Abstract

The sustainable land management program (SLMP) of Ethiopia aims to improve livelihoods and create resilient communities and landscape to climate change. Soil organic carbon (SOC) sequestration is one of the key cobenefits of the SLMP. The objective of this study was to estimate the spatial dynamics of SOC in 2010 and 2018 (before and after SLMP) and identify the SOC sequestration hotspots at landscape scale in four selected SLMP watersheds in the Ethiopian highlands. The specific objectives were to: 1) comparatively evaluate SOC sequestration estimation model building strategies using either a single watershed, a combined dataset from all watersheds, and leave-one-watershed-out using Random Forest (RF) model; 2) map SOC stock of 2010 and 2018 to estimate amount of SOC sequestration and potential; 3) evaluate the impacts of SLM practices on SOC in four SLMP watersheds. A total of 397 auger composite samples from the topsoil (0?20 cm depth) were collected in 2010, and the same number of samples were collected from the same locations in 2018. We used simple statistics to assess the SOC change between the two periods, and machine learning models to predict SOC stock spatially. The study showed that statistically significant variation (P < 0.05) of SOC was observed between the two years in two watersheds (Gafera and Adi Tsegora) whereas the differences were not significant in the other two watersheds (Yesir and Azugashuba). Comparative analysis of model-setups shows that a combined dataset from all the four watersheds to train and test RF outperform the other two strategies (a single watershed alone and a leaveone-watershed-out to train and test RF) during the testing dataset. Thus, this approach was used to predict SOC stock before (2010) and after (2018) land management interventions and to derive the SOC sequestration maps. We estimated the sequestrated, achievable and target level of SOC stock spatially in the four watersheds. We assessed the impact of SLM practices, specifically bunds, terraces, biological and various forms of tillage practices on SOC using partial dependency algorithms of prediction models. No tillage (NT) increased SOC in all watersheds. The combination of physical and biological interventions (?bunds + vegetations? or ?terraces + vegetations?) resulted in the highest SOC stock, followed by the biological intervention. The achievable SOC stock analysis showed that further SOC stock sequestration of up to 13.7 Mg C ha?1 may be possible in the Adi Tsegora, 15.8 Mg C ha-1 in Gafera, 33.2 Mg C ha-1 in Azuga suba and 34.7 Mg C ha-1 in Yesir watersheds.

Keywords

Climate change; Ethiopia; Random forest; Resilience; Sustainable land management; SOC sequestration

Published in

Journal of Environmental Management
2021, Volume: 286, article number: 112191
Publisher: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD

SLU Authors

• Persson, Kristin

  • Department of Soil and Environment, Swedish University of Agricultural Sciences
    
  • International Center for Tropical Agriculture (CIAT)

• Söderström, Mats

  • Department of Soil and Environment, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

Take urgent action to combat climate change and its impacts
Ensure sustainable consumption and production patterns


UKÄ Subject classification

Environmental Sciences related to Agriculture and Land-use


Publication identifier

DOI: https://doi.org/10.1016/j.jenvman.2021.112191

Permanent link to this page (URI)

https://res.slu.se/id/publ/111561