Spatial variability of potassium in agricultural soils of the canton of Fribourg, Switzerland

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 was particularly influenced by soil parent materials, as suggested by a significant global spatial autocorrelation measure (Moran’s I10km = 0.43) and significant differences observed among soil types and soil parent materials. 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 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 and their high 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

Spatial variability of soil phosphorus in the Fribourg canton, Switzerland

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

Emergent Imaging and Geospatial Technologies for Soil Investigations

Soil survey investigations and inventories form the scientific basis for a wide spectrum of agronomic and environmental management programs. Soil data and information help formulate resource conservation policies of federal, state, and local governments that seek to sustain our agricultural production system while enhancing environmental quality on both public and private lands. The dual challenges of increasing agricultural production and ensuring environmental integrity require electronically available soil inventory data with both spatial and attribute quality. Meeting this societal need in part depends on development and evaluation of new methods for updating and maintaining soil inventories for sophisticated applications, and implementing an effective framework to conceptualize and communicate tacit knowledge from soil scientists to numerous stakeholders

Digital mapping of GlobalSoilMap soil properties at a broad scale: a review

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 (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources

Terrain attribute soil mapping for predictive continuous soil property maps

The current U.S. Soil Survey information is provided in polygon map units with soil property attributes represented with overlapping ranges between map units. The polygon maps do not represent the soil continuum that conforms to the actual landscape and the soil scientist expert knowledge accumulated during the field survey. Geographic Information System (GIS) technologies and software in combination with high resolution spatial data provide an opportunity to generate continuous raster based soil property maps based on terrain attribute soil mapping (TASM) that is compatible with distributed hydrologic modeling. This requires the development of methods to translate Soil Survey Geographic Database (SSURGO) qualitative soil-landscape models to numerical ones. Related to the current SSURGO data is the soil carbon (SC) stock, especially the spatial distribution and stability of SC pools. The recent concerns about climate change, associated with high levels of atmospheric CO2, has brought new interest in the role of the SC pool as a sink for the atmospheric CO 2. Human activities in the Corn Belt and parts of the Great Plains in the United States have reduced the SC stock by half from historic levels. Restoring some of the SC pool would require understanding of its spatial distribution and stability for future predictions. TASM improved the accuracy of predicted continuous soil property maps compared to SSURGO. On average, TASM predicted values for depth to lithic/paralithic contact and depth to till/outwash were within 26 cm and 40 cm of measured values compared to SSURGO with 57 cm and 60 cm, respectively. The Distributed Hydrology Soil Vegetation Model (DHSVM) predicted streamflow without calibration demonstrating the benefits of using the correct soil input information. In addition, the TASM predicted continuous soil depth maps provided a better performance for the DHSVM predicted streamflow compared to SSURGO soil maps, especially for 90 m pixel resolution. SC distribution and stability followed the patterns of soil wetness and was related to soil landscape position. Wetter soils on lower positions and depressions had the highest SC stock. At the beginning of soil incubation the daily CO2 evolved was higher and more intense for the wetter areas indicating a less stable SC pool. The SC pool distribution and stability can be predicted based on soil wetness and landscape position. The management of these areas can potentially increase SC stock. These examples demonstrate the potential benefits of making continuous rater soil maps and property maps using expert knowledge; existing soil information and terrain attribute analysis

IMPACT OF CLIMATE CHANGE SCENARIOS ON THE EXTEND OF PLANT HARDINESS ZONES OF SOUTH WEST ALBANIA

Abstract Albania is part of th

Identification and Delineation of Broad-Base Agricultural Terraces in Flat Landscapes in Northeastern Oklahoma, USA

Broad-base agricultural terraces can be difficult to delineate in flat landscapes, particularly when covered by crops, due to subtle changes in elevation over relatively wide distances. In northeastern Oklahoma, these terraces are usually less than half a meter high and 15 to 20 m wide. The objective of this research was to develop and test a technique for identifying and classifying terraces using computer vision applied to terrain derivatives calculated from digital elevation models at five sites. We tested 38 terrain-derivative grid combinations or sets that represented 19 terrain characteristics, calculated from elevation models after two Gaussian smoothing strategies to provide some degree of generalization and a removal of excess noise. The best subsets achieved a 98% classification accuracy (kappa 0.96) and consisted of derivatives representing hydrology, morphometry, and visibility categories. Inaccuracies occurred primarily at the edges of some of the study sites, where agricultural fields bordered incised drainage areas where changes in elevation were similar to those for the terraces. Further study will elucidate the relationships between terrace “borrow” and “deposition” areas in the terrace areas and their relationships to yield and salinity issues. This work seeks to automate terrace identification for digital soil mapping on terraced fields for the improved delivery of soil information for resource conservation and land use

Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Soil organic matter content (SOM) and cation exchange capacity (CEC) are important agronomic soil properties. Accurate, high-resolution spatial information of SOM and CEC are needed for precision farm management. The objectives of this study were to: (1) map SOM and CEC in a low relief area using only lidar elevation-based terrain attributes, and (2) compare the prediction accuracy of SOM and CEC maps created by universal kriging, Cubist, and random forest with Soil Survey Geographic (SSURGO) database. For this study, 174 soil samples were collected from a depth from 0 to 10 cm. The topographic wetness index, topographic position index, multi resolution valley bottom flatness, and multi resolution ridge top flatness indices generated from the lidar data were used as covariates in model predictions. No major differences were found in the prediction performance of all selected models. For SOM, the predictive models provided results with coefficient of determination (R2) (0.44–0.45), root mean square error (RMSE) (0.8–0.83%), bias (0–0.22%), and concordance correlation coefficient (ρc) (0.56–0.58). For CEC, the R2 ranged from 0.39 to 0.44, RMSE ranged from 3.62 to 3.74 cmolc kg−1, bias ranged from 0–0.17 cmolc kg−1, and ρc ranged from 0.55 to 0.57. We also compared the results to the USDA Soil Survey Geographic (SSURGO) data. For both SOM and CEC, SSURGO was comparable with our predictive models, except for few map units where both SOM and CEC were either under or over predicted

Influence of Land Use and Topographic Factors on Soil Organic Carbon Stocks and Their Spatial and Vertical Distribution

Soil organic carbon (SOC) plays a critical role in major ecosystem processes, agriculture, and climate mitigation. Accurate SOC predictions are challenging due to natural variation, as well as variation in data sources, sampling design, and modeling approaches. The goal of this study was to (i) understand SOC stock distribution due to land use (restored prairie grass—PG; lawn grass—LG; and forest—F), and local topography, and (ii) assess the scalability of SOC stock predictions from the study site in North Carolina (Lat: 36°7′ N; Longitude: 80°16′ W) to the geographic extension of the Fairview soil series based on the US Soil Survey Geographic (gSSURGO) database. Overall, LG had the highest SOC stock (82 Mg ha−1) followed by PG (79 Mg ha−1) and forest (73.1 Mg ha−1). SOC stock decreased with the depth for LG and PG, which had about 60% concentrated on the surface horizon (0–23 cm), while forest had only 40%. The differences between measured SOC stocks and those estimated by gSSURGO and modeled based on land use for the Fairview series extent were comparable. However, subtracting maps of the uncertainty predictions based on the 90% confidence interval (CI) derived from the measured values and estimated gSSURGO upper and lower values (an estimated CI) resulted in a range from −17 to 41 Mg ha−1 which, when valued monetarily, varied from USD 33 million to USD 824 million for the Fairview soil series extent. In addition, the spatial differences found by subtracting the gSSURGO estimations from measured uncertainties aligned with the county administrative boundaries. The distribution of SOC stock was found to be related to land use, topography, and soil depth, while accuracy predictions were also influenced by data source