Allocation factors and issues in agricultural carbon footprint: a case study of the Canadian pork industry

Journal of Cleaner Production 113 (2016) 587e595

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Allocation factors and issues in agricultural carbon footprint: a case study of the Canadian pork industry
a, *, D. Maxime b, R.L. Desjardins c, A.C. VanderZaag c X. Verge a

Ottawa, ON, Canada Interuniversity Research Centre for the Life Cycle of Products, Processes and Services (CIRAIG), Polytechnique Montr
eal, Montreal, QC, Canada c Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 5 November 2014 Received in revised form 16 November 2015 Accepted 19 November 2015 Available online 12 December 2015

The choice of the calculation pathways used to estimate the environmental impact of human activities is of importance since it could modify the results of such studies. This is the case for the Life Cycle Analysis (LCA) which is now commonly used to perform environmental assessments: the allocation methods used have an important impact on calculations and can potentially affect the final results. This could have a very negative impact on the LCA in terms of adoption and trust in the results. In the current study, the Canadian swine sector has been used as a case study and the carbon footprint of pork production has been estimated regionally for the year 2006. In this study, these calculations were performed using different allocation approaches to study the impact and usefulness of each method. No-allocation, economic-allocation, and mass-allocation approaches were used. Owing to climate and productiontype specificities, calculations were done for eastern and western Canada in addition to the national estimates. Total greenhouse gas emissions were higher in the east (3.5 Mt CO2e) than in the west (3.1 Mt CO2e). However, the carbon footprint followed an opposite trend. Considering the primal cut products and, in turn, the mass allocation, the economic allocation and no allocations, the CFs were 2.6 kgCO2e, 3.8 kgCO2e and 4.0 kgCO2e per kg of product for the east and 3.2 kgCO2e, 4.7 kgCO2e and 5.0 kgCO2e per kg of product for the west. The current study shows that, in fact, allocation methods are not interchangeable and should be selected based on the specificity of each study: the no-allocation approach can be used to analyze on-farm production, economic allocation is oriented to market studies, and mass allocation is well suited to environmental sustainability assessments. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Allocation Life cycle analysis Greenhouse gases Carbon footprint Swine

1. Introduction 1.1. Background Livestock activities have a significant impact on virtually all aspects of the environment, including air quality, climate, land and soil, water and biodiversity. Concerning greenhouse gases (GHG) emissions specifically the Canadian agriculture accounted for 7.7% of the country's total emissions in 2011 (Environment Canada, 2013) with 43% of that total coming from the livestock sector.

* Corresponding author. 1016-2055 Carling Avenue, Ottawa, ON K2A 1G6, Canada. Tel.: þ1 613 759 2369.
), dominique.maxime@polymtl. E-mail addresses: [email protected] (X. Verge ca (D. Maxime), [email protected] (R.L. Desjardins), andy.vanderzaag@agr. gc.ca (A.C. VanderZaag). http://dx.doi.org/10.1016/j.jclepro.2015.11.046 0959-6526/© 2015 Elsevier Ltd. All rights reserved.

Animal-derived food products such as meat products contribute large quantities of GHGs per kilogram of product. In our previous work, the GHG emissions for all major meat production sectors, such as cattle and pork were calculated for the census years from
et al., 2008, 2009a). The carbon footprint (CF) 1981 to 2001 (Verge of Canadian pork production from cradle to the farm gate decreased
et al., 2009a) due to a by about 20% over the 20 year period (Verge drastic industrialization of the swine sector. Pushed by heavy market constraints, the sector has become more and more concentrated. The number of farms decreased by about 90% over the past 40 years between 1976 and 2014 (Statistics Canada, 2003, 2015), but the total live animal population kept growing during the same period. This was made possible by a sharp increase in the average on-farm hog population (from 91 heads in 1976 up to about 1850 heads in 2014). The current work used the pork sector as a case study to analyze the impact of the allocation factors in calculations, but it also presents the total GHG emissions and its

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associated CF for the census year 2006 which was not included in the previous estimates because data was unavailable at that time
et al., 2009a). (Verge
et al. (2009a), the CF estimates include direct emissions In Verge on the farm such as those from energy use, manure management and enteric fermentation (the latter is less important for swine than for ruminants). The estimates also include significant off-farm emissions such as those released during the production of agri
cultural inputs such as fertilizers and farm machinery (e.g. Verge et al., 2012). Greenhouse gas emissions from the food processing industry have also been reported through intensity indicators (Maxime et al., 2008). These indicators keep track of the GHG emissions due to fossil fuel combustion in manufacturing plants and GHGs associated with transportation to the plants. Doing this previous work it was noted that allocations remained an unresolved issue which had to be analyzed. 1.2. Allocation issues In the current study CFs have been calculated following an LCA approach. The strength of LCA studies is that they can be as exhaustive as possible, that is, they can consider all processing steps of the product evaluated and most of the related impacts accessible to calculations (midpoints) (Bare et al., 2000). However, several authors have mentioned important limitations that could affect consumer confidence in the LCA results. These limitations
et al., 2013) as concern the unit chosen to present the results (Verge well as the method of calculation itself (Beer and Grant, 2007). Indeed, this holistic approach has also controversial issues precisely because of its comprehensiveness. One of the main issues is related to the multifunctional process and to the manner of distributing the environmental impact to all valuable outputs from the same production process. Several activities and co-products requiring the use of allocation have been identified by Rougoor et al. (2015) for the pork production system. Different recommendations are provided by methodologies and standards that aim to evaluate the environmental impacts of human activities. Most of these methods are aligned with the International Organization for Standardization (ISO) standards for LCA, which recommend avoiding allocation as much as possible (ISO, 2006). Two opposing approaches are thus suggested for solving the multifunctionality problem, which are either to subdivide the system or to expand its boundaries. System subdivision is not always feasible if the process under study is a black box that cannot be broken down into several single processes or if individual data for the single processes cannot be gathered. System expansion consists in modeling a larger system that provides an additional function that is equivalent to the one that needs to be removed from the original system in order to make it monofunctional. For instance, a dairy farm that produces milk as well as meat from culled cows and calves could be made into a monofunctional system (e.g. that produces milk only) by also considering a beef farming system that produces the same amount of meat and subtracting its impact from the impact of the dairy farm. One can easily understand that the system expansion approach significantly increases the burden for the practitioner, who has to model and collect data for another system. Furthermore, the choice of the production avoided (beef in this case) has to be determined on the basis of market demand, which is either volatile or speculative and thus remains debatable. For instance, the demand for meat might be satisfied through a displacement of pork or poultry production rather than beef if culled cows and calves were not available on the meat market. Hence, the impact of the avoided production of swine or poultry would be better credited to the dairy farm.

If the problem of the multifunctionality of the system cannot be solved, then an allocation between co-products needs to be performed; physical relationships are preferred (ISO, 2006), or the economic value can be used (BSI, 2011). The ISO 14044 standard mentions economic allocation when no other possibility is available. Many studies have demonstrated that LCA results can be highly sensitive to the allocation approach chosen, and the conclusion of a comparison between alternative products may even be reversed from one approach to another. This has been shown for several biobased production systems, such as biofuel from first-generation feedstocks (Beer and Grant, 2007; Kim and Dale, 2002; Malça and Freire, 2006) or higher-generation feedstocks (Luo et al., 2009) as well as milk and meat (Cederberg and € et al., 2011). Luo et al. (2009) mentioned that Stadig, 2003; Flysjo allocation based on mass, energy, or economic criteria can lead to very different results, for instance when the mass ratio and price ratio of co-products are very different. They also note that the global warming potential is very sensitive to the choice of the allocation method. Another issue with economic allocation is the possible price change over time, whereas mass and energy content ratios are constant properties. The main objective of this study was therefore to discuss the issues related to allocating environmental burdens in LCA studies. The paper uses the Canadian pork industry as a case study: first, the total GHG emissions for this sector are presented and discussed; second, the carbon footprint is calculated using several allocation approaches; third, the impact and value of the mass, economic, and no-allocation approaches are compared and discussed. 2. Methodology 2.1. Systems and boundaries of the study In this study, GHG emission calculations have been performed by merging the production and the processing steps as well as emissions related to transportation activities occurring in between. A more comprehensive picture of the GHG emissions of the meat production sector from the farm to the exit gate of the processing plant is therefore obtained. Another reason for choosing these boundaries in this study is that allocation factors are needed only when several sellable products are identified from a unique production process. Because only one product is sold at the farm gate (the animal), no allocation is needed at this step. But at the exit gate of the processing plant, several products are manufactured and therefore the use of allocation factors is required. All downstream steps after processing e wholesaling, retailing, product end of life and all transportation between these steps e are not within the scope of this study. The functional unit used was the mass of the products delivered at the exit gate of the processing plant. An example of calculation is presented Section 2.2.3. 2.2. Greenhouse gas calculations The following three calculators have been used in this study: ULICEES, F4E2 and (cafoo)2. They were developed to be used in synergy, which makes the calculation paths as consistent as possible. The flow chart in Fig. 1 presents the main components included in each calculator and how they interact. The ULICEES and F4E2 calculators estimate the on-farm GHG emissions “from the cradle to the farm gate” and the (cafoo)2 calculator uses the outputs of ULICEES and F4E2 to calculate all GHGs “from the farm to the exit gate of the processing plant.” Each of the calculators is presented in the following sections.

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Fig. 1. Presentation of the three calculators (Unified Livestock Industry and Crop Emission Estimation Systems e ULICEES, Canadian Food Carbon Footprint e (cafoo)2 and Fossil Fuel for Farm Fieldwork Energy and Emissions e F4E2) and the way they interact between each other.

2.2.1. On-farm greenhouse gas emissions (ULICEES and F4E2) At the farm level, direct and indirect emissions of the main agricultural GHGs (methane, CH4, and nitrous oxide, N2O) have been estimated using the Unified Livestock Industry and Crop
et al., Emissions Estimation System (ULICEES) calculator (Verge 2012). This calculator estimates all GHG emissions associated with the main types of livestock production in Canada. For this study, ULICEES was run for the swine sector. Because this calculator
et al., has already been presented in previous publications (Verge 2007, 2008, 2009a,b, 2012), only the main characteristics are mentioned here. Calculations for CH4 and N2O follow the Intergovernmental Panel on Climate Change (IPCC) methodology (IPCC,
2000, 2006), which was adapted to Canadian conditions by Verge et al. (2006) for CH4 and by Rochette et al. (2008) for N2O. The N2O emissions come from manure storage and crop cultivation and the CH4 emissions come from the animal digestive system and manure storage. The carbon dioxide (CO2) emissions from the use of fossil energy were also accounted for and calculations were done using the Farm Fieldwork and Fossil Fuel Energy and Emissions (F4E2) model (Dyer and Desjardins, 2003). The CO2 emission sources were field work, energy use and manufacturing (building and farm inputs such as fertilizers). Emissions related to crop cultivation (N2O and CO2) were calculated for the livestock crop complex, which refers to the area


used to grow the crops that feed the livestock considered (Verge et al., 2007, 2008, 2009a,b). 2.2.2. Off-farm greenhouse gas emissions 2.2.2.1. (cafoo)2-meat calculator. The off-farm GHG emissions were obtained using the Canadian Foods Carbon Footprint (cafoo)2 calculator. It provides national estimates of the CF of primal cuts of meat, after slaughtering and the cutting of carcasses into primal cuts or cut-out parts. Similar to the version developed for the milk
et al., 2013), the (cafoo)2sector (Maxime et al., 2010, 2011; Verge meat calculator is a tool for estimating GHG emissions from the farm gate to the exit gate of the processing plant. The (cafoo)2 calculator is intended primarily for a sector-wide assessment (e.g. at the national or provincial scale), where generic steps are modeled for transportation, slaughtering and primary processing. To reduce the burden on the user of the tool, data input requirements are limited to key steps and “hot spots” within system boundaries. In addition to the GHG emissions for transportation from the farm to the slaughtering plant, all calculations related to the processing step are included within the (cafoo)2-meat calculator. Key inputs of the calculator are the number of animals passing through slaughterhouses (federally and provincially inspected plants); the warm carcass weight or shrunk live weight, which corresponds to live weight on arrival at the slaughterhouse; and the dressing

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percentage, which is the mass ratio (in %) between the shrunk live weight and the warm carcass weight and expresses the weight loss after the bleeding, dehairing, and eviscerating of the animal. These key data are gathered from national and provincial statistics. Data compiled by the Canadian Food Inspection Agency and provincial agencies have been preferred and are used, because they are easily accessed from the Red Meat Market portal (Agriculture and Agri-Food Canada, 2013) on the Agriculture and Agri-Food Canada website and are updated every year. These three key input data are important because they control the amount of farm production and transportation needed to satisfy the volume of slaughtering. Indeed, (cafoo)2 back-calculates the number of animals to be produced on the farm and scales the transportation step accordingly, to fulfill the actual demand of slaughterhouse plants. It is important to note that only the animal population slaughtered is accounted for in this study. This means that the resulting intensity indicator (kg CO2e/kg of meat) compares GHG emissions from the slaughtered population only to the mass of meat produced in slaughterhouses. This is different from
et al., 2008, 2009a,b), previous studies (Dyer et al., 2010; Verge where meat production was compared to the GHGs emitted by the entire on-farm animal population and not by the slaughtered population only. In this study, the (cafoo)2 model recalculates the total on-farm GHG emissions produced by the slaughtered animal population only using the emission factor calculated with ULICEES. The death rate and weight loss of animals during hauling are also accounted for. In addition, when data are available (from the same source) about the province of origin of slaughtered animals, (cafoo)2 can consider different transportation conditions (especially the distance, and therefore fuel consumption, as well as weight loss) and the specific on-farm CF of live animals. Other input data for the (cafoo)2-meat calculator are directly related to the processing step and include energy use, water use, materials inputs (e.g. chemicals), and wastewater management characteristics. 2.2.2.2. Off-farm greenhouse gas emission calculations. The (cafoo)2 calculator contains emission factors to estimate GHGs in CO2 equivalents from user input data such as mass and transportation distance for animals, use of electricity, natural gas, water and other utilities at the slaughterhouse, wastewater treatment technology, etc. Emission factors have been calculated after modeling every unit operation with the SimaPro v7.3.3 LCA software (www.pre.nl) and using the ecoinvent v2.2 life cycle inventory database (www. ecoinvent.org), with 100-year IPCC 2007 global warming potentials taken into consideration. Tables 1 and 2 present the data, assumptions and sources of data and results for most of the emission factors. The tool comes with default input data that are also intended to be used for sectoral assessment at the national level. In this case, Canadian data are used for energy consumption, by type (Canadian Industrial Energy End-Use Data and Analysis Centre, 2013), and other Canadian data are used as much as possible, otherwise US data or data from other countries collected from the literature are used. Furthermore, to increase the representativeness of the geographic context, the entire ecoinvent database has been contextualized with an average North American grid mix for electricity. For wastewater treatment, GHG emissions are estimated according to the IPCC methodology (IPCC, 2006). 2.2.3. Allocation factors Since swine is not a dual purpose animal, like dairy cattle (meat/ milk) or poultry (meat/egg), the choice of this animal type limits the number of allocations to consider which would have, otherwise, made this study more complex and less demonstrative. In our

calculations, the only remaining upstream allocation factor concerns the soybean and canola meal used in the feed rations. The impacts of these allocations (to meal vs oil) have been estimated through a sensitivity analysis: the final results obtained when no allocation is used (all GHGs allocated to the oil and no GHG allocated to the meal used by the swine sector) have been compared to the results obtained when all GHGs are allocated to the meal fed to the swine sector. Due to the relatively small amount of meal in the swine diet, the difference obtained between these two extremes corresponds to only a small change of about 0.1% of the final result which is negligible. This explains why the current study focuses on the allocation factor associated with the final pork products. Three CFs are then presented for each animal product according to the type of allocation applied to the final pork products: no allocation, physical (mass) allocation and economic allocation. The factors used are presented in Table 3. The mass allocation factors are based on the shrunk live weight minus the unmarketable remainder or “waste”. Waste has been estimated to be 6.5% for swine. For the economic allocation factors, percentages are based on the price per kilogram of animal products, which have been weighted using the marketable shrunk live weights. The CF corresponds to the amount of GHG emissions associated with each product divided by the mass of the product considered. The CFs presented in Table 5 are calculated as follows:


. CFp MA or EA ¼ Total GHGp ðkgÞ  AFp MA or EA ð%Þ Mprodp ðkgÞ (1) where MA is the mass allocation, EA is the economic allocation, AF corresponds to the allocation factor and Mprod corresponds to the mass of the product p. When no allocation factor is used, the CF for each animal product is calculated using the total GHG emissions. The values presented in Table 5 were calculated using equation (1), the GHG data presented in Fig. 2 and the mass of products presented in Table 4. The following example illustrates the calculation approach, using the “Primal cuts (Total GHG)” data in Table 5 and the mass allocation approach:

ð3 526 ktCO2 eq  0:65Þ=872 kt ¼ 2:63 kgCO2 eq=kg of product

3. Results 3.1. Total GHG emissions Total GHG emissions from the swine industry in 2006 based on the animal population slaughtered are shown in Fig. 2. The on-farm and off-farm emissions are presented nationally and for eastern and western Canada. Eastern Canada combines the Atlantic prov
bec and Ontario and western Canada combines the inces, Que provinces of Manitoba, Saskatchewan, Alberta and British Columbia. On-farm production is by far the main GHG source. On average in Canada, on-farm emissions were about 11 times higher than the emissions from off-farm activities. Between regions, total GHG emissions were about 15% higher in the east than in the west. However, off-farm emissions were higher in the west than in the east. 3.2. Carbon footprint The intensity indicators are presented in Table 5. Results are presented for eastern Canada (Table 5), western Canada (Table 5)

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Table 1 Off-farm greenhouse gas emission factors. Step

Unit operation

Process description

Source (in addition to ecoinvent life cycle inventory data)

GHG emission factor

Transportation

Animal hauling

Double flat trailer-10 wheels; average loading density: 80 heads/haul; truck payload: 9.5 tonnes; short route; typical distance: 75 km Pot belly trailer; average loading density: 212 heads/haul; truck payload: 25 tonnes; long route; typical distance: 115 km Ecoinvent process: ‘Electricity, medium voltage’; gridmix per province, including import/export Alberta Atlantic provinces British Columbia Canada Manitoba Ontario Prairies Quebec Saskatchewan From natural gas; ecoinvent process: ‘Heat, natural gas, at boiler modulating >100 kW RNA’ From heavy fuel oil/middle distillate; ecoinvent process: ‘Heavy fuel oil, burned in industrial furnace 1 MW, non-modulating’ From propane; adapted from ecoinvent process: ‘Heat, natural gas, at boiler modulating >100 kW RNA’ Ecoinvent process: ‘tap water, at user’; emission factor calculated per province since electricity gridmix influences significantly the emission factor Alberta Atlantic provinces British Columbia Canada Manitoba Ontario Prairies Quebec Saskatchewan acid detergent; at 53%; includes transport from the plant to user (a default 800 km distance and 53 feet dry van truck is assumed) and dilution water Acid detergent; at 50%; includes transport from the plant to user (a default 800 km distance and 53 feet dry van truck is assumed) and dilution water Acid detergent; at 50%; includes transport from the plant to user (a default 800 km distance and 53 feet dry van truck is assumed) and dilution water Alkali detergent; at 50%; includes transport from the plant to user (a default 800 km distance and 53 feet dry van truck is assumed) and dilution water Disinfectant; at 15%; includes transport from the plant to user (a default 800 km distance and 53 feet dry van truck is assumed) and dilution water Refrigerant; includes transport from the plant to user (a default 800 km distance and 53 feet dry van truck is assumed)

OCO Technologies (2010); Gonyou (2009)

0.307 kg CO2e/t.km

Slaughtering and processing

Electricity use

Heat production

Water use

HNO3 H3PO4 H2SO4 NaOH (@50%) NaOCl HCFC-22

and the country as a whole (Table 5). Three product types have been considered: primal cuts, rendering products and offal. Different calculations have been performed based on the three allocation approaches (Table 3): no allocation, economic allocation and mass allocation. In each part of Table 5, the first two rows present indicators for the same meat category. The indicators in the first row cover offfarm GHG emissions only, which explains the very low values observed. For the indicators in the second row, all GHGs (on- and off-farm) have been considered in the calculations. Considering the results obtained after allocating GHGs (last two columns), when the CFs for the three animal products are summed up (last three rows), the totals obtained are not the same. This is normal, because it is not the allocated GHG emissions that are presented (first term Eq. (1)) but rather the CF. For the same reason the allocation factors (Table 3) cannot be recalculated based on the results presented in Table 5. Looking at the product categories, offal has the highest CF and primal cuts have the lowest when no allocation is used. With economic allocation and considering the indicators based on the total GHG emissions (second row), the primal cuts category has the highest value. Among the by-products, rendering products have by

0.129 kg CO2e/t.km Statistics Canada (2007) 1.029 kg CO2e/kWh 0.593 kg CO2e/kWh 0.046 kg CO2e/kWh 0.256 kg CO2e/kWh 0.054 kg CO2e/kWh 0.249 kg CO2e/kWh 0.730 kg CO2e/kWh 0.023 kg CO2e/kWh 0.820 kg CO2e/kWh 0.0723 kg CO2e/MJ 0.0904 kg CO2e/MJ 0.0798 kg CO2e/MJ

0.528 kg CO2e/m3 0.358 kg CO2e/m3 0.144 kg CO2e/m3 0.226 kg CO2e/m3 0.148 kg CO2e/m3 0.223 kg CO2eq/m3 0.411 kg CO2e/m3 0.135 kg CO2e/m3 0.446 kg CO2e/m3 3.36 kg CO2e/kg HNO3 (@53%) 1.716 kg CO2e/kg H3PO4 (@50%) 0.311 kg CO2e/kg H2SO4 (@50%) 1.57 kg CO2e/kg NaOH (@50%) 0.25 kg CO2e/kg NaOCl (@15%) 76.8 kg CO2e/kg HCFC-22

far the lowest result. Considering calculations based on mass allocation and using the total GHG emissions (on- and off-farm), there are no differences among all three product categories. It is interesting to note that the values for primal cuts (Table 5) are relatively close between the non-allocated calculations (e.g. 4.43 kg CO2e/kg of product) and those using the economic allocation (4.15 kg CO2e/kg of product). But both of those values are much higher than the values for primal cuts based on mass allocation (2.88 kg CO2e/kg of product). Between regions (Table 5), the intensity indicators calculated for the east are always lower than those calculated for the west. 4. Discussion 4.1. Total greenhouse gas emissions On-farm activity is the main GHG source, totaling between 89% (west) and 94% (east), with a Canadian average of 93% of total GHG emissions. This is comparable to the value reported by Dalgaard et al. (2007). Based on this study the on-farm GHG emissions (animal feed and pig housing) represented more than 90% of the total Danish pork CF.

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Table 2 Water use and wastewater treatment at the slaughterhouse. Step

Unit operation

Description

Source

Slaughtering and processing

Water use

Default volume: 2.11 L/kg shrunk live weight of swine

Wastewater generation

Volume is assumed equal to water use volume lowered 10e30% (20% default); default pollution: 4.71 (þ/ 2.36) kg BOD5/m3 wastewater (blood is mixed with wastewater); 2.88 kg COD/kg BOD5; Max methane-producing capacity: 0.25 kg CH4/kg COD Share of technology (MCF: methane conversion factor):

Wu and Mittal (2011);
and Masse
(2000) Masse Wu and Mittal (2011)

Wastewater treatment

Untreated, sent to public wastewater treatment plant Untreated, discharged to sea, river and lake; MCF ¼ 0.1 Aerobic treatment plant, well managed; MCF ¼ 0 Aerobic treatment plant, not well managed, overloaded; MCF ¼ 0.3 Anaerobic digester for sludge; MCF ¼ 0.8 Anaerobic reactor (e.g., UASB, Fixed Film Reactor); MCF ¼ 0.8 Anaerobic shallow lagoon, <2 m depth; MCF ¼ 0.2 Anaerobic deep lagoon, >2 m depth; MCF ¼ 0.8 Biogas recovery for anaerobic wastewater management: (recovered biogas destruction efficiency ¼ 0.98) Covered anaerobic lagoon (biogas capture) with cover type ¼ Bank to bank, impermeable; biogas collection efficiency ¼ 0.975 Covered anaerobic lagoon (biogas capture) with cover type ¼ Modular, impermeable; biogas collection efficiency ¼ 0.70 Anaerobic sludge digester or anaerobic reactor with cover type ¼ Enclosed vessel; biogas collection efficiency ¼ 0.99 Treatment of wastewater sent to public wastewater treatment plant

Table 3 Allocation on mass and economic bases of the swine processing sector (%). Agricultural production

Primal cuts Rendering products Offal Total

Swine Mass

Economic

No allocation

65.00 31.10 3.90 100

93.60 4.25 2.15 100

100 100 100 300

Table 4 Mass of pork products (kt) produced in eastern and western Canada in 2006. Agricultural production

East

West

Primal cuts Rendering products Offal

872 421 52

621 298 37


and Masse
(2000); Masse IPCC (2006)


and Masse
(2000); Masse IPCC (2006)

ecoinvent 2.2

% wastewater volume 15% 5% 10% 6% 3% 3% 12% 46% 60% of anaerobic WW volume 50% of recovered volume 40% of recovered volume 10% of recovered volume 0.536 kg CO2e/m3

western climate. As a result, the N2O emission factors are higher in the east (Rochette et al., 2008) and the total N2O emissions follow the same trend. Conversely, off-farm GHG emissions are higher in the west. This is due to the energy source used to produce electricity and also to hauling distances. In the east, electricity is based
bec, heavily on renewable energy, especially hydroelectricity in Que whereas the west relies mainly on fossil energy. Also, production is more spatially concentrated in eastern Canada, and the hauling distances between farms and slaughterhouses are shorter. For
bec, the main pork-producing province in eastern instance, in Que Canada and the country as a whole (accounting for about 36% of Canadian production), the hauling distance has been estimated to be around 75 km, whereas it is about 650 km in the west. These results show the value and importance of including off-farm processes whenever statistics makes it possible. 4.2. Carbon footprint

In this study, the CF obtained was 3.6 kg CO2e/kg pork. The average European value estimated by Reckmann et al. (2012) was also 3.6 kg CO2e/kg pork ranging from 2.6 to 6.3 kg CO2e/kg pork. It is difficult to compare these values to the results obtained in the current study where data are provided per kg of product. However, as explained later in this discussion, results obtained using the mass allocation approach are equivalent to kg CO2e/kg of live weight. These values are 2.63, 3.24 and 2.88 kg CO2e/kg pork for the east, west and for Canada respectively (Table 5). In the studies mentioned earlier it is carcass weight that is used as functional unit. Using the live weight of 105 kg and carcass weight of 79.2 kg mentioned by Dalgaard et al. (2007), 3.6 kg CO2e/kg carcass weight is then equivalent to 2.7 kg CO2e/kg live weight. This value is in the range of those obtained in the current study. Table 5 shows that the eastern emissions were higher than the western ones. Two main reasons explain this result. First, pork production is higher in the east than in the west: in terms of animals slaughtered, the east accounts for about 60% of total production. Second, the eastern Canadian climate is wetter than the

4.2.1. Allocation issues The three allocation approaches used have led to very different results. Calculations based on mass allocation show no difference between types of production, in contrast with the two other calculations (no allocation and economic allocation). Whatever the region considered, the values obtained for primal cuts are close when no allocation or economic allocation is used, but both methods result in values that are about one third higher than those for mass allocation. Between the economic-allocation and massallocation methods, the results for offal and rendering products based on mass allocation are two and seven times higher, respectively, than those based on economic allocation. These very different results demonstrate the importance of the allocation factors and highlight a major issue: each calculation gives very different CFs for the same products and each CF can be justified. The purpose of the following discussion is to analyze the strengths and weaknesses of each of the allocation approaches used, in order to find their value and help standardize the LCA calculation method.

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Table 5 Intensity indicators (no-allocation, economic-allocation, and mass-allocation values) per region for different product categories. Carbon footprint of the pork industry East (Unit ¼ kgCO2e/kg of product)

No allocation

Economic allocation

Mass allocation

Primal cuts (Off-Farm GHG ) Primal cuts (Total GHGb) Rendering products (Total GHG) Offal (Total GHG) West (Unit ¼ kgCO2e/kg of product)

0.25 4.04 8.44 68.20 No allocation

0.23 3.78 0.35 1.47 Economic allocation

0.16 2.63 2.63 2.63 Mass allocation

Primal cuts (Off-Farm GHGa) Primal cuts (Total GHGb) Rendering products (Total GHG) Offal (Total GHG) Canada (Unit ¼ kgCO2e/kg of product)

0.54 4.99 10.42 84.22 No allocation

0.51 4.67 0.44 1.81 Economic allocation

0.35 3.24 3.24 3.24 Mass allocation

0.37 4.43 9.25 74.77

0.34 4.15 0.39 1.61

0.24 2.88 2.88 2.88

a

Primal cuts (Off-Farm GHGa) Primal cuts (Total GHGb) Rendering products (Total GHG) Offal (Total GHG) a b

GHG from transportation and slaughtering only. GHG from transportation, slaughtering and on-farm production.

Fig. 2. Total on-farm and off-farm greenhouse gas (GHG) emissions per region for the swine sector.

4.2.2. No allocation The non-allocated results are dependent only on the mass of the animal product considered, because the same amount of GHGs is used to calculate the indicator. For instance, the offal category has the highest value because it has the lowest mass and the reverse is true for the primal cuts category, which is the heaviest (Table 3). Therefore, results based on non-allocated calculations do not provide new information, given that the mass comparison leads to the same conclusions. Also, because of double-counting issues, the labeling of products cannot be based on these calculations either. These results could, however, be used by producers for ranking animals on the basis of GHG efficiency. Indeed, given that GHGs are not allocated and assuming that the agricultural practices stay the same, this indicator will change on the basis of animal characteristics only (e.g. carcass yield). Animal characteristics, such as breed, slaughter age, feed-conversion efficiency related to age, breeding or genetic improvements, etc. are directly dependent on farm management. The non-allocated CF can, therefore, be used to differentiate and rank each management decision in terms of GHG efficiency. In other words, this indicator will answer questions such as what animal type or breed will emit the fewest GHGs per

kilogram of meat or by-product. Also, if one considers the same type of animal, this indicator can be used to rank management practices or genetic improvements in relation to GHG emissions. For example, focusing on a specific animal category (e.g. marketed swine), results will depend only on management practices and/or genetic improvement to control feed digestibility, carcass yield or daily weight gain. The CF results using allocation cannot be used as easily and efficiently for such comparisons because the results are not directly dependent on animal characteristics as a whole: economic allocation uses parameters that are not necessarily related to the animal and mass allocation uses animal characteristics but produces results that are not as valuable as non-allocated results, because CFs consider only a part of animal GHG emissions rather than those emissions as a whole. 4.2.3. Economic allocation From a company's point of view, economic allocation makes more sense, because the value of a product is indicated by its price. It is important to realize that economic allocation directly links GHGs not to the product itself but to its economic value. For example, if the price of meat increases, the share of GHGs

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associated with meat production will increase too, even though the on-farm management practices and the product considered stay the same. This type of result will therefore change over time because of fluctuating prices, and this could be an important issue. However, studying changes in the CF of a specific product over time is generally not what is being asked of LCA and does not produce the first result looked for by industries. The important thing is to be able to compare products that are in the same market niche and thus have the same function at the time the product is put on the market. In that sense, a CF based on economic allocation calculations is mainly a new indicator that allows companies to better position products on the market, not assign a value to the product itself. Economic allocation can lead industries and producers to avoid animal “waste” (products without economic value). This means recycling wastes into by-products as much as possible: the more by-products that are found, the lower the indicator value will be, because the same amount of GHGs will be distributed to more valuable products. In other words, a good way to decrease the CF of agricultural products is to find uses that generate profits for each agricultural output. For example, manure becomes a valuable product if it is considered fertilizer and used appropriately (overfertilization means that manure is still considered a waste in farm management). In this case, manure replaces chemical fertilizers, and GHG emissions from manure management should not be attributed to livestock but to crops. On the off-farm side, many valuable products already come from slaughtered animals, especially swine (Environmental Protection Agency, 2014). Therefore, it is the entire spectrum of animal products that has to be considered with the result that many different industries are involved. The main and unsolvable problem related to economic allocation is that it follows market prices and the resulting factor is therefore subject to potentially large year-to-year variations. Comparisons and trends are thus very difficult or simply not possible. As mentioned before, it is not necessarily an issue for industries or from a market point of view, but for environmental sustainability assessments, it represents a fundamental limitation that is impossible to circumvent. The reason is that the calculated allocation factor is dependent mainly on parameters that are totally disconnected from the product itself, given that the prices of products and co-products are dictated mainly by market circumstances and rules. For instance, climatic events, such as severe flooding or droughts in large and important agricultural areas, or international financial speculation can greatly affect the price of agricultural products either directly (e.g. crop production) or indirectly (e.g. animal production through animal feed). Assuming that agricultural practices do not change and given that the characteristic of the grain crops or animal products are the same, the corresponding environmental burden should therefore be constant over time. However, that is not the case when economic allocation is used. Given that the CF of a specific product can vary from year to year without any modifications in terms of management practices and food characteristics, the results of economic allocation cannot be used for environmental sustainability assessments. From an environmental point of view, a product is dependent on the method used to produce it and on its own characteristics. 4.2.4. Mass allocation We recognize that, from a market perspective, it makes sense to allocate more GHGs to meat than to by-products and waste because, from a food perspective, meat is the most valuable product. From an environmental perspective, however, this point of view must be revised and corrected. What is valuable in the agricultural system can be considered a waste for the livestock industry

(e.g. manure). Also, what is considered animal waste in the food processing system (a product with low or no monetary value, such as offal) is of importance for the animal. This shows that to conduct true environmental assessments, the function of the “products” has to be based on an ecosystem point of view and not on their use by human societies. This change of perspective is crucial. Because the CFs calculated using the mass-allocation method are the same for all animal products (Table 5), mass allocation appears to be better suited for estimating the environmental impacts. From a calculation point of view, the reason is that both the allocation method and the results use mass. For example, if the mass difference between two animal products is 40%, the associated GHG emissions will differ by 40% as well. Therefore the ratio of GHGs to the product mass (Eq. (1)) stays the same and the CF remains unchanged. From an agricultural system point of view, this means that the CF calculations are associated with the animal type and not the animal products. This means that the value calculated in this study (for example 2.88 CO2e/kg product for Canada e Table 5) is equivalent to the amount of GHG emissions per kg of live weight and not carcass weight. Indeed, this approach considers that, if part of the animal is not accounted for, the same share of the GHG emission has to be discarded. Having only one value per animal type, and so characterizing this animal, makes sense in the context of environmental sustainability, because what is important is not the meat, bones or hides but rather the functional unit in the agroecosystem, which, in our case, is the animal itself. Conversely, it does not make sense to assume that meat emits more GHGs than bones or hides do, because it is the animal as a whole that produces them. With this type of calculation, the animal is considered an “indivisible” component in the agricultural system. This approach is therefore the best suited for studying environmental sustainability, because it is not a specific part of the animal (hides, offal, rendering products or even the “waste” category) that is at the origin of the environmental impact considered but rather the individual animal as whole. 5. Conclusion Carbon footprint results are highly dependent on how the total GHG emissions are allocated. With economic allocations most of the 6.6 Mt CO2e of the Canadian pork industry were attributed to meat (94%) whereas the GHG partition based on the mass of the animal products is more balanced: about two-thirds is allocated to meat and a little less than one-third is allocated to rendering products. Without any allocation, the results are directly and negatively correlated to the mass of each animal product (high mass, low CF and vice versa). When each product is considered separately, the differences in the CF are related only to the method of calculation and do not depend on farm management, production processes or product characteristics. These inconsistencies are important and could have very negative impacts on LCA studies in terms of trust in the results and the adoption of recommendations. Currently, all allocation methods are presented as having the same single purpose: to distribute the environmental burden among products obtained from the same production process. This study shows that this understanding is not sufficient since they not only greatly influence the LCA results and so the importance of the environmental footprint of products studied, but they also explore the environmental impact in different ways. We demonstrated that the allocation factor influences the indicator value of the result. Therefore, the allocation factors are not interchangeable and must be used in relation to what they are calculated for. Non-allocated CFs can be used by producers to rank farm management practices by GHG emission efficiencies. The use of economic allocation as an indicator is valuable for industries to better position products on

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the market. Economic allocation also provides a good incentive to make better use of animal products, that is, to avoid “waste” as much as possible. Lastly, because the mass-allocation method leads to CFs specific to each functional unit in the ecosystem which, in our case, is each animal, this allocation method is well suited for evaluating the environmental sustainability of production systems. Therefore, if it is necessary to define the goal and scope of a study to identify important parameters such as the functional unit, this step should also be used to justify the choice of the allocation factor. In this study we analyzed the importance and interest of the method used to partition the total GHG emissions of specific production sectors, but the same research should now be done with the GHG method of calculations themselves (empirical or process based models) since they have been developed for different purposes and geographic scales (field, farm, region, country). Doing so, the goal of the study could then be used to guide LCA practitioners finding method of calculations adapted to their specific needs. References Agriculture and Agri-Food Canada, 2013. Red Meat Market Information. Canadian Industry. http://www.agr.gc.ca/redmeat-vianderouge/index_eng.htm?menup os¼1.02&CFID¼2221150&CFTOKEN¼43964868 (last visit: 2013-09-01). Bare, J.C., Hofstetter, P., Pennington, D.W., Udo de Haes, H.A., 2000. Midpoints versus endpoints: the sacrifices and benefits e life cycle impact assessment workshop summary. Int. J. Life Cycle Assess. 5, 319e326. Beer, T., Grant, T., 2007. Life-cycle analysis of emissions from fuel ethanol and blends in Australian heavy and light vehicles. J. Clean. Prod. 15, 833e837. BSI, 2011. PAS 2050:2011 Specification for the Assessment of the Life Cycle Greenhouse Gas Emissions of Goods and Services. British Standards Institution. http://shop.bsigroup.com/en/Browse-By-Subject/Environmental-Managementand-Sustainability/PAS-2050/ (last visit: 2014-09-29). Canadian Industrial Energy End-Use Data and Analysis Centre, 2013. Energy Consumption and Energy Intensity Indicators Database. NAICS Industry 311600 Meat Product Manufacturing. http://www2.cieedac.sfu.ca/index.html (last visit: 2013-10-27). Cederberg, C., Stadig, M., 2003. System expansion and allocation in life cycle assessment of milk and beef production. Int. J. Life Cycle Assess. 8, 350e356. Dalgaard, D., Halberg, N., Hermansen, J., 2007. Danish pork production - an environmental assessment. D.J.F. Animal Sci. 82. November 2007. 38p. Dyer, J.A., Desjardins, R.L., 2003. Simulated farm fieldwork, energy consumption and related greenhouse gas emissions in Canada. Biosyst. Eng. 85, 503e513.
, X.P.C., Desjardins, R.L., Worth, D.E., 2010. The protein-based GHG Dyer, J.A., Verge emission intensity for livestock products in Canada. J. Sustain. Agric. 34, 618e629. http://dx.doi.org/10.1080/10440046.2010.493376. Environment Canada, 2013. National Inventory Report 1990e2011: Greenhouse Gas Sources and Sinks in Canada e Part 3. Pollutant Inventories and Reporting Division, Environment Canada, Ottawa, ON, Canada. http://unfccc.int/national_ reports/annex_i_ghg_inventories/national_inventories_submissions/items/ 7383.php (last visit: 2013-09-07). Environmental Protection Agency, 2014. Pork Production. http://www.epa.gov/ oecaagct/ag101/pork.html (Last visit: 2014-09-15). €, A., Cederberg, C., Henriksson, M., Ledgard, S., 2011. How does co-product Flysjo handling affect the carbon footprint of milk? Case study of milk production in New Zealand and Sweden. Int. J. Life Cycle Assess. 16, 420e430. Gonyou, H.W., 2009. Handling and transport of pigs: report to Industry. In: Presented at the Canadian Swine Handling and Transport Meeting, October 2009. http://www.prairieswine.com/pdf/39863.pdf (last visit: 2015-09-05). IPCC, 2000. Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. Chapter 4: Agriculture. Intergovernmental Panel on Climate Change, Montreal, Canada. IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. In: Agriculture, Forestry and Other Land Use, vol. 4. Intergovernmental Panel on Climate Change, Hayama, Japan. ISO, 2006. ISO 14044:2006. Environmental Management e Life Cycle Assessment e Requirements and Guidelines. International Organization for Standardization.

595

http://www.iso.org/iso/home/store/catalogue_tc/catalogue_detail.htm? csnumber¼38498 (last visit: 2015-09-10). Kim, S., Dale, D.E., 2002. Allocation procedure in ethanol production system from corn grain. I. System expansion. Int. J. Life Cycle Assess. 7, 237e243. Luo, L., van der Voet, E., Huppes, G., Udo de Haes, H.A., 2009. Allocation issues in LCA methodology: a case study of corn stover-based fuel ethanol. Int. J. Life Cycle Assess. 14, 529e539. Malça, J., Freire, F., 2006. Renewability and life-cycle energy efficiency of bioethanol and bio-ethyl tertiary butyl ether (bioETBE): assessing the implications of allocation. Energy 31, 3362e3380.
, D.I., Masse
, L., 2000. Characterization of wastewater from hog slaughterMasse houses in Eastern Canada and evaluation of their in-plant wastewater treatment systems. Can. Agric. Eng. 42, 139e146. Maxime, D., Arcand, Y., Landry, D., Marcotte, M., 2008. Eco-efficiency of the Canadian food and beverage industry: key indicators of the environmental perfor
re
gie, Saintmance. In: 24th ZIP Technologique e Cintech AA/ACCORD Monte Hyacinthe, QC, Canada, 13 November 2008. Maxime, D., Arcand, Y., Landry, D., Marcotte, M., 2010. Energy use and greenhouse gas emissions. In: Eilers, W., MacKay, R., Graham, L., Lefebvre, A. (Eds.), Environmental Sustainability of Canadian Agriculture. Agri-Environmental Report Series, pp. 143e154. Report #3. Agriculture and Agri-Food Canada, Ottawa, ON, Canada. http://publications.gc.ca/collections/collection_2011/agr/A22-2012010-eng.pdf (last visit: 2015-08-27.
, X., Arcand, Y., Desjardins, R., 2011. A cradle-to-gate assessment Maxime, D., Verge of the carbon footprint of Canadian dairy products. In: Presentation at the LCA XI Conference, Chicago, IL, 4e6 October 2011. http://lcacenter.org/lcaxi/final/ 426.pdf (last visit: 2015-08-27.
de
ration des producteurs de porcs du Que
bec: AssessOCO Technologies, 2010. Fe ment of the Scope of an Upgrade to the FPPQ Greenhouse Gas Emissions Calculator in Order to Quantify the Carbon Footprint of Pork Production in Quebec. Available at: http://www.agrireseau.qc.ca/porc/documents/Publique_ AN.pdf (last visit: 2015-08-27). Reckmann, K., Traulsen, I., Krieter, J., 2012. Environmental impact assessment e methodology with special emphasis on European pork production. J. Environ. Manag. 107, 102e109. Rochette, P., Worth, D.E., Lemke, R.L., McConkey, B.G., Pennock, D.J., WagnerRiddle, C., Desjardins, R.L., 2008. Estimation of N2O emissions from agricultural soils in Canada e development of a country-specific methodology. Can. J. Soil Sci. 88, 641e654. Rougoor, C., Elferink, E., Lap, T., Balkema, A., 2015. LCA of Dutch Pork - Assessment of Three Pork Production Systems in the Netherlands. Available at: http://glamur. eu/wp-content/uploads/2015/06/glamur-wp3-dutch-pork.pdf (last visit: 201510-29). Statistics Canada, 2003. Livestock Statistics e Fourth Quarter 2002, p. 90. Catalogue no. 23-603-XIE. Statistics Canada, 2007. Electric Power Generation, Transmission and Distribution. Statistics Canada, Ottawa, ON, Canada. Catalogue No. 57-202-X. Statistics Canada, 2015. Hogs statistics, number of farms reporting and average number of hogs per farm. Available at: http://www5.statcan.gc.ca/cansim/a05 (last visit: 2015-09-28).
, X.P.C., Worth, D., Hutchinson, J., Desjardins, R.L., 2006. Greenhouse Gas Verge Emissions from Canadian Agro-Ecosytems. Technical report. Cat. No. A22-414/ 2006E. Agriculture and Agri-Food Canada, Ottawa, Ontario, p. 38.
, X.P.C., Dyer, J.A., Desjardins, R.L., Worth, D., 2007. Greenhouse gas emissions Verge from the Canadian dairy industry during 2001. Agric. Syst. 94, 683e693.
, X.P.C., Dyer, J.A., Desjardins, R.L., Worth, D., 2008. Greenhouse gas emissions Verge from the Canadian beef industry. Agric. Syst. 98, 126e134.
, X.P.C., Dyer, J.A., Desjardins, R.L., Worth, D., 2009a. Greenhouse gas emissions Verge from the Canadian pork industry. Livest. Sci. 121, 92e101.
, X.P.C., Dyer, J.A., Desjardins, R.L., Worth, D., 2009b. Long-term trends in Verge greenhouse gas emissions from the Canadian poultry industry. J. Appl. Poult. Res. 18, 210e222.
, X.P.C., Dyer, J.A., Worth, D.E., Smith, W.N., Desjardins, R.L., McConkey, B.G., Verge 2012. A greenhouse gas and soil carbon model for estimating the carbon footprint of livestock production in Canada. Animals 2, 437e454. http://www. mdpi.com/2076-2615/2/3/437 (last visit: 2015-09-10).
, X.P.C., Maxime, D., Dyer, J.A., Desjardins, R.L., Arcand, Y., Vanderzaag, A., 2013. Verge Carbon footprint of Canadian dairy products: calculations and issues. J. Dairy Sci. 96, 6091e6104. http://www.journalofdairyscience.org/article/S0022-0302% 2813%2900479-7/fulltext (last visit: 2015-09-10). Wu, P.F., Mittal, G.S., 2011. Characterization of provincially inspected slaughterhouse wastewater in Ontario, Canada. Can. Biosyst. Eng. 53, 6.9e6.18.