Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia

Rainford, Shauna-Kay; Martín-López, Javier M.; Da Silva, Mayesse (2021). Approximating Soil Organic Carbon Stock in the Eastern Plains of Colombia. Frontiers in Environmental Science, 9 Frontiers Media 10.3389/fenvs.2021.685819

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > Oeschger Centre for Climate Change Research (OCCR)
08 Faculty of Science > Department of Biology > Institute of Plant Sciences (IPS) > Palaeoecology
08 Faculty of Science > Department of Biology > Institute of Plant Sciences (IPS)

UniBE Contributor:

Rainford, Shauna-Kay Doraine

Subjects:

500 Science > 580 Plants (Botany)

ISSN:

2296-665X

Publisher:

Frontiers Media

Language:

English

Submitter:

Peter Alfred von Ballmoos-Haas

Date Deposited:

08 Sep 2021 08:17

Last Modified:

08 Sep 2021 08:17

Publisher DOI:

10.3389/fenvs.2021.685819

Uncontrolled Keywords:

digital soil mapping, soil organic carbon, machine learning, random forest, spatial modeling

BORIS DOI:

10.48350/158315

URI:

https://boris.unibe.ch/id/eprint/158315

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