Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France
Anne C Richer-De-ForgesDominique ArrouaysLaura PoggioSongchao ChenMarine LacosteBudiman MinasnyZamir LibohovaPierre RoudierVera Leatitia MulderHervé NÉDÉLECGuillaume MarteletBlandine LemercierPhilippe LagacherieHocine Bourennane
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Soil texture
Digital Soil Mapping
Texture (cosmology)
Summary We investigated the utility of three interpolation techniques that ignored descriptive `soft' information and one that used it for mapping topsoil texture classes: re‐coding of soil map units within Geographical Information Systems (GIS), Thiessen polygons, and classifcation of probability vectors estimated by ordinary indicator kriging and simple indicator kriging with local prior means. The results were compared with texture maps based on a classifcation of kriged maps of particle size distribution. The methods were applied to two distinct regions, which represent large areas in rain‐fed rice ecosystems and irrigated rice ecosystems. The `hard' databases for both areas contained soil information needed for mapping at regional scales (1:100 000‐1:150 000). These data were complemented with 'soft' information derived from farmers and soil experts (Northeast Thailand) and soil maps (Nueva Ecija, Philippines). Interpolated maps agreed with the texture map based on interpolation of particle size distribution, and ®eld estimates of soil texture proved to be valuable surrogates for laboratory measurements of soil texture classes. The interpolation of categorical data such as soil texture classes allows for upgrading and increasing the resolution of maps in sparsely sampled regions by using simple field measurements. Validation using independent test sets showed that indicator kriging with local prior means performed best in the rain‐fed lands, whereas soft information did not improve the predictions in Nueva Ecija. Local knowledge in a formalized form was valuable in Northeast Thailand and the interpolated soil texture map for this area had an accuracy and resolution to support agronomic decisions at the village scale. The poor quality of the soil map and the fact that the gradually changing variability in young alluvial soils can be modelled with fewer data explained the lower accuracy of simple indicator kriging with local prior means in Nueva Ecija. Thiessen polygons performed well in the undulating rain‐fed lands but were not as reliable as indicator kriging in the gradually changing irrigated lands.
Soil texture
Digital Soil Mapping
Interpolation
Topsoil
Geostatistics
Texture (cosmology)
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Soil texture
Digital Soil Mapping
Texture (cosmology)
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High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.
Topsoil
Digital Soil Mapping
Soil texture
Agricultural soil science
Soil series
Soil survey
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Soil texture
Digital Soil Mapping
Texture (cosmology)
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Spatial information about physical soil properties is in great demand, being basic input data in numerous applications. Soil texture can be characterized by different approaches, such as particle size distribution, plasticity index or soil texture classification. In accordance with the increasing demands for spatial soil texture information, our aim was to compile a topsoil texture class map for Hungary with an appropriate spatial resolution, using the United States Department of Agriculture soil texture classes. The 'Classification and Regression Trees' method was applied because it is widely used in Digital Soil Mapping, and has numerous advantages. Primary soil data were provided by the Hungarian Soil Information and Monitoring System. A digital elevation model and its derived components, geological and land cover map, and appropriate remotely sensed products together with the soil map featuring overall physical properties provided by the Digital Kreybig Soil Information System were used as auxiliary environmental co-variables. The resulting map can be used as direct input data in meteorological and hydrological modelling as well as in spatial planning.
Topsoil
Soil texture
Digital Soil Mapping
Texture (cosmology)
Land Cover
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Digital soil mapping (DSM) is commonly conducted using input soil attributes derived from laboratory analyses of geographically referenced samples. Field observations are often abundant and can offer a dense source of soil data that has the potential to enhance DSM predictions. However, they are not widely used due to to concerns about subjectivity and data quality. This study investigates the usefulness of hand-feel soil texture (HFST) data for DSM. We processed HFST data obtained from forest soils in France from two inventory campaigns: (i) HFST determination from systematic 1 km2 grid observations in France utilizing a specialized soil texture triangle, and (ii) HFST observations from soil survey samples, using a different texture triangle. Both sets of HFST data were used as input soil variables, with the same covariates, for predicting topsoil texture In a sizable, forested area through a DSM method. By employing independent sampling and laboratory soil analyses in selected areas, we uncovered measurement bias in one of the datasets. However, intriguingly, these biased observations identified subtle yet highly specific and unexpected patterns of sands in terraces due to alluvial deposits along small rivers. Thus, field soil observations, even if they are biased, should not be dismissed solely based on their overall predictive performance. It is essential to carefully examine predicted maps and covariates to determine whether patterns may have pedological and/or lithological origins and if they are pertinent for enhancing DSM predictions, enhancing soil process understanding, and meeting the requirements of end users. Numerous HFST are available worldwide, these datasets are usually disregarded for DSM. Here we contend that efforts should be put in recovering these data, and their potential for enhancing DSM and deepening our understanding of soil processes.
Digital Soil Mapping
Soil texture
Topsoil
Soil survey
Texture (cosmology)
Compositional data
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Digital Soil Mapping
Soil survey
Digital mapping
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