Replacing peat in green roof substrates with sustainable alternatives while maintaining plant performance and ecosystem services remains a critical challenge. We studied biochar-substrate interactions across four commercial green roof formulations (based on the type of organic component) in a greenhouse experiment: pure vermicompost, vermicompost + fen peat, fen peat, and mixed fen/high-moor peat. Substrates were amended with straw biochar, pine bark biochar, or left unamended (5% v/v, n = 4 replicates) and planted with a grass seed mixture mimicking early green roof establishment. Plant growth, nutrient contents (nitrate and phosphate contents), and microbial indicators (microbial biomass carbon (MBC), qCO2, and enzyme activities) were measured 30 days after the experiment began. Straw biochar in vermicompost boosted nitrate (90.8 mg kg−1) and root N (3.1%) compared to the control, while pine bark biochar in mixed peat released phosphate (+375%) and maximized MBC (874 µg g−1). Biochar intensified substrate effects, suppressing CO2 in peat through liming effects (pH from 4.6 to 6.5–7.1) but priming respiration in vermicompost via labile C supply. PCA explained 63% of the variance, with nitrate, plant N, and microbial parameters driving substrate separation. These short-term greenhouse results demonstrate critical biochar-substrate specificity for green roof substrate development, emphasizing formulation-specific matching over universal biochar application.

Rapid and cost-effective estimation of soil total nitrogen (TN) is essential for soil fertility assessment and nutrient management. However, the performance of laboratory visible–near-infrared (Vis–NIR) models is shaped not only by preprocessing and modeling strategy but also by sample preparation and the soil’s compositional background. In this study, TN prediction was evaluated using 376 topsoil samples from two contrasting datasets: Mollisols from the black-soil region of Northeast China and Ultisols from Qiyang County, Hunan Province, southern China. Spectra acquired over 350–2500 nm for three particle-size fractions were preprocessed using Savitzky–Golay smoothing combined with standard normal variate (SNV), first-derivative, or second-derivative transformations, and modeled using partial least squares regression (PLSR), support vector regression (SVR), and extreme gradient boosting (XGBoost). Model development used a 5 × 5 nested cross-validation followed by evaluation on a sample-grouped held-out test set. Among all combinations, XGBoost with first-derivative preprocessing on the 0.25 mm fraction produced the best performance, with test R2 values of 0.91 for Mollisol and 0.78 for Ultisol. Shapley additive explanations (SHAP) and principal component analysis (PCA) consistently identified informative spectral regions at 430–480 and 1330–1450 nm for Mollisol and at 585–635, 820–900, and 2180–2240 nm for Ultisol. Prediction errors were larger in the sampled Ultisol dataset and increased with DCB-extractable Fe and mineral backgrounds. A second-stage log-domain residual correction incorporating ancillary soil properties further reduced the Ultisol RMSE from 0.30 to 0.27 g kg−1. These findings support the 0.25 mm, first-derivative, XGBoost workflow as a robust laboratory Vis–NIR approach for TN prediction and indicate that composition-aware residual correction can improve prediction in oxide- and mineral-rich soils.

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.

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.

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.

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.

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.

Climate change and soil degradation threaten agricultural sustainability in arid oases, where water and nutrient limitations constrain crop production. In Tunisia, date palm residues are abundant but frequently burned despite their potential as soil amendme. This study assessed the effects of date palm waste biochar (B; 10 t ha−1), mineral fertilizers (NPK), and their combination as enriched biochar (BNPK) on soil fertility, including total organic carbon (TOC) and total nitrogen (TN), as well as barley (Hordeum vulgare L.) yield over two consecutive cropping seasons (2023–2024) using a randomized complete block design with three replications. During 2024, B increased TOC to 0.5% (control: 0.18%), while NPK enhanced TN to 0.037% in 2023; however, in 2024, nitrogen levels returned to values comparable to the control condition (0.017%). BNPK combined these beneficial improvements, maintained them in 2024, and resulted in a C/N ratio of 16.7 (control: 9.6), reflecting the most favorable balance between soil carbon accumulation and nitrogen retention. Grain yield increased by 21% (B), 80% (NPK), and 79% (BNPK) relative to the control (3.12 t ha−1), while BNPK reduced soluble sugars in grains (fructose 100%), glucose 86% (control: 0.09, 0.014) and increased grain nitrogen content to 1.80% (control: 0.74). Principal component analysis revealed a clear separation among treatments, with BNPK strongly associated with improved soil fertility, grain yield, and grain quality. These results demonstrate that integrating biochar with nutrient management enhances soil fertility and supports sustainable agriculture in arid oasis agroecosystems.

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.

Drought reduces soil moisture and impairs root function, posing a significant threat to rice production in arid regions. The influence of soil amendments on early rice root development under semi-dry cultivation remains insufficiently characterized, especially when assessed using non-destructive rhizotron techniques. This study employed a scanner-based rhizotron system to evaluate early root responses of rice seedlings to six amendments under semi-dry irrigation: vermicompost and peat moss, spirulina powder, gypsum, rice husk biochar, zeolite, and an unamended control. The vermicompost plus peat moss (VC+PM) treatment demonstrated the highest water-holding capacity (26%), root projected area (9.60 cm2 plant−1), and root surface area (84.79 cm2 plant−1). VC+PM also promoted extensive lateral branching (233 secondary and 1709 tertiary roots) and the greatest total lateral root length (363.09 cm plant−1), resulting in superior biomass (shoot: 140.00 mg plant−1; root: 56.70 mg plant−1) and the lowest root-to-shoot ratio (0.90). These improvements are attributed to the enhanced moisture retention of peat moss and the nutrient and phytohormone contributions of vermicompost. In contrast, rice husk biochar exhibited the lowest water-holding capacity (14%), while other amendments produced moderate or limited effects. The results establish a direct relationship between improved soil water retention and early-stage drought-avoidant root development. The combination of VC and PM emerges as a promising approach to enhance root plasticity and seedling establishment in water-saving rice systems. As this study was conducted under controlled rhizotron conditions and limited to the seedling stage (20 days after sowing), future research should prioritize multi-season field trials to assess yield translation and economic feasibility assessments to support farmer adoption.