In this report input parameters used for the calculation of carbon footprints of Danish and Swedish milk are presented. It should be noticed that all results and interpretations for the carbon footprints of Danish and Swedish milk are presented in Schmidt and Dalgaard (2012). Further, the used terms, definitions and methodological framework is also described in Schmidt and Dalgaard (2012).
In Chapter 1 general activities and data (e.g. electricity, fertilisers, capital goods etc.) are presented. In Chapter 0 the Danish and Swedish milk and beef systems and the Brazilian beef system are presented. The plant cultivation system, which includes 12 different crops from various countries, is presented in Chapter 4. Finally, the food industry system is presented in Chapter 1.
Arla Foods wants to estimate and track the development in greenhouse gas (GHG) emission per kg raw milk - both at farm level, national level as well as corporate level which include emissions in several countries. The current report concerns a CF model for raw milk from cradle to farm gate.
The modelling of life cycle emissions for agricultural products is associated with several challenges. The production systems are most often characterised by having several co-products, and the most significant emissions are related to biological processing, such as enteric fermentation and altering of nutrient balances as opposed to LCAs in other sectors where most emissions are related to the combustion of fuels (Schmidt 2010a). The modelling of co-products is one of the major challenges in the modelling of life cycle emissions. The modelling of emissions in agricultural production systems involves a large number of activity and product parameters and the models (IPCC models for GHG-emissions) are often related to significant uncertainties.
A key challenge for Arla is that different methods for calculating the carbon footprint (CF) are often used in the countries where Arla operates. The following relevant modelling approaches have been identified:
Arla Foods therefore needs a flexible tool that enables different types of modelling depending on the context. It should be possible to calculate the CF at farm level and national level according to the used practises in the given country, but it should also be possible to compare results between countries and to calculate the aggregated CF at corporate level. The latter requires that the same model is used in all countries. The model developed in the present project, therefore have built-in switches that enables to use the same data, but to get the CF results according to the different modelling approaches. Hence, the model makes it possible for Arla to compare results across markets as well as within markets. The purpose of the present project is to:
Compared to a ‘normal’ CF model, the current model is generically described with input parameters and formulas. Then the same model can be used for calculating the CF baseline for different countries as well as farm specific CF. The generic model and country baseline results are described in the current report. All input parameters are described in an inventory report (Dalgaard and Schmidt 2012).
The special features and the generic nature of the Arla model require that the framework for the life cycle inventory is defined consistently. Therefore, before the actual CF model is described in chapter 4 to 9, the inventory framework is described in chapter 3.
In 2010 the Danish Plastics Federation (Plastindustrien) initiated the development of a web-based tool that enabled their members to produce life cycle environmental information on their products. The members of the Danish Plastics Federation are plastics processing companies in Denmark.
The tool was available via a webpage www.plastberegner.dk (no longer available). To access the tool, it was required to register and to create an account as the tool saved all information entered by the user.
In the tool it was possible to create own LCA activities and to link to activities in a database. The database contained pre-calculated life cycle emissions for a large number of LCA activities, e.g. electricity, transport, raw materials etc. It was not possible to modify the data in the database. Data were maintained by the tool administrator. The processes created by the user could be linked to other processes created by the user as well as to processes in the database.
The LCA activities in the database were stored as so-called terminated processes. This meant that the database did not contain information on intermediate product transactions between processes - only the calculated life cycle emissions were stored in the database. The reason for this was that the calculation speed would become slower if the database contained more than e.g. 4000 linked activities that would have to be re-calculated (matrix inversion) for each calculation by the tool.
The report documents all life cycle inventory data that were available in the database, and how the life cycle results were calculated, i.e. how the user specific linked LCA activities were combined with processes in the database.
