Application of the Cereal Unit in a new allocation procedure for agricultural life cycle assessments
Introduction
In recent years, the need for an accurate quantification of the environmental impacts of products and services has grown rapidly, as evidenced by enhanced information requirements in the supply chain of products (Cooper and Fava, 2006, Teixeira and Pax, 2011) and increased public awareness and communication of environmental footprints, such as carbon footprints (Finkbeiner, 2009), in addition to full LCAs. One risk associated with this development is the fact that most consumers and policy makers are not fully aware of the uncertainty of LCA results related to methodological choices. Driven by the carbon footprint discussion, LCAs for agriculture have gained increasing interest. There are methodological particularities and challenges in agricultural LCAs, reflected, for instance, in specific conference series, such as the International Conference series on Life Cycle Assessment in the Agri-Food Sector – LCA Food (Corson and van der Werf, 2012).
Agricultural LCAs contain products and co-products that cover a broad range, from cereal straw to fattening calf. Allocation procedures are used to attribute environmental burdens between the products. ISO 14044 describes a hierarchy of allocation approaches, which are preferably based on scientific aspects (e.g., mass allocation or lower heating value allocation) rather than economic relationships (e.g., market price allocation) (ISO 14044, 2006). For the sake of credible results, LCA practitioners try to treat all products and co-products as fairly and as adequately as possible, but existing allocation approaches focus on the specific functionalities of individual co-products and do not reflect in all cases the purposes of all co-products at the same time. Agricultural LCAs are particularly affected because allocation steps often take place several times and errors introduced by each allocation step propagate. An adequate allocation approach is crucial for the credibility of the LCA results of this sector. This aim could be achieved by finding an allocation solution that reliably represents the common functions provided by the wide range of agricultural products. Lundie et al. (2007) recommend establishing sector-specific allocation procedures based on physico-chemical relationships. Pelletier and Tyedmers (2011) suggest biophysical criteria as a basis for co-product allocations. They argue that allocation approaches should be causality driven and should express the motivation for a certain activity. Furthermore, biophysical approaches are more flexible and can be better adapted to the motivation and causalities of the processes (Pelletier and Tyedmers, 2011). Within this paper, we focus on co-product allocation within LCAs for agricultural products and products derived from raw agricultural materials.
Various approaches have been developed for allocating environmental burdens among multiple inputs and outputs. Well-known examples include mass allocation, energy allocation and economic allocation. Alternatives that avoid allocation include system expansion, subdivision or substitution (ISO 14040, 2006, ISO 14044, 2006). The standards ISO 14040 and ISO 14044 give guidance on how to address allocation situations, but they offer a hierarchy of choices rather than a particular method (Ekvall and Finnveden, 2001, Finkbeiner et al., 2006). The ISO hierarchy leaves room for different allocation procedures, whereas other LCA standards, such as PAS 2050 and BP X 30-323-0, give clear recommendations for the use of economic allocation or physical allocation, respectively (ADEME, 2011, PAS 2050, 2011). Such contradictory hierarchies within the LCA-based standards for attributional modeling complicate the allocation choices.
Additional strategies for dealing with co-products exist within the consequential LCA modeling approach that need to be treated separately from attributional LCA modeling. Ekvall and Weidema (2004) demonstrate the use of system expansion to handle the co-products of renewable materials leading to the approach of consequential LCA (Ekvall and Weidema, 2004, Weidema, 2000, Weidema, 2003). The allocation problem can be avoided in consequential LCAs by applying system expansion, but system expansion and avoided burden approaches cause additional uncertainties (Lundie et al., 2007). Consequential LCAs do not fall within the scope of this paper.
For attributional LCAs, the allocation problem, both in general and in particular for agricultural products, is still a topic of concern and debate; several studies provide more detailed analyses of this topic (Audsley et al., 2003, Ekvall and Finnveden, 2001, Finnveden et al., 2009, Lundie et al., 2007, Mendoza et al., 2008, Pawelzik et al., 2013). The approach presented in this paper is intended for use in attributional LCA modeling for agricultural products.
The next two sections describe situations where the application of allocation procedures may introduce uncertainty to the results or even lead to situations where environmental burden might be double counted or even ignored.
The different approaches for co-product allocation are one of the major reasons for the uncertainty in LCA results caused by methodological choices (Curran, 2008, Gnansounou et al., 2009, Kim and Dale, 2002, Singh et al., 2010). To address the allocation problem, various strategies have been developed, including system expansion, system reduction, allocation based on physical causality, mass allocation and economic allocation, but none of them are completely satisfactory (Klöpffer and Grahl, 2009).
Agricultural LCAs are particularly sensitive because allocations are carried out several times in succession. For example, during harvesting, the wheat plant split into grains and straw. During the milling process, wheat grains are split into flour, bran and middlings. Therefore, the inaccuracies introduced by each allocation step propagate and potentially amplify. For the calculation of agriculture-based chains, Chiaramonti and Recchia (2010) describe “dramatic variations [of the results] (up to approximately 300% or more if different approaches toward co-product allocation are considered). This will happen even in case a very simple and small biofuel chain […] is considered”. Another example pertaining to allocation choice is provided by Cavalett and Ortega (2010), who performed a case study for soybean biodiesel; they conclude that the allocation choice is a "very significant calculation step" that "strongly affects" the results. Using different allocation methods, Luo et al. (2009) compare the environmental effects of gasoline and bioethanol. The outcome is fundamentally affected by the choice of the allocation method. The results were even inverted by changing the allocation method from economic to mass or energy allocation. Against the backdrop of having different allocation approaches as sources of uncertainty, Lundie et al. (2007) recommend developing physico-chemical and sector-specific allocation procedures. Pelletier and Tyedmers (2011) advocate for biophysical criteria that simultaneously reflect physical properties and social functions.
Another consequence of the allocation problem is the phenomenon of ignoring or double counting environmental burden. This methodological artifact occurs if the allocation approaches of two (or more) LCAs that contain co-products grown in the same agricultural system are not aligned to each other. The sum of the sub-systems’ burdens is not equal to the total environmental burden of their underlying agricultural production process. If both sub-systems are considered in one LCA study, this should not happen because ISO 14040 and 14044 requires use of the same allocation approach (ISO 14040, 2006, ISO 14044, 2006).
However, if two co-products from the same production process are considered within different studies, e.g., they are used in different sectors, the LCAs are calculated independently. The individual LCA practitioners are not restricted in their decision of allocation approach (e.g., wheat flour and wheat bran in the food and feed sectors; vegetable oil and oilmeal in the bioenergy and feed sectors). If those practitioners are using different LCA standards (ISO 14040 and 14044, PAS 2050 or French BP X 30-323-0) or sector-specific LCA guidelines (European Renewable Energy Directive, International Dairy LCA Guide), it is reasonable to expect different allocation approaches to be used, even if the co-products originate from the same production process. As a result, the phenomenon of ignoring or double counting environmental burden is likely to occur.
For example, dairy production and biodiesel production use rapeseed meal and rapeseed oil. Both are most likely derived from the same rape seeds because, e.g., in Germany, the use of rapeseed meal for animal feeding and the use of rapeseed oil as a raw material for biodiesel production are quantitatively the most important uses (BMU, 2012, OVID, 2013, VDB, 2013). Typically, Life Cycle Assessments of dairy and biodiesel are performed separately. Relevant guidance documents recommend the use of different allocation methods for the same rapeseed processing step. The International Dairy Federation (IDF) advises in its LCA guideline “to use economic allocation for co-product in feed production” (IDF, 2010), whereas the European renewable energy directive requires that “greenhouse gas emission shall be divided between fuel or its intermediate product and the co-products in proportion to their energy content” (RED, 2009). Therefore, it is very likely that different allocation methods are being used for meal and oil, even though they may originate from the same rape seeds.
To illustrate the phenomena of ignoring and double counting environmental burden in quantitative terms, two allocation choice scenarios for determining the environmental burdens of rapeseed oil and meal are shown in Fig. 1.
The figure demonstrates the importance of the choice and consistency of allocation methods. Depending on the allocation approach, LCA practitioners might end up with diverging results for the same agricultural system (30% ignoring on one hand and 17% double counting of environmental burden on the other hand). In general terms, if independent LCAs use different allocation approaches for co-products that originate from the same agricultural system, the sum of the environmental interventions considered in each individual LCA is not equal to the actual environmental intervention of their common agricultural production process. This aspect should be carefully considered within the general interpretation of LCA results with any connection to agricultural processes.
With the goal of helping to address the allocation problem for agricultural LCAs, we identified the Cereal Unit (CU), a well-established unit in agricultural statistics in Germany. The CU makes possible the comparing of various agricultural products based on the animal feeding value. The animal feeding value is measured in repeatable and reproducible feeding experiments. Therefore, we consider the CU a physico-chemical and biophysical parameter.
A detailed description of the determination of the CU is given in the Supplementary data section. Because we applied no changes to the CU itself, the detailed description in the Supplementary data is only explanatory. The core of our work is Section 3. Here, we describe a new biophysical allocation approach that is based on the CU. Furthermore, we provide a comparison of the results from the CU allocation to those obtained via mass, energy and economic allocations.
Section snippets
Material and methods – the Cereal Unit
The Cereal Unit (CU) was developed by German agricultural authorities and scientists decades ago (Becker, 1988). Since 1944, it has been used and optimized continuously (Becker, 1988, BMELV, 2012, BMELV, 2013, Klapp, 2011, Mönking et al., 2010). The CU is based on the animal feeding value, a relevant function of agricultural products. As a common denominator, the CU makes it possible to compare different agricultural products, including both vegetable and animal products.
Because publications on
The Cereal Unit allocation procedure
Using several examples, we describe the implementation of the CU into an allocation procedure within this section.
Cereal Unit allocation versus other allocation alternatives
Depending on the choice of allocation method, the functionalities of the co-products are expressed only to a limited extent. If applied to co-products used for energy production, the energy allocation might be more appropriate; however, this is not the case when one of the co-products from the same process does not have energy applications, such as sugar beet leaves and soybean meal. A comparable situation is given for mass allocation, which only renders a quantitative view of the co-products
Conclusions
The Cereal Unit (CU), an established measure in agricultural statistics, was identified as a suitable parameter for agricultural allocation. In this study, a new allocation procedure for agricultural LCAs based on the CU is proposed and tested for selected products. From a theoretical point of view and supported by our results, the CU allocation approach offers vast potential; however, broad practical application is not yet available. To road test the proposed approach and either reconfirm its
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