This paper summarizes the hydrological modeling research results of the national CPR flood project developed by the National Center for Cartography and Remote Sensing to create a warning system for predicting and combating future floods. The watershed studied is located in northwest Tunisia, characterized by an elongated shape and bordered on one side by high reliefs that favor the convergence of flowing streams towards the Medjerdawadi. In terms of pedology, the soil texture is mainly characterized by a dominance of complex soil units and poorly evolved alluvial deposits. Following the flooding phenomenon that affected several towns around the Medjerda during the last century, our study aimed to map and delineate risk areas using hydraulic modeling methods. Flood risk analysis is performed using the HEC-RAS 2D hydraulic model and geographic information systems (GIS), based on several rainfall events. The exceptional floods that occurred in 2003, 2012, and 2015 brought a very large volume of water upstream of the Sidi Salem dam. Inflows from the intermediate basins on both banks, as well as the volumes released from the Mellègue and Bouhertma dams, caused spectacular flooding along the region between Ghardimaou and the Sidi Salem dam. We focused on very high-risk areas located in sectors with medium or low slopes, with varying degrees of cover. The "floodability" method is used to quantify vulnerability. Finally, risk mapping is obtained by cross-referencing the flood hazard map and the vulnerability map of the exposed issues. The aim was to determine the extent of the flooding and identify flood-prone areas. The mapping results enabled the identification of areas affected by flooding during the February 2015 flood. The flooded areas are located mainly in the town of Jendouba.

Ecophysiological vulnerability and thermal niche shifts of an extremophile lizard under climate change in the Sonoran desert using hybrid mechanistic-correlative SDM.

Complex Interactions Among Plant Residue Quality, Soil Texture, and Soil Moisture Determine Nitrogen Mineralization in Tropical Soils

Soil moisture plays a vital role in ecological dynamics, the hydrological cycle, and climate monitoring. Traditional prediction methods include physical models and data-driven approaches, with the latter gaining popularity due to its ability to capture complex data patterns. This study utilizes the Extreme Learning Machine (ELM) model to predict surface soil moisture (0–5 cm) by incorporating meteorological, topographic, and soil texture data. To improve model stability and accuracy, we integrate the Particle Swarm Optimization (PSO) algorithm for global parameter optimization. Results indicate that incorporating static features, such as topography and soil texture, significantly enhances prediction accuracy. The particle swarm optimization–extreme learning machine (PSO-ELM) model achieves a 14.6% increase in the coefficient of determination (R²), with reductions of 27.5% in root-mean-square error (RMSE) and 23.8% in mean absolute error (MAE). Validation with ground-based observations demonstrates superior performance. The PSO-ELM model provides high-resolution, accurate soil moisture predictions, supporting agricultural management and land surface process studies.

Leguminous crop rotation (LC) and conservation tillage (CT) are nature-based solutions to mitigate climate change. Previous studies have shown significant variations in crop productivity and soil organic carbon (SOC) under LC and CT, largely influenced by site-specific conditions. However, the mechanisms driving the interactions between LC and CT to enhance compatibility across diverse environmental conditions remain unclear. This study conducted a meta-analysis combined with machine learning, using a high-resolution global database of 271 site experiments to evaluate the impact of LC, CT, and their interaction on crop yield and SOC, clarify the underlying mechanisms, and assess their global potential. Results indicated synergistic effects of LC and CT led to additional increases of up to 13.4% in yield and 8.6% in SOC. These benefits were more pronounced in warm-humid regions, with low initial soil fertility, fine soil texture, and low nitrogen (N) input. Among key factors influencing these interactive effects, N input and the initial soil carbon to nitrogen (C/N) ratio emerged as the top two determinants for crop yield and SOC changes. Globally, integrating LC and CT in farmlands could potentially increase crop production and SOC stock by 16.9% and 7.6%, respectively. Looking ahead, these practices could enhance crop production by up to 400 Tg (24.6%) and SOC stock by 8.4 Pg (10.0%), helping to address climate change under various future scenarios. These results highlight that optimising N input and the initial soil C/N ratio through LC-CT integration achieves a win-win scenario of increased crop yield and enhanced SOC sequestration, with significant potential under future climate conditions. This study provides a scientific basis for developing targeted farmland management strategies tailored to diverse environmental conditions worldwide.

Mining for fossil fuels and minerals generates spoil heaps and open pits, which have significant environmental impacts in addition to their economic contributions. Afforestation of these disturbed areas can improve soil properties, thereby increasing the functionality and resilience of terrestrial ecosystems. However, the extent of changes in soil properties following afforestation varies depending on the methods used for tree introduction. There is a need for knowledge on the effects of afforestation on soil properties, especially in post-mining Techonosols. Therefore, the objective of this research is to evaluate the effects of three afforestation methods, succession on barren spoil top (SBT), succession on reclaimed topsoil (STS), and plantation on reclaimed topsoil (PTS), on soil properties in a coal post-mining site. Soil samples were collected from 30 randomly established plots (10 × 10m) for physical and chemical analyses, focusing on the upper layer (0-10cm depth). The collected samples were analyzed for soil texture, bulk density (BD), porosity, air capacity, capillary water capacity (CWC), moisture content (MC), exchangeable base cations (Ca2⁺, Mg2⁺, K⁺, and Na⁺), total organic carbon (SOCt), SOC fractions, total nitrogen (Nt), and total sulfur (St). The results showed that PTS had significantly higher CWC, Nt, Ca2⁺, K⁺, occluded light fraction of carbon (ColF), and mineral-associated carbon fraction (CMAF) compared to SBT. These improvements highlight the effectiveness of active reclamation in enhancing soil structure stability, nutrient retention, and long-term carbon stabilization, critical elements for post-mining ecosystem restoration. In contrast, SBT had greater porosity, Mg2⁺, and free light fraction of carbon (CflF) than STS. In addition, SBT had greater St compared to STS and PTS. This indicates that both natural succession and active restoration contribute to soil change through different mechanisms. Therefore, the choice between afforestation strategies should depend on factors such as restoration objectives, topsoil availability, and resource constraints, as active restoration is labor-intensive and costly.

Abstract Soil salinity is one of the most widespread causes of declines in agricultural yields in drylands, consequently threatening environmental sustainability and food security. Productive restoration is an opportunity to repair the structure and function of these agroecosystems. Among possible restoration measures, afforestation with native trees ( Neltuma spp., ‘algarrobos’) is a promising alternative, particularly in the Cuyo oasis (Argentina). Although the relevance of restoration planning has been widely recognised, there is a knowledge gap regarding the planning of productive restoration in agroecosystems. Our objective is to identify potential sites for reforestation in the irrigated drylands of San Juan. To that end, we used biomass productivity prediction based on soil salinity, texture, and moisture derived from a reforestation experiment. We evaluated bands from individual Sentinel‐2 acquisitions to account for these soil properties. We selected them based on their correlation with field sample data to use as predictor variables. Biomass productivity was modelled using Random Forests, with the growing‐season integral of the Normalised Difference Vegetation Index red edge (iNDVIre) as a proxy for productivity and 29 predictor variables. Based on variable importance, RMSE, and MAE, we selected a subset of seven predictors. We performed a spatial 3‐fold cross‐validation for the final model, and R 2 was 0.68. We identified 134 potential restoration sites affected by soil salinity and capable of supporting the highest forest biomass values. Sites totalled 248.50 ha, representing 10% of the area most affected by soil salinity in this sector of the oasis. Sites varied in size and were distributed across the entire study area. Biomass productivity prediction can be used to plan restoration actions in agroecosystems affected by soil salinity to ensure a higher probability of success and lower costs. Moreover, the proposed approach enabled the scaling up of experimental findings. Synthesis and applications . The proposed approach is a valuable tool for productive restoration planning in irrigated drylands and is adaptable to other agroecosystems constrained by different soil factors.

Earthworms affect soil organic carbon (SOC) decomposition and C stabilization into mineral associated organic matter (MAOM) following fresh organic matter input. However, it remains untested how these earthworm-induced C dynamics vary with the rate of fresh organic matter input and soil texture and how they are associated with soil microbial C use efficiency (CUE). Herein, we conducted a 48-day incubation to investigate the impact of earthworms on soil C dynamics following litter input, as well as the relationships of C dynamics with microbial CUE. The experimental set-up consisted of three factors including earthworms (with and without), 13C-labeled grass litter input rate (0, 1 and 6 g C kg−1 soil) and soil texture (grassland soils with either clay or sand addition). Earthworms increased SOC decomposition without litter input by 9 % − 13 %, while amplifying the priming effect (PE) in soil with clay and sand addition at the highest litter addition by 24 % − 139 %, but decreasing the PE in soil with sand and low litter addition by 32 %. In soil with sand addition, earthworms increased MAOM formation efficiency from litter (fraction of added litter C stabilized in MAOM) by 17 % − 23 %, and the litter C sequestration quotient (litter-derived C in MAOM divided by the sum of litter derived C in MAOM and respiration) by 10 % − 27 %. However, earthworm-induced changes in SOC decomposition, PE and MAOM formation were not associated with earthworm-induced changes in microbial CUE. In conclusion, earthworms can facilitate SOC accrual more in soils with sand addition through disproportional amplification of SOC stabilization compared with SOC loss through decomposition. The influence of earthworms on SOC accrual is more likely driven by physicochemical protection of SOC rather than by changes in microbial metabolism.

Soil chemical composition and texture determinations using neutron-gamma analysis

Phosphorus leaching in alkaline soils: the role of soil texture and pore structure

Soil texture modulates microbial responses to irrigation: Implications for nutrient cycling in arid agroecosystem

Although root hairs (RHs) have been shown to enhance root-soil contact and soften the gradients in matric potential across the rhizosphere, our mechanistic understanding of how RH traits (length, density, and shrinkage) collectively affect water uptake and drought response remains elusive. Using a soil-plant hydraulic model, we investigated how these traits alter root water uptake dynamics in two contrasting soil textures (sandy loam and loam) and evaluated two scenarios: (1) RHs increasing the effective root radius and (2) RHs extending the effective absorption length (L eff ). In the first scenario, longer RHs enhanced effective root radius and delayed the soil hydraulic limit on transpiration, but only when RHs were shrinkage-resistant (shrinkage initiated at -0.2 MPa), with no significant difference between soil textures. In the second scenario, increased L eff improved uptake under moderate drought (soil matric potential around -0.15 MPa), mitigating declines in soil-plant hydraulic conductance. Long, dense, and shrinkage-resistant RHs broadened the moisture window for water uptake, with the largest relative gains in sandy loam. During soil drying, these trait interactions mitigated the gradients in matric potential at the root-soil interface, delayed stomatal closure, and shifted water use toward more anisohydric behavior, particularly in sandy loam. Our findings provide a step toward establishing a mechanistic basis for optimizing RH traits to enhance water uptake efficiency and drought resilience. • Root-hair traits modulate soil–plant hydraulic connectivity. • Increased effective absorption length (Leff) boosts uptake in sandy loam. • Shrinkage-resistant, long, dense hairs delay stomatal closure under drought. • Leff links measurable hair traits to crop-scale drought-resilience targets.

A dataset-based statistical comparison of gamma-ray and visible-near-infrared sensors for soil texture prediction with ensemble learning

A novel and simple method for accurate prediction of soil particle-size distribution from limited soil texture data

Sustainable soil management is increasingly recognized as essential for crop health, productivity, and resilience, especially in vineyard ecosystems. Within the B-Wine project, biochar was evaluated as a soil amendment to improve physicochemical properties, water availability, plant eco-physiological functions, and yield. The trial was carried out in one growing season, one year after biochar application (16 t ha−1 fresh weight ≈ 10.4 t ha−1 dry weight) on three organically managed vineyards in the Chianti Classico region (Tuscany, Italy), integrating soil parameters (e.g., organic carbon content, soil moisture, saturated hydraulic conductivity, bulk density) and eco-physiological measurement (e.g., leaf water content, photosynthetic performance) with remote-sensing analysis of multispectral Sentinel-2 level-2A imagery from the Copernicus program and soil spectral measurements. Results indicated that biochar significantly improved key soil properties, although the magnitude of these improvements varied according to soil characteristics. Bulk density decreased by 5–16%, while soil organic carbon increase differed in the three sites, being nearly 50% in the medium-to-fine textured soils and exceeding 200% in the coarse-textured soil. The impact of biochar on saturated hydraulic conductivity varied depending on the soil, the type of biochar, and the moisture conditions. However, it improved the water balance of the vines and yield. Considering all three vineyard sites, the average yield increase was approximately 42%. However, this result was largely driven by pronounced responses at two sites, while the third showed no measurable increase, likely due to site-specific differences in soil properties and climatic conditions. Overall, biochar proved to be an effective, soil-dependent strategy for enhancing vineyard resilience, plant performance, and productivity under challenging conditions.

Soil degradation from intensive agriculture and the overuse of inorganic fertilizers has raised global concerns associated with reduced crop yields and environmental pollution. Algae including cyanobacteria, micro, and macro-algae offer a sustainable solution as new age biofertilizers, capable of enhancing soil health and crop productivity. This review examines key mechanisms such as nitrogen fixation, secretion of phytohormones, bioactive compounds and organic compounds, and improvements in soil structure, texture, fertility and moisture retention. Notable species such as Anabaena, Nostoc, and microalgae consortia have demonstrated significant increases in nutrient availability and plant growth. Based on the current literature, algae based biofertilizers represent a promising component of integrated nutrient management and acting as a suitable biofertilizers for various crop needs.

This study evaluated the effects of ammonium sulfate [(NH4)2SO4] addition and land-use history on greenhouse gas emissions (CH4, CO2, N2O) and inorganic nitrogen dynamics (NH4+ and NO3−) in Brazilian Cerrado soils. The objective was to determine how fertilization interacts with native and agricultural soils to regulate key biogeochemical processes. Soil samples from native and agricultural areas were collected in four regions (Araras, Sorocaba, Itirapina, and Brasília), representing contrasting pedoclimatic conditions and soil textures under different cropping systems. Samples were incubated under controlled conditions, with greenhouse gas fluxes analyzed by gas chromatography and inorganic nitrogen concentrations determined by colorimetric methods. Nitrogen fertilization inhibited CH4 consumption in native and agricultural soils and reversed fluxes to emissions in sandy soils. CO2 emissions increased in native soils but decreased in agricultural soils, suggesting effects of soil fertility and carbon stocks. N2O emissions increased mainly in native soils, reflecting intensified nitrification and denitrification, whereas agricultural soils responded heterogeneously. Nitrogen addition altered NH4+ and NO3− consumption, indicating enhanced oxidation and microbial assimilation. These results demonstrate that land-use history influences soil biogeochemical responses to nitrogen, underscoring the importance of site-specific fertilization in mitigating emissions and promoting sustainability in the Cerrado.

The development of cocoa plants requires data and information regarding land potential and land suitability class assessment based on the physical and chemical properties of the soil in order to achieve optimal productivity. The objective of this study was to provide information on land suitability for cocoa cultivation in the Rembangan Sub-watershed. The research was conducted using a descriptive-exploratory method, with soil sampling carried out through a purposive random sampling technique, and by collecting both primary and secondary data. Land evaluation was performed using the matching method. Soil samples were analyzed in the laboratory, followed by matching land qualities with land characteristics. The results showed that the land characteristics in the Sub watershed area were divided into two actual land suitability classes: S3 (marginally suitable) convering 88.65% of the area and N (not suitable) convering 11.35% with limiting factors including soil texture, base saturation, pH H₂O, total nitrogen (N-total), available phosphorus (P₂O₅), available potassium (K₂O), slope gradient, and erosion hazard. Improvement efforts that can be implemented include the application of dolomite, urea fertilizer, SP-36 fertilizer, KCl fertilizer, as well as the construction of terraces and infiltration pits (rorak). After improvement measures were applied, the potential land suitability classes became S2 (moderately suitable) convering 52.46% of the area, S3 (marginally suitable) convering 36.19%, and N (not suitable) convering 11.35%.

This study aimed to develop a quantitative Cultivation Suitability Index (CSI) and identify growth-limiting environmental factors for the stable cultivation of Ainsliaea acerifolia, an understory perennial native to the southern and south-central mountainous regions of Korea. Climatic conditions, site topography, microenvironment, soil physicochemical properties, vegetation structure, and plant growth indices were investigated at six representative natural habitats. The soils were generally acidic and nutrient-limited, with low available phosphorus and low exchangeable Ca and Mg. Community diversity indices indicated stable understory assemblages across sites. Thirteen environmental indicators were normalized and weighted to construct the CSI, and suitability classes were defined as highly suitable (≥0.75), suitable (0.5–0.75), potential (0.25–0.5), and unsuitable (<0.25). The Maximum Limiting Factor Method (MLFM) was applied to identify site constraints, yielding a Limiting Factor Index (LFI) of 0.29–0.42, with light, humidity, temperature, EC, Ca and Mg emerging as dominant limiting factors. CSI and LFI exhibited a negative linear relationship (R2 = 0.6353), demonstrating that alleviation of limiting conditions directly improves site suitability. Optimal cultivation environments were characterized by moderately acidic soils, adequate Ca and Mg availability, moderate shade, and improved moisture balance. From a management perspective, maintaining soil pH around 4.5–5.0, supplementing Ca and Mg, enhancing drainage, and applying organic mulching or clay amendments to coarse textured soils are recommended. The CSI–MLFM framework provides a practical and transferable tool for selecting suitable cultivation sites and establishing sustainable understory and ecological mountain cultivation systems for A. acerifolia.

andfill leachate (LL) from landfills and open dumpsites poses significant risks to surrounding soils and water bodies. This study investigated seasonal variations in physicochemical and heavy metal characteristics of LL at the newly operational Bancharedada landfill site in Nepal using the Leachate Pollution Index (LPI). It also evaluated the attenuation of organic content, measured as chemical oxygen demand (COD), across different soil textures using fixed-bed column tests. Kinetics analyses employed the Yoon-Nelson model (YNM), Thomas model (TM), and Adam and Bohart model (ABM). The biological oxygen demand (BOD5)/COD ratio ranged from 0.44 to 0.51, with higher values in the dry seasons and lower values during the monsoon, indicating rainfall-induced dilution of organic pollutants. The bed saturation time for COD removal was longest in clayey soil (35 days) and shortest in sandy soil (4 days). YNM provided the best model fit and was therefore applied for COD breakthrough prediction across soil textures. YNM rate constants (KYN) were lower in clayey soils and higher in sandy soils, thereby increase in breakthrough times (τ) and adsorption capacities (Qo) in clayey soils whereas the sandy soils shows the opposite trend, highlighting the strong influence of soil texture on COD attenuation potential.