Search Results (339)

The occurrence and concentration of potentially toxic elements (PTE) in fertilizers are a concern in tropical regions, and soil properties affect their bioavailability for crops. Cadmium is the most easily bioavailable for plants and so the food chain, and it represents a stepping-stone toward safe food production. So, this study aimed to evaluate the ionomics, metabolism, and growth of potato, tobacco, and rice in response to liming and to monoammonium phosphates (MAP) from different geographic origins and PTE contents (MAP 1, MAP 2, MAP 3). For this, independent experiments were conducted with each crop using MAP fertilizers as a phosphorus source applied to a Red-Yellow Latosol, with and without liming. Our findings indicated that physiological changes were primarily influenced by liming rather than PTE. Most acidic soils negatively impacted plant growth and sugar content and induced metabolic adjustments related to proline. The higher level of Cd in MAP 3 reduced manganese and zinc and increased sugar in plant shoots. Rice also had a lower Cd bioaccumulation than potato and tobacco, followed by a higher tolerance to acidic soil. The concentrations of As, Cd, and Cr present in fertilizers did not impair the growth and life cycle of the evaluated plants; however, metabolic adjustments were observed. Full article

Microplastics (MPs) pose a significant threat to soil ecosystems based on their small size and resistance to biodegradation. Soil organic carbon (SOC) in saline–alkaline ecosystems has significantly affected maintain the ecological balance. This paper aims to review the mechanisms underlying the influence of MPs on SOC in saline–alkaline soils combining bibliometric mapping (VOSviewer). The results revealed that: (1) MPs mainly enter the saline–alkaline soil through water irrigation, sewage sludge, and agricultural films. (2) The interaction between the salt ions in saline–alkaline soils and the negatively charged surface of MPs will intensify the dispersion of soil aggregates, resulting in a significant decline in soil structure stability and nutrient imbalance. (3) MPs and the high-salt environment of saline–alkaline soils form a synergistic stress, significantly reducing the activities of key enzymes such as catalase and dehydrogenase in the soil, and it selectively promotes the enrichment of salt-tolerant bacterial communities (such as Halomonas and Bacillus species). (4) Using biodegradable plastic materials, setting up ecological buffer zones and planting halophytic plants (in coastal saline–alkaline areas), adding windbreak and sand-fixing buffer zones (in inland desert-type saline–alkaline areas), promoting precise irrigation and fertilization technologies (in areas with uneven irrigation conditions), and emergency soil amendment treatment (for severely polluted and ecologically fragile saline–alkaline soils) were all effective measures to dealing with the MPs pollution in saline–alkaline soils. This review provides a theoretical basis for the prevention and control of MPs pollution and the sustainable use of saline–alkaline soils. Full article

Soil health monitoring is critical for the sustainable management of the black soil region, a key resource for global food security. However, traditional field surveys are constrained by high operational costs, limited spatial coverage, and low temporal frequency, making them inadequate for high-resolution and time-sensitive soil monitoring. The recently launched ZY1-02E satellite, equipped with an advanced hyperspectral imager, offers a new potential data source, yet its capability for quantitative soil modelling requires rigorous cross-sensor validation. This study conducts a cross-sensor evaluation of ZY1-02E and its predecessor, ZY1-02D, for mapping soil organic matter (SOM) and soil texture (sand, silt, and clay) in Northeast China. Optimal spectral indices were constructed through exhaustive band combination and correlation screening, and quantitative inversion models were established using a hybrid framework integrating Random Frog feature selection with Gaussian Process Regression (GPR) and Boosting Trees, based on synchronous ground observations. Results demonstrate strong cross-sensor consistency, with spectral indices showing significant linear correlations (${\mathrm{R}}^2>0.65$) between ZY1-02E and ZY1-02D. Furthermore, the quantitative retrieval models applied to ZY1-02E imagery achieved robust performance, with cross-sensor retrieval consistency exceeding ${\mathrm{R}}^2=0.60$ for all parameters and SOM exhibiting the highest agreement (${\mathrm{R}}^2=0.74$). These findings confirm the radiometric stability and algorithm transferability of ZY1-02E, demonstrating its capability to generate soil parameter products comparable to ZY1-02D without extensive model recalibration. The validated interoperability of the twin-satellite constellation substantially enhances temporal observation capacity during the narrow bare-soil window, effectively mitigating cloud-induced data gaps in high-latitude agricultural regions. Importantly, the enhanced monitoring framework provides a scalable technical paradigm for high-frequency hyperspectral soil mapping, offering critical spatial decision support for precision fertilization, soil degradation mitigation, and conservation tillage management in the Mollisol belt. Full article

Soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry is essential for maintaining fertility and ecosystem functioning, yet its spatial patterns and drivers in large-scale agricultural regions remain unclear. We collected 225 topsoil samples across the Songnen Plain, Northeast China, and used geostatistical methods to map the spatial distributions of soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and their ratios (C:N, C:P, N:P). Feature importance and correlation analyses were employed to assess the relative influence of environmental factors. Results revealed significant spatial heterogeneity, with a consistent north-high, south-low pattern for all elements and ratios. Mean C:N, C:P, and N:P ratios were 11.6, 32.8, and 2.8, respectively. SOC was the dominant controlling factor (importance: 0.5–0.6), showing strong positive correlations with all ratios. Mean annual temperature exerted significant negative effects, while precipitation had limited influence, primarily on C:N. Soil type also mattered, with black soils exhibiting the highest C:N and C:P ratios (11.8 and 36.7). We conclude that soil C:N:P stoichiometry in the Songnen Plain is governed by hierarchical interactions of SOC, climate, and soil type. These findings provide a mechanistic framework for understanding regional nutrient patterns and support the development of spatially targeted management strategies for sustainable soil health. Full article

In this study, a survey was conducted in 14 fields (6 in Thessaloniki and 8 in Serres) before barley harvest during three growing seasons (2019–20, 2020–21, 2021–22) to map the occurrence of ACCase and ALS multi-resistant populations and evaluate the influence of agronomic practices and environmental conditions on their dynamics. Specifically, weed cover and tiller number/plant were assessed in each field using a W pattern, while questionnaires were used to collect information from farmers on agronomic practices applied, such as seedbed preparation, the rate of fertilization at sowing, the time of sowing, the time and rate of top-dressing nitrogen fertilizer, the time of application of the herbicide pinoxaden, and the harvest time. Soil properties and climatic conditions were also recorded. These results indicated that regardless of the different agricultural practices applied in the fields of the studied regions, no significant association was found with L. rigidum’s ground cover or number of tillers/plant. Additionally, no association was identified between weed ground cover and climatic characteristics. Therefore, the findings of this study strongly support the dependence of the dispersal of the resistant strain L. rigidum on the interactions between genetic, biological, and soil factors; fertilizer or herbicide use; sowing or other agronomic practices; and climatic factors that drive resistance dynamics, rather than any individual practice alone. Full article

Soil salinity and nutrient availability are major constraints affecting crop productivity, soil quality, and agroecosystem sustainability, particularly in coastal regions vulnerable to seawater intrusion. This study provides a comprehensive spatial and temporal assessment of soil properties and quality dynamics on Hainan Island by integrating field observations and multi-temporal remote sensing (RS) datasets. In 2024, a total of 152 sampling sites were surveyed, with three topsoil soil samples collected at each location. Multi-year RS data (2024–2021), including soil salinity reflectance indices (SRSI and SI), the Normalized Difference Vegetation Index (NDVI), and land use and land cover (LULC), were analyzed to evaluate temporal and spatial variability. The soil fertility index was calculated using alkali-hydrolyzed nitrogen (AN), available phosphorus (AP), available potassium (AK), soil pH, and soil organic matter (SOM). The soil quality index was calculated using the same parameters with the addition of chromium (Cr) to account for potential heavy metal contamination. Furthermore, in this study the Inverse Distance Weighting (IDW) method was used for spatial distribution maps of soil properties and other indices. The results indicated that soils were predominantly acidic (pH < 6.0) with generally low electrical conductivity (0.01–0.53 mS cm⁻¹) across inland areas, whereas higher salinity levels (2.28–5.31 mS cm⁻¹) were observed in southern and eastern coastal zones, suggesting potential seawater intrusion. Nutrient concentrations ranged from 60.1 to 150 mg kg⁻¹ (AN), 4 to 332 mg kg⁻¹ (AP), and 50.1 to 100 mg kg⁻¹ (AK). NDVI values (0.70–0.94) indicated high vegetation density over most agricultural landscapes. Significant positive correlations were observed between soil EC and the SRSI (r = 0.781) and SI (r = 0.663; p < 0.01), demonstrating the reliability of RS-derived indices for salinity assessment. The integrated indicator-based framework developed in this study provides a scientific basis for precision agriculture, soil health monitoring, and sustainable land management in coastal agroecosystems. Full article

This study evaluated the potential of Sentinel-2-derived NDVI (Normalized Difference Vegetation Index) for predicting within-field variability of winter wheat grain yield in central Lithuania during the 2024 growing season. Reliable within-field yield prediction remains challenging in regions with heterogeneous soils and limited region-specific models, particularly in northeastern Europe. Grain yield data were obtained from combine harvesters equipped with GPS yield monitoring across 13 fields with a total area of 283.6 ha. NDVI values were calculated for four half-monthly periods from March to May, corresponding to key phenological stages (from tillering to spike emergence). Spatial and temporal variability in NDVI–yield relationships was observed, with early May consistently showing the strongest correlations (r up to 0.49), particularly in lower-fertility fields, indicating its critical role in yield prediction. Machine learning models (Random Forest, XGBoost, and Deep Neural Networks), along with linear regression, were applied to predict yields based on NDVI from four growth stages. Random Forest achieved the highest predictive accuracy (MAE = 0.951 t/ha), outperforming the other models. The model also showed the highest correlation with observed yields (Pearson r = 0.717), indicating strong agreement between predicted and measured values. Feature importance analysis confirmed NDVI from 1 to 15 May as the most influential predictor across all models. Predicted yield maps closely matched observed patterns, with the largest discrepancies near field edges due to combine harvester effects. These findings highlight the utility of mid-season NDVI for precise estimation of within-field grain yield variability and demonstrate that Random Forest models can effectively capture the NDVI–yield relationship, particularly under heterogeneous field conditions. Full article

This Special Issue, entitled “Digital Soil Mapping for Agri-Environmental Management and Sustainability”, gathers nine studies from around the globe that illustrate how digital soil mapping (DSM) is being applied to support agri-environmental management and sustainability. Field- and farm-scale studies are emphasized, where informed decisions are essential for efficient day-to-day management and profitability. The articles highlight the integration of remote/proximal sensing, along with modern machine learning techniques, to produce high-resolution soil maps, soil fertility and nutrient management zoning, and to monitor salinity and soil moisture to inform irrigation and land management. Another key focus is improving sampling strategies and assessing prediction uncertainty and model interpretability. This collection sets future DSM priorities, including cost-effective sampling, robust uncertainty assessments, and reliable cost–benefit and risk assessment approaches that link map accuracy/uncertainty to management outcomes and economic performance. Full article

Saline irrigation water is increasingly used in arid and coastal regions, posing serious constraints to soil health and wheat yield, particularly in saline–sodic soils. A two-season field experiment was conducted to evaluate the effects of compost, biofertilizers (Azospirillum brasilense and Azotobacter chroococcum), and their combinations on soil physicochemical properties, microbial activity, wheat growth, yield, and physiological traits under two irrigation water salinity levels (3 and 6 dS m⁻¹). Two wheat varieties differing in salt tolerance (Miser 4 and Sakha 95) were tested. Salinity significantly increased soil EC and ESP and reduced plant growth, yield, and nutrient content, while integrated bio-organic treatments markedly alleviated these adverse effects. Compost combined with Azotobacter chroococcum markedly improved soil physical conditions, enhanced microbial biomass carbon, reduced sodicity indicators, and promoted wheat productivity across both seasons. Multivariate analyses including principal component analysis (PCA), redundancy analysis (RDA), and self-organizing maps (SOMs) revealed a strong positive association between yield traits, microbial activity, and soil fertility, and negative correlations with salinity stress indicators. The results demonstrate that combining compost with biofertilizers induces both immediate and residual improvements in saline–sodic soils, enhances wheat resilience to salinity stress, and offers a sustainable approach for improving cereal production under salt-affected environments. Full article

Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, and non-destructive soil analysis. This study aimed to evaluate the potential of a low-cost, portable NIR sensor (NeoSpectra) for the quantitative prediction of key soil properties in paddy fields from Southeastern China. The target properties were soil organic matter (SOM), total nitrogen (TN), pH, and particle size fractions (clay, silt, and sand). A total of 995 soil samples were collected from representative paddy fields in the region and spectra measurements were conducted in the laboratory on air-dried samples. We developed and compared the performance of multiple machine learning algorithms, including partial least squares regression (PLSR), Cubist, random forest (RF) and memory-based learning (MBL), to build robust calibration models. The predictive models showed substantial performance for SOM and TN, indicating high accuracy (R² > 0.75, LCCC > 0.85, RPD > 2) for quantitative prediction. Predictions for pH, silt, sand, and clay were less accurate (R² of 0.48–0.53, LCCC of 0.67–0.71, RPD of 1.39–1.49), suggesting the sensor’s utility is limited to indicating general trends for these properties. Among the tested algorithms, MBL consistently provided the most accurate and robust predictions across the majority of soil properties. Our findings demonstrate that the low-cost portable NIR sensor, when coupled with appropriate machine learning algorithms, is a powerful and viable tool for the rapid and reliable estimation of critical paddy soil fertility properties (SOM and TN). This technology has significant potential to support field-level soil health monitoring, precision fertilization strategies, and sustainable land management in the agricultural systems of Southeastern China. Full article

The spatial variability of soil physicochemical properties significantly influences irrigation efficiency, nutrient availability, and the long-term sustainability of irrigated agriculture in semi-arid regions. This study aimed to quantify and model the spatial distribution of soil properties in a semi-arid irrigation district in central Mexico (Irrigation District 001 “Pabellón de Arteaga”, Aguascalientes), providing spatially explicit information for differential irrigation and fertilization management. Ninety-seven crop and four natural sampling sites were established under a stratified random design at two soil depths (0–30 and 30–60 cm). Geostatistical and machine learning models (Ordinary Kriging, OK; Generalized Additive Models, GAM; and Random Forest, RF) were applied to predict spatial patterns, and their performance was evaluated using statistical metrics. The findings reveal high spatial and vertical variability, with most properties (such as organic matter, total nitrogen, and texture) showing significant stratification with depth. In contrast, others (pH and electrical conductivity, EC) remained remarkably homogeneous vertically. Correlation patterns were identified, highlighting the negative influence of alkaline pH (≈8.0) on the availability of micronutrients (Fe²⁺ and Mn²⁺) and the positive association between EC and soluble cations (Ca²⁺, K⁺, and Na⁺). Moran’s Index confirmed significant spatial autocorrelation for most properties, reducing the effective sample size by 30–70%. The comparative evaluation of predictive models demonstrated the superiority of RF over OK and GAMs for predicting chemical properties, thanks to its ability to capture nonlinear relationships and complex interactions. However, the overall predictive performance was moderate, reflecting the multifactorial complexity of the edaphic system. This study lays the foundation for the development of an accessible, low-cost Decision Support System by providing a robust methodological framework for spatial soil characterization and contributing to more sustainable, resilient agriculture, where decision-making is based on quantitative data and predictive models. Full article

In drip-irrigated maize of arid Xinjiang, seedling hardening (withholding irrigation) is used to induce deep rooting, but the conventional practice of banding phosphorus (P) fertilizer without basal application creates a spatial mismatch—roots are forced downward while P remains trapped in drying topsoil. We hypothesized that co-applying superabsorbent polymer (SAP) with banded P fertilizer can form a localized, persistently hydrated P-enriched patch that synchronizes root–resource distribution. A two-year field experiment (2024–2025) was conducted with three treatments: no P (P0), banded monoammonium phosphate (B-MAP, 120 kg P₂O₅ ha⁻¹), and B-MAP + SAP (15 kg ha⁻¹). Soil properties, root growth, canopy physiology, dry matter accumulation, nutrient uptake, and grain yield were measured. Results: At the V4 stage, B-MAP + SAP increased available P and soil water content in the 0–10 cm layer by 9.4% and 16.1%, respectively, relative to B-MAP. This patch triggered vigorous root proliferation: topsoil root length at V4 rose by 23.9%, and root length density in the 30–40 cm subsoil at V9 and R1 increased by 59.0% and 36.5%. Consequently, B-MAP + SAP sustained the highest leaf area index, net photosynthetic rate, and biomass accumulation. Two-year average grain yield reached 18.2 t ha⁻¹, 9.7% and 20.7% higher than B-MAP and P0. Crucially, P use efficiency (PUE) and water productivity (WP) under B-MAP + SAP improved by 76.2% and 9.8% over B-MAP. Co-applying SAP with banded P fertilizer resolves the spatial mismatch in hardening systems, optimizes root architecture, and synergistically boosts yield, PUE, and WP. This one-time amendment offers a simple, scalable strategy for efficient P management in arid drip-irrigated maize. Full article

Soil fertility is a critical factor for sustainable agricultural production, yet traditional soil analysis methods are often time-consuming and costly. As such, the aim of our research was to evaluate the potential of near-infrared (NIR) spectroscopy as a rapid, cost-effective, and non-destructive tool to assess soil fertility properties in agricultural soils. The soil samples were collected from the Quinta da Foz located in Benavente (Portugal) across agricultural regions including rice paddies, tomato fields, and Montado ecosystems. The sampling locations were guided by the Normalized Difference Vegetation Index (NDVI) to capture spatial variability, and soil analyses included near-infrared (NIR) spectral measurements and laboratory-based chemical determinations of soil fertility parameters (pH, electrical conductivity, total carbon and nitrogen, organic matter, macro- and micronutrients) as well as multiple soil carbon fractions. Two predictive modeling approaches (Random Forest (RF) and Partial Least Squares Regression (PLSR)) were developed to estimate soil chemical properties from spectral data. The RF models consistently outperformed PLSR, achieving high accuracy (R² = 0.85) for nutrients such as Mg, Fe, Ni, Ca, and Na and organic matter. A moderate predictive performance (R² between 0.70 and 0.80) was observed for different elements, namely, K and Mn. On the other hand, P, S, Zn, electrical conductivity, pH, total N, and various carbon fractions were poorly predicted. The spatial interpolation of predicted values enabled the generation of soil fertility maps that informed site-specific nutrient management. The results indicate that NIR spectroscopy combined with robust modeling offers a promising approach for rapid spatial assessment of selected soil nutrients, supporting precision agriculture and sustainable land management. Full article

Artificial intelligence (AI) is rapidly affecting soil science by allowing the analysis of large, complex, and heterogeneous datasets that were previously difficult to exploit. The current review synthesizes the recent advances of AI and highlights how these tools are applied in key soil science domains, such as digital soil mapping, soil fertility management, soil moisture prediction, contamination monitoring, soil carbon assessment, and precision agriculture. This study evaluates the performance of different AI methods, showing that techniques such as random forests, neural networks, and convolutional neural networks often outperform traditional methods in capturing non-linear soil-environment. At the same time, it identifies major limitations such as data scarcity, reproducibility, lack of large datasets, uncertainty, and the “black-box” nature of many models. This review concludes that AI has strong potential to support sustainable soil management, but its real-world impact will depend on better data integration, explainability, standardization, and closer collaboration with scientists, technologists, and end-users. Full article

Current agriculture faces the challenge of producing sufficient food from diminishing land resources, due to deteriorating soil quality and accelerated population growth. Numerous studies have demonstrated that biochar/hydrochar can serve as efficient soil amendments by improving soil fertility and enhancing crop productivity. Various food wastes are promising raw materials for biochar/hydrochar production due to their abundant organic matter. Recently, thermochemical techniques such as pyrolysis, hydrothermal carbonization (HTC), and microwave-assisted pyrolysis (MAP) have been widely proposed for converting food waste into biochar/hydrochar for soil amendment. However, the composition of food waste is complex and the parameters for its thermal treatment are highly variable, leading to uncertainties in the performance of the derived biochar/hydrochar for soil applications. This study aims to establish a structure–activity relationship linking food waste carbonization technology, the properties of the obtained biochar/hydrochar, and its functions as a soil amendment. Furthermore, the detailed mechanisms by which biochar improves plant growth or poses potential ecological risks to agricultural land are discussed. This review is intended to provide a guideline for the large-scale application of food waste-derived char for soil amendment. Full article