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Information obtained from landform classification is fundamental for understanding physical, chemical, and biological soil processes. Digital elevation models (DEMs) can be used for landform classification using a geomorphic pattern recognition and classification approach such as geomorphons. In this study, we used geomorphons to predict soil types of the Uasin Gishu Plateau in western Kenya with the aim of improving the existing soil type maps. We ran the geomorphons classification on the 30 m Shuttle Radar Topographic Mission (SRTM) DEM using the module r.geomorphons “add-on” in GRASS GIS with look up distance (L) values of 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 cells (300 m to 3,000 m) and flatness threshold values of 2, 0.5 and 0.01 degrees. We grouped the resulting geomorphons into three classes: upland summits, upland midslopes, and bottomlands. We then assigned soil types according to the World Reference Base soil classification system to these three landscape positions after dividing the study area into the Lower Plateau and the Upper Plateau based on elevation and geomorphology. The predicted soil types were quantitatively evaluated against 50 soil point observations based on overall accuracy, precision, recall and Cohen’s kappa coefficient (k) metrics. Evaluation results showed that an L value of 20 cells performed best (k = 0.52; overall accuracy = 0.62) followed by an L value of 30 cells (k = 0.50; overall accuracy = 0.58). L values of 60, 70 and 80 cells performed worse (k = 0.35; overall accuracy = 0.48). Although an L value of 20 cells performed slightly better than an L value of 30 cells, the L value of 30 cells better captured the geomorphology and soil-landscape relationships based on our own expert knowledge, legacy data, and the fact that the bottomlands pattern was more continuous for an L value of 30 cells than for an L value of 20 cells. Upland summits occurred over ~ 32% of the plateau and were occupied by Nitisols on the Upper Plateau and Ferralsols on the Lower Plateau. Upland midslopes occurred over ~ 42% of the plateau and were occupied by Acrisols on the Upper Plateau and Acrisols/Ferralsols on the Lower Plateau. Bottomlands occurred over ~ 26% of the plateau and were occupied by Luvisols/Gleysols on both the Upper and Lower Plateau. Geomorphons, as a method of landform classification, correlated to geomorpho-pedological processes and captured soil variations and differences in the study area. The approach is computationally efficient and can be used for large areas, but is limited in that it only classifies landscapes according to shape. This means that the algorithm does not separate or identify soil types with different parent materials occurring within one landform class. Thus, Regosols and Cambisols on very steep slopes and Acrisols and Ferralsols on gentle slopes could not be separated.

Soil survey organizes the landscape into units with common soil properties, characteristics, and classification. Soil survey units can be used to predict soil behavior and thus are useful for making management decisions and evaluating soil change. Traditionally, in the USA, soil survey mapping concepts have been developed with the dominant use of the landscape in mind. Current enhancement of soil survey includes documenting dynamic soil properties and soil change due to ecosystem management. Ecological sites are a concept used to describe “kinds of land” that have common potential kinds and amounts of vegetation and characteristic response to disturbance. In intensively managed (agronomic) systems, inputs (e.g., energy, fertilizer, irrigation water) can confound and homogenize vegetation indicators. In these situations, ecological site concepts, as constructed through state and transition models, can be differentiated based upon levels of soil function (indicated by dynamic soil properties) that occur as a result of the management (disturbance). Groupings and interpreting soil properties using an ecological site framework can serve as a useful tool for soil resource management and assessment and bring whole ecosystem insight into management decisions. Such organizational frameworks should provide information about both reference conditions and alternative management systems of soil functions or dynamic soil properties within an ecological site. Reference conditions might reflect either native or naturalized vegetation or the highest possible function that an ecological site could support. A framework for assessing soil condition in two ecological sites/soil types is examined. The capacity of each ecological site is different as indicated by soil carbon content and aggregate stability. This information allows for documentation of soil change (from reference to alternative states or management systems); it could also be used as a reference for soil health assessments and could be used to enhance soil survey with land use and management-specific information.

Potassium (K) is a crucial element for plant nutrition and its availability and spatial distribution in agricultural soils is influenced by many agro-environmental factors. In Switzerland, a soil monitoring network (FRIBO) was established in 1987 with 250 sites distributed over the whole of the canton of Fribourg (representing 4% of the surface area of Switzerland), whose territory is shared between the Swiss Midlands and the Western Alp foothills. In this study area, diverse geological deposits (sandstone, marlstone, silts and calcareous rocks), soil types (Cambisols, Gleysols, Rendzinas, Luvisols and Fluvisols) and land uses (cropland, permanent grassland and mountain pasture) are present, making the network interesting for assessing the relative contribution of environmental variables and land use management on soil properties. The aims of the present study were to (i) characterize the soil K status in the Fribourg canton according to four different extraction methods; (ii) analyse the spatial variability of soil K in relation to land use, soil type, soil parent material and topography; (iii) evaluate the spatial predictability of K at the canton level; and (iv) analyse the implications for K fertilization management. The overall amount of soil total K averaged 13.6 g·kg − 1 with significant variations across the sites (5.1–22.1 g·kg − 1 ). The spatial distribution of total K content was influenced by relatively extended soil forming processes, as suggested by (i) a significant global spatial autocorrelation measure at the 10 km scale (Moran's I = 0.43); (ii) significant differences observed among soil types and soil parent materials and (iii) significant correlations with land attributes such as elevation ( r = − 0.51). On the other hand, available mean K forms were significantly different among land uses, with the highest mean values of available K encountered in permanent grasslands, from 46.3 mg·kg − 1 (water extraction) to 198 mg·kg − 1 (acetate ammonium + EDTA extraction). All K forms (total and available) showed similar spatial regional patterns for all spatial interpolation methods, with areas dominated by permanent grassland and crops presenting higher values. However, these trends were less pronounced for the available K forms due to the prevalence of on-farm management practices for these K forms ( e.g. fertilization), likely inducing high spatial and temporal variability. This hypothesis was supported by spatial clustering of low and/or high K fertility status that could be related to local particular farming practices. Grasslands require particular attention with regard to overall high K fertility status.

Eutrophication associated with high concentrations of phosphorus in Missisquoi Bay has raised concerns about its influence on submersed aquatic vegetation. Subaqueous soils could play an important role in nutrient cycling, algae blooms, the spread of invasive aquatic vegetation, and water quality. The objectives of this study were to (a) characterize some of the morphological, physical and chemical properties of subaqueous soils in Missisquoi Bay based on ground-penetrating radar and soil analysis; and (b) assess relationships among the subaqueous soil landscapes, radar facies, and submersed aquatic vegetation. Coarse Stratified Sediments and Lacustrine Silt radar facies associated with Delta/Nearshore and Lakebed/Bay Bottom subaqueous soil-landscape units covered 90 percent of the study area. The Lacustrine Silt radar facies occurred in relatively deep, low-energy, depositional areas and had the highest P concentrations. The majority of the submersed aquatic vegetation was associated with Delta/Nearshore and Fringing Peatland subaqueous soil landscapes under shallow water.

Phosphorus (P) is the second essential nutrient for plant growth but can become an ecological and economical concern in case of over-fertilization. Soil P dynamic is influenced by many parameters like soil physical-chemical properties and farming practices. A better understanding of the factors controlling its distribution is required to achieve best P crops management. In Switzerland, the FRIBO network was launched in 1987 and comprises of 250 sites covering a wide diversity of soils and three different land uses (croplands, grasslands and mountain pastures) across the Fribourg canton. A spatial investigation of the different P forms for the FRIBO network led to the following main conclusions: i) The P status in agricultural soils was significantly different among the three land uses encountered, with the highest mean values of available P found in croplands (from 2.12 to 81.3 mg.kg-1 according to the indicator used), whereas total P was more abundant in permanent grasslands (1186.2 mg.kg-1), followed by mountain pastures (1039.0 mg.kg-1) and croplands (935.0 mg.kg-1). A full characterization of the soil P status provides necessary data on P distribution related to soil properties and land use, and should help to develop more accurate estimation procedures and fertilization strategies in a near future; ii) Environmental variables derived from digital elevation model (DEM) only explained a small part of the spatial variation of the different P forms (20 to 25%). Thus, the geostatistic analyses revealed that land use play a major role in soil P distribution. However, this pattern was less visible for total P than for available P. Future studies should include more data points as well as additional variables such as parent material and soil type to accurately estimate the role of soil parameters on the distribution of P-related forms.

The GlobalSoilMap initiative calls for the generation of continuous maps for soil properties, including pH in a 1:5 suspension of soil in water (pH 1:5 W ) based on a standard method, ISO 10390. The United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS) employs a 1:1 suspension of soil in water (pH 1:1 W ), and a 1:2 suspension of soil in CaCl 2 (0.01 M) (pH 1:2 CaCl 2 ) for routine pH analysis (Soil Survey Staff, 2009). The objective of this study was to determine the most efficient way to convert these pH values to the GlobalSoilMap standard. For this analysis, 563 soil samples from the USDA-NRCS-National Soil Survey Center (NSSC) soil archive, which had been previously analysed for pH 1:1 W and pH 1:2 CaCl 2 , were selected for determination of pH 1:5 W , pH 1:5 CaCl 2 and electrical conductivity (EC) in 1:2 suspension of soil in water (EC 1:2 W ). The samples represented 11 soil orders, 8 mineralogy classes, 5 family particle size classes, 4 genetic master horizons, and 7 depth intervals. For each category, 25–30 samples were selected to represent a comprehensive pH range. Regression analysis showed strong and significant relationships (R 2 > 0.92) between pH methods across all categories. The simple linear regression equation, pH 1:5 W = − 0.51 + 1.06 pH 1:1 W , had an RMSE = 0.44 pH units. Smoothing spline, did not significantly improve pH 1:5 W predictions, nor did the incorporation of EC. Genetic horizons and soil depth intervals did not have a significant effect on pH 1:5 W . The linear regression models for predicting pH 1:5 W using pH 1:1 W or pH 1:2 CaCl 2 as predictors emerged as the best candidates for a standard pedotransfer function. Using pedotransfer functions such as these will allow for the simple conversion of existing measured and estimated pH 1:1 W or pH 1:2 CaCl 2 values from NRCS databases to the GlobalSoilMap standard of pH 1:5 W .

Digital soil mapping (DSM) approaches can be used to create new soil maps or enhance existing maps, particularly in areas where only general soil maps are available. In this study, we utilized a knowledge-based inference soil mapping approach to develop a first-generation digital soil map for part of the Uasin Gishu Plateau in western Kenya. We calculated the following environmental covariates from the Shuttle Radar Topographic Mission (SRTM) 30 m digital elevation model (DEM): slope gradient, multi-resolution valley bottom flatness index (MrVBF), multi-resolution ridgetop flatness index (MrRTF), topographic position index (TPI), elevation, and profile curvature. These covariates were then used along with existing soil information and expert knowledge from soil scientists familiar with the area to produce new raster-based maps of soil types, effective soil depth, soil moisture storage capacity and soil drainage class. The soil type maps predicted using clustering analysis and fuzzy logic methods showed good agreement with field observations based on the overall accuracy values. The fuzzy logic map performed slightly better (kappa coefficient (k) = 0.68; overall accuracy = 0.76) than the map based on clustering analysis (k = 0.59; overall accuracy = 0.68). The accuracy for the effective soil depth fuzzy logic map was higher (R2 = 0.56; RMSE = 11; ME = 1.1) compared to the existing soil map (R2 = 0.34; RMSE = 27; ME = 8). Seven major soil types occur in the study area: Ferralsols, Nitisols, Gleysols, Luvisols, Acrisols, Cambisols and Regosols, according to the World Reference Base soil classification system. This study generated detailed and improved predictions of soil types and properties at 30 m grid resolution. These maps should be more useful for soil, crop and land use management decisions than existing maps.

Land Resource Regions (LRR) M and L occupy predominantly the northcentral part of USA with the exception of MLRA 101 (LRR L) located on the northeastern part bordering Lake Eire. Both regions have been influenced by several glaciations that occurred between 10,500 and 2,500,000 years ago. The geology is dominated by Devonian/Ordovician rocks, sedimentary and granitic, and to a lesser extent by Cambrian (9 %) and volcanic (8 %) rocks. Soils in the regions have developed mostly on loess, till, and outwash materials deposited after glacier melt. The predominant soils are Mollisols and Alfisols formed on loess followed by Spodosols, Entisols, and Inceptisols formed on outwash rmaterials on stream terraces and valleys. Histosols are limited in extend occurring throughout the region on localized depressions. The region that is also known as the “Bread Basket” of the USA is comprised of soils that are relatively young and among the most fertile in the world. The rolling hills, loess plains, and outwash valleys support a wide variety of crops. Soils are neutral to slightly alkaline for the most part and relatively high in organic matter due to favorite climate conditions and historical predominantly prairie vegetation. Intensive agricultural practices have caused extensive erosion and systematic depletions of fertility and organic matter and continue to remain major concerns for soil resource management and environment issues such as water quality.

The distribution of continuous soil properties and their environmental covariates within soil classes are often times unknown or not evaluated. Understanding and defining the distribution of environmental covariates within soil classes is fundamental to the fuzzy logic inference mapping processes. Under knowledge-based applications of fuzzy logic mapping, the user typically utilizes predetermined distribution functions to define a representative relationship between soils and their covariates. If the predetermined distributions do not adequately describe the soil–covariate relationship, the misrepresentation can lead to inadequate prediction of soil properties while requiring a high level of user input. To move away from knowledge-based “guesses” of distributions, we present a new and innovative method of modeling the distribution of environmental covariates, specifically terrain attributes (TAs), within the landform-based soil classes. This eliminates the need for manually manipulated, user-defined curves and works to more accurately represent the distribution of TAs within soil classes. The fully automated method fits a variety of probability distribution functions (PDFs) to TA values within algorithm-derived landform classes. We compared the Pearson’s correlation coefficient for goodness of fit to determine which PDF best models the distribution of TAs within soil classes. This fully automated method works to improve our understanding of how terrain attributes vary within soil classes, allowing for more accurate and reliable model predictions.

Saturated hydraulic conductivity (K sat) is an important soil parameter that governs water movement through horizons, pedons, and soil landscapes. K sat is infamous for its spatial and temporal variability, which contributes to the difficulty and considerable expense in measuring or otherwise quantifying it. Consequently, predictive methods such as pedotransfer functions (PTFs) that use physical soil properties, such as texture and bulk density, have been developed to derive K sat values. Soil texture and structure are key factors influencing K sat because of their direct relationship to pore size distribution. Quantitatively defining the combined effects of texture and structure on pore size distribution in a PTF is a difficult task. The objectives of this research were to: (i) estimate K sat based on pore characteristics derived from soil thin sections via image analysis; and (ii) compare the resultant values with field-measured K sat and with K sat estimated by a PTF using soil texture and bulk density parameters. We digitally scanned 39 thin sections from 11 pedons of soils derived from loess over till and/or over weathered sandstone. Soil voids were classified based on their size and shape. K sat was measured in the field using a Compact Constant-head Permeameter (Amoozemeter) and estimated using a Rosetta PTF. Simple and multiple linear regression (MLR) analyses were used to relate pore indexes and soil physical properties with measured and estimated K sat. The mean measured K sat was 0.74 cm h−1, whereas the PTF-estimated K sat from Rosetta and MLR were 0.36 cm h−1 and 0.49 cm h−1, respectively. The addition of pore characteristics into the model improved K sat predictions compared to predictions using Rosetta alone. The estimated K sat based on the model with added pore characteristics was better correlated with field-measured K sat (r = 0.82) than that based on Rosetta (r = 0.62). The addition of pore characteristics can improve K sat predictions. However, thin section void analysis from additional parent materials is needed.

There is a growing need for raster-based soil data to support modelling at regional and continental scales. The GlobalSoilMap consortium aims to satisfy this need with the production of a suite of digital soil maps of various soil properties at six standard depths for most of the land surface of the Earth. Initially, the maps will be produced using legacy soil data (soil data already available). In the United States, the State Soil Geographic (STATSGO2) database is a rich source of legacy soil information, but the STATSGO2 map is vector-based and soil components' soil property data is organised by horizon. We therefore applied the equal-area spline function to the soil components of STATSGO2 map units in order to obtain estimates of soil organic carbon (SOC) content at the GlobalSoilMap standard depth increments. Using these estimates, we calculated the weighted mean of SOC for each STATSGO2 map unit at each GlobalSoilMap.net depth increment and gridded these weighted means at 100 m resolution for the contiguous United States. The result is a set of maps of SOC content for each GlobalSoilMap depth increment accompanied by an indication of the within-map unit variability. In addition, we show how the use of other “metadata maps” is essential for avoiding misunderstandings about reported values and discuss some of the limitations of the approach.

Few studies have attempted to isolate the various factors that may cause the observed increases in peak flows and erosion after high-severity wildfires. This study evaluated the effects of burning by: (i) comparing soil water repellency, surface cover, and sediment yields from severely burned hillslopes, unburned hillslopes, and hillslopes where the surface cover was removed by raking; and (ii) conducting rainfall simulations to compare runoff, erosion, and surface sealing from two soils with varying ash cover. The fire-enhanced soil water repellency was only stronger on the burned hillslopes than the unburned hillslopes in the first summer after burning. For the first 5 yr after burning, the mean sediment yield from the burned hillslopes was 32 Mg ha ―1 , whereas the unburned hillslopes generated almost no sediment. Sediment yields from the raked and burned hillslopes were indistinguishable when they had comparable surface cover, rainfall erosivity, and soil water repellency values. The rainfall simulations on ash-covered plots generated only 21 to 49% as much runoffand 42 to 67% as much sediment as the plots with no ash cover. Soil thin sections showed that the bare plots rapidly developed a structural soil seal. Successive simulations quickly eroded the ash cover and increased runoff and sediment yields to the levels observed from the bare plots. The results indicate that: (i) post-fire sediment yields were primarily due to the loss of surface cover rather than fire-enhanced soil water repellency; (ii) surface cover is important because it inhibits soil sealing; and (iii) ash temporarily prevents soil sealing and reduces post-fire runoff and sediment yields.

The concerns about climate change have increased interest in understanding differences in soil carbon pools and availability. The objective of this study was to assess total Soil Organic Carbon (SOC) and mineralizable SOC (Cmin) dynamics and spatial distribution as controlled by slope position, in glaciated northern Indiana. We collected 210 soil samples from the 0 to 25 cm surface layer along 10-point transects along a soil catena. Total SOC was determined by dry combustion and Cmin by incubation. The spatial distribution of total SOC followed patterns related to soil wetness. Overall, the depression areas stored between 50 and 141 Mg C ha−1 or between 50 and 68 % more total SOC when compared to the drier areas. After 28 days of incubation (Cmin), depressions released 1.2 Mg C ha−1, which was significantly more than the drier areas at 0.8 Mg ha−1. These differences indicate the potential of wetter areas, to store C if converted to C accruing management practices. The mean daily rate of C-CO2 evolved decreased exponentially during the first 28 days from 1.5 to 0.2 μg g−1 h−1. The management of these targeted areas can potentially increase soil C stock in arable lands and assist managers in developing systems that will sequester soil carbon.

The GlobalSoilMap project is representative of a global consortium of scientific institutions involved in soil survey and soil science. The GlobalSoilMap group was formed as an outgrowth of the International Union of Soil Sciences Working Group for Digital Soil Mapping with the purpose of providing consistently produced soil property information at 100 m resolution across the world. This information will aid in solving some of the key environment and societal issues of the day, including food security, global climate change land degradation and carbon sequestration. Data would be produced using mostly the storehouse of existing legacy soils data along with geographic information and a range of covariates. A range of modeling techniques is used dependant on the complexity of the background soil survey information. The key soil properties that would be most useful to the modeling community and other users are: organic carbon (g/kg), texture (sand %, silt %, clay % and coarse fragments %), pH, depth to bedrock or restrictive layer. Predictions are made at specified depths with uncertainty values assigned to each prediction. An innovative splining technique will be employed to provide a continuous prediction of soil property values throughout the depth of each profile. Maps have been produced at the country level in the Australia, Canada, Denmark, Nigeria, South Korea and the US and work is on-going in many other parts of the world.

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1 km in 2014, followed by an update at a resolution of 250 m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

Magnesium (Mg) is an essential element for plant growth and human health. Its availability and spatial distribution in soils depends on a wide variety of intrinsic and extrinsic factors. Understanding how Mg availability changes in space and time is crucial for preventing potential deficiencies. In 1987, a soil-monitoring network (FRIBO) was launched in the canton of Fribourg, Switzerland. It was based on 250 sites distributed evenly throughout the canton so as to include a large variety of soil types (Cambisols, Gleysols, Rendzinas, Regosols, Lithosols, Luvisols and Fluvisols) under three different land use types (croplands, permanent grasslands and mountain pastures). The aim of this research was to characterize the spatial and temporal variation of total and available forms of Mg in the agricultural soils of the canton of Fribourg and to discuss potential implications for Mg fertilization management. Total Mg concentration (MgT) averaged 5.5 g kg−1, with small differences between land use types. Spatial distribution of MgT showed higher values on the southern part of the study area, mostly on Rendzinas and Cambisols. Average concentrations of available Mg forms were significantly different according to extraction methods, with water extraction (MgH2O) having the lowest value (14.2 mg kg−1) followed by calcium extraction (MgCaCl2, 109.4 mg kg−1) and ammonium acetate + EDTA extraction (MgAAE, 148.7 mg kg−1). On average, permanent grasslands had significantly higher values for all Mg forms compared to croplands and mountain pastures, a result further corroborated by the analysis of spatial distribution. Intrinsic factors, such as soil type and terrain attributes, appeared to have a major influence on total Mg content, whereas available Mg forms depended mostly on extrinsic factors, such as land use type. Temporal analysis of soil available Mg forms revealed an overall increase between 1987 and 2016, especially after a land use change from croplands to permanent grasslands. In the light of the essential role of Mg for plant growth and development, as well as its critical role in animal health, the status of Mg should continue to be monitored in the FRIBO network and plant analysis should be implemented.

International audience; Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent 10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. Half the articles focused on soil information in topsoil only (