Errors in soil maps: The need for better on-site estimates and soil map predictions

• Published in: PloS one Vol. 18; no. 1; p. e0270176
• Main Authors: Buenemann, Michaela, Coetzee, Marina E, Kutuahupira, Josephat, Maynard, Jonathan J, Herrick, Jeffrey E
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.01.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Africa, Southern; Analysis; Applications programs; Biology and Life Sciences; Carbon; Climate; Crop production; Cultural heritage; Databases, Factual; Decision making; Earth Sciences; Ecology and Environmental Sciences; Errors; Estimates; Geospatial data; Irrigation systems; Land use planning; Mapping; Mobile computing; Namibia; Natural resources; Online data bases; Onsite; People and Places; Physical Sciences; Predictions; Runoff; Soil; Soil Erosion; Soil maps; Soil properties; Soil quality; Soil surveys; Soil texture; Soil water; Spatial distribution; Systems design; Terrain; Texture mapping; Topography; Topsoil; Africa, Southern; Namibia; United States; United States > US; Southern Africa
• DOI: 10.1371/journal.pone.0270176
• ISSN: 1932-6203

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.