Background, aim, and scope
Carbon footprint (CF) has become a hot topic as public awareness of climate change is placing demands on manufacturers to declare the climate impact of their products. Calculating the CF of food products is complex and associated with unavoidable uncertainty due to the inherent variability of natural processes. This study quantifies the uncertainty of a common food product and discusses the results in relation to different types of CF systems for food product labelling.
Materials and methods
A detailed LCI with global warming potential as the only impact category was performed on King Edward table potatoes grown in the Östergötland region of Sweden. Parameters were described using one probability distribution for spatial and temporal variation and one separate distribution describing measuring/data uncertainty, allowing the effect of parameter resolution on CF uncertainty to be studied. Monte Carlo simulation was used to quantify the overall uncertainty. The influence of individual parameters on the CF was analysed and differences in CF for food products from different production systems, with and without climate impact reduction rules, were simulated.
The potato CF fell in the range 0.10–0.16 kg CO2e per kilogram of potatoes with 95% certainty for an arbitrary year and field. Emissions of N2O from soil dominated the CF uncertainty. Locking the temporal variation to a specific year lowered the uncertainty range by 19%. Parameter collection on a spatial scale of one field did not reduce the uncertainty. The most sensitive parameters were the yield, the soil humus content and the emissions factors for N2O emissions from soil. Potatoes grown according to climate rules lowered the CF by 9% with a probability of 53% for an arbitrary year and field.
The importance of yield, which proved to be the most influential parameter, is a common characteristic of agricultural products in general, since the accumulated emissions from a cultivated area are divided across the yield from that area. Maximising the yield reduces the CF but could have negative impacts on other environmental aspects. The purpose of the CF labelling scheme, together with uncertainty analysis, needs to be considered when determining how the CF should be calculated, as an average or for a specific year, farm, field, region, etc.
The CF of a potato crop calculated for an arbitrary year and field varied between approximately -17% and +30% of the average value with 95% certainty, showing that uncertainty analysis in the design, calculation and evaluation of food product CF labelling schemes is important to ensure fair comparisons.
Recommendations and perspectives
Similar studies comparing different production systems for the same type of product and products from different categories, on large and small scale depending on the purpose of the CF system, are needed in order to determine how the CF of food products can be compared and the precision with which data have to be collected in order to allow fair and effective comparison of the CF of food products.
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KRAV is an incorporated association with 28 members representing farmers, producers, trade, and consumers as well as environmental and animal welfare interests. KRAV develops organic standards and promotes the KRAV label for organic products. Seal Quality Systems Ltd is a wholly owned subsidiary of the Federation of Swedish Farmers (LRF). Seal Quality Systems Ltd owns and develops rules for the Swedish Seal of Quality (Svenskt Sigill) label (conventional farming).
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Röös, E., Sundberg, C. & Hansson, P. Uncertainties in the carbon footprint of food products: a case study on table potatoes. Int J Life Cycle Assess 15, 478–488 (2010). https://doi.org/10.1007/s11367-010-0171-8
- Carbon declaration
- Carbon footprint
- Carbon labelling
- Food products
- Monte Carlo simulation
- Table potatoes
- Uncertainty analysis