A global dataset on phosphorus in agricultural soils

Numerous drivers such as farming practices, erosion, land-use change, and soil biogeochemical background, determine the global spatial distribution of phosphorus (P) in agricultural soils. Here, we revised an approach published earlier (called here GPASOIL-v0), in which several global datasets descr...

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Veröffentlicht in:Scientific data 2024-01, Vol.11 (1), p.17-34, Article 17
Hauptverfasser: Ringeval, Bruno, Demay, Josephine, Goll, Daniel S., He, Xianjin, Wang, Ying-Ping, Hou, Enqing, Matej, Sarah, Erb, Karl-Heinz, Wang, Rong, Augusto, Laurent, Lun, Fei, Nesme, Thomas, Borrelli, Pasquale, Helfenstein, Julian, McDowell, Richard W., Pletnyakov, Peter, Pellerin, Sylvain
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Sprache:eng
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Zusammenfassung:Numerous drivers such as farming practices, erosion, land-use change, and soil biogeochemical background, determine the global spatial distribution of phosphorus (P) in agricultural soils. Here, we revised an approach published earlier (called here GPASOIL-v0), in which several global datasets describing these drivers were combined with a process model for soil P dynamics to reconstruct the past and current distribution of P in cropland and grassland soils. The objective of the present update, called GPASOIL-v1, is to incorporate recent advances in process understanding about soil inorganic P dynamics, in datasets to describe the different drivers, and in regional soil P measurements for benchmarking. We trace the impact of the update on the reconstructed soil P. After the update we estimate a global averaged inorganic labile P of 187 kgP ha −1 for cropland and 91 kgP ha −1 for grassland in 2018 for the top 0–0.3 m soil layer, but these values are sensitive to the mineralization rates chosen for the organic P pools. Uncertainty in the driver estimates lead to coefficients of variation of 0.22 and 0.54 for cropland and grassland, respectively. This work makes the methods for simulating the agricultural soil P maps more transparent and reproducible than previous estimates, and increases the confidence in the new estimates, while the evaluation against regional dataset still suggests rooms for further improvement.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-023-02751-6