Search

Bare soil mapping is inevitable for the monitoring of soil resources, as it acts as a major indicator of urban development. Although remote sensing based indices are largely used for the delineation of bare soil, their effectiveness has always been a challenge due to overlapping spectral characteristics of the bare soil and built up areas. Recently, built up indices have been employed to increase the accuracy of bare soil mapping, which can induce discrepancies due to lesser sensitivity in smaller patches of bare soil areas in high density urban regions. In this context, modified normalized difference bare soil index (MNDBSI) is developed using a logical combination of index with additional constraint for the precise delineation of bare soil area using Sentinel 2 images. The qualitative and quantitative analyses adopted to evaluate the performance of the MNDBSI revealed an overall agreement value of above 95% and the improvement percentage of MNDBSI over the other indices compared in terms of spectral discrimination index and transformed divergence varied from 8% to 166% and 15% to 97%, respectively, across all study areas. The comparative analyses of results indicate that MNDBSI is an effective alternative to the existing indices for precise bare soil mapping which can be used as a promising indicator of urban expansion. [ABSTRACT FROM AUTHOR]

Arsenic (As) pollution in soil poses a significant risk to human health and sustainable development goals. Accurate measurement of the degree of As contamination and mapping in soil are important for identifying high-risk areas and developing remediation strategies. In this study, 201 soil samples (0–15 cm) were collected from As contaminated rice growing regions in Nadia, West Bengal, India. Various pollution indices (geoaccumulation index, Igeo; contamination factor, CF; ecological risk index, Eri) were computed to understand the contamination level and its health implications. Geostatistical analysis using ArcGIS mapped the spatial distribution of As contamination in soil. The accuracy of different interpolation techniques were evaluated using root mean square error (RMSE) and mean CV (cross-validation) parameters. Results showed that total As content in soil ranged from 3.47 to 53.1 mg kg‒1 (mean-19.3 mg kg‒1). The pollution indices such as Igeo, CF, and Eri varied from ‒0.22 to 3.72 (mean-2.11 mg kg‒1), 0.23 to 3.54 (mean-1.29 mg kg‒1), and 2.32 to 35.4 (mean-12.9 mg kg‒1), respectively. The northern side of Nadia showed higher As contamination, while comparatively lower levels were observed in southern side. Two interpolation techniques, viz. ordinary kriging (OK) and inverse distance weighting (IDW) were identified to precisely predict the spatial distribution of As in soil based on RMSE and mean CV values. Health risk assessment showed that children were more vulnerable to As toxicity in the study region. The findings support the development of location-specific remediation strategies and policies to minimize health risks associated with As-contaminated soils. [ABSTRACT FROM AUTHOR]

Crop yield is heavily influenced by soil salinity. The objective of the research is to assess the depth wise soil salinity distribution pattern and groundwater quality by measuring physico-chemical parameters in the agricultural land of Nijhum Island, Noakhali, Bangladesh. This study also explicates the spatial variation of soil salinity in the study area. Soil samples were collected from three different depths to estimate the Electrical Conductivity (EC) using a paste of 1:5 soil and distilled water suspension (1:5 weight-to-volume method). Spatial analyst tool of ArcGIS was used and observed that the land of Nijhum Island was affected with severe salinity in almost 85% of sampling plots and 15% of sampling plots were extremely saline. The soil salinity process has been noticed due to seawater intrusion from the Bay of Bengal through the Meghna estuary which threatens crop production and soil fertility, threatening the population's livelihood. This study provides a baseline understanding of soil salinity in Nijhum Island to help decision-makers and smallholder farmers improve their livelihoods. Thus, groundwater depth did not affect soil salinity significantly. The result contradicts most studies that found a strong influence on soil salinity. Because the study area is coastal, most of the salt comes from seawater inundation and capillary rise, which reduces groundwater availability. [ABSTRACT FROM AUTHOR]

Sensed and soil sample data are used in two approaches for mapping soil properties in precision agriculture: management zone (MZs) and contour maps. This is the second paper in a two-part series that focuses on contour maps. Detailed and accurate contour maps of soil properties for precision agriculture are often costly to produce because of the large sampling effort required. Such maps or those of sensed ancillary data are often simplified to represent MZs. This research investigated the accuracy of detailed maps of soil properties produced inexpensively from sensed data by transforming them to z-scores. The z-scores of ancillary values are then transformed to values of soil variables using the mean and standard deviation of a small soil data set. The errors from this mapping approach are examined with historic soil data from three field sites with different scales of spatial variation in the United Kingdom. Errors from the conversion of z-scores of sensed data to soil variable ranges are compared with those from MZ averages (Paper I in this series). For soil properties with a moderate relation to ancillary data, the errors related to the z-score conversion were small irrespective of sample size. The root mean squared errors associated with the MZ mean rather than values from the digital map were generally smaller except when sample size was very small. The results suggest that when the scale of variation is small and more samples are required to define MZs, calibrating z-scores of sensed ancillary data may provide better MZ averages than sampling on a grid; it also provides a detailed map of spatial variation within the field. The z-score conversion approach is less sensitive to sample size and captures small features of the variation compared to the standard 100 m grid sampling to determine MZ averages. [ABSTRACT FROM AUTHOR]

Sensed and soil sample data are used in two main approaches for mapping soil properties in precision agriculture: management zones (MZs) and contour maps. This is the first of two papers that explores maps of MZs. Management zones based on variation in sensed data that are related to the more permanent soil properties assume that the zones are multi-purpose. Soil properties are then often sampled on a grid to provide the average values of each property per zone. This paper examines the plausibility of this approach by examining how the number of samples taken on a grid and the application of kriging affect mean soil property values for MZs. The suitability of MZs based on ancillary data for managing several agronomically important properties simultaneously is also considered. These concepts are examined with historic soil data from four field sites in southern UK with different scales of spatial variation. Results showed that when the grid sampling interval is large, there is less difference in the means of properties between MZs, but kriging the soil data increased the differences between zones when the sampling interval was large and sample small. Sensed data are used increasingly to aid the identification of MZs, but these could not be considered multi-purpose at all sites. The MZs produced were most useful for phosphorus (P), pH and volumetric water content (VWC) at the Wallingford site and useful for most properties at the Clays and Y215 sites. For the latter site this was true only when the most dense data were used to calculate MZ averages. The results show that sampling interval for MZ averages should relate to the scale of variation or the size of the MZs at a site. The sampling density could be based on the variogram range of ancillary data. This research suggests that there should be 6–8 samples per zone to obtain accurate averages of soil properties. Nutrient data for more than one year were examined at two sites and showed that patterns remained consistent in the short term unless variable-rate management was used, but also the range of values changed in the short term. [ABSTRACT FROM AUTHOR]

Soil sampling constitutes a fundamental process in agriculture, enabling precise soil analysis and optimal fertilization. The automated selection of accurate soil sampling locations representative of a given field is critical for informed soil treatment decisions. This study leverages recent advancements in deep learning to develop efficient tools for generating soil sampling maps. We proposed two models, namely UDL and UFN, which are the results of innovations in machine learning architecture design and integration. The models are meticulously trained on a comprehensive soil sampling dataset collected from local farms in South Dakota. The data include five key attributes: aspect, flow accumulation, slope, normalized difference vegetation index, and yield. The inputs to the models consist of multispectral images, and the ground truths are highly unbalanced binary images. To address this challenge, we innovate a feature extraction technique to find patterns and characteristics from the data before using these refined features for further processing and generating soil sampling maps. Our approach is centered around building a refiner that extracts fine features and a selector that utilizes these features to produce prediction maps containing the selected optimal soil sampling locations. Our experimental results demonstrate the superiority of our tools compared to existing methods. During testing, our proposed models exhibit outstanding performance, achieving the highest mean Intersection over Union of 60.82% and mean Dice Coefficient of 73.74%. The research not only introduces an innovative tool for soil sampling but also lays the foundation for the integration of traditional and modern soil sampling methods. This work provides a promising solution for precision agriculture and soil management. [ABSTRACT FROM AUTHOR]

Background: Soil fertility is a significant natural property which decides the health and quality of land resources. Pulse productivity diminishes nowadays under rain fed condition due to improper nutrient management practices. Hence the present study was carried out to assess the soil fertility status under major pulses growing area by using advanced geo statistical tool with ArcGIS. Methods: Soil survey was taken in 14 revenue villages in Melur block, Madurai district. Soil samples were collected and analysed for its nutrient properties. The Parker's Nutrient Index and soil fertility maps were prepared by using ArcGIS. Result: Pulses growing soils were mostly neutral in pH and soluble salt content was low. The organic carbon and available nitrogen content was low to medium. The available phosphorus and potassium status were medium to high. Hence proper nutrient management practices are to be needed for augment pulse production in such area. [ABSTRACT FROM AUTHOR]

Annual surveys of Irish soil samples identified three isolates, CBS 16921 (UCD88), CBS 18246 (UCD443), and CBS 18247 (UCD483), of an apiculate yeast species within the Hanseniaspora genus. The internal transcribed spacer (ITS) and D1/D2 region of the large subunit (LSU) rRNA sequences showed that these are isolates of the recently described species Hanseniaspora menglaensis, first isolated from Southwest China. No genome sequence for H. menglaensis is currently available. The genome sequences of the three Irish isolates were determined using short-read (Illumina) sequencing, and the sequence of one isolate (CBS 16921) was assembled to chromosome level using long-read sequencing (Oxford Nanopore Technologies). Phylogenomic analysis shows that H. menglaensis belongs to the fast-evolving lineage (FEL) of Hanseniaspora. Only one MAT idiomorph (encoding MATα1) was identified in all three sequenced H. menglaensis isolates, consistent with one mating type of a heterothallic species. Genome comparisons showed that there has been a rearrangement near MATα of FEL species compared to isolates from the slowly evolving lineage (SEL). [ABSTRACT FROM AUTHOR]

Nematode (Phylum nematoda) infestation is a major set back in mango (Mangifera indica) production world-wide. Control of nematodes requires understanding of their diversity, distribution and population densities. The objective of this study was to identify plant-parasitic nematodes associated with mangoes and their population densities at seedling and maturity stages in production regions of Ghana. The study was conducted in Greater Accra (Manya and Klagon) and the Eastern region (Somanya) during December 2016 to January 2018. Mango farmlands were surveyed for soil and root samples, from which nematodes were extracted, identified and quantified. Twenty-four nematode genera were identified in mango seedlings and matured trees from Somanya; and 14 genera dentified in seedlings and 22 in matured trees from Manya; while at Klagon, 15 nematode genera were identified in matured trees. Helicotylenchus spp. were the most populated in seedlings obtained from Somanya and Manya, with relative abundance of 23.29%. Tylenchus spp. had the highest population in mature trees, with relative abundance of 24.49% at Somanya and 38.86% at Manya; while Helicotylenchus spp. had the highest relative abundance of 43.96% at Klagon. Mature trees had higher nematode population densities, compared to seedlings. There were significant variation in nematode populations between seedlings and matured stages of mango. [ABSTRACT FROM AUTHOR]

THE MAJOR aim of the study was to assess and map the spatial variability of some soil properties in El-Gallaba Plain, New Aswan City, using a geostatistical technique. Forty topsoil samples were selected from forty profiles that were dug to represent the study area. The variability of the soil maps was drawn based on the ordinary kriging interpolation method based on the geostatistical analysis. The data indicates that most of the soil samples were rough in texture. The organic matter was extremely low in most soil samples (≤ 4.03 g kg-1). The salinity of soil paste extract (ECe) ranges from 0.84 to 28.21 dSm-1. The soil reaction (pH) values of the surface soils vary between 7.69 and 8.89. The calcium carbonate values extend between 0.43 and 9.74 %. Gypsum contents in the soil samples range between 0.49 to 4.07%. The CEC of soil samples ranged between 3.73 and 25.35 cmolc (+)/kg. The coefficient of variation of soil pH was low (CV<5%), medium for sand fraction (CV<25%), and the rest of the soil properties were high to very high in the coefficient of variation. The normal histogram and QQPlots analysis of the physicochemical properties of the studied soil samples was applied to make the data more normally distributed. Logarithmic transformation of the soil properties data was used to normalize highly skewed and distant datasets because ordinary kriging methods work best if the data are approximately normally distributed. The ordinary Kriging (OK) method was used in the present study as an interpolation method compared to other Kriging methods due to it being simple and having high accuracy for prediction. The data reveal that the Gaussian, J-Bessel, Exponential, Rational Quadratic, and K-Bessel are the best-fitted semivariogram models for all properties selected. Accurate maps efficiently generated using geostatistics were essential to properly understand the spatial variability of the area under study. This study gives useful information about the physical and chemical characteristics and the spatial diversity of this soil. [ABSTRACT FROM AUTHOR]

The purpose of this study was to construct a map of expected fertilization rates for nitrogen (N) and phosphorus (P2O5) based on measurements of components in soil samples and to identify the spatial variabilities of four lots of a salt-affected paddy field in Korea. Four salt-affected paddy field lots in Korea were divided into 30 sectors for collecting soil samples. They were then analyzed for soil organic matter (SOM), silicon dioxide (SiO2), total nitrogen (TN), and available phosphorus (Av.P2O5) in accordance with international standards. Expected fertilization rates of N and P2O5 were developed as prescription standards for the application of fertilizer to paddy fields. They were derived using a model of the fertilization rates of N and P2O5. To determine the presence of spatial correlation and continuity in the given fields, a spherical variogram was used. Based on the spherical model with the application of a regular kriging interpolation, maps of the contents of TN and Av.P2O5 as well as the expected fertilization rates of N and P2O5 at each sector of 1 × 1   m2 were developed. The expected fertilization rate of N at each sector appeared in the range of min. 10.0 g to max. 25.7 g, while that of P2O5 appeared in the range of min. 0.68 g to max. 8.46 g. [ABSTRACT FROM AUTHOR]

Soil fungi play an indispensable role in the functioning of terrestrial habitats. Most landscape-scale studies of soil fungal diversity try to identify the fungal taxa present at a study site and define the relationships between their abundance and environmental factors. The specific spatial distribution of these fungi over the site, however, is not addressed. Our study's main objective is to propose a novel approach to landscape-scale mapping of soil fungi distribution using next generation sequencing and geographic information system applications. Furthermore, to test the proposed approach and discuss its performance, we aimed to conduct a case study mapping the spatial distribution of soil fungi on the Wielka Żuława island. The case study was performed on the Wielka Żuława island in northern Poland, where soil samples were collected every 100 m in an even grid. The fungal taxa and their relative abundance in each sample were assessed using the Illumina platform. Using the data obtained for the sampled points, maps of soil fungi spatial distribution were generated using three common interpolators: inverted distance weighted (IDW), B-spline, and ordinary Kriging. The proposed approach succeeded in creating maps of fungal distribution on Wielka Żuława. The most abundant groups of soil fungi were Penicillium on the genus level, Aspergillaceae on the family level, and ectomycorrhizal fungi on the trophic group level. Ordinary Kriging proved to be the most accurate at predicting relative abundance values for the groups of fungi significantly spatially autocorrelated at the sampled scale. For the groups of fungi not displaying spatial autocorrelation at the sampled scale, IDW provided the most accurate predictions of their relative abundance. Although less accurate at predicting exact relative abundance values, B-spline performed best in delineating the spatial patterns of soil fungi distribution. The proposed approach to landscape-scale mapping of soil fungi distribution could provide new insights into the ecology of soil fungi and terrestrial ecosystems in general. Producing maps of predicted fungal distribution in landscape-scale soil fungi diversity studies would also facilitate the reusability and replicability of the results. Outside the area of research, mapping the distribution of soil fungi could prove helpful in areas such as agriculture and forestry, nature conservation, and urban planning. [ABSTRACT FROM AUTHOR]

Soil acidification is a major problem in the inland Pacific Northwest. A potential solution is the application of lime to neutralize acidity and raise pH. As lime is an expensive input, precision variable rate application is necessary. However, high-resolution mapping of pH and buffer pH for lime prescription requires costly sampling and analysis. To reduce the amount of sampling needed, remote sensing, which correlates with soil pH and buffer pH, can be used to determine optimal sampling locations and allow optimal interpolation. We used soil and crop data from the Washington State University R.J. Cook Agronomy Farm to develop an optimal sampling plan for a farmer's property, followed that sampling design, and used the measured pH and buffer pH to fit a Bayesian hierarchical model using remote sensing variables specific to that farmer's land. Following this, a new model was developed for the research farm with similar covariates. Ultimately, on the farmer's field, we observed a root mean square error (RMSE) of 0.2487 for soil pH at a depth of 0–10 cm and 0.1221 for modified Mehlich buffer at 0–10 cm of depth. For the research farm, where buffer pH was not measured, we saw an RMSE of 0.3272 for soil pH at 0–10 cm of depth and 0.3381 for soil pH at 10–20 cm of depth. The ability make predictions of soil acidity with uncertainty using this technique allows for prescription lime application while optimizing soil sampling and testing. Further, this paper serves as a case study of on-farm research. [ABSTRACT FROM AUTHOR]

The communication of uncertainty is not only a challenge when soil information has been produced but also in the planning stage. When planning a survey of soil properties it is necessary to make decisions about the sampling density. Sampling density determines both the quality of predictions and the cost of fieldwork. In this study, we considered four ways in which the relationship between sample density and the uncertainty of predictions can be related, based on prior information about the variability of the target quantity. These were offset correlation, prediction intervals, conditional probabilities of the interpretation errors and implicit loss functions. Offset correlation is a measure of the consistency of kriging predictions made from sample grids with the same spacing but different origins. Prediction intervals and conditional probabilities are based on the prediction distribution of the variable of interest. All four of these methods were investigated using the information on soil pH and Se concentration in grain in Malawi. They were presented to a group of stakeholders, who were asked to use them in turn to select a sampling density. Their responses were evaluated and they were then asked to rank the methods based on their effectiveness, in their experience, and in terms of finding a level of uncertainty that they were able to tolerate when deciding about a sampling grid spacing. Our results show that the approach that stakeholders favoured was offset correlation, and some approaches were not well understood (conditional probability and implicit loss function). During feedback sessions, the stakeholders highlighted that they were more familiar with the concept of correlation, with a closed interval of [0,1] and this explains the more consistent responses under this method. The offset correlation will likely be more useful to stakeholders, with little or no statistical background, who are unable to express their requirements of information quality based on other measures of uncertainty. [ABSTRACT FROM AUTHOR]

Deriving an accurate three‐dimensional (3D) spatial distribution of heterogeneous soil pollutants at contaminated sites using traditional spatial interpolation methods, such as inverse distance weighting (IDW), is challenging; especially when only limited borehole data are available. This study presents a novel IDW‐COV method, where weighting is determined by optimizing relative importance between feature distances of covariates and spatial distances to soil sample locations. The method was tested by mapping the 3D distributions of Cr (VI) and total Cr at a Cr salt production workshop (Site A) and a legacy Cr slag stacking site (Site B). The results were compared with those of IDW, the soil land inference model (SoLIM), and the SoLIM combined with IDW method (SoLIM‐IDW). The proposed IDW‐COV method returned the highest accuracy, with R2 values of 0.83 for Cr (VI) in Site A and 0.75 for total Cr in Site B, compared with 0.65 and 0.57 for IDW, 0.16 and 0.58 for SoLIM, and 0.73 and 0.61 for SoLIM‐IDW. This study highlights the importance of considering differences in explanatory power between multidimensional covariate and geographical spatial distance when incorporating multisource auxiliary data into weighted average estimators and provides guidance for mapping 3D distributions of heterogeneous pollutants from sparse soil borehole data. [ABSTRACT FROM AUTHOR]

Pedometricians have spent a lot of effort on mapping soil types and basic soil properties. However, end‐users typically need a more elaborate soil quality index for land management. Soil quality indices are typically derived from multiple individual soil properties by evaluating whether specific criteria are met. If this is based on individually mapped soil properties, then an important consequence is that cross‐correlations between soil properties are ignored. This makes it impossible to quantify the uncertainties associated with the mapped indices. The objective of this study was to map a soil potential multifunctionality index for agriculture (Agri‐SPMI) over a 12,125 km2 study region located along the French Mediterranean coast to help urban planners preserve soils of the highest quality. The index considered the ability of soils to fulfil four functions under five land use scenarios. A binary map represented each soil function fulfilment for a given scenario. The final soil quality index map was the sum of the 20 binary maps. A regression cokriging model was developed to map the basic soil properties first individually from legacy soil data and spatial soil covariates using a random forest algorithm, and next, interpolate the residuals using cokriging and the linear model of coregionalisation. The mapping uncertainties of soil properties were propagated by calculating the soil quality index over 300 stochastic simulations of soil properties derived from the linear models of coregionalisation. Results showed a poor prediction accuracy of the quality index, mainly because some soil properties were poorly predicted (notably available water capacity and coarse fragments) and used in combination with extreme thresholds that defined land suitability. Overall, the uncertainty was correctly quantified because the stochastic simulations reproduced the width of the observed distribution well, but the shapes of the distributions differed considerably from those of the observations. We envisage some ways for improvement, such as creating probability maps instead of the mean from simulations, and changing the prediction support from point to area. Highlights: The study mapped a soil quality index (Agri‐SPMI) to help preserve soils of highest quality along the French Mediterranean coast.The Agri‐SPMI considered the ability of soils to fulfill four functions under five land use scenarios, derived from multiple individual soil properties.A regression cokriging model was developed to map the basic soil properties, followed by interpolating the residuals using cokriging and the linear model of coregionalisation.The study accurately quantified mapping uncertainties using stochastic simulations, but the soil quality index prediction accuracy was poor, with suggestions for improvement. [ABSTRACT FROM AUTHOR]

Soil sampling is critical to obtaining reliable input for farm field-level digital soil mapping (DSM). Sample size and location are the key issues for soil sampling. However, sample size is often restricted by available budgets. In this case, recognizing the key sample locations is necessary. Existing methods have optimized the sample locations in a global manner without considering the impacts of local heterogeneity of soil. In this paper, a novel sampling approach based on the local heterogeneity of soil with a limited sample size (40 samples in this research) was developed. First, the local heterogeneity of soil was inferred. Second, the sub-regions were divided based on the level of local soil heterogeneity and the corresponding sample numbers were determined. Finally, the key sample locations were determined based on the fuzzy memberships. To validate the proposed method, it was compared with stratified random sampling, k-means sampling and conditional Latin hypercube sampling. The ordinary kriging method was applied to map five soil properties, including soil organic matter, pH, total nitrogen, available phosphorus and available potassium. The comparative experiments showed that the proposed method has better robustness in satisfying good mapping accuracy for multi-soil properties at the farm field level compared with the competing sampling methods, as indicated by the relatively lower and more stable mean bias error (MBE) and root mean square error (RMSE) values. It can be concluded that the consideration of local heterogeneity of soil is helpful to recognize the key sample locations for limited sample sizes. [ABSTRACT FROM AUTHOR]

A soil‐landscape conceptual model developed in the Rhode River subestuary of Maryland was applied to create a soil survey for the adjacent West River subestuary. The survey for the West River subestuary was completed before samples were collected there to evaluate the soil‐landscape conceptual model used to generate the soil survey. The West River subestuary was then sampled along transects that crossed soil map units to compare observed soil taxa with predicted soil taxa. Observed transect samples were classified and scored based on their similarity to predicted taxa in soil map units. These data were resampled via a bootstrapping method to determine if the predictions of the West River subestuary soil survey were significantly different from random predictions. Significant information was provided by the survey, and therefore by the soil‐landscape conceptual model used to generate it. Core Ideas: Soil surveys can be statistically evaluated using bootstrapping statistical methods.A polygon‐based soil survey can function as a testable hypothesis.A soil‐landscape model developed in one setting can be evaluated in similar settings. [ABSTRACT FROM AUTHOR]

The mapping of cation exchange capacity (CEC) was assessed using the geostatistical technique based on principal component analysis (PCA) in the Khuzestan province, southwestern Iran, for the proper management of agricultural lands. To this end, 210 topsoil samples were obtained from the area at the depth of 0–30 cm by systematic sampling in regular networks with dimensions of 350 × 350 m. Soil attributes were fully analyzed using both classical and geostatistical methods, and the prediction map of soil CEC was created by ordinary kriging (OK) and cokriging (CK) methods. The findings revealed that soil properties dominating the first principal component (PC1) explained the high correlation observed between soil properties such as CEC (r2 = 0.89). Still, a weak or no correlation between other components and CEC was obtained in the correlation matrix. According to the PCA results, the PC1 was used as an auxiliary variable to estimate the soil CEC in the CK method. The precision of the prepared maps was determined by the cross-validation analysis using NRMSE, MAE, and R2. Based on the cross-validation result of the predicted dataset, the NRMSE and MAE for OK were 0.25, 2.9, and 0.34 cmol(+) kg–1, and 0.18 and 2.1 cmol(+) kg–1 for CK, respectively. The cross-validation R2 for the prediction dataset was 0.31 for OK and 0.84 for CK methods at the 0.01 level, respectively. The result also revealed that the CK interpolation method with PC1 could effectively predict soil CEC variation. Furthermore, the combination of the CK method and PC1 derived from soil attributes was reasonably able to predict soil CEC. Principal components, which have a good correlation with the dependent variables, the best potential for CK predictions, and the application of CK interpolation method with PC1 can reduce the time and cost for CEC mapping. [ABSTRACT FROM AUTHOR]

The mapping of soils in Africa is at least a century old. We currently have access to various maps depicting mapping units locally and for the continent. In the past two decades, there has been a growing interest in alternatives for generating soil maps through digital soil mapping (DSM) techniques. There are, however, numerous challenges pertaining to the implementation of DSM in Africa, such as the unavailability of appropriate covariates, age and positional error in the measurements, low sampling density, and spatial clustering of the soil data used to fit and validate the models. This review aims to investigate the current state of DSM in Africa, identify challenges specific to implementing DSM in Africa and the ways it has been solved in the literature. We found that nearly half of African countries had an existing digital soil map covering either a local or national area, and that most studies were performed at a local extent. Soil carbon was the most common property under study, whereas soil hydraulic variables were seldom reported. Nearly all studies performed mapping for the topsoil up to 30 cm and calculated validation statistics using existing datasets but without collecting a post-mapping probability sample. Few studies (i.e., 11%) reported an estimate of map uncertainty. Half of the studies had in mind a downstream application (e.g., soil fertility assessment) in the map generation. We further correlated the area of study and sampling density and found a strong negative relationship. About 30% of the studies relied on legacy soil datasets and had a lack of sufficient spatial coverage of their area of study. From this review, we highlight some research opportunities and suggest improvements in the current methodologies. Future research should focus on capacity building in DSM, new data collection, and legacy data rescue. New initiatives, that should be initiated and led from within the continent, could support the long-term monitoring of soils and updating of soil information systems while ensuring their contextualised usability. This pairs with better delivery of existing DSM studies to stakeholders and the generation of a value-added proposition to governmental institutions. • Half of African countries have an existing digital soil mapping study. • There are numerous challenges for generating soil maps in Africa. • Local initiatives for a sustainable soil data collection is required. • More efforts should be invested in capacity building and soil monitoring systems. • Delivery of detailed soil information to stakeholders is insufficient. [ABSTRACT FROM AUTHOR]

The Canarian archipelago comprises seven major oceanic volcanic islands located off the northwest coast of Africa. Due to recent volcanic activity, the Canary Islands boast significant high enthalpy geothermal potential. Extensive soil gas surveys, combined with magnetotelluric and ambient noise tomography studies for geothermal exploration, have been conducted on the island of Tenerife (Canary Islands). The findings from these studies have highlighted the necessity of undertaking detailed surface exploration work in areas with the greatest geothermal potential. Here we present the findings from a comprehensive soil gas survey (∼500 sampling sites/km2) conducted in a 0.7 km2 area known as Madre del Agua on the Tenerife north-south rift zone (SRZ) volcano, where surface geothermal features are not readily apparent. The selection of the study area followed a preliminary low-density soil gas survey (5 sampling sites/km2) and a magnetotelluric survey, which indicated a thinning of a broad-scale clay alteration cap. At each of the 362 sampling sites, measurements of soil CO 2 efflux and 222Rn activity were conducted in situ. Additionally, soil gas samples were collected at a depth of 40 cm for further chemical and isotopic analysis (δ13C-CO 2). Statistical-graphical analysis and the assessment of spatial distribution of the soil physico-chemical data confirms the presence of a relative enrichment of deep-seated gases in the soil gas atmosphere. The detection of these soil gas anomalies holds potential for identifying permeable areas and possible upwelling or boiling zones. [ABSTRACT FROM AUTHOR]

Soil texture has a great influence on the physical–hydric and chemical behavior of soils. In the Amazon regions, due to the presence of dense forest cover and limited access to roads, carrying out surveys and mapping of soils is challenging. When data exist, they are relatively sparse and the distribution is quite uneven. In this context, machine learning algorithms (ML) associated with remote sensor covariates offer a framework to derive digital maps of soil attributes. The objective of this study was to produce maps of surface and subsurface soil clay, silt, and sand contents in a 13.440 km2 area in the Amazon. The specific objectives were to (a) evaluate the gain in prediction accuracy when using the P-band of airborne radar as a covariate; (b) evaluate two sampling approaches (Reference Area—RA and Total Area—TA); and (c) evaluate the transferability and performance of three ML algorithms: regression tree (RT), random forest (RF), and support vector machine (SVM). The study site was divided into three blocks, called Urucu, Araracanga, and Juruá, respectively. The soil dataset consisted of 151 surface and subsurface sand, silt, and clay observations and 21 covariates (20 relief variables and the backscattering coefficient from the P-band). Both the RA and TA sampling approach used 114 observations for training the prediction models (75%) and 37 for validation (25%). The RA approach was better for the development of sand and silt models. Overall, RF derived the most accurate predictions for all variables. The effect of introducing the P-band backscattering coefficient improved the sand prediction accuracy at the surface and subsurface in Araracanga, which had the highest sand content, with relative improvements (RI) of the R2, root mean square error (RMSE), and mean absolute error (MAE) of 46%, 3%, and 4% at the surface, respectively, and 66.7%, 4.4%, and 5.2% at the subsurface, respectively. For silt, the P-band improved the predictions at the surface in Araracanga, which had the lowest silt contents among the blocks. For clay, adding the P-band improved the RF predictions at the subsurface, with RI of the R2, RMSE, and MAE of 29%, 5%, and 5%, respectively. Despite the low observation density, inherently hindered by the low accessibility of the area and high costs of sampling thereof, the results showed the potential of ML algorithms boosted by airborne radar P-band to map soil clay, silt, and sand contents in the Amazon. [ABSTRACT FROM AUTHOR]

Experiences from 30 years of geochemical mapping at the (sub)continental scale in Europe using a wide range of different sample media are reviewed and discussed with a focus on the most recent GEMAS (GEochemical Mapping of Agricultural Soils) project. Comparing results from the different surveys it is possible to come to conclusions as to how geochemical surveys at the continental scale could best be designed. High analytical quality and lowest possible detection limits are key requirements. In Europe good outcomes were achieved with the <2 mm fraction of soil samples and aqua regia extraction. Focus should be on high quality of sampling and analyses and more determined parameters rather than on more samples. The sample density of 1 site/2500 km2 provides a good overview of the processes governing geochemistry at the continental scale. Results should be extensively published by the project team to allow the dataset to be known and utilized by the wider scientific community. Thematic collection: This article is part of the Continental-scale geochemical mapping collection available at: https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping [ABSTRACT FROM AUTHOR]

Simple Summary: Buffalo breeding in the Amazon biome can contribute significantly to local community development and, thus, is considered an essential income source. However, in Amazon regions, the inadequate breeding of these animals can lead to considerable negative consequences for the environment. Therefore, it is crucial to develop methodologies to improve animal management and grass yield. One of these methodologies is related to Precision Agriculture (PA), adapted for pasture and animal monitoring. Along these lines, we seek to utilize geostatistical techniques and remote sensing applications to better understand Buffalo grazing under a rotating system. In particular, we analyze forage Dry and Green Matter, as well as pH in pasture soils, demonstrating the obstacles against and advantages of the implementation of precise techniques for decision making and increasing grass productivity. We describe ways in which geostatistical soil pH mapping can be conducted, as well as the premises necessary to include remote sensing data in the analysis of pasture variables. Implementing these results in buffalo management systems can contribute to greater productivity and increasingly sustainable livestock. The mapping of pastures can serve to increase productivity and reduce deforestation, especially in Amazon Biome regions. Therefore, in this study, we aimed to explore precision agriculture technologies for assessing the spatial variations of soil pH and biomass indicators (i.e., Dry Matter, DM; and Green Matter, GM). An experiment was conducted in an area cultivated with Panicum maximum (Jacq.) cv. Mombaça in a rotational grazing system for dairy buffaloes in the eastern Amazon. Biomass and soil samples were collected in a 10 m × 10 m grid, with a total of 196 georeferenced points. The data were analyzed by semivariogram and then mapped by Kriging interpolation. In addition, a variability analysis was performed, applying both the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite remote sensing data. The Kriging mapping between DM and pH at 0.30 m depth demonstrated the best correlation. The vegetative index mapping showed that the NDVI presented a better performance in pastures with DM production above 5.42 ton/ha−1. In contrast, DM and GM showed low correlations with the NDWI. The possibility of applying a variable rate within the paddocks was evidenced through geostatistical mapping of soil pH. With this study, we contribute to understanding the necessary premises for utilizing remote sensing data for pasture variable analysis. [ABSTRACT FROM AUTHOR]

Since the early 2000s, digital soil maps have been successfully used for various applications, including precision agriculture, environmental assessments and land use management. Globally, however, there are large disparities in the availability of soil data on which digital soil mapping (DSM) models can be fitted. Several studies attempted to transfer a DSM model fitted from an area with a well‐developed soil database to map the soil in areas with low sampling density. This usually is a challenging task because two areas have hardly ever the same soil‐forming factors in two different regions of the world. In this study, we aim to determine whether finding homosoils (i.e., locations sharing similar soil‐forming factors) can help transferring soil information by means of a DSM model extrapolation. We hypothesize that within areas in the world considered as homosoils, one can leverage on areas with high sampling density and fit a DSM model, which can then be extrapolated geographically to an area with little or no data. We collected publicly available soil data for clay, silt, sand, organic carbon (OC), pH and total nitrogen (N) within our study area in Mali, West Africa and its homosoils. We fitted a regression tree model between the soil properties and environmental covariates of the homosoils, and applied this model to our study area in Mali. Several calibration and validation strategies were explored. We also compared our approach with existing maps made at a global and a continental scale. We concluded that geographic model extrapolation within homosoils was possible, but that model accuracy dramatically improved when local data were included in the calibration dataset. The maps produced from models fitted with data from homosoils were more accurate than existing products for this study area, for three (silt, sand, pH) out of six soil properties. This study would be relevant to areas with very little or no soil data to carry critical soils and environmental risk assessments at a regional level. Highlights: Soil mapping models were fitted with soil data within the homosoils of Mali.The fitted models were applied to our study area.Model accuracy dramatically improved when including local data.Homosoil maps were more accurate for 3 out of 6 soil properties compared to global and continental maps.New opportunity to map the regional soil pattern of areas with limited soil data coverage. [ABSTRACT FROM AUTHOR]

Digital soil mapping provides estimates of soil properties and, in turn, an understanding of the spatial variation in soil across landscapes. In recent years, several digital soil mapping products have been released across the world. An end user in New South Wales (NSW), Australia, could choose between a global product (SoilGrids), a national product (Soil and Landscape Grid of Australia) and a state product (NSW digital soil maps). All predict at grid resolutions of 250 m or less, which approaches the required detail for within‐field site‐specific management such as variable‐rate application of agricultural inputs. In this study, the quality of clay and soil organic carbon (SOC) products (0–30 cm) from these three publicly available digital soil maps (DSM) were validated with an external dataset of 394 soil observations, collected from 14 farms across eastern Australia. Each farm was sampled using stratified random sampling, allowing for unbiased estimates of prediction quality at three spatial supports: point (soil core), strata (management zone) and farm. For the clay attribute, this study observed Lin's concordance correlation coefficient (LCCC) values of 0.22–0.37 at the point spatial support, far below reported values. This improved at the strata spatial support with LCCC values of 0.20–0.60 and the farm spatial support with values of 0.07–0.54. For SOC, LCCC values ranged from 0.25 to 0.45 for points, 0.31–0.55 for strata and 0.31–0.60 for farms. The grid resolution of DSMs depends largely on the resolution of the covariates used, and current efforts look to incorporate more recent, finer resolution covariates to create higher‐resolution soil maps. However, the results of this study suggest that both the grid resolution and spatial support used in DSMs need further consideration. Rather than letting the resolution of covariates determine the resolution of DSMs, this should also be determined by the number of soil observations available for modelling, their distribution in geographic and attribute space and the prediction quality. Highlights: DSM products do not meet published accuracies when assessed with an independent datasetThe large‐extent DSM products were unable to represent within‐farm variabilityAggregation of the DSM products over large spatial supports improved accuraciesDSM products should be created at different spatial supports, each with an associated uncertainty [ABSTRACT FROM AUTHOR]

On the cover: Soil samples were packed into wells, then imaged at 91 known configurations using a custom designed and 3D‐printed sample well array to discern the influence of surface orientation on VNIR hyperspectral signatures. This method advances the ability to map soil chemistry at high resolution on intact soil samples using laboratory‐based imaging. See A.M. Duro al., "Topographic correction of visible near‐infrared reflectance spectra for horizon‐scale soil organic carbon mapping," https://doi.org/10.1002/saj2.20612. Photo by Alyssa Duro.. [Extracted from the article]

Kriging has been shown to be the best interpolator to interpolate maps in precision agriculture. However, Kriging requires a high number of sampling points to generate accurate maps. Recently, machine learning (ML) techniques have shown the potential to produce maps with a lower number of sampling points. In addition, using ML map generation can be automated and use much more feature information to improve map quality. Therefore, the objective of this study was to implement a ML technique and compare it to IDW and to Ordinary Kriging (OK). The ML algorithm used was the Support Vector Machine (SVM). Software based on the SVM method was developed (Smart-Map) using the Python language. This software was tested in an area of 204 ha cultivated with soybeans. The performance of the SVM method was compared to traditional interpolation methods, IDW and Ordinary Kriging (OK). Based on the analysis of 10 soil attributes, OK had better performance than IDW and the ML method when the Moran's I (Index) values were significant and higher than 0.67. With a low density of points and low degrees of spatial autocorrelation, the ML method performed better than IDW and OK. [ABSTRACT FROM AUTHOR]

A capability for mapping meter-level resolution soil moisture with frequent temporal sampling over large regions is essential for quantifying local-scale environmental heterogeneity and eco-hydrologic behavior. However, available surface soil moisture (SSM) products generally involve much coarser grain sizes ranging from 30 m to several 10 s of kilometers. Hence, a new method is proposed to estimate 3-m resolution SSM using a combination of multi-sensor fusion, machine-learning (ML), and Cumulative Distribution Function (CDF) matching approaches. This method established favorable SSM correspondence between 3-m pixels and overlying 9-km grid cells from overlapping Planet SuperDove (PSD) observations and NASA Soil Moisture Active-Passive (SMAP) mission products. The resulting 3-m SSM predictions showed improved accuracy by reducing absolute bias and RMSE by ~0.01 cm3/cm3 over the original SMAP data in relation to in situ soil moisture measurements for the Australian Yanco region while preserving the high sampling frequency (1–3 day global revisit) and sensitivity to surface wetness (R 0.865) from SMAP. Heterogeneous soil moisture distributions varying with vegetation biomass gradients and irrigation regimes were generally captured within a selected study area. Further algorithm refinement and implementation for regional applications will allow for improvement in water resources management, precision agriculture, and disaster forecasts and responses. [ABSTRACT FROM AUTHOR]

The low natural fertility and compaction of agricultural soils in the Cerrado are the main limitations for agricultural production. Changes in management systems are necessary to optimize the use of natural resources. The objective of this study was to map and evaluate the presence of penetration resistance and its relationship with soil chemical attributes in pasture areas in the Cerrado of Piauí. The experiment was carried out in the municipality of Corrente-PI, Brazil. The area was divided into two sub-areas of 0.5 ha each: the first is remaining vegetation and the second is pasture. Fifty soil samples were collected in the 0.00-0.20 and 0.20-0.40 m layers, with a sampling grid of 10 x 10 m. The pH in water, organic matter, calcium, magnesium, and soil penetration resistance were analyzed. The introduction of animals in the pasture area increased soil compaction by 2%. Chemical attributes, pH, organic matter, calcium and magnesium vary according to soil compaction by 2%. The native forest area has soil penetration resistance at acceptable levels (<2 MPa) and availability of nutrients at adequate levels. The maps of variability of organic matter, calcium, magnesium and penetration resistance showed the heterogeneity of the areas, allowing decisions based on specific management zones, localized application of nutrients and decompaction, and recovery of areas with inefficient management. [ABSTRACT FROM AUTHOR]

Soil–plant–atmosphere models and certain land surface models usually require information about the ability of soils to store and release water. Thus, a critical soil parameter for such reservoir-like models is the available water capacity (AWC), which is usually recognized as the most influential parameter when modeling water transfer. AWC does not have a single definition despite its wide use by scientists in research models, by regional managers as land-management tools and by farmers as decision-aid tools. Methods used to estimate AWC are also diverse, including laboratory measurements of soil samples, field monitoring, use of pedotransfer functions, and inverse modeling of soil-vegetation models. However, the resulting estimates differ and, depending on the method and scale, may have high uncertainty. Here, we review the many definitions of AWC, as well as soil and soil–plant approaches used to estimate it from local to larger spatial scales. We focus especially on the limits and uncertainties of each method. We demonstrate that in soil science, AWC represents a capacity—the size of the water reservoir that plants can use—whereas in agronomy, it represents an ability—the quantity of water that a plant can withdraw from the soil. We claim that the two approaches should be hybridized to improve the definitions and estimates of AWC. We also recommend future directions: (i) adapt pedotransfer functions to provide information about plants, (ii) integrate newly available information from soil mapping in spatial inverse-modeling applications, and (iii) integrate model-inversion results into methods for digital soil mapping. [ABSTRACT FROM AUTHOR]

The spatiotemporal analysis and mapping of soil organic matter (SOM) play a pivotal role for evaluating soil health and for implementing preservation and restoration actions. In this context, the first aim of the study is to furnish a high-resolution mapping of current SOM content in eastern Croatia. The second aim is to perform a diachronic analysis of SOM content, comparing two datasets characterized by an extreme data imbalance. The more recent dataset (SOM2010), representative of 2010s, comprises 19,386 samples and the older dataset (SOM1970), representative of the 1970s, comprises 152 samples. The marked data imbalance and the different modalities in soil sampling and laboratory analysis of the two datasets are taken into consideration in performing the comparison. The study reveals a general depletion trend of SOM from the 1970s to the 2010s, more evident in with regard to Fluvisols and Gleysols. At a regional scale, the SOM2010 is characterized by lower variability compared to SOM1970, indicating a process of homogenization of SOM spatial distribution in recent years. Considering the local scale, there is limited information for the 1970s; for the 2010s the SOM spatial distribution is characterized by a high short-range spatial variability, with a characteristic spotty appearance, likely related to agricultural practices. [ABSTRACT FROM AUTHOR]

The incredible drying of the Aral Sea has resulted in a large area of exposed seafloor with saline soils, which has led to catastrophic consequences. This study investigated ground-truth soil salinity data and used Landsat data to map the soil salinity distribution of the exposed seabed of the Aral Sea from 1960 onwards. The soil salinity distribution, with the depth from 0 cm to 100 cm, was analyzed. The correlation analysis was applied to find the best performance index in describing soil salinity changes. The results showed that ground-truth data of topsoil salinity (depth of 0−5 cm) exhibited a significantly strong correlation with soil salinity index 4 (SI4) among seven indices, where the Pearson correlation coefficient (r) was up to 0.92. Based on the relationship between soil salinity sampling data and SI4, a linear regression model was employed to determine the capability of evaluating the soil salinity distribution of the Aral Sea with the coefficient of determination (R2), root mean squared error (RMSE), and ratio of performance to deviation (RPD) values of 0.84 and 0.86 dS m−1 and 2.36, respectively. The SI4 performed well and was used to predict the soil salinity distribution on the exposed seabed. The distribution showed that soil salinity increased from the former to current shoreline. In the North Aral Sea, compared to 1986, the water area remained stable, accounting for 50.3% in 2020, and the soil salinization level was relatively low. However, the moderately and slightly saline areas dominated 73.8% and 7.5% of the South Aral Sea in 2020, with an increase of 53% and 6% transformed from the water area. The area of salinized soils dramatically increased. The strongly and extremely saline areas were mainly located in the northeastern part of the eastern basin and western part of Vozrozhdeniya Island, respectively, and were the main source of salt-dust storms. These results support the dynamic monitoring and distribution patterns of soil salinization in the Aral Sea. [ABSTRACT FROM AUTHOR]

The study is aimed at identifying patterns in distribution of pollutants in the elementary landscape-geochemical systems (ELGS) of the temperate zone. The study used 137Cs as a tracer, which allows a highly detailed analysis of the nature of the heterogeneity of secondary migration in the toposequence: summit—slope—closing depression, treated as the elementary landscape-geochemical system. The study site was located in the Bryansk region in the Chernobyl abandoned area with an initial level of 137Cs contamination exceeding 1480 kBq/m2 (40 Ci/km2). An original technique of repeated 137Cs measurements along cross-sections accompanied by topographic survey and soil cores sampling has been applied. The obtained results showed a complete absence of constant increase of 137Cs concentration downslope but revealed a steady regular variability of 137Cs activity of a cyclical type. Given uniformity of the initial 137Cs fallout within a small-sized plot, variation of 137Cs due to its secondary distribution in ELGS was 2–2.7-fold according to field gamma-spectrometry data which corresponded to the radionuclide contamination density of the top 20-cm layer of the soil containing 96–99% of the total radionuclide amount (correlation between the parameters equaled to r0.01 = 0.782, n = 20). A specifically regular structure obviously formed under the set of radionuclide water migration processes seems to be inherent in all systems of the studied type. The results obtained are believed to be of both theoretical and practical importance, since they can contribute to making decisions on the precise monitoring of zones of technogenic accumulation, as well as solving fundamental problems of soil formation and its restoration after technogenic pollution. [ABSTRACT FROM AUTHOR]

The ability to define site‐specific variability affects the acceptability of precision farming. A study was conducted to determine the soil nutrient spatial variability in the Nagpur Rural block of Nagpur district, Maharashtra, India. A total of 1707 surface soil samples (0‐to‐15‐cm soil depth) were collected from a 65,386‐ha region. Soil samples were analyzed for pH, electrical conductivity (EC), soil organic carbon (SOC), and available macronutrients (N, P, K, and S) and micronutrients (Fe, Mn, Cu, and Zn). Geostatistical modeling (kriging) was run on soil geodatabase to obtain the best‐fit experimental semivariogram having the lowest RMSE. Exponential (for pH, SOC, and N) and spherical (for P, K, and S) variogram model of ordinary kriging type as well as exponential variogram of simple kriging (for EC) were found to be best fitted to the soil parameters studied. The spatial distribution of maps showed deficiency of available N, P, and Zn, whereas soil test K, Mn, and Cu was high in most of the study area. Spatial dependence was moderate for all soil fertility parameters (N/S ratio 0.25–0.75), whereas soil test P exhibited strong spatial dependency (N/S ratio 0.19). The soil fertility maps developed through geostatistical techniques can be successfully used for managing site‐specific nutrients, enhancing fertilizer use efficiency, minimizing cost of cultivation, improving socioeconomic status of farmers, and increasing farm income without adverse effects on soil environment. Core Ideas: High‐density sampling produces better soil fertility maps.GIS and remote sensing supports cadastral‐level planning for plant nutrient management.Precision soil fertility maps can reduce cultivation costs and improve socioeconomic returns. [ABSTRACT FROM AUTHOR]

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In‐field soil testing and soil mapping can contribute to addressing the challenge of poor soil fertility and limited fertilizer application across sub‐Saharan Africa. Semi‐quantitative colorimetric methods, such as paper test strips, are frequently employed in soil nutrient assessment across developing countries, especially in South‐East Asia. This research investigated the accuracy of nutrient‐sensitive paper strips and smartphone, which was re‐purposed to act as a reflectometer, to assess soil nitrate‐N, and different methods for mapping soil fertility to identify areas of land that are suitable for human waste‐derived fertilizers (HWDF) application. The study entailed testing soil samples across 42 different farms in Kenya and Ghana and compared it to laboratory results in‐country. It was found that paper strips were capable of assessing available nitrate‐N concentration present in the soil within ±20 kg ha−1 of the standard method for 86% of the farms. Paper strips were less effective in Ghana as they had been calibrated for a method that was not used by local laboratories. Paper strips were not effective for HWDF samples, where chemical interferences and concentration of different forms of nitrates were too high, resulting in overestimation of readings and thus negatively affecting any associated nutrient management advice. Soil mapping has the potential to use open‐source data to inform farmers through mobile technology. For soil mapping two methods were deployed which includes targeting organic matter deficient areas and stakeholder led mapping, with the latter shown to be more effective in identifying areas for HWDF application. [ABSTRACT FROM AUTHOR]

Open access, high-resolution soil property maps have been created for Africa at 30 m resolution, using machine learning trained on over 100,000 analysed soil samples. Combined with other field-level information, iSDAsoil enables the possibility of site-specific agronomy advisory for smallholder farmers. Recent developments in remote sensing and machine learning have allowed African soil properties to be mapped at an unprecedented resolution of 30 m. iSDAsoil is an open access resource that will empower the community to embrace 'hyperlocal' approaches such as field-level agronomy advice for smallholder farmers. [ABSTRACT FROM AUTHOR]

Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches. [ABSTRACT FROM AUTHOR]

Salinity is a critical feature for the management of agricultural soil, particularly in arid and semi-arid areas. The present study was conducted to develop an effective soil salinity prediction model using Sentinel-2A (S2) satellite data. Initially, the collected soil samples were analysed for soil salinity (ECe). Subsequently, multiple linear regression analysis was carried out between the obtained ECe values and S2 data, for the prediction of soil salinity models. The relationship between ECe and S2 data, including individual bands, band ratios and spectral indices showed moderate to highly significant correlations (R² = 0.43-0.83). A combination of SWIR-1 band and the simplified brightness index was found to be the most appropriate (R² = 0.65; P < 0.001) for prediction of soil salinity. The results of this study demonstrate the ability to obtain reliable estimates of EC using S2 data. [ABSTRACT FROM AUTHOR]

For developing countries, detailed soil resource data and maps are essential in land-use planning. Unfortunately, obtaining detailed soil data for mapping is expensive. Detailed soil studies and mapping in developing countries often use the grid method. In addition to being time-consuming, the grid method needs a lot of sample points and surveyors. Geomorphometry can be a less expensive alternative for detailed soil mapping. Geomorphometry uses computationally measured terrain characteristics to describe other hard-to-measure terrain and soil properties. In our study, landform arrangements and slopes were analyzed together to create a map of soil pH. Bompon watershed, Indonesia, was used as a case study. Soil mapping units with potentially similar soil pH were created based on a classification system of the two geomorphometric parameters. Soil samples were taken from each of the units. The samples' soil pH was measured and compared to the geomorphometric predicted result. Regression tests were performed to see the significance of geomorphometric parameters on soil pH conditions. Regression tests show that the results of p value of the four soil layers are 0.046, 0.019, 0.037, and 0.047, respectively, on a 5% confidence level. According to the test result, landform arrangements and slopes can indicate soil pH conditions in Bompon. Our estimate suggests that our geomorphometric method is cheaper than the grid method by a factor of seven. The ability to use geomorphometric parameters to describe other soil properties could enable a cheap and fast production of detailed soil maps for developing countries. [ABSTRACT FROM AUTHOR]

Monitoring the status of natural and ecological resources is necessary for conservation and protection. Soil is one of the most important environmental resources in agricultural lands and natural resources. In this research study, we used Landsat 8 and Artificial Neural Network (ANN) to monitor soil salinity in Qom plain. The geographical location of 72 surface soil samples from 7 land types was determined by the Latin hypercube method, and the samples were taken to determine the electrical conductivity (EC). Thirty percent of the data was considered as a validation set and 70% as a test set. In addition to the Landsat 8 bands, we used spectral indices of salinity, vegetation, topography, and drainage (DEM, TWI, and TCI) because of their impacts on soil formation and development. We used ANN with different algorithms to model soil salinity. We found that the GFF algorithm is the best for soil salinity modeling. Also, the TWI topography index and SI5 salinity index and NDVI vegetation index had the most effect on the outputs of the selected model. It was also found that flood plains and lowlands had the highest levels of salinity accumulation. [ABSTRACT FROM AUTHOR]

The land quality geochemical evaluation database was mainly based on soil geochemical surveys. During its construction process, geochemical survey data involving atmospheric dry (wet) sediments, irrigation water, fertilizers and crops was simultaneously collected, analyzed and compiled into result maps using MAPGIS. ACCESS was used to manage standardized result data to establish an evaluation database. The database included a basic spatial database involving regional geography, topography, soil type and land use, a spatial evaluation database centered on the sampling layout of the work areas of soil, irrigation water, crops and other media, the distribution and changes of soil chemical elements, the classification of soil pollution and the classification of soil element environment and nutrients, and an evaluation database of various result maps such as land planning map, characteristic agricultural product planting plan suggestion map and fertilization suggestion map. The database realized the scientific and effective management of the samples and corresponding numerous laboratory analysis data, and provides basic information for the geochemical survey and evaluation of the land quality in the evaluation areas and the overall comprehensive research, and an important basis for promoting the adjustment of agricultural economic planning, the development of modern green agriculture and the development and utilization of high-quality characteristic land resources. [ABSTRACT FROM AUTHOR]

To cope with the challenges in forest management that are contemporarily caused by climate change, data on current chemical and physical soil properties are more and more necessary. For this purpose, we present a further amalgam of depth functions and SCORPAN modelling to provide data at arbitrary depth layers. In this concept, regionalisation is split up into the modelling of plot totals and the estimation of vertical distributions. The intended benefits by splitting up are: consistency between estimates on plot level and depth layer level, avoidance of artificial depth gradients, straightforward interpretation of covariates in the sense of pedogenetic processes, and circumnavigation of the propagation of uncertainties associated with separation between horizons during field sampling. The methodology was tailored to the circumstances within the north-eastern lowlands and the utilisation of current inventory data of the National Forest Soil Inventory (NFSI) in Brandenburg (Germany). Using the regionalisation of soil organic carbon (SOC) as an example, the application is demonstrated and discussed in detail. The depth to groundwater table and terrain parameters related to the catchment area were the main factors in SOC storage. The use of kriging did not improve the model performance. The relative depth gradients of SOC were especially distinguished by tree species composition and stand age. We suppose that interesting fields of application may be found in scenario-based modelling of SOC and when SOC serves as a basis for hydrological modelling. [ABSTRACT FROM AUTHOR]

Anthrax is one of the most important diseases that can be transmitted from cattle to humans, especially in countries with a high density of cattle. Cattle are usually infected by the ingestion of Bacillus anthracis spores with food ingredients. For that reason, the adequacy of anthrax spores in the soil is considered as one of the causes of anthrax in cattle. The present study is based on the identification of anthrax spores in the soil and the development of a map based on the presence of spores, so that the people of the community can take a practical step in this regard in selecting the place for their cattle to graze. For this purpose, soil samples were collected from the studied areas. After extraction of soil DNA with DNA extraction kits, all samples were tested using a qualitative detection method. For a rapid and reliable qualitative detection system for Pathogenic B. anthracis spores isolated from soil samples, the specific primer of two plasmids, pXO1 (gene Pag), pXO2 (genes Cap), and S-layer locus (gene Sap) were used. The presence of spores was found in 7 out of 50 samples. The results of a qualitative detection method of Bacillus anthracis spores in the pastureland inserted as per their GPS coordinates. Based on the presence of pathogenic anthrax spore in a specific location, a map was then prepared and displayed in the community. So that the cattle owners can decide where their cattle will graze and be careful to prevent spore contact of their animal. Among the various effective anthrax prevention measures, rich mapping according to the presence of spores can be helpful in community level that will protect people from anthrax disease in the future. [ABSTRACT FROM AUTHOR]

Statistical and spatial approaches have been used to determine the relationship between in-situ resistivity, soil moisture content and pH. These are done to establish the extent to which resistivity measurements can be used in mapping selected soil properties. Soil resistivity measurements were taken via a Wenner array platform having an electrode spacing of 8 cm. Soil samples were taken at 0-3 cm depth. The samples were analysed for moisture content and pH. Clayey sand and sand textural classes were deduced from resistivity values (210-750 ohm-m and >750 ohm-m respectively). Moisture content values were classified in conjunction with texture as unavailable water (UW) and available water (AW). pH values were classified as moderately acidic (5.2 - 6.0), slightly acidic (6.1 - 6.5) and neutral (6.6 - 6.9). A moderate inverse relationship was observed between resistivity and moisture content. The spatial correlation between resistivity-derived textural classes and moisture content classes was 62%. Resistivity was also found to be inversely correlated with pH. Spatially, higher resistivity zones were abundantly associated with moderately acidic pH zones, while lower resistivities correlated with slightly acidic to neutral pH zones - thereby bringing the spatial association between resistivity and pH to 60%. The pilot/plot-scale study concluded that to a satisfactory extent, in-situ soil resistivity measurements can be adopted as a complimentary tool for selected soil properties mapping. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil Science & Plant Nutrition / Toprak Bilimi ve Bitki Besleme Dergisi is the property of Soil Science Society of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Anabolic/catabolic index greater in sites invaded by invasive alien species. • Boron and phosphorus related to tree invasion, supporting the Catabolic Theory. • Boron poverty constrains anabolism and hence tree establishment. • Phosphorous enrichment promotes catabolism and hence non-tree species. Invasion by invasive alien trees is a major threat to the biodiversity of the fynbos biome in South Africa. The environmental factors influencing the intensity of invasion are, however, largely unknown. In particular, the effects of soil nutrients on the invasion of alien trees in the fynbos biome have not been investigated. The Catabolic Theory predicts that trees will be more competitive than shrubs and herbaceous plants where anabolic nutrients (e.g. Mg, Mn and B) are comparatively more abundant than catabolic nutrients (e.g. P, Cu and Zn). We tested this theory at seven study sites by comparing soil nutrient concentrations in non-invaded and adjacent invaded sub-sites. Soil samples from 275 plots across 17 sub-sites were analysed for pH, Na, Mg, K, Ca, P, S, C, B, Mn, Cu and Zn. The results corroborated the Catabolic Theory. An index of anabolic to catabolic nutrients was greater in all ten sub-sites invaded by invasive alien trees than in sub-sites without invasion. Nutrients strongly related to tree invasion included B and P. As per the Catabolic Theory, poverty of B at certain sites is likely to constrain anabolism and consequently tree establishment. Some non-invaded sites had B concentrations of 0.02 mg kg−1, which is an order of magnitude less than critical deficiency thresholds in an agronomic context. By contrast, richness of P (as a result of prior fertilization) in fallow agricultural fields would, as per the Catabolic Theory, promote catabolism and the competitive strength of shrubs and/or herbaceous plants relative to trees. Practical implications of these findings include: 1) the mapping of soil properties such as B and P to reveal the inherent propensity of landscapes in the Western Cape to be invaded by trees; and 2) the application of B-binding substances such as illite and vermiculite to constrain invasion of trees in fynbos. [ABSTRACT FROM AUTHOR]

Enhancing the spatial resolution of pedological information is a great challenge in the field of digital soil mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially and are available at a coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining soil map units (SMUs), where each SMU can include one or several soil type units (STUs) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at a finer spatial resolution by machine-learning algorithms, using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of the spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithms (DSMART with extra soil profiles; DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at a 50 m resolution was assessed over a large study area (6775 km 2) using an external validation based on 135 independent soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1:25000 soil maps. Pairwise comparisons were also performed, using the Shannon entropy measure, to spatially locate the differences between disaggregated maps. The main results show that adding soil landscape relationships to the disaggregation process enhances the performance of the prediction of soil type distribution. Considering the three most probable STUs and using 135 independent soil profiles, the overall accuracy measures (the percentage of soil profiles where predictions meet observations) are 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost 2 times higher when validated using 3×3 windows. They achieved 28.5 % for DSMART with soil landscape relationships and 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships and extra soil observations constraints allow the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial for obtaining consistent soil maps with a clear internal disaggregation of SMUs across the landscape. [ABSTRACT FROM AUTHOR]