High‐resolution mapping of soil phosphorus concentration in agricultural landscapes with readily available or detailed survey data. (May 2017) - Matos‐Moreira, M.; Lemercier, B.; Dupas, R.; Michot, D.; Viaud, V.; Akkal‐Corfini, N.; Louis, B.; Gascuel‐Odoux, C.
Summary: High‐resolution mapping of soil phosphorus (P) concentration is necessary to identify critical source areas reliably where a large risk of transport coincides with a large potential source of P in agricultural landscapes. However, dense soil P data are not usually available to produce such maps and to obtain them is expensive. In this study, we modelled and mapped soil extractable P (ExtP) and total P (TP) concentrations in an intensively farmed 12‐km 2 catchment in Brittany (NW France) with two different datasets to test the suitability of readily available regional or national databases for high‐resolution mapping. We used a machine learning tool (Cubist) to develop rule‐based predictive models from a calibration dataset. Covariates included pedological, geological, agricultural, terrain and geophysical‐related attributes obtained specifically in the study area (SURVEY) or derived from readily available regional or national databases (DATABASE). Even though better predictions were obtained with the SURVEY data (RMSE = 0.018 g kg −1 for ExtP and RMSE = 0.219 g kg −1 for TP), the DATABASE data produced acceptable predictions (RMSE = 0.024 g kg −1 for ExtP and RMSE = 0.253 g kg −1 for TP). The machine learning tool helped to identify key covariates that would improve the prediction of soil P when detailed data are not available. Readily available data about crop rotations could increase the accuracy of existing ExtP maps. These maps, combined with additional soil analysis for extractable Al, would improve the mapping of TP and the identification of areas with a large potential source of P. Highlights: Modelling and mapping of soil phosphorus with the machine learning algorithm Cubist. Comparison of regional or national databases and detailed survey data for prediction. Models with regional and national data performed well, but some areas with large concentrations of P were not identified. Information about crop rotation and soil extractable Al improved model performance.