Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"

• Main Authors: Siqueira, Rafael, Moquedace, Cássio, Francelino, Márcio, Schaefer, Carlos, Fernandes-Filho, Elpídio
• Format: Other/Unknown Material
• Language: English
• Published: Zenodo 2023
• Subjects: Digital Soil Mapping; Antarctica; Soil chemistry; Antarc*; Antarctic
• Online Access: https://doi.org/10.5281/zenodo.8347083

Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbondistribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions. As soon as the work"Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica" is published, the paper will be cited here. The .zip file contains the following folders: 1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution 2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction 4) soil_texture_quantile05: quantile 5% of the soil attributes prediction 5) soil_texture_quantile95: quantile 95% of the soil attributesprediction