Jump to Main Content
A comparison of farm-level greenhouse gas calculators in their application on beef production systems
- Sykes, Alasdair J., Topp, Cairistiona F.E., Wilson, Ron M., Reid, Gillian, Rees, Robert M.
- Journal of cleaner production 2017 v.164 pp. 398-409
- beef, beef cattle, carbon footprint, data collection, emissions, emissions factor, farms, greenhouse gases, livestock production, meat production, models, production technology
- Farm-level greenhouse gas (GHG) footprinting tools produce markedly different results from common input datasets. These tools are typically empirical, broad scope models which are valuable for their ability to account for a range of on-farm GHG sources using non-specialist data. Many of these tools are publicly available, and are employed by users from a range of backgrounds to provide enterprise-level carbon footprints. They may be used to inform mitigation strategies and policy developments, though are often developed outside the peer-review system, and as such the methodology employed may be sparsely documented.The study reported here rigorously tests these tools and discusses differential findings. Five farm-level tools were tested using data from a variety of beef production enterprises. Beef production was chosen as an emissions intensive form of livestock production, and the focus of considerable mitigation effort globally. Considerable inconsistencies between tools were found in the resulting estimates.Estimates of emissions stemming directly from livestock were variable, and the largest contributor to the overall farm footprint (43–92% of total). As such, consistent calculation of these emissions is of considerable importance. Similar variability was found in other emissions categories. The emissions intensity of beef production was calculated for each estimate and compared to published values from LCA literature. Some tools produced estimates concurrent with these values, whilst others markedly underestimated in comparison.This study highlights the differences between estimates produced by these tools, and explores the reasons behind them. Of relevance to users is the finding that even where farm-level estimates appear similar between tools, the composition of these estimates can vary. As such, different tools respond differently to system changes. In highlighting and exploring the impacts this can have, the conclusions of this study provide a key reference point for tool users and developers.