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Carbon sequestration potential in croplands in Lesotho

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  • Ramakhanna, Selebalo Joseph
  • Mapeshoane, Botle Esther
  • Omuto, Christian Thine

Abstract

Soil organic carbon (SOC) plays a crucial role in physical, chemical and biological soil properties and maintains the sustainability of cropping systems. However, SOC stocks in croplands decline due to lower carbon inputs and faster decomposition rates. Therefore, sustainable soil management (SSM) practices are crucial to replenish the carbon inputs. Mapping carbon (C) stocks and sequestration potential allows determination of SOC stock level and identifies potential areas for improvement in Lesotho. The quantile random forest model determined SOC stock spatial distribution for top 0 to 30 cm. Soil data from 161 profiles and environmental covariates were used to predict SOC concentration, bulk density, and coarse fragments, which together with soil depth, helped determine SOC stock density averaging 64.76±25.59 Mg C ha−1. The SOC stock was used as an input in SOC potential determination. Then, SOC sequestration potential was determined using soil, management and climate layers as inputs in the Rothamsted Carbon model (RothC) for three SSM practices and business-as-usual (BAU). For BAU, SSM1, SSM2 and SSM3, 0, 5, 10 and 20% carbon inputs were added respectively. The attainable SOC stocks after 20 years were 67.02, 67.76, 68.49 and 69.65 Mg C ha−1, with uncertainty of 22.69 and 26.13% for BAU and respective SSM scenarios. Despite the projections showing annual mean gain of SOC stocks for BAU of 0.11 Mg C ha−1 per year, there are losses in other regions. Thus, as shown that in SSM the losses are reduced while the gains increase, their use is advised. The establishment of research areas which can be used for monitoring SOC stock levels and research on SSM are a major priority.

Suggested Citation

  • Ramakhanna, Selebalo Joseph & Mapeshoane, Botle Esther & Omuto, Christian Thine, 2022. "Carbon sequestration potential in croplands in Lesotho," Ecological Modelling, Elsevier, vol. 471(C).
  • Handle: RePEc:eee:ecomod:v:471:y:2022:i:c:s0304380022001624
    DOI: 10.1016/j.ecolmodel.2022.110052
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    References listed on IDEAS

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    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Timothy E. Crews & Brian E. Rumsey, 2017. "What Agriculture Can Learn from Native Ecosystems in Building Soil Organic Matter: A Review," Sustainability, MDPI, vol. 9(4), pages 1-18, April.
    3. Hoyoung Kwon & Carmen M Ugarte & Stephen M Ogle & Stephen A Williams & Michelle M Wander, 2017. "Use of inverse modeling to evaluate CENTURY-predictions for soil carbon sequestration in US rain-fed corn production systems," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-18, February.
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