Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia

In Colombia, the rise of agricultural and pastureland expansion continues to exert increasing pressure on the structure and ecological processes of savannahs in the Eastern Plains. However, the effect of land use change on soil properties is often unknown due to poor access to remote areas. Effectiv...

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Main Authors: Shauna-kay Rainford, Javier M. Martín-López, Mayesse Da Silva
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-07-01
Series:Frontiers in Environmental Science
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fenvs.2021.685819/full
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spelling doaj-e8f4b3e6f07d468384f542c4263ea8712021-07-21T10:50:46ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2021-07-01910.3389/fenvs.2021.685819685819Approximating Soil Organic Carbon Stock in the Eastern Plains of ColombiaShauna-kay Rainford0Javier M. Martín-López1Mayesse Da Silva2Institute of Plant Sciences and Oeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandSoil and Water Management in Latin America and Caribbean, Multifunctional Landscapes, International Center for Tropical Agriculture (CIAT), Cali, ColombiaSoil and Water Management in Latin America and Caribbean, Multifunctional Landscapes, International Center for Tropical Agriculture (CIAT), Cali, ColombiaIn Colombia, the rise of agricultural and pastureland expansion continues to exert increasing pressure on the structure and ecological processes of savannahs in the Eastern Plains. However, the effect of land use change on soil properties is often unknown due to poor access to remote areas. Effective management and conservation of soils requires the development spatial approaches that measure and predict dynamic soil properties such as soil organic carbon (SOC). This study estimates the SOC stock in the Eastern Plains of Colombia, with validation and uncertainty analyses, using legacy data of 653 soil samples. A random forest model of nine environmental covariate layers was used to develop predictions of SOC content. Model validation was determined using the Taylor series method, and root-mean-squared error (RMSE) and mean error (ME) were calculated to assess model performance. We found that the model explained 50.28% of the variation within digital SOC content map. Raster layers of SOC content, bulk density, and coarse rock fragment within the Eastern Plains were used to calculate SOC stock within the region. With uncertainty, SOC stock in the topsoil of the Eastern Plains was 1.2 G t ha−1. We found that SOC content contributed nearly all the uncertainty in the SOC stock predictions, although better determinations of SOC stock can be obtained with the use of a more geomorphological diverse dataset. The digital soil maps developed in this study provide predictions of extant SOC content and stock in the topsoil of the Eastern Plains, important soil information that may provide insight into the development of research, regulatory, and legislative initiatives to conserve and manage this evolving ecosystem.https://www.frontiersin.org/articles/10.3389/fenvs.2021.685819/fulldigital soil mappingsoil organic carbonmachine learningrandom forestspatial modeling
collection DOAJ
language English
format Article
sources DOAJ
author Shauna-kay Rainford
Javier M. Martín-López
Mayesse Da Silva
spellingShingle Shauna-kay Rainford
Javier M. Martín-López
Mayesse Da Silva
Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia
Frontiers in Environmental Science
digital soil mapping
soil organic carbon
machine learning
random forest
spatial modeling
author_facet Shauna-kay Rainford
Javier M. Martín-López
Mayesse Da Silva
author_sort Shauna-kay Rainford
title Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia
title_short Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia
title_full Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia
title_fullStr Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia
title_full_unstemmed Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia
title_sort approximating soil organic carbon stock in the eastern plains of colombia
publisher Frontiers Media S.A.
series Frontiers in Environmental Science
issn 2296-665X
publishDate 2021-07-01
description In Colombia, the rise of agricultural and pastureland expansion continues to exert increasing pressure on the structure and ecological processes of savannahs in the Eastern Plains. However, the effect of land use change on soil properties is often unknown due to poor access to remote areas. Effective management and conservation of soils requires the development spatial approaches that measure and predict dynamic soil properties such as soil organic carbon (SOC). This study estimates the SOC stock in the Eastern Plains of Colombia, with validation and uncertainty analyses, using legacy data of 653 soil samples. A random forest model of nine environmental covariate layers was used to develop predictions of SOC content. Model validation was determined using the Taylor series method, and root-mean-squared error (RMSE) and mean error (ME) were calculated to assess model performance. We found that the model explained 50.28% of the variation within digital SOC content map. Raster layers of SOC content, bulk density, and coarse rock fragment within the Eastern Plains were used to calculate SOC stock within the region. With uncertainty, SOC stock in the topsoil of the Eastern Plains was 1.2 G t ha−1. We found that SOC content contributed nearly all the uncertainty in the SOC stock predictions, although better determinations of SOC stock can be obtained with the use of a more geomorphological diverse dataset. The digital soil maps developed in this study provide predictions of extant SOC content and stock in the topsoil of the Eastern Plains, important soil information that may provide insight into the development of research, regulatory, and legislative initiatives to conserve and manage this evolving ecosystem.
topic digital soil mapping
soil organic carbon
machine learning
random forest
spatial modeling
url https://www.frontiersin.org/articles/10.3389/fenvs.2021.685819/full
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