Search Results (4,185)

Zhejiang Province, as a key ecological region in southeastern China, plays a vital role in ensuring regional ecological security and sustainable development through its soil conservation services (SCS). Based on remote sensing data, this study employed the InVEST model to evaluate the characteristics of SCS in Zhejiang from 2001 to 2020. Long-term trends were identified using Sen’s Slope and the Mann–Kendall test, spatial autocorrelation was assessed through Moran’s I, the contributions of driving factors were quantified using XGBoost combined with SHAP, and spatial heterogeneity was further explored using Geographically Weighted Regression (GWR). The results indicate that: (1) from 2001 to 2020, SCS exhibited a fluctuating trend of “decline followed by recovery,” with significantly higher values in the western mountainous areas than in the eastern coastal and plain regions; approximately 58% of the area remained stable, while 40% experienced degradation; (2) Spatial autocorrelation analysis showed that areas with strong SCS were concentrated in the western mountains, while low-value areas were mainly distributed in the eastern coastal and urban regions; (3) natural factors contributed the most, followed by climatic and human activity factors; and (4) the GWR model outperformed the OLS model in revealing the spatial variation in the effects of natural and anthropogenic drivers. These findings provide valuable scientific references and decision-making support for ecological conservation, watershed management, and sustainable land use in Zhejiang Province. Full article

Accurate and spatially detailed soil information is essential for supporting sustainable land use planning, particularly in data-scarce regions such as Cyprus, where soil degradation risks are intensified by land fragmentation, water scarcity, and climate change pressure. This study aimed to generate national-scale predictive maps of key soil health descriptors by integrating satellite-based indicators with a recently released geo-referenced soil dataset. A machine learning model was applied to estimate a suite of soil properties, including organic carbon, pH, texture fractions, macronutrients, and electrical conductivity. The resulting maps reflect spatial patterns consistent with previous studies focused on Cyprus and provide high resolution insights into degradation processes, such as organic carbon loss, and salinization risk. These outputs provide added value for identifying priority zones for soil conservation and evidence-based land management planning. While predictive uncertainty is greater in areas lacking ground reference data, particularly in the northeastern part of the island, the modeling framework demonstrates strong potential for a national-scale soil health assessment. The outcomes are directly relevant to ongoing soil policy developments, including the forthcoming Soil Monitoring Law, and provide spatial prediction models and indicator maps that support the assessment and mitigation of soil degradation. Full article

As an important reserve resource for cultivated land, the improvement and fertility enhancement of saline-alkali land are key to alleviating the pressure on cultivated land and ensuring the sustainable utilization of land resources. Studying the regulatory effect of rotation patterns on the soil fertility of saline-alkali land is one of the core research contents in exploring low-cost and environmentally friendly comprehensive management strategies for saline-alkali land. This study focuses on Zhaoyuan County, a representative saline and alkaline area within the Songnen Plain. Utilizing remote sensing technology, crop information was systematically collected across 13 time periods spanning from 2008 to 2020. These data were employed to construct a comprehensive crop information change atlas. This atlas categorized crop rotation patterns based on crop combinations, rotation frequencies, and the number of consecutive years of planting. Using soil sampling data from 2008 and 2020, a soil fertility evaluation was conducted, and the changes in soil chemical properties and fertility under various crop rotation patterns were analyzed. The results of the study show that, during the study period, crop rotation patterns in Zhaoyuan County were dominated by paddy-upland rotations and upland crop rotations. Crop rotation patterns, categorized by crop combination, were dominated by soybean–maize–other crops rotation (S-M-O) and rice–soybean–maize–other crops rotation (R-S-M-O). The frequency of crop rotation is dominated by low- and medium-frequency crop rotation. Crop rotation significantly increased soil organic matter, total nitrogen content, and overall soil fertility in the study area, while simultaneously lowering soil pH levels. Crop rotation patterns with different crop combinations had significant effects on soil chemical properties, with smaller differences in the effects of different rotation frequencies and years of continuous cropping. Crop rotation patterns incorporating soybean demonstrate a significant positive regulatory impact on the soil fertility of saline-alkali land. Low-frequency crop rotation (with ≤5 crop changes) has a relatively better effect on improving soil fertility. This research provides important empirical support and decision-making references for establishing sustainable farming systems in ecologically fragile saline-alkali areas, ensuring regional food security, and promoting the long-term sustainable utilization of land resources. Full article

This paper investigates the optimization of an irrigation system distributed over an agricultural area discretized into unit cells, using evolutionary algorithms for the control of water irrigation points (taps). The model simulates the distribution of water through strategically placed irrigation points, considering the individual requirements of each cell. The main objective is to minimize the difference between the amount of water needed and delivered, while reducing the total consumption. The dynamics of fitness over generations are analyzed, as well as the average behavior of deficit, surplus, and relative humidity. The results highlight a relatively uniform distribution of delivered water and a stable convergence of the fitness function, demonstrating the efficiency of the proposed method in managing water resources in a sustainable way. In this matter, compared to the full-activation scenario, the presented model reduced total water use by more than 50%, achieving zero deficit, minimal surplus, and a 46% improvement in overall fitness. Although the approach demonstrates promising results in simulated scenarios, it does not currently incorporate real-time sensor data or field validation, which are planned for future development. The study provides a solid basis for the development of smart irrigation systems, adaptable to the variability of soil and climatic conditions. Full article

Soil salinity stress, intensified by extreme weather patterns, significantly threatens global watermelon [Citrullus lanatus (Thunb.) Matsum & Nakai] production. Watermelon, a moderately salt-sensitive crop, exhibits reduced germination, stunted growth, and impaired fruit yield and quality under saline conditions. As freshwater resources decline and agriculture’s dependency on irrigation leads to soil salinization, we need sustainable mitigation strategies for food security. Recent advances highlight the potential of using salt-tolerant rootstocks and breeding salt-resistant watermelon varieties as long-term genetic solutions for salinity. Conversely, agronomic interventions such as drip irrigation and soil amendments provide practical, short-term strategies to mitigate the impact of salt stress. Biostimulants represent another tool that imparts salinity tolerance in watermelon. Plant growth-promoting microbes (PGPMs) have emerged as promising biological tools to enhance watermelon tolerance to salt stress. PGPMs are an emerging tool for mitigating salinity stress; however, their potential in watermelon has not been fully explored. Nanobiochar and nanoparticles are another unexplored tool for addressing salinity stress. This review highlights the intricate relationship between soil salinity and watermelon production in a unique manner. It explores the various mitigation strategies, emphasizing the potential of PGPM as eco-friendly bio-inoculants for sustainable watermelon management in salt-affected soils. Full article

Groundwater is essential for irrigated agriculture, yet its use remains unsustainable in many regions worldwide. In countries like Pakistan, the situation is particularly pressing. The irrigated agriculture of Pakistan heavily relies on groundwater resources owing to limited canal-water availability. The groundwater quality in the region ranges from good to poor, with the lower-quality water adversely affecting soil structure and plant health, leading to reduced agricultural productivity. The delineation of quality zones with respect to irrigation parameters is thus crucial for optimizing its sustainable use and management. Therefore, this research study was carried out in the Lower Chenab Canal (LCC) irrigation system to assess the spatial distribution of groundwater quality. The geostatistical analysis was conducted using Gamma Design Software (GS+) and the Kriging interpolation method was applied within a Geographic Information System (GIS) framework to generate groundwater-quality maps. Semivariogram models were evaluated for major irrigation parameters such as electrical conductivity (EC), residual sodium carbonate (RSC), and sodium adsorption ratio (SAR) to identify the best fit for various Ordinary Kriging models. The spherical semivariogram model was the best fit for EC, while the exponential model best suited SAR and RSC. Overlay analysis was performed to produce combined water-quality maps. During the pre-monsoon season, 17.83% of the LCC area demonstrated good irrigation quality, while 42.84% showed marginal quality, and 39.33% was deemed unsuitable for irrigation. In the post-monsoon season, 17.30% of the area had good irrigation quality, 44.53% exhibited marginal quality, and 38.17% was unsuitable for irrigation. The study revealed that Electrical Conductivity (EC) was the primary factor affecting water quality, contributing to 71% of marginal and unsuitable conditions. In comparison, the Sodium Adsorption Ratio (SAR) accounted for 38% and Residual Sodium Carbonate (RSC) contributed 45%. Therefore, it is recommended that groundwater in unsuitable zones be subjected to artificial recharge methods and salt-tolerated crops to enhance its suitability for agricultural applications. Full article

Global climate change has intensified the frequency and severity of drought events, posing significant threats to agricultural sustainability, particularly for water-sensitive crops such as tea. In northern China, where precipitation is unevenly distributed and evapotranspiration rates are high, tea plantations frequently experience water stress, leading to reduced yields and declining quality. Therefore, accurately simulating soil water content (SWC) is essential for drought forecasting, soil moisture management, and the development of precision irrigation strategies. However, due to the high complexity of soil–vegetation–atmosphere interactions in field conditions, the practical application of the HYDRUS-1D model in northern China remains relatively limited. To address this issue, a three-year continuous monitoring campaign (2021–2023) was conducted in a coastal area of northern China, covering both young tea plantations and adjacent grasslands. Based on the measured meteorological and soil data, the HYDRUS-1D model was used to simulate SWC dynamics across 10 soil layers (0–100 cm). The model was calibrated and validated against observed SWC data to evaluate its accuracy and applicability. The simulation results showed that the model performed reasonably well, achieving an R² of 0.739 for the tea plantation and 0.878 for the grassland, indicating good agreement with the measured values. These findings demonstrate the potential of physics-based modeling for understanding vertical soil water processes under different land cover types and provide a scientific basis for improving irrigation strategies and water use efficiency in tea-growing regions. Full article

Stable crop yields require an appropriate supply of essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) based on the accurate diagnosis of soil nutrient status. Traditional laboratory analysis of soil nutrients is often complicated and time-consuming and does not provide real-time nutrient status. Visible–near-infrared (Vis-NIR) spectroscopy has emerged as a non-destructive and rapid method for estimating soil nutrient levels. Vis-NIR spectra reflect sample characteristics as the peak intensities; however, they are often affected by various artifacts and complex variables. Since Vis-NIR spectroscopy does not directly measure nutrient levels in soil, improving estimation accuracy is essential. For spectral preprocessing, the most important aspect is to develop an appropriate preprocessing strategy based on the characteristics of the data and identify artifacts such as noise, baseline drift, and scatter in the spectral data. Machine learning-based modeling techniques such as partial least-squares regression (PLSR) and support vector machine regression (SVMR) enhance estimation accuracy by capturing complex patterns of spectral data. Therefore, this review focuses on the use of Vis-NIR spectroscopy for evaluating soil properties including soil water content, organic carbon (C), and nutrients and explores its potential for real-time field application through spectral preprocessing and machine learning algorithms. Vis-NIR spectroscopy combined with machine learning is expected to enable more efficient and site-specific nutrient management, thereby contributing to sustainable agricultural practices. Full article

The complex trade-offs and synergies among ecosystem services (ESs) in arid regions influence the stability and sustainable development of regional ecosystems. As a representative oasis–desert transition zone, the Kashgar region requires quantifying the key drivers and thresholds influencing ecosystem services, which is crucial for regional management. This study examines the spatio-temporal changes and interactions of five types of ES (grain production, water yield, soil retention, carbon storage, and habitat quality) and employs Restricted Cubic Splines to quantify the nonlinear changes and threshold effects of natural and social drivers. The results indicate the following: (1) During the period from 2000 to 2020, supply services (grain production) and regulatory services (water yield and soil retention) showed growth, while support services (carbon storage and habitat quality) declined slightly; (2) the synergistic effects of ecological services improved across the entire region, but trade-off effects emerged in certain local areas; and (3) the NDVI is the core natural factor driving the spatio-temporal differentiation of ESs. In 2020, when the NDVI exceeded 0.35, it had an adverse impact on habitat quality and carbon storage. Among social factors, water yield and habitat quality exhibit the highest threshold points with land use development intensity. An increase in land development intensity significantly impacts the trade-off and synergistic relationships among ESs, leading to local imbalances in ES resource supply and demand. These findings enhance our understanding of the nonlinear characteristics and potential mechanisms of ecosystems in arid regions, providing a scientific basis for ecosystem management in these areas. Full article

Heavy metals represent long-lasting contaminants that pose significant risks to both human health and ecosystem integrity. Originating from both natural and anthropogenic activities, they bioaccumulate in organisms through the food web, leading to widespread and long-lasting contamination. Industrialization, agriculture, and urbanization have exacerbated soil and water contamination through activities such as mining, industrial production, and wastewater use. In response to this challenge, biochar produced from waste materials such as sewage sludge has emerged as a promising remediation strategy, offering a cost-effective and sustainable means to immobilize heavy metals and reduce their bioavailability in contaminated environments. Here we explore the potential of phosphate-enriched biochar, derived from sewage sludge, to adsorb and stabilize heavy metals in polluted soils. Sewage sludge was pyrolyzed at various temperatures to produce biochar. A soil incubation experiment was conducted by adding phosphate-amended biochar to contaminated soil and maintaining it for one month. Heavy metals were extracted using a CaCl₂ extraction method and analyzed using atomic absorption spectrophotometry. Results demonstrated that phosphate amendment significantly enhanced the biochar’s capacity to immobilize heavy metals. Amending soils with 2.5 wt% phosphate-enriched sewage sludge biochar led to reductions in bioavailable Cd (by 65–82%), Zn (40–75%), and Pb (52–88%) across varying pyrolysis temperatures. Specifically, phosphate-amended biochar reduced the mobility of Cd and Zn more effectively than unamended biochar, with a significant decrease in their concentrations in soil extracts. For Cu and Pb, the effectiveness varied with pyrolysis temperature and phosphate amendment, highlighting the importance of optimization for specific metal contaminants. Biochar generated from elevated pyrolysis temperatures (500 °C) showed an increase in ash content and pH, which improved their ability to retain heavy metals and limit their mobility. These findings suggest that phosphate-amended biochar reduces heavy metal bioavailability, minimizing their entry into the food chain. This supports a sustainable approach for managing hazardous waste and remediating contaminated soils, safeguarding ecosystem health, and mitigating public health risks. Full article

Heavy machinery degrades agricultural soils, with severity influenced by wheel type, contact area, and soil moisture. Tropical agriculture is characterized by the constant maintenance of straw on the ground. This permanent cover, among other benefits, can mitigate the stress imposed by wheels on the physical structure of the soil. This study aimed to evaluate the effect of tire types and straw amounts on soil stresses. Static studies were carried out under controlled conditions in a static tire test unit (STTU), equipped with standardized sensors and systems that simulated real farming conditions. Three tire models were tested: road truck double wheelset—2 × 275/80R22.5 (p1); agricultural radial tire—600/50R22.5 (p2); and bias-ply tire—600/50-22.5 (p3) on four contact surfaces (rigid surface; bare soil; soil with 15 and 30 Mg ha⁻¹ straw cover). We performed comparative statistical tests and subsurface stress simulations for each tire and surface condition. On the hard surface, the contact areas were 4.7 to 6.8 times smaller than on bare soil. Straw increased the tire’s contact area, reducing compaction and subsoil stresses. Highest pressure was imposed by the road tire (p1) and lowest by the radial tire (p2). Adding 15 Mg ha⁻¹ of straw reduced soil SPR by 18%, while increasing it to 30 Mg ha⁻¹ led to an additional 8% reduction. Tire selection and effective straw management improve soil conservation and agriculture sustainability. Full article

This study investigates the drivers of individual and joint adoption of sustainable land use (SLU) practices—specifically crop choice and soil and water conservation—and their impact on farm performance (crop revenue) and production risk (crop yield skewness). Using a farm-level dataset of 504 households across three agro-ecological zones in Punjab, Pakistan, we address selectivity bias through the newly developed multinomial endogenous switching regression (MESR) model. Additionally, we assess land use sustainability across ecological, social, and economic dimensions using a comprehensive non-parametric approach. Our findings identify key determinants of SLU adoption, including farmer education, access to advisory services, FBO membership, hired labor, climate information, farm size, and perceptions of drought and heatwaves. We demonstrate that joint adoption of SLU practices maximizes crop revenue and reduces production risk, lowering the likelihood of crop failure. The study further suggests complementarity between these SLU practices in enhancing crop revenue. Moreover, joint adopters of SLU practices significantly outperform non-adopters in ecological, social, and economic sustainability dimensions. We recommend improving access to public sector farm advisory services and climate information to enable farmers to make well-informed decisions based on reliable data. Implementing these measures can support the transition toward sustainable land management, helping to mitigate risks like crop failure and declining revenues, which threaten farm income. Full article

Soil salinity stands among the most critical abiotic stressors, imposing severe limitations on global rice cultivation. Emerging evidence highlights the potential of beneficial microorganisms to enhance crop salt tolerance. In this study, a halotolerant bacterial strain, Rhodobium marinum RH-AZ (Gram-negative) was identified and analyzed. It exhibited exceptional survival at 9% (w/v) NaCl salinity. Whole-genome sequencing revealed a circular chromosome spanning 3,875,470 bp with 63.11% GC content, encoding 5534 protein-coding genes. AntiSMASH analysis predicted eight secondary metabolite biosynthetic gene clusters. Genomic annotation identified functional genes associated with nitrogen cycle coordination, phytohormone biosynthesis, micronutrient management and osmoprotection. Integrating genomic evidence with the existing literature suggests RH-AZ’s potential for enhancing rice salt tolerance and promoting the growth of rice plants. Subsequent physiological investigations revealed that the RH-AZ strain had significant growth-promoting effects on rice under high salinity stress. Compared with a non-inoculated control, RH-AZ-inoculated rice plants exhibited stem elongation and fresh biomass enhancement under salt stress conditions. The RH-AZ strain concurrently affected key stress mitigation biomarkers: it enhanced the activity of antioxidant enzymes including superoxide dismutase, peroxidase, catalase and ascorbate peroxidase, and the contents of proline and chlorophyll in plants, and reduced the content of malondialdehyde. These findings demonstrate that R. marinum RH-AZ, as a multifunctional bioinoculant, enhances rice salt tolerance by enhancing the stress responses of the plants, presenting a promising solution for sustainable agriculture in saline-affected ecosystems. Full article

Pesticides play a significant role in agriculture, but their leaching into soil and water poses serious environmental risks. This study examines pesticide contamination in surface and groundwater in northern Tunisia, specifically in Kef governorate, involving a survey of 140 farmers to gather data on agricultural practices and pesticide use. Twenty-four pesticides were monitored and utilized within the Pesticide Environmental Risk Indicator (PERI) model to evaluate environmental risk scores for each substance. Soil and water samples were analyzed using a multi-residue method and liquid chromatography–tandem mass spectrometry. Results showed that 50% of the pesticides assessed had an Environmental Risk Score of 5 or higher. Contamination was identified in water and soil, with 18 and 15 pesticide residues, respectively. Notable concentrations included 7.8 µg/L of linuron and flupyradifurone in water and 1718.4 µg/kg of linuron in soil. Commonly detected substances included the insecticide acetamiprid and fungicides like cyflufenamid and penconazole in water, while soil contamination was linked to fungicides metalaxyl and metalaxyl-m, as well as herbicides linuron and s-metolachlor. Factors such as proximity to treated water points and poor packaging management were discussed as risks. The findings emphasize the need for better monitoring and sustainable agricultural practices to mitigate contamination. Full article

Water-use efficiency (WUE) plays a crucial role in sustainable crop production, particularly in water-limited environments where maximizing natural resource use is essential. This study evaluated the physiological and agronomic performance of two Piper nigrum cultivars, Clonada and Uthirankotta, grown under different soil water potential conditions. The trial was conducted in a 1930 m² field using a randomized block design and drip irrigation system, calibrated to 3.55 L h⁻¹ with a uniformity of 97%. Soil water availability was managed based on daily tensiometer readings at 20 and 30 cm depths, triggering irrigation at defined tensions (10–55 kPa). Clonada exhibited higher net CO₂ assimilation rates (A) and stomatal conductance (g_s), but these responses did not lead to higher yields. In contrast, Uthirankotta consistently maintained superior water-use efficiency and yield across all soil moisture conditions by favoring water conservation and targeted biomass allocation over maximized gas exchange. Both cultivars performed optimally at a soil water potential range of 25–35 kPa, with declines in yield and gas exchange parameters at higher tensions (45–55 kPa). Under such conditions, Uthirankotta was 51.3% more water-use efficient and 40.8% more productive than Clonada. Based on this, a Principal Component Analysis (PCA) further demonstrated distinct physiological profiles, underscoring trade-offs between yield and water-use strategies. These results highlight the significance of cultivar selection for optimizing WUE and provide valuable insights into irrigation management and breeding programs aimed at boosting black pepper performance under water-limited conditions. Full article