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Soil infiltration is critical for water security and related ecosystem services. This infiltration, the ability of soils to absorb water at their surface, is controlled by the soil hydraulic conductivity. Despite recent efforts in assembling measurements of soil hydraulic conductivity, global databases and derived pedotransfer functions lack coverage in the tropics. Here, we present soil infiltration measurements and other indicators of soil and land health collected systematically in 3,573 plots from 83 100 km 2 sites across 19 countries in sub‐Saharan Africa. We use these data to (a) determine field‐saturated hydraulic conductivity ( K fs ) and (b) explore which variables best predict variation in K fs . Our results show that sand content, soil organic carbon (SOC), and woody cover had a positive relationship with K fs , whereas grazing intensity and soil pH had a negative relationship. Our findings highlight that, despite soil texture being important, structure also plays a critical role. These results indicate considerable potential to improve soil hydrological functioning through management and restoration practices that target soil structure. Enhancing SOC content, limiting animal stocking, promoting trees, shrubs, and other vegetation cover, and preventing soil erosion can increase K fs and improve water security. This data set can contribute to improving Earth system and land surface models for applications in Africa. We present field infiltration measurements and accompanying indicators of soil and land health from 3,573 plots across sub‐Saharan Africa Field‐saturated hydraulic conductivity ( K fs ) is associated with soil texture and factors related to soil structure Our results suggest that soil hydrological functioning can be enhanced through management practices that target soil structure

Soil infiltration is critical for water security and related ecosystem services. This infiltration, the ability of soils to absorb water at their surface, is controlled by the soil hydraulic conductivity. Despite recent efforts in assembling measurements of soil hydraulic conductivity, global databases and derived pedotransfer functions lack coverage in the tropics. Here, we present soil infiltration measurements and other indicators of soil and land health collected systematically in 3,573 plots from 83 100 km2 sites across 19 countries in sub‐Saharan Africa. We use these data to (a) determine field‐saturated hydraulic conductivity (Kfs) and (b) explore which variables best predict variation in Kfs. Our results show that sand content, soil organic carbon (SOC), and woody cover had a positive relationship with Kfs, whereas grazing intensity and soil pH had a negative relationship. Our findings highlight that, despite soil texture being important, structure also plays a critical role. These results indicate considerable potential to improve soil hydrological functioning through management and restoration practices that target soil structure. Enhancing SOC content, limiting animal stocking, promoting trees, shrubs, and other vegetation cover, and preventing soil erosion can increase Kfs and improve water security. This data set can contribute to improving Earth system and land surface models for applications in Africa. Key Points We present field infiltration measurements and accompanying indicators of soil and land health from 3,573 plots across sub‐Saharan Africa Field‐saturated hydraulic conductivity (Kfs) is associated with soil texture and factors related to soil structure Our results suggest that soil hydrological functioning can be enhanced through management practices that target soil structure

PurposeSoil texture identification is vital for various agricultural and engineering applications but generally involves rigorous laboratory work, especially for estimating USCS (Unified Soil Classification System) soil texture classes. Soil texture influences soil water storage capacity, soil fertility, compaction characteristics, and soil strength. Soil spectroscopy offers a reliable approach that is non-destructive, rapid, and cost-effective to estimate several soil properties including texture. For engineering applications, the USCS soil texture classes are preferred, but very few studies have focussed on estimating USCS soil texture using soil spectroscopy or remote sensing data in general.MethodsTwo large soil spectral libraries (SSLs), viz., Kellog Soil Spectral Library (KSSL) and Open-source Soil Spectral Library (OSSL), as well as three deep learning algorithms (VGG-16, ResNet-16, and Swin transformers), were used in this study to predict six USCS soil texture classes and three USCS soil texture groups. The USCS soil texture classes and groups were derived by grouping clay, sand, and silt fractions that are closely associated with the corresponding USCS soil texture classes.ResultsThe results indicate that the Swin transformer model performed the best with an accuracy of 67% for six USCS soil texture class predictions and 81% for three USCS soil texture group predictions. Cohen’s kappa value implies a moderate agreement (0.55) for soil texture class predictions and a substantial agreement (0.64) for soil texture group predictions.ConclusionThe proposed methodology offers a novel approach for USCS soil texture class predictions utilizing SSLs and deep learning techniques.

Phosphorus is a critical nutrient that significantly enhances tomato production, so maintaining an adequate level of phosphorus plays an essential role in enhancing the growth of tomato by being present in the soil. This study assessed the impact of soil texture and phosphorus content on tomato plant properties using a factorial, complete, randomized design with four replications. Treatments included clayey and sandy soils with varying phosphorus sources: non-phosphorus (P0), calcium phosphate (CaP1 and CaP2), and nano-hydroxyapatite (PN1 and PN2), where 1 indicates a concentration of 0.12 g and 2 indicates a concentration of 0.23 g per 5-kilogram pot of fertilizer. Results indicated that treatments significantly influenced yield parameters such as average fruit weight, juice content, antioxidant activity, and fruit volume. In the clayey soil, CaP2 treatment had a superior effect on yield, average fruit weight, and shoot fresh weight. In comparison with sandy conditions, CaP2 produced a 50% increase in fruit number, 29% increase in average fruit weight, and 91% increase in fruit yield. The treatments then impacted the shoot fresh weight and root length, while the phosphorus concentration appeared to be more dependent on soil type than on phosphorus sources. Similar to the CaP1 and CaP2 treatments, the PN1 treatment in clay soil also resulted in the highest fresh and dry weights of tomato shoots when compared with the control group. Generally, the findings from this study suggest that the use of CaP2 can serve as a reliable method to improve the growth, yield, and fruit quality of tomatoes, especially in clayey soil environments. However, nano-based phosphorous sources need to be tested more to see if they can improve tomato performance in a range of soil conditions. Also, further research should look into the long-term effects of phosphorous interventions on soil health and sustainability.

Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R2) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R2 = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R2 = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future.

In soil texture classification, accuracy with interpretability is the key to sustainable agriculture and environmental management. The presented ATFEM (Advanced Triptych Feature Engineering and Modeling framework) framework synergizes handcrafted texture features with learned deep representations through a three-stream architecture: VGG-RTPNet (Residual Texture-Preserving Network based on Visual Geometry Group-16) for texture, ResNet-DANet (Residual Network integrated with Dual Attention Network) for semantics, and Swin-FANet (Shifted Window-based Frequency-Aware Network based on Transformer) for spectral spatial correlation. Subsequently, these branches help in extracting fine-grained structural, dual-attention-enhanced semantic, and spectral-spatial correlation-wise features of soil-image data. To further eliminate redundancy from the feature sets and arrive at the best representation, a Feature Fusion and Selection strategy employing an enhanced hybrid metaheuristic method termed EWJFO (Enhanced Wombat-Jellyfish Feature Optimization) is proposed. It synthesizes the adaptive exploration behavior of Wombat Optimization Algorithm (WOA) with the swift control convergence tempo of the Jellyfish Search Optimizer (JSO) to select the best feature subset. In addition, a new handcrafted descriptor for soil texture image analysis referred as Farthing Ornament of Histogram of Oriented Gradients (F-HOG) has been introduced with adapative. Conventional HOG is burdened with having high-dimensional redundancy and hence suffers from noise sensitivity, F-HOG combines the effect of a Butterworth frequency filter to remove the unwanted high-frequency artifacts and then goes on to perform the statistical selection of the most frequent gradient bins, thus reducing dimensions and retaining quite a bit of the discriminative structural information. The experiments were conducted on a self-built soil texture image dataset consisting of 4,000 labeled images distributed among five texture classes. ATFEM achieved an accuracy of 98.10%, an F1 score of 89.60%, Cohen’s kappa rating of 94.80%, and an AUC of 98.10%, outperforming cutting-edge methods such as CatBoost-DNN, GBDT-CNN, and SVC-RF. This work offers an upscalable, explainable, and expressively accurate solution for soil texture mapping in precision agriculture and environmental monitoring.

Stabilization of aeolian sand is essential for achieving desertification control, soil and water conservation, and agricultural development in sandy lands. Feldspathic sandstone is a soft clay rock widely found in the Mu Us Sandy Land. The purpose of this study was to ascertain the mechanism for aeolian sand stabilization with feldspathic sandstone from the perspective of particle size distribution. Feldspathic sandstone was added to aeolian sand at different ratios ( m f : m s = 1:0, 1:1, 1:2, 1:5, and 0:1, where m f is the mass of feldspathic sandstone and m s is the mass of aeolian sand). The results showed that the soil texture was modified upon addition of feldspathic sandstone. The content of particles <0.05 mm increased with increasing addition ratio of feldspathic sandstone, in contrast to the downward trend observed for particles >0.05 mm. Consequently, the soil texture changed from sand to sandy loam, then loam, and finally silty loam. The addition of feldspathic sandstone ameliorated aeolian sand, resulting in a broader particle size distribution and lower particle size uniformity. Continuously well-graded soil was obtained at m f : m s = 1:5 (coefficient of uniformity: 54.71; coefficient of curvature: 2.54) or 1:2 (coefficient of uniformity: 76.21; coefficient of curvature: 1.12). While the addition of feldspathic sandstone solved the problem of single particle size distribution in aeolian sand, the presence of aeolian sand prevented soil compaction caused by the high clay content of feldspathic sandstone. Findings of this study indicate that the addition of feldspathic sandstone to aeolian sand leads to the mixing of various sized particles and continuous gradation of the soil. Although a higher addition ratio of feldspathic sandstone is more favorable for soil texture improvement, m f : m s = 1:5 is recommended for practical application in terms of particle gradation and cost effectiveness.

Context In the Rio Grande Plains of southern Texas, subtropical savanna vegetation is characterized by a two-phase pattern consisting of discrete woody patches embedded within a C 4 grassland matrix. Prior trench transect studies have suggested that, on upland portions of the landscape, large woody patches (groves) occur on non-argillic inclusions, while small woody patches (clusters) are dispersed among herbaceous vegetation where the argillic horizon is present. Objective To test whether spatial heterogeneity of subsurface soil texture drives the landscape-scale pattern of woody patches in this subtropical savanna. Methods Landscape-scale spatial patterns of soil texture were quantified by taking spatially-specific soil samples to a depth of 1.2 m in a 160 m × 100 m plot. Kriged maps of soil texture were developed, and the locations of non-argillic inclusions were mapped. Results Visual comparison of kriged maps of soil texture to a high resolution aerial photograph of the study area revealed that groves were present exclusively where the non-argillic inclusions were present. This clear visual relationship was further supported by positive correlations between soil sand concentration in the lower soil layers and total fine root biomass which mapped the locations of groves. Conclusions Subsurface non-argillic inclusions may favor the establishment and persistence of groves by enabling root penetration deeper into the profile, providing greater access to water and nutrients that are less accessible on those portions of the landscape where the argillic horizon is present, thereby regulating the distribution of grove vegetation and structuring the evolution of this landscape.

Differences in land use systems may resulted in different soil cover, litter input, and soil management practices, and consequently affect to soil nutrient stock. The study aimed to assess soil carbon (C) and nitrogen (N) storages on various soil depths from difference land use systems within UB forest. The research was conducted in UB forest, Malang – Indonesia, from April to November 2017. Soil sample was collected from four soil depths (0-0.1, 0.1-0.3, 0.3-0.5, and 0.5-1.0 m) within five land use systems, including (1) protected area; (2) pine + coffee; (3) pine + crops; (4) mahogany + coffee and (5) mahogany + crops, each with three replicate plots. Soil C and N concentrations, soil texture, and bulk density, were measured. The study showed significant difference in soil C and N storages among land use systems. In 0.5 m depth of soil, soil C and N storages was higher in protected area (64% and 53%, respectively) as compared to other land use systems. The result support clay content controls soil C and N stock, whereas vegetation determines soil N stocks. Therefore, proper management in vegetation and soil were needed to conserve soil C and N storages.

PurposePrecision mapping of soil texture is critical for hydrological, ecological, environmental, and agricultural modeling and field management. However, the mapping precision is generally restricted by the limited number of soil sampling and insufficient use of available information in spatial interpolation.MethodsTo map layered soil texture with higher precision, we propose an additive log-ratio (ALR) transformation and exploratory factor analysis (EFA)-based co-kriging (CK) method (ALR-EFA-CK) to study the spatial variability of multi-layered soil particle-size fractions and soil texture. In this method, the ALR transformation is used to reduce the closure effect of soil particle-size fractions as compositional data that are characterized by non-negativity and a constant sum of 100%, and EFA is used to extract common factors from variables related to soil texture that are further used as auxiliary variables of CK. Six interpolation methods, ordinary kriging (OK), traditional CK (CC-CK), and EFA-CK for both the original and ALR transformed data, were evaluated in a case study with data collected at seven soil layers of 108 sampling points in the middle reach of Heihe River basin in Northwest China.ResultsCC-CK is superior to OK by including auxiliary data in interpolation, EFA-CK is more effective in improving the interpolation precision by taking full advantages of auxiliary information, and ALR transformation can improve the interpolation precision effectively for soil particle-size fractions as compositional data.ConclusionsTherefore, the proposed ALR-EFA-CK method is beneficial in improving the interpolation precision and recommended to interpolate multi-layered soil texture.