تاثیرات کربن در تولید گوشت گاو
Abstract
1. Introduction
2. Methods
3. Results
4. Discussion
5. Conclusion
Funding
Acknowledgements
Appendix A. Supplementary data
References
Abstract
Greenhouse gas (GHG) accounting models facilitate mitigation of emissions from livestock systems. Such models are approximations, and uncertainties in their output may stem from a) uncertainty or variability in input data, b) uncertainty resulting from model scope and allocation methods, or c) uncertainty in modelling approach used. While sources a) and b) vary depending on the modelled scenario, c), referred to as epistemic uncertainty, relates to the modelling process, and as such is inherent in the methodology used rather than the specific scenario. This study combines a farm-level model comprised of widely used GHG accounting methodologies with a typical northern hemisphere suckler beef production system, and employs Monte Carlo simulation to assess the sensitivity of the modelled GHG footprint to epistemic uncertainty in the model. Following a cradle-to-gate approach, an emissions intensity of 19.20 ± ۲٫۴۹ kg CO2-eq kg live weight۱ was estimated for the modelled system. The study also highlights a discrepancy of 8.3% between deterministically and stochastically calculated emissions; this results from skewness in key modelling coefficients, primarily those relating to nitrous oxide emissions. Sensitivity analysis showed coefficients relating to emissions of nitrous oxide from land and methane from enteric fermentation were most influential in the modelled uncertainty, though coefficients relating to livestock feed production also contributed substantially. In conducting a root-cause analysis of uncertainty in GHG accounting from beef production, this study makes a novel contribution to the literature surrounding uncertainty in livestock emissions modelling. Developers of GHG accounting methodologies may use these insights to focus efforts on refining the most influential elements of these approaches, while researchers applying the models should be aware of the associated uncertainty. The latter should be quantified and effectively communicated where these models are used to support policy decisions.
Introduction
Global livestock production is faced with the twin challenges of increasing productivity to meet demand and reducing emissions to meet climate commitments (Opio et al., 2013; OECD/FAO, 2017). Cattle production systems (i.e. beef and dairy) contribute almost three quarters to total livestock emissions (Caro et al., 2014) and are therefore under considerable scrutiny in many national greenhouse gas budgets (e.g. Moran et al., 2011; Schulte et al., 2012; Pellerin et al., 2013). Greenhouse gas (GHG) accounting models are important for understanding and quantifying emissions from complex livestock production systems (Opio et al., 2013). These tools provide a better understanding of emissions hotspots, and opportunities for mitigation. The models used differ in goal and scope; system-level life cycle assessments (e.g. Beauchemin et al., 2010), farm-level GHG accounting tools (e.g. Sykes et al., 2017), and national inventory assessments (e.g. Milne et al., 2014) are common implementations. While such models draw on common methodologies (e.g. IPCC, 2006), there is recognition that broadbrush approaches necessary in national-level assessments are often insufficient to facilitate detailed policy analysis of the heterogeneous livestock sector (Moran et al., 2011). As such, a requirement is growing for system-level assessments of GHG emissions from livestock systems, and scalable GHG accounting tools are increasingly sought to facilitate this on an ongoing basis (Hall et al., 2010; Macleod et al., 2017; CSA Wales, 2017). However, livestock production systems are fundamentally complex, and limitations in the methodological ability of extant modelling approaches to accurately capture these intricacies represent a major challenge both to modellers (Ro€os and Nylinder, € ۲۰۱۳) and those seeking to utilise such approaches for decision making (Milne et al., 2015).