A global dataset on phosphorus in agricultural soils

Author:

Ringeval BrunoORCID,Demay Josephine,Goll Daniel S.,He XianjinORCID,Wang Ying-PingORCID,Hou EnqingORCID,Matej Sarah,Erb Karl-HeinzORCID,Wang RongORCID,Augusto LaurentORCID,Lun Fei,Nesme ThomasORCID,Borrelli Pasquale,Helfenstein Julian,McDowell Richard W.ORCID,Pletnyakov PeterORCID,Pellerin Sylvain

Abstract

AbstractNumerous 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.

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability

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