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Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants

Author

Listed:
  • Erica Hargety Kimei

    (Nelson Mandela African Institution of Science and Technology, Arusha P.O. Box 447, Tanzania)

  • Devotha G. Nyambo

    (Nelson Mandela African Institution of Science and Technology, Arusha P.O. Box 447, Tanzania)

  • Neema Mduma

    (Nelson Mandela African Institution of Science and Technology, Arusha P.O. Box 447, Tanzania)

  • Shubi Kaijage

    (Nelson Mandela African Institution of Science and Technology, Arusha P.O. Box 447, Tanzania)

Abstract

Uncertainty quantification and sensitivity analysis are essential for improving the modeling and estimation of greenhouse gas emissions in livestock farming to evaluate and reduce the impact of uncertainty in input parameters to model output. The present study is a comprehensive review of the sources of uncertainty and techniques used in uncertainty analysis, quantification, and sensitivity analysis. The search process involved rigorous selection criteria and articles retrieved from the Science Direct, Google Scholar, and Scopus databases and exported to RAYYAN for further screening. This review found that identifying the sources of uncertainty, implementing quantifying uncertainty, and analyzing sensitivity are of utmost importance in accurately estimating greenhouse gas emissions. This study proposes the development of an EcoPrecision framework for enhanced precision livestock farming, and estimation of emissions, to address the uncertainties in greenhouse gas emissions and climate change mitigation.

Suggested Citation

  • Erica Hargety Kimei & Devotha G. Nyambo & Neema Mduma & Shubi Kaijage, 2024. "Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants," Sustainability, MDPI, vol. 16(5), pages 1-15, March.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2219-:d:1352495
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    References listed on IDEAS

    as
    1. Kun Mo Lee & Min Hyeok Lee & Jong Seok Lee & Joo Young Lee, 2020. "Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method," Energies, MDPI, vol. 13(18), pages 1-15, September.
    2. Sabrina Hempel & Diliara Willink & David Janke & Christian Ammon & Barbara Amon & Thomas Amon, 2020. "Methane Emission Characteristics of Naturally Ventilated Cattle Buildings," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    3. Bożena Króliczewska & Ewa Pecka-Kiełb & Jolanta Bujok, 2023. "Strategies Used to Reduce Methane Emissions from Ruminants: Controversies and Issues," Agriculture, MDPI, vol. 13(3), pages 1-26, March.
    4. Mathieu Fortin, 2021. "Comparison of uncertainty quantification techniques for national greenhouse gas inventories," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 26(2), pages 1-20, February.
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