Estimating soil organic carbon stocks under commercial forestry using topo-climate variables in KwaZulu-Natal, South Africa Article Swipe
Omosalewa Odebiri
,
Onisimo Mutanga
,
John Odindi
,
Kabir Peerbhay
,
Steven Dovey
,
Riyad Ismail
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.17159/sajs.2020/6339
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.17159/sajs.2020/6339
Commercial forests are expanding globally, with great potential to absorb carbon and mitigate climate change. However, whereas the role of natural forests in carbon sequestration has been widely investigated, there is a paucity in the literature on the role of commercial forests in carbon assimilation. Hence, understanding the role of commercial forests in carbon storage is essential for quantifying local, regional or global carbon balances, which is valuable for climate change mitigation. Soil carbon is known to be the largest pool within any forest landscape, and is controlled by a wide range of physical and climatic factors. However, the relationship between soil organic carbon (SOC) and topo-climatic variables controlling its distribution within commercial forests is still poorly understood. Due to the limitations encountered in traditional systems of SOC determination, particularly at large spatial extents, geospatial techniques have recently emerged as a viable alternative for mapping soil properties. Therefore, this study sought to map SOC stocks variability within the commercial forest landscape, using landscape topo-climatic variables. A total of 81 soil samples was analysed for SOC concentrations and 31 topographic and climatic variables were used as predictors to SOC variability. To reduce multicollinearity, these variables were reduced to 11 using stepwise backward elimination and the maximum entropy (Maxent) algorithm was used for regression analysis to determine the relationship between SOC and the selected topo-climatic variables. Good accuracies were obtained for both training (area under the curve = 0.906) and test (area under the curve = 0.885) data sets, and demonstrate the effectiveness of selected topo-climatic variables and the Maxent algorithm in predicting SOC stocks. This study provides a framework for monitoring the status of soil carbon in commercial forest compartments and provides a viable approach for local, national or regional carbon accounting – valuable for climate change mitigation. Significance: Rainfall and temperature, as well as topographic variables (such as slope, elevation and topographic wetness index) are effective in mapping SOC distribution. The model developed is useful in predicting SOC occurrence and yielded an effective framework for continuous monitoring and assessment of SOC. The method developed in this study is cost-effective and suggests the use of other readily available climatic and topographic information for the prediction of SOC under commercial forestry in South Africa and indeed globally. Results from this study are important to achieve the national carbon accounting objective and are also valuable to forest managers, ecologists and relevant stakeholders in understanding the spatial distribution of SOC.
Related Topics
Concepts
Soil carbon
Environmental science
Climate change
Carbon sequestration
Geospatial analysis
Multicollinearity
Physical geography
Forestry
Hydrology (agriculture)
Geography
Soil science
Ecology
Regression analysis
Soil water
Carbon dioxide
Mathematics
Statistics
Cartography
Geology
Biology
Geotechnical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17159/sajs.2020/6339
- https://sajs.co.za/article/download/6339/9856
- OA Status
- diamond
- Cited By
- 30
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3012599333
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3012599333Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.17159/sajs.2020/6339Digital Object Identifier
- Title
-
Estimating soil organic carbon stocks under commercial forestry using topo-climate variables in KwaZulu-Natal, South AfricaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-22Full publication date if available
- Authors
-
Omosalewa Odebiri, Onisimo Mutanga, John Odindi, Kabir Peerbhay, Steven Dovey, Riyad IsmailList of authors in order
- Landing page
-
https://doi.org/10.17159/sajs.2020/6339Publisher landing page
- PDF URL
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https://sajs.co.za/article/download/6339/9856Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://sajs.co.za/article/download/6339/9856Direct OA link when available
- Concepts
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Soil carbon, Environmental science, Climate change, Carbon sequestration, Geospatial analysis, Multicollinearity, Physical geography, Forestry, Hydrology (agriculture), Geography, Soil science, Ecology, Regression analysis, Soil water, Carbon dioxide, Mathematics, Statistics, Cartography, Geology, Biology, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
30Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 8, 2023: 7, 2022: 4, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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