Year: 2013
Type: report
Abstract: Livestock production is a major contributor to anthropogenic climate change, being responsible for 18% of global greenhouse gas (GHG) emissions. In the quest to reduce emissions, the amount of GHG released during the production of livestock products is commonly quantified by calculating the carbon footprint (CF), which includes all GHG emitted during the life cycle of the product. \nQuantification of the CF is challenging for several reasons. The majority of GHG emissions from agricultural systems arise from complex microbial processes that are difficult to fully understand and highly variable in time and space. Changes in carbon pools above and below ground can have huge impacts on GHG emissions from agricultural systems. The increasing demand for food, feed and biofuel on the global market is leading to deforestation and thus increased emissions of GHG. In addition, there is great diversity between livestock systems, e.g. in feeding strategies, animal growth and production, housing systems and manure handling. \n \nThis report describes uncertainties and variations in input data and models used to calculate the CF of livestock products. It discusses when uncertainty assessments are important and how uncertainty can be included in CF calculations. \n \nUncertainty in the CF of livestock products arises from: 1) uncertain input data; 2) the choice of model used for calculating emissions of e.g. N2O from soil, CH4 from enteric fermentation in ruminants, CO2 emissions or sequestration in soils and emissions as a result of land use change, as well as uncertainties in these models; and 3) uncertainty due to scenario choices in modelling the livestock system, e.g. how system boundaries are drawn and how allocation between co-products is handled. It is important to account for uncertainty when comparing different production systems where emissions arise from different sources. However, when similar production systems are compared, e.g. when they only differ in the amount of feed used, it is possible to draw solid conclusions without comprehensive uncertainty assessments. Uncertainty in input data and model parameters can be propagated through the CF model using stochastic simulation, which gives an uncertainty range for the resulting CF. Sensitivity analysis can be used to test how different modelling choices affect the results, thus providing a measure of their robustness. In a full sustainability assessment, it is important not to focus solely on the CF, but to include other environmental impact categories and social and economic aspects.
Authors: Elin Röös, Josefine Nylinder
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDGs: Climate action, Zero hunger
Open Access status: green
OpenAlex ID: W16392435