Abstract
Phosphorus (P) retention capacity of soils directly affects the management of phosphate fertilization, and has economic and environmental importance. Brazil has expressive agricultural production and high edaphoclimatic variability; still, the detailed spatial distribution of P retention capacity is not known. Thus, machine learning models were created to estimate the P retention capacity of soils at superficial horizons using the variables: clay content, sand content, soil organic matter, pH, base saturation. The four best models were combined to create an ensemble (a combination of models), which was applied in a dataset (5524 samples) that comprises a sample data from the entire Brazilian territory. The ensemble was used to build a map of P retention capacity. Most of the country presented soils with medium P retention (40–60%), and large areas with very high retention capacity were found in the south of Brazil, a subtropical region mostly associated with clayey soils. High P retention was observed for Histosols, probably related to humic-Al(Fe) complexes. Nitisol, Ferralsol, Gleysol and Cambisol classes presented high P retention, especially for clayey soils with qualifiers related to bases/weathering (Dystric) and organic matter (Umbric and Humic). The correlation between Fe oxide minerals and P retention varied with soil color and drainage conditions mainly associated to redoximorphic reactions; the same was not observed for Al oxide minerals. This pioneer study revealed the distribution of P retention capacity across different soil classes in Brazil, which can help to define land use suitability and soil P management strategies that ensure profitable agricultural exploration.
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