Predicting soil organic carbon stocks in different layers of forest soils in the Czech Republic

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F23%3A97370" target="_blank" >RIV/60460709:41210/23:97370 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/23:00131498 RIV/00020702:_____/23:N0000078

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2352009423000548" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352009423000548</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.geodrs.2023.e00658" target="_blank" >10.1016/j.geodrs.2023.e00658</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting soil organic carbon stocks in different layers of forest soils in the Czech Republic

Popis výsledku v původním jazyce

Carbon dioxide, the most produced anthropogenic greenhouse gas, could be moderated by sequestering carbon in forest soils. Forest soils store more carbon than there is in the atmosphere. Thus, the smallest variation in soil carbon levels could trigger a significant change in atmospheric carbon. This study focused on predicting the spatial distribution of carbon stocks within surface organic and mineral topsoil and subsoil layers of the forty-one natural forest areas of the Czech Republic. Cubist and Random Forests machine learning algorithms were employed with a grid search hyper tuning to improve prediction accuracy. We used the five-fold cross-validation to verify the model accuracy using Root Mean Square Error (RMSE), coefficient of determination (R2), and Mean Absolute Error (MAE). Random Forests yielded lower RMSE of 1.10 kg/m2, 3.85 kg/m2, and 4.77 kg/m2 in the surface organic horizon (F + H layer), mineral topsoil (0-30 cm layers) and subsoil horizons (30-80 cm layers), respectively, compared to the RMSE values of Cubist, which were 1.14 kg/m2, 3.90 kg/m2, and 4.91 kg/m2 in the surface organic, mineral topsoil and subsoil horizons, respectively. R2 values of both models were low for all three horizons considered. Random Forests were the preferred algorithm for SOC stock prediction in all layers of the forest soils. Cubist predicted the spatial distribution of SOC stocks with more covariates than Random Forests. Altitude was the most important covariate for the spatial distribution of SOC stocks for both Random Forests and Cubist in all soil horizons considered. High SOC stocks for all soil horizons are spatially concentrated in soil horizons along the country borders in the mountaineous natural forest areas.

Název v anglickém jazyce

Predicting soil organic carbon stocks in different layers of forest soils in the Czech Republic

Popis výsledku anglicky

Carbon dioxide, the most produced anthropogenic greenhouse gas, could be moderated by sequestering carbon in forest soils. Forest soils store more carbon than there is in the atmosphere. Thus, the smallest variation in soil carbon levels could trigger a significant change in atmospheric carbon. This study focused on predicting the spatial distribution of carbon stocks within surface organic and mineral topsoil and subsoil layers of the forty-one natural forest areas of the Czech Republic. Cubist and Random Forests machine learning algorithms were employed with a grid search hyper tuning to improve prediction accuracy. We used the five-fold cross-validation to verify the model accuracy using Root Mean Square Error (RMSE), coefficient of determination (R2), and Mean Absolute Error (MAE). Random Forests yielded lower RMSE of 1.10 kg/m2, 3.85 kg/m2, and 4.77 kg/m2 in the surface organic horizon (F + H layer), mineral topsoil (0-30 cm layers) and subsoil horizons (30-80 cm layers), respectively, compared to the RMSE values of Cubist, which were 1.14 kg/m2, 3.90 kg/m2, and 4.91 kg/m2 in the surface organic, mineral topsoil and subsoil horizons, respectively. R2 values of both models were low for all three horizons considered. Random Forests were the preferred algorithm for SOC stock prediction in all layers of the forest soils. Cubist predicted the spatial distribution of SOC stocks with more covariates than Random Forests. Altitude was the most important covariate for the spatial distribution of SOC stocks for both Random Forests and Cubist in all soil horizons considered. High SOC stocks for all soil horizons are spatially concentrated in soil horizons along the country borders in the mountaineous natural forest areas.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40104 - Soil science

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

GEODERMA REGIONAL

ISSN

2352-0094

e-ISSN

2352-0094

Svazek periodika

34

Číslo periodika v rámci svazku

SEP 2023

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

001029542300001

EID výsledku v databázi Scopus

2-s2.0-85161722925