Soil Systems

Co-utilization of milk vetch as green manure (GM) and rice straw is an effective practice for reducing nitrogen (N) input while maintaining crop productivity in rice-based agroecosystems in southern China. The effects of soil carbon (C) and N pools and their fractions under green manuring and rice straw return, combined with reduced N fertilization remain to be clarified. A four-year field experiment was carried out to explore the effects of synergistic utilization of GM and rice straw (GMS) on rice yield, soil C and N fractions, and their contributions to rice productivity. The study demonstrated that compared with winter fallow (WF), GMS increased rice yield by 20.3% under 40% reduction in N fertilization (N60). GM application increased soil total N content by 16.5% and 18.0% significantly relative to WF under N0 and N60, respectively. GMS treatment demonstrated improvements in the soil organic C pool and enhanced soil N activity. Compared with WF, soil organic C, mineral-associated organic C and particulate organic C under GMS increased by 11.1% and 24.9%, 31.3% and 13.8%, 13.1% and 47.3% at N0 and N60 levels, respectively. Under N60, GMS increased heavy-fraction organic C content by 42.6% while reducing light-fraction organic C content by 28.0% compared to WF, thereby enhancing soil C pool stability. Regarding soil N fractions, GMS increased particulate organic N content by 60.8% and 79.3%, and mineral-associated organic N content by 89.7% and 43.4% at N0 and N60 levels, respectively. Under N60, GMS reduced heavy-fraction organic N content while increasing light-fraction organic N content, thereby enhancing soil N availability. Based on the results of Mantel tests and random forest prediction, our analysis found that N and particulate organic C served as the key factors affecting rice yield. In conclusion, GMS combined with 60% of the conventional N rate enhanced rice yield by mediating soil C sequestration and N availability, proving to be an effective strategy for improving soil fertility and ensuring food security in the rice-growing region of southern Jiangsu, China. Full article

Conventional tillage, a soil preparation practice used to produce a fine seedbed, can disturb the soil profile by promoting soil compaction and soil organic matter (SOM) degradation. In contrast, conservation tillage, such as no-till, has the potential to sustain or increase SOM. This study aimed to (1) quantify soil organic carbon (SOC) content under conservation tillage and conventional tillage practices, (2) describe the degree of aromaticity of bioavailable SOC using fluorescence spectroscopy, and (3) correlate SOC quantity with nitrogen and phosphorus retention in soils. Fluorescence spectroscopy is a sensitive and non-destructive tool that allows for the assessment of bioavailable SOC quality related to the molecular structure, degree of aromaticity (cyclic molecules with carbon double bonds), and recalcitrance (difficulty of decomposition) of organic compounds. This study employed fluorescence excitation–emission matrices combined with parallel factor analysis (EEM-PARAFAC) to identify humic-like, fulvic-like, and protein-like substances. Data on agricultural management practices were collected from spring 2014 until fall 2017. We obtained soil samples (fall 2017) from farms in the Western Lake Erie Basin, Ohio, and performed geochemical characterization in the bulk soil and aqueous extraction. Our results showed that no-till and minimal tillage fields consistently had greater SOC and fluorescence intensity in the humic-like acids region when compared to conventional tilled fields (no-till: 34,000 mg TOC kg⁻¹; tilled six times: 16,000 mg TOC kg⁻¹). No-till enhanced SOC stabilization. In addition, conservation tillage practices retained the largest total nitrogen (no-till: 2800 mg TN kg⁻¹; tilled six times: 1350 mg TN kg⁻¹) and total phosphorus (no-till: 470 mg TP kg⁻¹; tilled six times: 250 mg TP kg⁻¹) concentrations at all studied depths (0–30 cm) when compared to conventional tilled fields. Conservation tillage promotes the accumulation of highly aromatic organic compounds favoring high cation exchange capacity, and NO₃⁻ and PO₄³⁻ retention and plant bioavailability. Full article

Soil–Forage–Livestock systems (SFL-systems) integration is fundamental for sustainable land management in arid lands, where conventional crop production is often unfeasible. Aridisols dominate dryland agroecosystems and their edaphic constraints, together with climatic limitations, constitute a major bottleneck for fertility and productivity in key arid regions worldwide. This narrative review provides a taxonomic and edaphic framework to guide sustainable SFL-systems and integrates current approaches and technological applications for forage production in arid environments, focusing on an edaphic-digital scheme that combines organic and inorganic soil amendments with AI-based decision support to improve Aridisols productivity and resilience. Searches of the literature (ScienceDirect, EBSCOhost, Clarivate Web of Science; English, 2021–2025) screened 309 records and selected 169 references; seminal older works were consulted for context. Representative quantitative outcomes from the reviewed literature include SOC increases of ~15–30% after multi-year organic inputs; forage biomass gains of ~10–25% following amendments that correct sodicity; and water-productivity improvements up to ~30% with hydrogels or biochar. AI tools can improve soil diagnostics and amendment selection (diagnostic accuracy improvements of ~15–30% in recent studies) and generate predictive models of amendment–response that facilitate optimization of application rates and water use. The novel contribution of this review is the explicit linkage of SFL-systems and amendment-based soil restoration with AI-driven diagnostics and decision support, providing actionable metrics and research priorities to translate digital diagnostics into measurable forage gains in arid and semi-arid regions. Overall, the evidence suggests that targeted soil restoration, reinforced by AI-based support systems, is a feasible strategy to increase forage availability and ecosystem service provision in drylands. Full article

Vegetation strongly influences soil formation, yet its effect on Rare Earth Element (REE) distribution and fractionation across treeline ecotones remains insufficiently constrained. The present study investigated how contrasting plant communities, Vaccinium myrtillus heathlands and Picea abies forests, affect pedogenetic pathways and REE behavior in sandstone-derived soils of the Northern Apennines (Italy). Six soil profiles were characterized for bulk geochemistry, selective Fe–Al extractions, particle-size distribution, and REE concentrations. Principal component analysis and hierarchical clustering identified pedogenetic drivers and horizon groupings. Under Vaccinium myrtillus, thick acidic organic horizons promoted organo-metal complexation and incipient podzolization, whereas Picea abies soils showed thinner organic layers and enhanced mineral weathering, leading to Bw development with higher silt–clay contents and elevated Al/N ratios. These pathways were captured by Fe–Al indicators and the Spodic Index. REE distributions showed vegetation-related differences in surface horizons and Eu–Ce anomalies, but they did not reproduce Fe–Al pedogenetic clusters, reflecting strong parent-material control. The coexistence of podzolic and cambic pathways at the treeline highlights pronounced spatial heterogeneity and vegetation effects. Plant composition may redirect pedogenesis, influencing nutrient cycling and metal mobility. Additionally, these findings emphasize the need to integrate multivariate statistics with established pedogenetic indicators when evaluating geochemical properties in mountain soils. Full article

This study is the first to investigate volcanic ash (VA) as a soil amendment to mitigate nitrous oxide (N₂O) emissions, a potent greenhouse gas mainly produced through nitrification and denitrification processes in agricultural soils. The experiment assessed the effects of VA mixed with soil and combined with mineral (NH₄NO₃, N) or organic (poultry manure, O) fertilizer on N₂O emissions, soil mineral nitrogen (NO₃⁻ and NH₄⁺), trace metals (Zn, Cu, Mn), and crop yield in a 4-month pot experiment including treatments with and without VA. Results showed that VA reduced N₂O emissions by 55% in mineral fertilizer treatments and 71% in organic fertilizer treatments compared to soils without VA. This reduction was associated with significant changes in nitrogen availability. In mineral fertilizer treatments with VA, soil NO₃⁻ concentrations remained high, potentially limiting denitrifier activity, while in organic treatments VA appeared to inhibit nitrogen mineralization. Additionally, VA increased soil concentrations of Zn, Cu, and Mn, which were negatively correlated with N₂O emissions, suggesting an influence on microbial processes. Importantly, crop yields were not affected by VA application. Although promising, these preliminary findings highlight the need for further research to optimize application rates and evaluate long-term effects across soil types and management systems. Full article

Reactive oxygen species (ROS) are a class of molecules or free radicals with strong oxidizing properties. They have attracted increasing attention in soil research in recent years because of their perceived importance in many soil biochemical processes. Previous reviews of ROS in soil mainly focused on their impacts on carbon emissions and organic pollutant remediation, with few descriptions of the mechanisms responsible for ROS generation, and a comprehensive understanding of their environmental effects is still lacking. Therefore, the present review provides details on the sources and underlying generation mechanisms of ROS in soil. These mechanisms include inputs via atmospheric deposition, metal–mineral reactions, root exudation, microbial metabolism, enzymatic reactions and various organic matter transformations. In contrast to previous reviews, we also discuss mutual conversion between different types of ROS in soil. The impacts of ROS on the soil environment are further explored, such as element cycling, pollutant degradation, and the growth and reproduction of plants and microorganisms, in order to provide a systematic understanding of the various processes involving ROS in soil, thereby guiding better soil management decisions. Finally, we highlight future research trends, suggesting that the advancement of in situ detection methods is crucial for establishing the precise contribution of abiotic ROS processes to global soil carbon and nutrient models. Full article

Soil erosion is a significant environmental concern in arid regions, particularly in dam-regulated watersheds, where intermittent flows from sprinkler irrigation can exacerbate land degradation. This study assesses soil erosion susceptibility in the Sidi Aich watershed using a combined approach of the Revised Universal Soil Loss Equation (RUSLE) and the Analytic Hierarchy Process (AHP), enabling the integration of both regional characteristics and expert-driven weighting. The RUSLE model accounts for natural and human-induced factors, whereas AHP provides a hierarchical weighting system that highlights rainfall erosivity and the local impacts of dam-regulated discharges. Results show that 26.12% of the area falls into the very high susceptibility category, 25.45% into high, 23.91% into moderate, and 24.51% into low susceptibility. Model validation demonstrates satisfactory predictive performance, with Area Under the Curve (AUC) values of 0.85 for AHP and 0.78 for RUSLE. Overall, the findings emphasize the critical role of dam-controlled releases in increasing soil vulnerability, a factor that may not be fully captured when using RUSLE alone. By combining RUSLE and AHP, this research provides a more realistic and regionally tailored assessment of erosion risk, offering valuable guidance for watershed management and erosion mitigation strategies in arid environments. Full article

Aquaculture effluent appears as an alternative for reuse, given its significant generation. However, its use must be reasonable to avoid damage to the environmental quality of the soil. In this context, the objective was to evaluate the chemical changes in Ultisol cultivated with small prickly pear cactus and irrigated with different dilutions of aquaculture effluent in the supply water. The experiment was conducted at the Water Reuse Experimental Unit, located in the Brazilian semi-arid region, Mossoró, RN, Brazil. Planting was carried out in a randomized block design with five treatments and five replications. A small prickly pear cactus was irrigated weekly for 365 days, with the gross water depth determined based on the crop’s evapotranspiration. During the experimental period, the physical-chemical characterization of the effluent dilutions was conducted every 60 days, with initial and final descriptions of the soil in the 0.0–0.20 m and 0.20–0.40 m layers. Additionally, cation exchange capacity and the exchangeable sodium percentage were determined. Multivariate statistical analysis was applied to understand chemical changes in the soil. The dilutions containing a higher proportion of aquaculture effluent in the supply water, primarily consisting of 100% effluent, exhibited the chemical changes in the soil. Using a dilution containing 25% aquaculture effluent in 75% supply water may be the most viable alternative for water supply in prickly pear cactus irrigation, with non-relevant changes in soil chemical characteristics. Full article

Soil contamination in Sub-Saharan Africa (SSA) is increasingly driven by rapid industrialization, intensive agriculture, mining activities, and urban expansion, posing significant risks to food safety, ecosystem services, and human livelihoods. Despite the growing scale of the problem, low-cost, locally adaptable remediation technologies are widely available and technically feasible within the region. Organic waste and waste-derived products—such as compost, manure, biochar, vermicompost, digestate, and agro-industrial residues—have emerged as sustainable and cost-effective amendments for the remediation of contaminated soils. These materials can immobilize heavy metals, enhance the microbial degradation of organic pollutants, and improve soil health, making them especially suitable for resource-constrained settings. This review synthesizes the current knowledge on the use of organic waste-based remediation approaches in SSA, highlighting technologies already applied at the laboratory, pilot, and field scales, as well as their effectiveness across different contaminant types. However, despite their demonstrated potential, their widespread adoption remains limited. The primary challenge is not the absence of affordable solutions, but rather the systemic constraints characteristic of many SSA countries, including limited technical capacity, weak policy and regulatory frameworks, low stakeholder awareness, and insufficient financial and institutional support for large-scale implementation. To enable broader uptake, there is a need to strengthen waste segregation and treatment systems, standardize composting and pyrolysis processes, and develop robust regulatory guidelines and certification schemes. Investments in monitoring infrastructure, practitioner training, and knowledge transfer mechanisms will also be critical to translating scientific advances into scalable, field-ready solutions for sustainable soil remediation in SSA. Full article

Earthworms are ecosystem engineers that are sensitive to land-use intensification and edaphic conditions, yet their ecology remains poorly understood in transformed semi-arid landscapes. We hypothesized that, in recently colonized agroecosystems, land-use intensity and physicochemical soil conditions jointly filter the earthworm assembly. In the recently irrigated Lower Valley of the Negro River, Patagonia, Argentina, we sampled earthworms and soils across five land uses—riparian reference sites, fruit orchards, pastures, cereal crops, and horticulture plots—in landscapes dominated by Natrargid Ustolls and Fluventic Haplocambids. We found five species, all of which were exotic Lumbricidae, including the first Argentine record for Murchieona minuscula, indicating a recent colonization following human-mediated niche construction that created an ecological island. The earthworm abundance and biomass were highest in permanent and semi-permanent uses and were driven primarily by soil moisture, pH, and particulate organic matter. Crucially, our results reveal that land-use intensity filters communities by restricting the initial colonization rather than through local extinctions. These findings confirm that soil properties mediate the impact of land use on earthworm assemblages. The inclusion of pastures and fruit orchards in the rotations favors the earthworm populations that, despite low diversity, enhance soil functioning and contribute to agricultural sustainability in semi-arid irrigated agroecosystems. Full article

High-resolution soil depth maps are valuable for environmental modelling, yet reliable data remains scarce in the tropics. This study evaluates the feasibility of mapping depth to bedrock (DTB) in Sri Lanka using a legacy dataset (n = 88) and global environmental covariates (n = 247). A robust machine learning workflow was employed—including feature selection, hyperparameter tuning, and a stacked ensemble of four algorithms (Random Forest, XGBoost, Cubist, SVM)—to test the limits of global data for local mapping. Despite rigorous optimization, the final ensemble model achieved a performance of R² = 0.197 (RMSE = 35.4 cm) under spatial cross-validation. While still modest, this result significantly outperforms existing global products and quantifies the “prediction gap” inherent in using ~1 km resolution global covariates to model micro-scale soil variability. An initial exploration involved log-transforming the target variable; however, following rigorous testing, the untransformed depth was modelled directly to avoid bias in back-transformation. A robustness experiment was further conducted, reducing predictors from 24 to 12, which degraded performance, confirming that the model captures complex, physically meaningful climatic interactions rather than fitting noise. The study concludes that while global covariates can capture regional meso-scale trends (explaining ~20% of variance), they are insufficient for resolving local micro-relief (<50 m). The resulting map and uncertainty products provide a critical “baseline” for national planning, but effectively demonstrate that future improvements will require investment in higher-resolution local covariates (e.g., LiDAR) rather than more complex algorithms. Full article

Compost is widely used to improve soil fertility and structure, yet its effects on soil physical properties in Vertosols remain insufficiently documented. This study evaluated the effects of repeat compost application on soil carbon and nitrogen contents and selected soil physical properties in Vertosols from three farms in Queensland, Australia (Roma, Dalby, and Goovigen). Compost had been applied at rates between 5 and 22 Mg ha⁻¹ yr⁻¹ for periods ranging from 3 to 11 years, depending on the site. Intact and disturbed soil samples from the top 0–8 cm were analyzed for bulk density, water retention, hydraulic properties, aggregate stability, and water repellence. Aggregate stability was assessed using laser diffraction before and after ultrasonic dispersion. Compost application significantly increased total carbon and nitrogen contents at all sites (p ≤ 0.01), although effects on soil physical properties varied by site. In Dalby, compost improved water retention and aggregate stability; in Goovigen, it resulted in lower Disaggregation Ratios. Compost did not induce soil water repellence at any site. The results indicate that compost amendments improve soil carbon and nitrogen concentrations and can modify soil physical properties in Vertosols, although responses depend on site conditions and management history. Full article

Soil enzyme activities are sensitive biochemical indicators that could benefit soil health assessments, especially in coarse-textured soils. Current protocols are inconsistent for fluorimetric assays and an optimized assay would facilitate comparisons of activities across climates and soils. A factorial experiment was conducted to evaluate how assay conditions affect the activity of three enzymes (acid phosphatase, β-glucosidase, and N-acetyl-β-glucosaminidase) across seven Florida mineral soils (>89% sand) by crossing two temperatures, four pH values, and two reaction termination reagents. Results between microplate fluorimetry and benchtop colorimetry and between air-dried and frozen (−80 °C) soils were also compared. For these soils, a pH of 4.5 with sodium hydroxide termination and a temperature of 25 °C were deemed “optimal” for maximizing activities and maintaining consistent trends. Activities measured with benchtop colorimetry and microplate fluorimetry were related for each enzyme (R² range: 0.58–0.83) and activities from air-dried soils were 50–90% of those from frozen soils (R² range: 0.75–0.91). Enzyme activities were positively correlated with other indicators (total C, nutrients), supporting their use in soil health assessments. As the rankings of soil samples by highest enzyme activities were similar regardless of protocol variations, this suggests that inherent soil properties were the dominant drivers of enzymatic activity. Full article

Climate change brings about changes in precipitation and temperatures, significantly increasing aridity in many areas. The southeast of the Iberian Peninsula is affected by climate change and increased aridity, which, together with anthropogenic factors, has increased the area affected by erosion. It is interesting to learn about aspects of aridity, desertification, and erosion in the southeast of the Iberian Peninsula. A literature review was conducted on issues related to climate change, aridity, desertification, and erosion, focusing on the southeast of the peninsula. In addition, field visits were made to verify some of the situations described in the literature. The results highlight the relationships among climate change, aridity, desertification, and erosion, and illustrate their impacts on the landscape and territory of the southeastern Iberian Peninsula. Furthermore, the results indicated a clear anthropogenic influence on the aridity–desertification–erosion loop. There has been a notable and rapid increase in erosion and aridification. Aridity is closely linked to erosion, and its harmful effects on soils in the southeastern Iberian Peninsula have intensified significantly. Full article

In permanently submerged coastal wetlands, interactions between biogeochemical processes and microbial communities strongly influence greenhouse gas (GHG) fluxes. To improve our understanding of how redox-driven processes shape GHG dynamics in these ecosystems, we investigated the relationships among iron (Fe) pools, microbial dynamics, and the potential GHG production in subaqueous soils from an interdunal wetland in San Vitale Park (Italy), permanently submerged and affected by seasonal oscillations of the saline water table. Two subaqueous soil columns (WAS-2 and WAS-4), collected from similar settings, were analyzed. Surface layers of WAS-4 showed higher salinity and carbonate content, whereas WAS-2 was characterized by overall higher Fe concentrations. Distinct vertical distributions of organic matter and sulfur (S) were shown along depth. Laboratory incubations revealed that nitrous oxide (N₂O) production was up to ten times higher in WAS-2 than in WAS-4, with peaks in the top 13–14 cm, consistent with more active nitrification-denitrification in surface layers. Methane (CH₄) and carbon dioxide (CO₂) fluxes decreased with depth, reflecting reduced availability of labile carbon. Methanomicrobiales dominated CH₄-producing layers, indicating hydrogenotrophic methanogenesis, while amoA-carrying Nitrosomonadales and Thaumarchaeota, occurred in shallow, organic-rich layers where ammonia supported nitrification and denitrification. Denitrifiers mainly belonged to α- and β-Proteobacteria, consistent with their direct contribution to N₂O peaks. Spearman’s correlations showed N₂O positively correlated to sulfur and labile carbon (C), supporting denitrification under moderately reducing conditions. CH₄ and CO₂ positively correlated with organic C (Corg), total nitrogen (TN), and reactive Fe forms, reflecting redox-mediated microbial respiration and methanogenesis. Trace elements (B, Cr, Cu, Ni) acted as micronutrients or inhibitors depending on concentration. Canonical correspondence analysis indicated depth-structured links among gas fluxes, soil chemistry (Corg, TN, S/C, CaCO₃, P), and microbial distributions: surface layers, rich in labile C and nutrients, supported active bacteria and archaea involved in decomposition, nitrification, and denitrification, whereas deeper layers hosted oligotrophic archaea adapted to inorganic substrates. Overall, Fe pools appeared to be associated with soil processes relevant to GHG dynamics, although the extent of their regulatory role remains uncertain due to potential alterations of redox-sensitive Fe fractions during sample handling. These results contribute to broader efforts to predict GHG emissions in submerged wetland soils by linking redox stratification, inorganic chemistry, and microbial functional groups. Full article

Micronutrient addition to soil is crucial for improving crop yield. Within the framework of the circular economy, it is necessary to seek more efficient fertilizers. This would reduce fertilizer consumption while serving as a strategy to mitigate the negative effects of climate change. This study proposes the combined use of a traditional source of a Zn fertilizer (ZnSO₄) together with wood biochar to improve lettuce (Lactuca sativa L.) crop yield. An experiment was designed in which a dose of 8 mg Zn kg⁻¹ as ZnSO₄·7H₂O was added to Cambisol soil, mixed with or without biochar (5%), for lettuce growth. Among other soil properties, Zn bioavailability, microbial biomass, and available water were monitored in the soil, while photosynthetic pigments, Zn content, and biomass production were determined in plants. All treatments increased plant biomass production. Biochar treatments (biochar and biochar/ZnSO₄) increased fresh biomass by 324%, while ZnSO₄ addition resulted in a 158% increase in lettuce yield. This can be due to several factors, such as biochar being a C source, the improvement of soil water content after biochar addition, and the increase in Zn leaf content in all treatments with respect to the control soil. All of these likely had a positive effect on photosynthesis. This is corroborated by the increase in total chlorophyll, chlorophyll, and carotenoids in the treatments with ZnSO₄, biochar/ZnSO₄, and biochar. The application of biochar alone increased this property by more than 168%, with a positive impact on soil quality. Our research demonstrates that it is possible, in some cases, to prepare fertilizers combining ZnSO₄ and biochar, leading to increased plant Zn uptake and improved crop yield. Full article

Predictive Digital Soil Mapping (PDSM) in Eastern Amazonia faces challenges due to its environmental complexity, difficult access, and scarce legacy data. While legacy soil maps contain valuable tacit knowledge, updating them requires methods that can handle uncertainty. This study evaluates the integration of old soil maps with machine learning to update soil information in Tracuateua, Pará, with a specific focus on the performance of ensemble learning and the explicit incorporation of uncertainty metrics in soil mapping units under hydromorphic influence, which, in addition to being difficult to access, are influenced by complex pedogenetic processes. We combined 270 sampling points, equivalent to the total pixels that captured the variability of soil mapping units, with environmental covariates and historical data. Several algorithms were tested, including an ensemble approach, to predict mapping units and quantify uncertainty through entropy and confusion indices. The ensemble model demonstrated improved stability and reduced classification uncertainty compared to single models, particularly in challenging hydromorphic environments. Although accuracy gains were modest, the models captured soil–environment relationships, with climate as: Annual Mean Temperature 22,000 years ago (Tmean_22k), relief: Channel Network Base Level (CNBL and altitude) and organism variables: Land Surface Temperature (LST) emerging as the main predictors. Spatialized uncertainty estimates, expressed through entropy and the confusion index, provide a practical decision-support tool for guiding field surveys and identifying areas of low mapping reliability. By explicitly transferring the pedologist’s mental model—encoded as tacit knowledge in legacy soil maps—into ensemble learning, this study presents a robust and transferable framework for updating soil maps in data-scarce tropical regions, balancing predictive performance, spatial consistency, and uncertainty-aware interpretation. Full article

Integrating cover crops (CCs) into crop rotations has gained interest in the Southeastern United States due to the benefits that CCs offer, which improve soil health for agricultural production. However, more information is needed on how CCs may affect the development of soil water repellency (SWR), which can negatively impact soil hydrology. The development of SWR threatens crop yields, food security, and farmer livelihoods. To address this knowledge gap, a field experiment measured the water repellency (WR) of four common CC species and a fallow treatment. CC samples were oven-dried, ground, and analyzed for WR using the water drop penetration time (WDPT) test. The mean WDPTs of the CC residues collected at termination and four weeks post-termination ranged from 49 to 4174 and 8 to 2627 s, respectively. Large WDPTs (>5 s) indicate that CC residues can potentially influence the development of SWR. All CC residues exhibited WR. The results suggest that farmers may need to consider alternative CC species depending on when they plant their cash crops in relation to CC termination. Considering the effects of CCs on SWR will enable farmers to make informed management decisions to mitigate SWR development and maintain soil health in a changing climate. Full article

Comprehensive soil surveys necessitate the integration of multidimensional pedological information, ranging from the morphological delineation of horizons and the taxonomic identification of soil groups to the quantitative assessment of soil organic matter (SOM). These attributes collectively constitute the basis for interpreting pedogenesis and guiding sustainable soil management. However, conventional methods are limited by the subjectivity of expert judgment for horizon and soil group identification, and the time-consuming nature of laboratory analyses for SOM quantification. We developed a novel multimodal deep learning framework based on an improved Mask R-CNN architecture that integrates soil profile images with auxiliary soil property data to concurrently delineate soil horizons, classify soil groups, and quantify SOM. The model was trained on high-resolution soil profile images from 451 soil survey sampling sites spanning ten soil groups across Anhui Province, China. Data augmentation and transfer learning with pre-training on large general image datasets were employed to address the dataset size limitations and improve model generalization. In addition to accurately delineating master horizons, we evaluated three schemes for classifying transitional horizons, which are often ambiguously determined by expert assessments: (i) assigning the transitional horizon to one adjacent master horizon; (ii) assigning it to both neighboring master horizons as an overlapping section; and (iii) treating the transitional horizon as an independent layer. Scheme (iii) achieved the best overall performance, e.g., horizon delineation with accuracy = 0.925, recall = 0.933, F1-score = 0.929, and segmentation mean average precision (seg-mAP) = 0.918, soil group classification accuracy = 0.717 and prediction of SOM with R² = 0.565. These results demonstrate that treating transitional horizons as independent layers yields superior segmentation. Consequently, this integrated framework provides a robust, automated solution for high-throughput soil resource assessment. Full article