Digital soil mapping using machine learning-based methods to predict soil organic carbon in two different districts in the Czech Republic

Digital soil mapping using machine learning-based methods to predict soil organic carbon in two different districts in the Czech Republic

Soil organic carbon (SOC) is an important soil characteristic as well as a way how to mitigate climate change. Information on its content and spatial distribution is thus crucial. Digital soil mapping (DSM) is a suitable way to evaluate spatial distribution of soil properties thanks to its ability t...

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Bibliographic Details
Main Authors: Shahin Nozari, Mohammad Reza Pahlavan-Rad, Colby Brungard, Brandon Heung, Luboš Borůvka
Format: Article
Language:English
Published: Czech Academy of Agricultural Sciences 2024-02-01
Series:Soil and Water Research
Subjects:
cubist
dsm
quantile random forest
random forest
soc
Online Access:https://swr.agriculturejournals.cz/artkey/swr-202401-0003_digital-soil-mapping-using-machine-learning-based-methods-to-predict-soil-organic-carbon-in-two-different-distr.php

Internet

https://swr.agriculturejournals.cz/artkey/swr-202401-0003_digital-soil-mapping-using-machine-learning-based-methods-to-predict-soil-organic-carbon-in-two-different-distr.php

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