Using homosoils for quantitative extrapolation of soil mapping models

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.

Automated summary

Digital soil mapping has been successfully used for various applications since the early 2000s. However, the availability of soil data globally is uneven, posing challenges for fitting digital soil mapping (DSM) models. This study explores the possibility of transferring soil information through geographic model extrapolation within homosoils. Soil data from Mali, West Africa and its homosoils were collected, and a regression tree model was fitted to the data. The study concludes that extrapolating models within homosoils is possible and leads to more accurate maps compared to existing global and continental maps.