Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants
Erica Hargety Kimei (),
Devotha G. Nyambo,
Neema Mduma and
Shubi Kaijage
Additional contact information
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
Sustainability, 2024, vol. 16, issue 5, 1-15
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.
Keywords: greenhouse gas emission; ruminant livestock; uncertainty analysis; sensitivity analysis; methane; carbon dioxide; nitrous oxide; EcoPrecision framework (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:5:p:2219-:d:1352495
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