Soil Property Data Research Articles

Enhancing slope stability prediction through integrated PCA-SSA-SVM modeling: a case study of LongLian expressway

China is one of the regions most frequently affected by landslides, which have significant socio-economic impacts. Traditional slope stability analysis methods, such as the limit equilibrium method, limit analysis method, and finite element method, often face limitations due to computational complexity and the need for extensive soil property data. This study proposes a novel approach that combines Principal Component Analysis (PCA), Sparrow Search Algorithm (SSA), and Support Vector Machine (SVM) to improve the accuracy of slope stability prediction. PCA effectively reduces data dimensionality while retaining critical information. SSA optimizes SVM parameters, addressing the limitations of traditional optimization methods. The integrated PCA-SSA-SVM model was applied to a dataset of 257 slope stability samples and validated using five-fold cross-validation to ensure the model’s generalization capability. The results show that the model exhibits superior performance in prediction accuracy, precision, recall, and F1-score, with the test set achieving an accuracy of 84.6%, a recall of 84.7%, a precision of 83.1%, and an F1-score of 84.6%. The model’s robustness was further validated using slope data from the LongLian Expressway, demonstrating high consistency with the actual stability status. These findings indicate that the PCA-SSA-SVM-based slope stability prediction model has significant potential for practical engineering applications, providing a reliable and efficient tool for slope stability forecasting. Classify the training samples through cross-validation, using the accuracy of cross-validation as the fitness of the sparrow individual. Retain the optimal fitness value and position information.

Mapping soil thickness by accounting for right‐censored data with survival probabilities and machine learning

AbstractIn digital soil mapping, modelling soil thickness poses a challenge due to the prevalent issue of right‐censored data. This means that the true soil thickness exceeds the depth of sampling, and neglecting to account for the censored nature of the data can lead to poor model performance and underestimation of the true soil thickness. Survival analysis is a well‐established domain of statistical modelling that can deal with censored data. The random survival forest is a notable example of a survival‐related machine learning approach used to address right‐censored soil property data in digital soil mapping. Previous studies that employed this model either focused on mapping the probability of soil thickness exceeding certain depths, and thereby not mapping soil thickness itself, or dismissed it due to perceived poor performance. In this study, we propose an alternative survival model to map soil thickness that is based on the inverse probability of censoring weighting. In this approach, calibration data are weighted by the inverse of the probability that soil thickness exceeds a certain depth, that is, a survival probability. These weights can then be used with most machine learning models. We used the weights with a regular random forest, and compared it with a random survival forest, and other strategies for handling right‐censored data, through a comprehensive synthetic simulation study and two real‐world case studies. The results suggest that the weighted random forest model produces competitive predictions, establishing it as a viable option for mapping right‐censored soil property data.

Implementing fuzzy SMRGT, ANN, and ANFIS for flow coefficient estimation in Antalya River Basin

The estimation of the flow coefficient is a vital hydrological procedure that holds considerable importance in flood prediction, water resource management, and flood mitigation. The precise estimation of the flow coefficient is imperative in mitigating flood-related damages, administering flood alert mechanisms, and regulating water discharge. It is hard to accurately determine the flow coefficient without a good understanding of the river basin’s hydrology, climate, topography, and soil characteristics. A range of methodologies have been documented in the most recent body of literaturefor flow coefficient modeling. The majority of these methods, however, depend on opaque techniques that lack generalizability. Therefore, this research employed three distinct methodologies—specifically, the Adaptive Neural Fuzzy Inference System (ANFIS), the Simple Membership Function, and the Fuzzy Rules Generation Technique (SMRGT) are all examples of fuzzy inference systems, and Artificial Neural Network (ANN), to achieve its objectives. The Aksu River Basin in Antalya, Turkey, was chosen as the study area. The models underwent multiple permutations of precipitation (P), temperature (T), relative humidity (Rh), wind speed (Ws), land use (LU), and soil properties (Sp) data that were tailored to the particular study region. The study analyzed the results using various performance metrics of the model such as mean absolute error (MAE), Nash–Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). The results indicate that the SMRGT method resulted in a remarkable degree of accuracy in forecasting the flow coefficient, as demonstrated with the minimal RMSE and MAE values and high correlation coefficient values. The study’s findings suggest that the SMRGT method was applied effectively in hydrological analysis to estimate the flow coefficient, contributing to more accurate flood prediction, water resource management, and flood mitigation strategies.

Compilation of Consolidation Properties Data of Champlain Sea Clay from Ottawa Region

Estimation of consolidation settlements in fine-grained soils due to various civil infrastructure loads is traditionally based on results derived from consolidation tests performed on undisturbed soil samples, combined with the data of other soil properties. In many geotechnical engineering applications, consolidation settlements are also estimated using empirical consolidation parameters derived from basic soil properties. This approach relies on correlations from the literature to bypass the time-consuming and expensive sampling techniques, laboratory testing, and other associated expenses. However, these correlations may not provide reasonable consolidation settlement estimations as these correlations are typically developed without considering the influence of stress history, geology, salinity of pore water, gradation, soil fabric, and chemical properties of the soils. This is especially true for Champlain Sea clay deposits from Eastern Ontario region of Canada that are typically with heterogeneous site conditions and exhibit spatial variability of soil properties. In this paper, data from the published literature and industrial and government reports on sensitive Champlain Sea clays were gathered for the Ottawa region. The data collection and clean-up methodology towards enhancing the reliability of the gathered data is comprehensively discussed. The summarized data from this study can be used with a greater degree of confidence towards developing reliable correlations in the estimation of consolidation settlements in geotechnical engineering practice.

Assessment of Deposited Red Clay Soil in Kirkuk City Using Remote Sensing Data and GIS Techniques

This study investigates the physical characteristics of red clay using the IDW approach and linear regression modeling in an area of 268.12 km2, focusing on Kirkuk, Bor, and Jambor structures. Through the analysis of 52 soil samples and the integration of laboratory data with IDW and regression results, several significant findings have emerged. The IDW method combined with linear regression proves to be a cost-effective and efficient approach for obtaining soil property data and generating accurate digital maps of red clay’s physical features. The Silt concentration exhibits a wide range, while the gravel content remains relatively low, indicating the predominance of silt in the soil composition. Analysis of Atterberg limits reveals the soil’s behavior and consistency in response to moisture, with the plasticity index generally falling within the low to medium range due to the considerable silt content in most soil samples. The linear regression model highlights positive correlations between the liquid limit, plastic limit, and plasticity index. Moderately positive relationships exist between the liquid limit and clay content, as well as a weak positive association between the liquid limit and specific gravity. Dry density, on the other hand, shows no significant correlation with other physical variables, suggesting its independence from the measured parameters. The plastic limit demonstrates a stronger relationship with the clay content compared to the liquid limit. Additionally, weak positive correlations are found between the liquid limit, plastic limit, and specific gravity and water content, indicating the influence of moisture on these parameters. Furthermore, gravel exhibits a moderate positive correlation with sand and silt concentrations, while a strong positive correlation is observed between sand and silt contents, underscoring their close association with the soil composition.

From field soil sampling to watershed model: Upscaling by integrating information entropy and interpolation method

Soil property data plays a crucial role in watershed hydrology and non-point source (H/NPS) modeling, but how to improve modeling accuracy with affordable soil samplings and the effects of sampling information on H/NPS modeling remains to be further explored. In this study, the number of sampling points and soil properties were optimized by the information entropy and the spatial interpolation method. Then the sampled properties were parameterized and the effects of different parameterization schemes on H/NPS modeling were tested using the Soil and Water Assessment Tool (SWAT). The results indicated that the required sampling points increased successively for soil bulk density (SOL_BD), soil saturated hydraulic conductivity (SOL_K) and soil available water capacity (SOL_AWC). Compared to the traditional database (Harmonized world soil database), the NSE and R2 performance by new scheme increased by 22.8% and 10.5%, respectively. The entropy-based optimization reduced the sampling points by 13.2%, indicating a more cost-effective scheme. Compared to hydrological simulation, sampled properties showed greater effects on NPS modeling, especially for nitrogen. This proposed method/framework can be generalized to other watersheds by upscaling field soil sampling information to the watershed scale, thus improving H/NPS simulation.

Improvement Of Landslide Handling On Filled Land In Cidahu-Cieurih Road, Cidahu District, Kuningan Regency

This research is based on the issue of soil landslide in the embankment area of Cidahu - Cieurih Road. Geotechnical analysis involves cone penetration testing, boreholes, and laboratory testing to obtain soil property data. The results indicate the presence of soil layers with different characteristics, such as clay, sand, and silty clay. The analysis method utilizes PLAXIS 3D V20 for slope stability modeling. The modeling results show that the existing slope condition is unsafe, and two alternative treatments are proposed. Alternative 1 involves replacing the existing retaining wall with a new one and adding ribs, resulting in an SF of 1.516. Alternative 2 involves using the existing sheet pile with the addition of a retaining wall and embankment, resulting in an SF of 1.63. This research concludes that Alternative 2, utilizing the existing sheet pile and retaining wall, provides better slope stability results with an SF that meets safety standards.

High‐resolution mapping of available water content in Senegal using iSDA Africa dataset and USDA Rosetta3 model

AbstractThis data article presents a high‐resolution map of available water content (AWC) for Senegal, derived from the iSDA Africa dataset and the USDA Rosetta3 model, as well as the method used for its production. The map covers the entire country at a resolution of 30 m and provides a valuable resource for hydrological studies and spatialized crop model simulations in the region where water is a limiting factor for crop production. The dataset is based on existing soil properties data and leverages pedotransfer functions (PTFs) to estimate water retention capabilities from soil properties. This AWC map derived from datasets with enhanced accuracy offers a more precise estimate of soil water retention capacity, which can be instrumental in informing water and agricultural management, policy decisions and investments. The dataset, including intermediate variables, is available in geotiff format at Cirad Dataverse under the DOI 10.18167/DVN1/SGNSII and complies with FAIR data principles, allowing its broad reuse. The code used to produce this dataset is also made available under the DOI 10.5281/zenodo.10078399, so that AWC maps can be produced for any territory covered by the iSDA Africa product.

Estimation of Infiltration Parameters: The Role of Pedotransfer Functions and Initial Moisture Conditions

Infiltration is one of the key components of rainfall-runoff processes at small catchment scale and is therefore a vital component of hydrological models. However, small catchments typically lack direct infiltration measurements and associated soil hydraulic characteristics, which are being substituted with soil databases and pedotransfer functions built upon these databases. We utilized two sets of pedotransfer functions Rosetta 3 and EUPTFv2 derived from two well known soil databases: UNSODA, based on US and global soil data, and EU-HYDI, based on EU soil data. Our study evaluates the impact of choosing one of these pedotransfer functions on modeled infiltration in relation to variable initial moisture content- It investigates the behavior of the simplified infiltration models of Green-Ampt and Philip under different initial conditions, in comparison with Richards’ complex solution, which is used as a benchmark. Our evaluation was conducted using soil property data available in the Czech Republic, covering ten USDA soil textures that are prevalent within the European Union. The applied initial moisture conditions were derived for specific suction pressure values that correspond to hydrological initial conditions ranging from near saturation to near the permanent wilting point. Variation in the derived parameters for the Green-Ampt and Philip models across the pedotransfer function sources results in considerable variation in the modeled infiltration. Our results imply different sensitivities of the simplified infiltration models to the initial moisture for different pedotransfer functions. The Green-Ampt and Philip infiltration models demonstrate better alignment with Richards’ complex solution with a low and consistent RMSE when using the Rosetta 3 pedotransfer function. By contrast, the EUPTFv2 model scenarios, show a clear trend of increasing RMSE towards dry initial conditions. As the European soil database is not an open data source, we only hypothesize that the distributions of soil characteristics in the European soil database differ systematically from those in UNSODA, and that this would cause different results of pedotransfer functions for different initial moisture conditions. The findings of this study are important for selecting appropriate infiltration models and, appropriate pedotransfer functions for estimating model parameters, and for assessing uncertainties connected with varying initial conditions.

Comparing impacts of corn residue removal and amelioration practices on soil properties after 3, 6, and 10 years

AbstractInformation on corn (Zea mays L.) residue removal and organic amendment additions to ameliorate removal effects on soil properties is limited. Thus, we compared impacts of corn residue removal at 59% and organic amendments (winter rye [Secale cereale L.]) cover crop and manure every other year at 24 Mg ha−1 on soil organic carbon (SOC) and physical properties after 10 years under irrigated no‐till continuous corn on a silt loam in the western US Corn Belt. We collected soil property data after 10 years and then compared it with earlier results from this experiment (3 and 6 years). Cover crop and manure amendments maintained SOC concentration, but no amelioration practice reduced SOC concentration (−15%) from 3 to 10 years when residues were annually removed in 0‐ to 5‐cm depth. Under residue retention, SOC concentration increased with time with largest increase (+41%) occurring under manure amelioration practice in the 0‐ to 5‐cm depth. Residue removal reduced soil wet aggregate stability by 34%, soil dry aggregate stability by 73%, bulk density by 6%, water content at −33 kPa matric potential by 14%, and plant available water by 17% after 10 years in the 0‐ to 5‐cm soil depth but did not affect infiltration rate. Cover crops increased wet aggregate stability (32%), but manure and cover crops had limited effects on other soil properties. Residue removal increased the susceptibility of the soil to erosion and reduced water retention and SOC after 3 years, but such effects were not fully offset by amendments.

Towards Reliable Barrier Systems: A Constrained XGBoost Model Coupled with Gray Wolf Optimization for Maximum Swelling Pressure of Bentonite

Bentonite and bentonite mixtures are used as buffer material for deep geological radioactive waste repositories. The proper determination of bentonite maximum swelling pressure is important as it influences the long-term stability and sealing characteristics of the barrier system. We developed a constrained machine learning model based on extreme gradient boosting (XGBoost) algorithm tuned with grey wolf optimization (GWO) to determine the maximum swelling pressure of bentonite and bentonite mixtures. We integrated a penalty term into the XGBoost algorithm’s loss function to constrain the model during the training phase ensuring meaningful predictions. To this end, we compiled a dataset containing 305 experimental data of bentonite maximum swelling pressure and other relevant soil properties including montmorillonite content, liquid limit, plastic limit, plasticity index, initial water content, and soil dry density to develop the model. The performance of the hybrid GWO-XGBoost model was benchmarked against feed-forward and cascade-forward neural network models. The GWO-XGBoost model showed a better performance in estimating the experimental values comparing to the other models, thereby demonstrating its effectiveness and reliability in determining the maximum swelling pressure of bentonite and bentonite mixtures.

Bantuan Teknis Perencanaan Dinding Penahan Tanah Pada Warga di Area Rentan Longsor

West Bandung Regency is one of the areas that frequently experiences landslides and landslides. Residents of Cililin Village, which is part of it, have concerns that the slope due to open excavation close to residential houses will have the potential for landslides but do not yet understand how to plan appropriate slope reinforcement. This service activity aims to review the stability of the excavation slopes around residents' houses and then plan a retaining wall that meets safe and stable technical rules. A field survey was carried out to measure the slope geometry and observe the situation around the area. Then slope stability modeling is carried out with input of slope geometry and soil property data. From the analysis results, it was obtained that the slope safety factor value was 1,518, exceeding the minimum safety factor of 1.5, so it was considered quite safe. However, this value is considered low considering that Cililin District is at high risk of landslides. Therefore, additional reinforcement of the slopes was made with earth retaining walls made of rock material. Planning for retaining walls is made of 4 types of varying dimensions. The size of the retaining wall varies, from a width of 3-4.5 m, and a height of 5.5 – 7.5 meters according to the height of each trap. From the planned dimensions, the overall safety factor against overturning, shearing and soil bearing capacity exceeds 1.5, the slope is declared safe. It is hoped that this service activity can become a reference for residents in making retaining walls to strengthen structures on similar slopes.

Chronosequential changes in soil-related ecosystem services after coastal reclamation: Insights for coastal cropland protection

Coastal reclamation has been used widely to accommodate urbanization and support increasing food demand. However, the responses of soil properties and soil-related ecosystem services (SES) to reclamation remain unclear. There are no effective operational methods for calculating the SES provided by reclaimed cropland. Using field sampling data of soil properties, we proposed a framework to evaluate the influence of reclamation on the soil's ability to provide five SESs: plant growth support (SES1), carbon storage (SES2), nutrient cycling (SES3), erosion control (SES4), and habitat provision (SES5). A series of SES indices were constructed accordingly and tested using data from a reclaimed soil chronosequence over 700 years in Hangzhou Bay, China. The results showed that all the SES indices increased significantly along the reclamation chronosequence, with the highest impact for SES2 (273.7% of soil C storage growth) owing to long-term continuous cultivation and fertilization. Most of the indices have a different logarithmic relationship with reclamation time, with a faster growth rate in the first 50 years, while SES1 increases linearly with reclamation time. Using soil organic matter as a reference, the sensitivity of SESs to reclamation is SES4 > SES2 > SES5 > SES3 > SES1. The significant positive correlations between all SES indices suggest that the provision of the five SESs is interdependent, with strong synergy. Our methods and index system can be effectively used to monitor coastal cropland protection and promote sustainable management of reclaimed land.

Rapid and portable mid-infrared analysis of wet sediment samples by a novel “filter-press” attenuated total reflectance method

Mid-infrared (MIR) analysis of wet sediments or soils usually requires freezing and drying, adding considerable analysis time and cost, and promoting changes in chemical properties. A novel attenuated total reflectance (ATR) mid-infrared (MIR) method was developed to enable analysis of non-processed, aqueous sediments. Wet sediment samples from the Coorong Lagoon, South Australia, were subjected to laboratory analysis for physical and chemical properties, our new filter-press ATR method, and conventional ATR and diffuse reflectance (DRIFT) analysis of dried samples. For ATR on wet sediments, samples were pressed onto the ATR crystal with a filter-paper backing and scanned. The spectra were analyzed by principal components analysis (PCA) and partial least squares regression (PLSR) to develop multivariate models for total organic carbon (TOC). ATR spectra of as-received wet sediments were poor due to water absorption features, but filter-pressing reduced these and greatly enhanced the solids spectral component. The filter-pressed PLSR cross-validation for TOC resulted in an R2 = 0.89 and RMSECV = 0.78%. Prediction accuracies were similar to freeze-dried ATR samples and far more accurate than testing wet samples without filter-pressing. Our findings confirmed the filter-press ATR method as proof-of-concept and showed that it has the potential to remove one of the major barriers to in-field application of MIR techniques, the high and variable amounts of moisture levels commonly present in many environmental samples. Extension of the filter-press ATR method to a wider range of analytes may enable it to rapidly acquire moist or wet sediment and soil property data while in the field.

Species distribution modeling for disease ecology: A multi-scale case study for schistosomiasis host snails in Brazil.

Species distribution models (SDMs) are increasingly popular tools for profiling disease risk in ecology, particularly for infectious diseases of public health importance that include an obligate non-human host in their transmission cycle. SDMs can create high-resolution maps of host distribution across geographical scales, reflecting baseline risk of disease. However, as SDM computational methods have rapidly expanded, there are many outstanding methodological questions. Here we address key questions about SDM application, using schistosomiasis risk in Brazil as a case study. Schistosomiasis is transmitted to humans through contact with the free-living infectious stage of Schistosoma spp. parasites released from freshwater snails, the parasite's obligate intermediate hosts. In this study, we compared snail SDM performance across machine learning (ML) approaches (MaxEnt, Random Forest, and Boosted Regression Trees), geographic extents (national, regional, and state), types of presence data (expert-collected and publicly-available), and snail species (Biomphalaria glabrata, B. straminea, and B. tenagophila). We used high-resolution (1km) climate, hydrology, land-use/land-cover (LULC), and soil property data to describe the snails' ecological niche and evaluated models on multiple criteria. Although all ML approaches produced comparable spatially cross-validated performance metrics, their suitability maps showed major qualitative differences that required validation based on local expert knowledge. Additionally, our findings revealed varying importance of LULC and bioclimatic variables for different snail species at different spatial scales. Finally, we found that models using publicly-available data predicted snail distribution with comparable AUC values to models using expert-collected data. This work serves as an instructional guide to SDM methods that can be applied to a range of vector-borne and zoonotic diseases. In addition, it advances our understanding of the relevant environment and bioclimatic determinants of schistosomiasis risk in Brazil.

Mining soil data of Switzerland: New maps for soil texture, soil organic carbon, nitrogen, and phosphorus

Maps of soil property are essential for soil monitoring, management, and conservation. These are available from regional to global scales, with global maps being urgently needed for global modeling and management endeavors (from soil degradation to climate change modeling and assessments). Currently, global maps are provided by SoilGrids (SG) and OpenLandMap (OLM). However, the number of samples used and spatial resolution for these maps indicate potentially a high uncertainty and leave room for discussion. A validation of global maps could be regional and national assessments with higher resolution data availability. Therefore, the objective of this study was to produce a new Swiss Soil Property Map (SSPM) for soil organic carbon, soil texture, total nitrogen, and phosphorus for Switzerland using higher resolution data compared to the previous global studies. To establish swiss scale maps, we fitted the Quantile Random Forest (QRF) machine-learning model for spatial predictions linking environmental covariates such as topography, climate, and vegetation with soil property data. Each model was evaluated using five-fold spatial cross-validation. The results showed concordance correlation coefficients (CCC) between 0.24 and 0.68 for predicted soil property. The new Swiss Soil Property Map (SSPM) as well as the global soil property maps were validated using the LUCAS dataset. The CCC for OC and clay maps of SSPM are 52%, and 24% higher, respectively, than the SG map. Therefore, this study demonstrates the potential impact of using sufficient data points to represent the individual country. Simultaneously we show that not just any additional data set will improve model performance. A well-planned high-resolution national soil monitoring is needed to improve the global maps in the future.

Distinct, direct and climate‐mediated environmental controls on global particulate and mineral‐associated organic carbon storage

AbstractIdentifying controls on soil organic carbon (SOC) storage, and where SOC is most vulnerable to loss, are essential to managing soils for both climate change mitigation and global food security. However, we currently lack a comprehensive understanding of the global drivers of SOC storage, especially with regards to particulate (POC) and mineral‐associated organic carbon (MAOC). To better understand hierarchical controls on POC and MAOC, we applied path analyses to SOC fractions, climate (i.e., mean annual temperature [MAT] and mean annual precipitation minus potential evapotranspiration [MAP‐PET]), carbon (C) input (i.e., net primary production [NPP]), and soil property data synthesized from 72 published studies, along with data we generated from the National Ecological Observatory Network soil pits (n = 901 total observations). To assess the utility of investigating POC and MAOC separately in understanding SOC storage controls, we then compared these results with another path analysis predicting bulk SOC storage. We found that POC storage is negatively related to MAT and soil pH, while MAOC storage is positively related to NPP and MAP‐PET, but negatively related to soil % sand. Our path analysis predicting bulk SOC revealed similar trends but explained less variation in C storage than our POC and MAOC analyses. Given that temperature and pH impose constraints on microbial decomposition, this indicates that POC is primarily controlled by SOC loss processes. In contrast, strong relationships with variables related to plant productivity constraints, moisture, and mineral surface availability for sorption indicate that MAOC is primarily controlled by climate‐driven variations in C inputs to the soil, as well as C stabilization mechanisms. Altogether, these results demonstrate that global POC and MAOC storage are controlled by separate environmental variables, further justifying the need to quantify and model these C fractions separately to assess and forecast the responses of SOC storage to global change.

Gradient boosting machine learning model to predict aflatoxins in Iowa corn.

Aflatoxin (AFL), a secondary metabolite produced from filamentous fungi, contaminates corn, posing significant health and safety hazards for humans and livestock through toxigenic and carcinogenic effects. Corn is widely used as an essential commodity for food, feed, fuel, and export markets; therefore, AFL mitigation is necessary to ensure food and feed safety within the United States (US) and elsewhere in the world. In this case study, an Iowa-centric model was developed to predict AFL contamination using historical corn contamination, meteorological, satellite, and soil property data in the largest corn-producing state in the US. We evaluated the performance of AFL prediction with gradient boosting machine (GBM) learning and feature engineering in Iowa corn for two AFL risk thresholds for high contamination events: 20-ppb and 5-ppb. A 90%-10% training-to-testing ratio was utilized in 2010, 2011, 2012, and 2021 (n = 630), with independent validation using the year 2020 (n = 376). The GBM model had an overall accuracy of 96.77% for AFL with a balanced accuracy of 50.00% for a 20-ppb risk threshold, whereas GBM had an overall accuracy of 90.32% with a balanced accuracy of 64.88% for a 5-ppb threshold. The GBM model had a low power to detect high AFL contamination events, resulting in a low sensitivity rate. Analyses for AFL showed satellite-acquired vegetative index during August significantly improved the prediction of corn contamination at the end of the growing season for both risk thresholds. Prediction of high AFL contamination levels was linked to aflatoxin risk indices (ARI) in May. However, ARI in July was an influential factor for the 5-ppb threshold but not for the 20-ppb threshold. Similarly, latitude was an influential factor for the 20-ppb threshold but not the 5-ppb threshold. Furthermore, soil-saturated hydraulic conductivity (Ksat) influenced both risk thresholds. Developing these AFL prediction models is practical and implementable in commodity grain handling environments to achieve the goal of preventative rather than reactive mitigations. Finding predictors that influence AFL risk annually is an important cost-effective risk tool and, therefore, is a high priority to ensure hazard management and optimal grain utilization to maximize the utility of the nation's corn crop.

Land degradability mapping using remote sensing data and soil chemical properties

Selection of the most influential soil properties to land degradability (LDy) mapping and their association is unclear. Accordingly, this study developed a geo-environmental modelling approach using multi-source datasets including remote sensing imagery, meteorological data, and soil properties (electrical conductivity (EC), soil organic matter (SOM), and calcium carbonate (CaCO3)) to LDy mapping in Jajrud and Karaj basin of Iran. LDy reference map obtained from soil properties data and Hedged Product overlay method was used to train and validate the random forest model. A sensitivity analysis was performed, which showed that normalized difference vegetation index (NDVI) is the most effective variable in LDy mapping. Under data constraints, the combination of biological soil crust index, leaf area index, and maximum wind speed is the best dataset for LDy mapping. Results revealed that the study area's most degraded lands were primarily barelands, followed by farmlands, grasslands, and fallow lands, respectively. Besides, considering their lower EC, the highlands and fallowlands did not witness land degradation.

Predicting soil fungal communities from chemical and physical properties

AbstractIntroductionBiogeography describes spatial patterns of diversity and explains why organisms occur in given conditions. While it is well established that the diversity of soil microbes is largely controlled by edaphic environmental variables, microbiome community prediction from soil properties has received less attention. In this study, we specifically investigated whether it is possible to predict the composition of soil fungal communities based on physicochemical soil data using multivariate ordination.Materials and MethodsWe sampled soil from 59 arable fields in Switzerland and assembled paired data of physicochemical soil properties as well as profiles of soil fungal communities. Fungal communities were characterized using long‐read sequencing of the entire ribosomal internal transcribed spacer. We used redundancy analysis to combine the physical and chemical soil measurements with the fungal community data.ResultsWe identified a reduced set of 10 soil properties that explained fungal community composition. Soil properties with the strongest impact on the fungal community included pH, potassium and sand content. Finally, we evaluated the model for its suitability for prediction using leave‐one‐out validation. The prediction of community composition was successful for most soils, and only 3/59 soils could not be well predicted (Pearson correlation coefficients between observed and predicted communities of <0.5). Further, we successfully validated our prediction approach with a publicly available data set. With both data sets, prediction was less successful for soils characterized by very unique properties or diverging fungal communities, while it was successful for soils with similar characteristics and microbiome.ConclusionsReliable prediction of microbial communities from chemical soil properties could bypass the complex and laborious sequencing‐based generation of microbiota data, thereby making soil microbiome information available for agricultural purposes such as pathogen monitoring, field inoculation or yield projections.