we sampled and analyzed soils from vegetable greenhouses with greenhouse lifespans of 2 years (G2), 5 years (G5), and 10 years (G10). The results showed that early-stage greenhouse operation (G2) significantly increased soil temperature (ST) by 8.38–19.93% and soil porosity (SP) by 16.21–56.26%, promoted nutrient accumulation and enhanced aggregate stability compared to OF. However, as the greenhouse lifespan increased, the soil aggregates gradually disintegrated, particle-size distribution shifted toward finer clay fractions, and pH changed from neutral to slightly alkaline, exacerbating nutrient imbalances. Compared with G2, G10 exhibited reductions in mean weight diameter (MWD) and soil organic matter (SOM) of 2.41–5.93% and 24.78–30.93%, respectively. Among greenhouses with different lifespans, G2 had the highest soil quality index (SQI), which declined significantly with extended operation; at depths of 0–20 cm and 20–40 cm, the SQI of G10 was 32.59% and 38.97% lower than that of G2, respectively (p < 0.05). Structural equation modeling (SEM) and random forest analysis indicated that the improvement in SQI during the early stage of greenhouse use was primarily attributed to the optimization of soil hydrothermal characteristics and pore structure. Notably, the 2–5 years was the critical stage of rapid decline in SQI, during which intensive water and fertilizer inputs reduced the explanatory power of soil nutrients for SQI. Under long-term continuous cropping, the reduction in MWD and SOM was the main reason for the decline in SQI. This study contributes to targeted soil management during the critical period for sustainable production of protected vegetables in southern China. Full article

Fungi in tropical ecosystems remain an understudied yet critical component of climate change mitigation, particularly within the Land Use, Land-Use Change, and Forestry (LULUCF) sector. This review highlights their dual role in reducing greenhouse gas (GHG) emissions by regulating carbon dioxide (CO₂), methane (CH₄), and nitrous oxides (N₂O) while enhancing long-term carbon sequestration. Mycorrhizal fungi are pivotal in maintaining soil integrity, facilitating nutrient cycling, and amplifying carbon storage capacity through symbiotic mechanisms. We synthesize how fungal symbiotic systems under LULUCF shape ecosystem networks and note that, in pristine ecosystems, these networks are resilient. We introduce the concept of Nature-based Culture (NbC) to describe symbiotic self-cultures sustaining ecosystem stability, biodiversity, and carbon sequestration. Case studies demonstrate how the NbC concept is applied in reforestation strategies such as AeroHydro Culture (AHC), the Integrated Mangrove Sowing System (IMSS), and the 4N approach (No Plastic, No Burning, No Chemical Fertilizer, Native Species). These approaches leverage mycorrhizal networks to improve restoration outcomes in peatlands, mangroves, and semi-arid regions while minimizing land disturbance and chemical inputs. Therefore, by bridging fungal ecology with LULUCF policy, this review advocates for a paradigm shift in forest management that integrates fungal symbioses to strengthen carbon storage, ecosystem resilience, and human well-being. Full article

Trees are vital to both environmental health and human well-being. They purify the air we breathe, support biodiversity by providing habitats for wildlife, prevent soil erosion to maintain fertile land, and supply wood for construction, fuel, and a multitude of essential products such as fruits, to name a few. Therefore, it is important to monitor and preserve them to protect the natural environment for future generations and ensure the sustainability of our planet. Remote sensing is the rapidly advancing and powerful tool that enables us to monitor and manage trees and forests efficiently and at large scale. Statistical methods, machine learning, and more recently deep learning are essential for analyzing the vast amounts of data collected, making data the fundamental component of these methodologies. The advancement of these methods goes hand in hand with the availability of sample data; therefore, a review study on available high-resolution aerial datasets of trees can help pave the way for further development of analytical methods in this field. This study aims to shed light on publicly available datasets by conducting a systematic search and filter and an in-depth analysis of them, including their alignment with the FAIR—findable, accessible, interoperable, and reusable—principles and the latest trends concerning applications for such datasets. Full article

In response to water scarcity and low agricultural water-use efficiency in arid regions in Northwest China, this study conducted field experiments in Wuwei, Gansu Province, from 2023 to 2024. It aimed to develop a water stress diagnosis model for spring maize to provide a scientific basis for precision irrigation and water management. In this work, two irrigation methods—plastic film-mulched drip irrigation (FD, where drip lines are laid on the soil surface and covered with film) and plastic film-mulched shallow-buried drip irrigation (MD, where drip lines are buried 3–7 cm below the surface under film)—were tested under five irrigation gradients. Multispectral UAV remote sensing data were collected from key growth stages (i.e., the jointing stage, the tasseling stage, and the grain filling stage). Then, vegetation indices were extracted, and the leaf water content (LWC) was retrieved. LWC inversion models were established using Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Different irrigation treatments significantly affected LWC in spring maize, with higher LWC under sufficient water supply. In the correlation analysis, plant height (hc) showed the strongest correlation with LWC under both MD and FD treatments, with R² values of −0.87 and −0.82, respectively. Among the models tested, the RF model under the MD treatment achieved the highest prediction accuracy (training set: R² = 0.98, RMSE = 0.01; test set: R² = 0.88, RMSE = 0.02), which can be attributed to its ability to capture complex nonlinear relationships and reduce multicollinearity. This study can provide theoretical support and practical pathways for precision irrigation and integrated water–fertilizer regulation in smart agriculture, boasting significant potential for broader application of such models. Full article

Non-point source pollution (NPS) from agriculture is a primary driver of water eutrophication, necessitating effective control for regional water ecological security and sustainable agricultural development. This study focuses on the Chenzhuang village watershed, a typical green agricultural demonstration area in Jiangsu Province, using the HYPE model to analyze hydrological processes and Total Nitrogen (TN) and Total Phosphorus (TP) migration patterns. The model achieved robust performance, with Nash–Sutcliffe Efficiency (NSE) values exceeding 0.7 for daily runoff and 0.35 for monthly TN and TP simulations, ensuring reliable predictions. A multi-scenario simulation framework evaluated the synergistic control effectiveness of Best Management Practices (BMPs), including agricultural production management, nutrient management, and landscape configuration, on TN and TP pollution. The results showed that crop rotation reduced annual average TN and TP concentrations by 11.8% and 13.6%, respectively, by shortening the fallow period. Substituting 50% of chemical fertilizers with organic fertilizers decreased TN by 50.5% (from 1.92 mg/L to 0.95 mg/L) and TP by 68.2% (from 0.22 mg/L to 0.07 mg/L). Converting 3% of farmland to forest enhanced pollutant interception, reducing TN by 4.14% and TP by 2.78%. The integrated BMP scenario (S13), combining these measures, achieved TN and TP concentrations of 0.63 mg/L and 0.046 mg/L, respectively, meeting Class II surface water standards since 2020. Economic analysis revealed an annual net income increase of approximately 15,000 CNY for a 50-acre plot. This was achieved through cost savings, increased crop value, and policy compensation. These findings validate a “source reduction–process interception” approach, providing a scalable management solution for NPS control in small rural watersheds while balancing environmental and economic benefits. Full article

Our study focused on the quantification of aboveground biomass stock and aboveground net primary productivity (ANPP) in young, planted beech (Fagus sylvatica L.). We selected 15 young even-aged stands targeting moderately fertile sites. Three rectangular plots were established within each stand, and all trees were annually measured for height and stem basal diameter from 2020 to 2024. For biomass modeling, we conducted destructive sampling of 111 beech trees. Each tree was separated into foliage and woody components, oven-dried, and weighed to determine dry mass. Allometric models were developed using these predictors: tree height, stem basal diameter, and their combination. Biomass accumulation was closely correlated with stand age, allowing us to scale tree-level models to stand-level predictions using age as a common predictor. Biomass stocks of both woody parts and foliage increased with stand age, reaching 48 Mg ha⁻¹ and 6 Mg ha⁻¹, respectively, at the age of 15 years. A comparative analysis indicated generally higher biomass in naturally regenerated stands, except for foliage at age 16, where planted stands caught up with the naturally regenerated ones. Our findings contribute to a better understanding of forest productivity dynamics and offer practical models for estimating carbon sequestration potential in managed forest ecosystems. Full article

Environmental conditions are increasingly recognized as important contributors to the emergence and spread of antimicrobial resistance (AMR), yet early detection of high-risk situations remains difficult. This study developed a data-driven framework to identify anomalous environmental profiles associated with potential AMR risk. Using an unsupervised anomaly detection method (Isolation Forest) applied to multivariate indicators—including pesticide use, land use change, precipitation, and crop type—we detected unusual environmental patterns without prior AMR data. The anomaly detection analysis highlighted pesticide use, population density, land use change, and fertilizer application as the dominant environmental factors, together explaining the largest share of variation in anomaly scores (each contributing around one-quarter to one-third of the model’s decisions). In the subset of anomalous cases, fertilizer and pesticide intensity exerted the strongest negative impact, confirming their role as key drivers of atypical environmental profiles. Extreme precipitation and crop-specific production patterns also emerged as influential in certain cases. These results show that our interpretable framework can both rank global drivers and reveal context-dependent risks, thereby enabling the development of early-warning strategies for AMR surveillance. Full article

Increasing demand for land and resources in Himalayan catchments is altering hydrological processes and threatening freshwater ecosystems. Sediment mobilization and nutrient fluxes, especially during monsoon rainfall events, are intensifying the degradation of water bodies. This study investigates land cover change and its effects on nutrient dynamics in the Phewa Lake catchment, Nepal. Landsat imagery from 1990 to 2021, processed through Google Earth Engine, was used to map land changes. Nutrient loading for the two time periods was estimated with the InVEST model. Surface soils were sampled across the catchment to analyze nitrogen and phosphorus distribution, while their particle-bound transport to the lake was assessed through riverbed sediments and the suspended sediments collected during monsoon rainfalls. Pre-monsoon water quality was examined to evaluate eutrophication levels across different lake zones. Results reveal forest recovery in the upper catchment, but agricultural land in the lower catchment is being rapidly converted to urban areas. While forest recovery has enhanced sediment retention, nutrient inputs to the lake, particularly nitrogen and phosphorus, have increased. Fertilizer leaching and untreated sewage emerge as key sources in rural and urban areas, respectively. Seasonal constraints of the dataset may underestimate the overall extent of water quality deterioration, as indicated by high nutrient loads in monsoon suspended sediments. Overall, this study highlights the dual effect of land cover change: forest regrowth coincides with rising nutrient discharge. Without timely interventions, growing urban populations in the region may face worsening water quality challenges. Full article

Soil erosion is a widespread problem leading to land degradation in many watersheds, including the Lato Basin, an Apulian permanent river that supplies water used for irrigation in many agricultural territories along the Ionian coast with considerable economic importance for crop production. The loss of fertile soil makes land less productive for agriculture; soil erosion decreases soil fertility, which can negatively affect crop yields. The present research aimed to determine soil loss (t/ha/year) in the Lato watershed in 2024, and then four ecosystem services—loss of carbon, habitat quality, crop productivity and sustainable tourism suitability—directly or indirectly linked to erosion, were defined and evaluated in monetary terms. These ecosystem service evaluations were made for the actual basin land use, and also for two hypothetical scenarios applying different afforestation strategies to the watershed. The first scenario envisages afforestation interventions in the areas with the highest erosion; the second scenario envisages afforestation interventions in the areas with medium erosion, cultivated with cereal crops. Each scenario was also used to evaluate the economic convenience and the effects of sustainable land management practices (e.g., reforestation) to reduce soil erosion and loss of ecosystem services. This study demonstrates that soil erosion is related to land use. It also underlines that reforestation reduces soil erosion and increases the value of ecosystem services. Furthermore, the economic analysis shows that crop productivity is the most incisive ecosystem service, as the lands with high productivity achieve higher economic values, making conversion to wooded areas economically disadvantageous if not supported with economic aid. The results of this study may help development of new management strategies for the Lato Basin, to be implemented through the distribution of community funds for rural development programs that consider the real economic productivity of each area through naturalistic engineering interventions. The reforestation measures need to be implemented over a long time frame to perform their functions; this requires relevant investments from the public sector due to cost management, requesting monetary compensation from EU funds for companies involved in forestation projects on highly productive areas that will bring benefits for the entire community. Full article

Soil organic carbon (SOC) constitutes the largest terrestrial carbon pool and plays a crucial role in climate regulation, soil fertility, and ecosystem functioning. Understanding its spatiotemporal dynamics is particularly important in semi-arid regions, where fragile environments and extensive ecological restoration may alter carbon cycling. The Loess Plateau, the world’s largest loess accumulation area with a history of severe erosion and large-scale vegetation restoration, provides a natural laboratory for examining how environmental gradients influence SOC storage over time. This study used a random forest model with multi-source environmental data to quantify soil organic carbon density (SOCD) dynamics in the 0–100 cm soil layer of the Loess Plateau from 2005 to 2020. SOCD showed strong spatial heterogeneity, decreasing from the humid southeast to the arid northwest. Over the 15-year period, total SOC storage increased from 4.84 to 5.23 Pg C (a 7.9% rise), while the annual sequestration rate declined from 0.046 to 0.020 kg·m⁻²·yr⁻¹, indicating that the regional carbon sink may be approaching saturation after two decades of restoration. Among soil types, Cambisols were the largest carbon pool, accounting for over 44% of total SOC storage. Vegetation productivity emerged as the dominant driver of SOC variability, with clay content as a secondary factor. These results indicate that although ecological restoration has substantially enhanced SOC storage, its marginal benefits are diminishing. Understanding the spatial and temporal patterns of SOC and their environmental drivers provides essential insights for evaluating long-term carbon sequestration potential and informing future land management strategies. Broader generalization requires multi-regional comparisons, long-term monitoring, and deeper soil investigations to capture ecosystem-scale carbon dynamics fully. Full article

Winter wheat is a major staple crop worldwide, and real-time monitoring of soil moisture content (SMC) is critical for yield security. Targeting the monitoring needs under arid conditions in southern Xinjiang, this study proposes a UAV multispectral-based SMC estimation method that constructs novel three-dimensional (3-D) texture indices. Field experiments were conducted over two consecutive growing seasons in Kunyu City, southern Xinjiang, China, with four irrigation and four fertilization levels. High-resolution multispectral imagery was acquired at the jointing stage using a UAV-mounted camera. From the imagery, conventional texture features were extracted, and six two-dimensional (2-D) and four 3-D texture indices were constructed. A correlation matrix approach was used to screen feature combinations significantly associated with SMC. Random forest (RF), partial least squares regression (PLSR), and back-propagation neural networks (BPNN) were then used to develop SMC models for three soil depths (0–20, 20–40, and 40–60 cm). Results showed that estimation accuracy for the shallow layer (0–20 cm) was markedly higher than for the middle and deep layers. Under single-source input, using 3-D texture indices (Combination 3) with RF achieved the best shallow-layer performance (validation R² = 0.827, RMSE = 0.534, MRE = 2.686%). With multi-source fusion inputs (Combination 7: texture features + 2-D texture indices + 3-D texture indices) combined with RF, shallow-layer SMC estimation further improved (R² = 0.890, RMSE = 0.395, MRE = 1.91%). Relative to models using only conventional texture features, fusion increased R² by approximately 11.4%, 11.7%, and 18.1% for the shallow, middle, and deep layers, respectively. The findings indicate that 3-D texture indices (e.g., DTTI), which integrate multi-band texture information, more comprehensively capture canopy spatial structure and are more sensitive to shallow-layer moisture dynamics. Multi-source fusion provides complementary information and substantially enhances model accuracy. The proposed approach offers a new pathway for accurate SMC monitoring in arid croplands and is of practical significance for remote sensing-based moisture estimation and precision irrigation. Full article

Advancements in unmanned aerial vehicle (UAV) multispectral systems offer robust technical support for the precise and efficient estimation of japonica rice yield in cold regions within the framework of precision agriculture. These innovations also present a viable alternative to conventional yield estimation methods. However, recent research suggests that reliance solely on vegetation indices (VIs) may result in inaccurate yield estimations due to variations in crop cultivars, growth stages, and environmental conditions. This study investigated six fertilization gradient experiments involving two conventional japonica rice varieties (KY131, SJ22) and two hybrid japonica rice varieties (CY31, TLY619) at Yanjiagang Farm in Heilongjiang Province during 2023. By integrating UAV multispectral data with machine learning techniques, this research aimed to derive critical phenotypic parameters of rice and estimate yield. This study was conducted in two phases: In the first phase, models for assessing phenotypic traits such as leaf area index (LAI), canopy cover (CC), plant height (PH), and above-ground biomass (AGB) were developed using remote sensing spectral indices and machine learning algorithms, including Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). In the second phase, plot yields for hybrid rice and conventional rice were predicted using key phenotypic parameters at critical growth stages through linear (Multiple Linear Regression, MLR) and nonlinear regression models (RF). The findings revealed that (1) Phenotypic traits at critical growth stages exhibited a strong correlation with rice yield, with correlation coefficients for LAI and CC exceeding 0.85 and (2) the accuracy of phenotypic trait evaluation using multispectral data was high, demonstrating practical applicability in production settings. Remarkably, the R² for CC based on the RF algorithm exceeded 0.9, while R² values for PH and AGB using the RF algorithm and for LAI using the XGBoost algorithm all surpassed 0.8. (3) Yield estimation performance was optimal at the heading (HD) stage, with the RF model achieving superior accuracy (R² = 0.86, RMSE = 0.59 t/ha) compared to other growth stages. These results underscore the immense potential of combining UAV multispectral data with machine learning techniques to enhance the accuracy of yield estimation for cold-region japonica rice. This innovative approach significantly supports optimized decision-making for farmers in precision agriculture and holds substantial practical value for rice yield estimation and the sustainable advancement of rice production. Full article

The biogenic organization of widespread valley pine ecosystems on sandy alluvium leads to an increase in soil fertility, productivity, and biodiversity through autogenic successions. Using our own stationary observations and literary data on the productivity of pine forests in Russia, Belarus, and Ukraine, we quantified the mechanism of autogenic forest successions associated with carbon sequestration and the influence of organic matter dynamics on the fertility and water retention of sandy soils. The low rate of organic matter turnover in primary succession leads to the intensive accumulation of thick (6–8 cm) forest litter and the formation of small humus-eluvial horizons with total carbon storage up to 50 Mg/ha. This soil structure retains 2–6 times more water and biophilic elements than in the original sandy alluvium. It is suitable for the settlement of more demanding broadleaf species and nemoral herbs with higher rates of litterfall, its decomposition and humification. As a result, simple pine forests on Arenosols and primitive Sod-podzolic soils are replaced by complex, more productive linden–oak–pine ecosystems on developed Cambisols with thick (up to 30 cm) humus horizons, carbon storage of 80–100 Mg/ha and higher (2–7 times compared to the previous soils) fertility and water-holding capacity. This mechanism is adequately described by a nonlinear process model with a trigger reaction of plant productivity to the storage and quality of soil organic matter, suitable for predicting long-term carbon sequestration during the succession of valley pine forests and the effectiveness of artificial afforestation. Full article