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Mapping the phosphorus sorption capacity of Danish soils in four depths with quantile regression forests and uncertainty propagation

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Managing soil phosphorus is essential for agricultural production and environmental protection. This requires information on the phosphorus sorption capacity (PSC) of the soil. In this study, we map the PSC for Danish soils in four depths. Measuring PSC directly is expensive and time-consuming, and we therefore used a pedotransfer function based on oxalate-extractable aluminum (Alo) and iron (Feo). We mapped Alo, Feo, and their uncertainties using Quantile Regression Forests (QRF). We then calculated the uncertainties for PSC with a quasi-Monte Carlo complete combinatorial convolution (CCC) of the prediction quantiles for Alo and Feo. The main factors for predicting Alo were the parent material, topography and precipitation. In many areas, podzolization also affected the vertical distribution of Alo. The main factors for Feo were the soil texture, organic matter and wetland areas. The average predicted PSC was 36 ± 9 mmol kg−1 (±1 SD) in topsoil, but it was generally highest at 25–50 cm depth with a mean value of 37 ± 9 mmol kg−1. The weighted root mean squared error of the mapped properties was 14 mmol kg−1 for Alo, 32 mmol kg−1 for Feo and 19 mmol kg−1 for PSC. The prediction accuracies were moderate at best, but the prediction quantiles were generally reliable. The mapped uncertainties were largest in wetland areas, while they were smallest in young loamy moraine deposits.

Original languageEnglish
Article number116316
Publication statusPublished - Feb 2023

Bibliographical note

Funding Information:
We are obliged to Dr. José Padarian for help with the declustering methodology. We would also like to thank the two anonymous reviewers, whose thorough and insightful comments helped to improve the final version of the manuscript.

Publisher Copyright:
© 2023 The Authors

    Research areas

  • Aluminum, Iron, Oxides, Pedotransfer functions, Podzolization, Probability

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