Can carbon footprint serve as an indicator of the environmental impact of meat production?

Ecological Indicators 24 (2013) 573–581

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Can carbon footprint serve as an indicator of the environmental impact of meat production? Elin Röös a,∗ , Cecilia Sundberg a , Pernilla Tidåker b , Ingrid Strid a , Per-Anders Hansson a a b

Department of Energy and Technology, Swedish University of Agricultural Sciences, Lennart Hjelms väg 9, Uppsala, Sweden Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Ulls väg 16, Uppsala, Sweden

a r t i c l e

i n f o

Article history: Received 11 July 2011 Received in revised form 3 August 2012 Accepted 7 August 2012 Keywords: Meat production Life cycle assessment Carbon footprint

a b s t r a c t The carbon footprint (CF), the amount of greenhouse gases (GHG) emitted during a product’s lifecycle, was evaluated as an indicator of the wider environmental impacts of meat production using existing life cycle assessments of different types of meat (pork, chicken and beef). The CF generally acts as an indicator of acidification and eutrophication potential, since more efficient use of nitrogen leads to less eutrophying and acidifying substances being released to the environment and to lower GHG emissions in nitrous oxide form. GHG mitigation strategies based on more efficient use of feed can therefore also lead to decreased acidification and eutrophication potential. Decreased GHG emissions due to increased productivity mean less land is required for feed production, so CF can act as a proxy for land use. For the impact category primary energy use, apparent conflicts with CF were identified. Pasture-based beef production can be either very energy-efficient or energy-demanding, but both forms produce high CH4 emissions from enteric fermentation. For monogastric animal production, CF can function as an indicator of primary energy use, as both energy use and GHG emissions originate mainly from feed production. It is unclear how the biodiversity impact category correlates to CF. More intensive production can allow more land to be left in its natural state, but can involve increased use of pesticides and fertilisers and monocropping locally, threatening biodiversity. Using CF as an indicator of the environmental impact of meat can generate conflicts with other environmental categories in some cases. However, the risk of damaging other environmental areas when acting on CF must be weighed against the risk of further neglecting to act on global warming by failing to exploit the current market momentum of carbon footprinting. © 2012 Elsevier Ltd. All rights reserved.

1. Introduction 1.1. Background The impact arising from food consumption is now making its way onto the environmental agenda. Recent attention has been on emissions of greenhouse gases (GHG), but the food sector also affects the environment considerably in many other ways. The global nitrogen cycle is under heavy stress, mainly due to the large addition of reactive nitrogen to soils via mineral fertilisers. Biodiversity is greatly affected by monoculture practices and deforestation in the quest for more agricultural land. Pesticide use causes leaching of toxic substances into groundwater and surface waters. Water shortage is a serious problem in many regions, as freshwater reserves are being overused. As regards resource use, modern agriculture is heavily dependent on fossil fuel, will probably suffer

∗ Corresponding author. E-mail address: [email protected] (E. Röös). 1470-160X/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2012.08.004

from future shortages of easily available phosphorus and, of course, uses and affects large areas of land (UNEP, 2010). Meat is the food product with the greatest environmental impact (Steinfeld et al., 2006; Weidema et al., 2008b). This is due to the inefficiency of animals in converting feed to meat, as 75–90% of the energy consumed is needed for body maintenance or lost in manure and by-products such as skin and bones. 1.2. Life cycle assessment The environmental impact of food is usually quantified using life cycle assessment, LCA (ISO, 2006a, 2006b). In LCA, emissions and resource use from all phases in a product’s lifecycle are quantified and used to calculate the environmental impact divided over a number of impact categories such as global warming potential, acidification and eutrophication potential, primary energy use, land use, toxicity, biodiversity, etc. (Baumann and Tillman, 2004). LCA results can be presented as mid-point indicators in the form of the impact categories listed above to show how a product affects the environment, e.g. how it contributes to climate change, potential eutrophication, land or energy use, etc. Use of

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mid-point indicators leaves it up to the individual to weight these different aspects together. LCA results can also be presented as end-point indicators, in which the mid-point indicators have been translated into actual damage levels. Several end-point methods based on different assumptions and perspectives are currently in use (Jungbluth et al., 2012). Examples of indicators include DALY (Disability-Adjusted Life Year) for human health, annual loss of species for ecosystem diversity, and increased cost for resource depletion (Goedkoop et al., 2008). Although handling just a few scores is tempting, the degree of uncertainty and subjectivity is increased in end-point indicators compared with mid-point indicators (Bare et al., 2000).

1.4. Objective of the study The overall objective was to evaluate CF as an indicator for the wider environmental impacts of meat production. The evaluation was based on two levels of consumer decisions (Jungbluth et al., 2012): (i) choice within the product group of meat, i.e. between chicken, pork or beef; and (ii) choice within one product category, i.e. between e.g. beef from different production systems. The environmental synergies and conflicts arising from a unilateral focus on the impact category global warming in the choice between meat products were also discussed.

1.5. GHG emissions from meat production 1.3. Carbon footprint With increased awareness of climate change, calculating the carbon footprint (CF) of products has become increasingly popular. CF is a mid-point indicator, commonly also entitled global warming potential (GWP), in which all GHG emitted during a product’s lifetime are quantified and transformed into CO2 -equivalents (CO2 e) depending on their ability to contribute to global warming. A number of standards and guidelines exist on how to calculate the CF of products. The Greenhouse Gas Protocol initiative recently launched a new standard for calculating life cycle GHG emissions (GHG Protocol, undated), while the British Standardisation Institute released its suggested methodology for CF calculations, PAS 2050, back in 2008 (BSI, 2008). ISO is also working on standardising the methodology for calculation of product CF (ISO 14067). The major reason for the widespread use of CF is the attention that climate change has received on the global environmental agenda, but CF also has several advantages. There are a limited number of anthropogenic substances that cause global warming; carbon dioxide (CO2 ), nitrous oxide (N2 O), methane (CH4 ) and fluorinated gases. Measuring and modelling GHG emissions involves some uncertainty (Röös et al., 2011) and although the IPCC (2007) method for calculating GWP from GHG emissions is generally accepted, it has been questioned (Smith, 2003; Tanaka et al., 2010). However, in comparison with assessing the effects on e.g. biodiversity and expressing these in one score, the use of CO2 e as prescribed by IPCC (2007) to express GWP is more straight-forward. In addition, unlike the impact categories eutrophication, acidification and biodiversity, which have highly site-specific effects, GWP is the same regardless of where the GHG emissions take place. The ability to produce values that are globally applicable and comparable across a wide range of products makes CF easy to understand and communicate. The obvious limitation of using the CF of products to guide consumers and design policy instruments is that it only includes the environmental impact of global warming. This violates the basic principle of LCA, which aims at being a comprehensive method for assessing environmental impact (Finkbeiner, 2009; Weidema et al., 2008a). However, CF is a reality, which makes it interesting to investigate how accurately it can represent other impact categories. Previous work on correlations between different environmental indicators in LCA has mostly focused on resource use and the use of different energy measurements as indicators (Bao and Multani, 2007; Berger and Finkbeiner, 2011; Bösch et al., 2007; Huijbregts et al., 2010; Ingwersen, 2011). Huijbregts et al. (2010) showed that for agricultural products, for which the CF is dominated by nonenergy related emissions, energy use is not an appropriate indicator of other environmental impacts. Monteny et al. (2006) examined mitigation alternatives for reduced GHG emissions from animal husbandry and highlighted some potential risk areas in pollution swapping, but more knowledge is needed regarding the use of CF as an indicator for wider environmental impacts from food products.

The processes contributing to major GHG emissions during meat production are: (1) production of feed, (2) enteric fermentation from feed digestion by animals (mainly ruminants), (3) manure handling and (4) energy use in animal houses (Steinfeld et al., 2006). Feed production causes GHG emissions due to energy use in field operations, feed processing and transport, manufacturing of mineral fertilisers, and soil emissions. Application of nitrogen fertilisers, crop residues and animal manure increases N2 O emissions from the processes of nitrification and denitrification (Monteny et al., 2006). N2 O is an approximately 300-fold more potent GHG than CO2 in a 100-year perspective (IPCC, 2007). When feed is cultivated on soils rich in organic matter or when forests are cleared to make room for e.g. feed cultivation and pasture, large amounts of CO2 are released from the standing biomass and the soil. If the new land use is associated with large inputs or release of nitrogen, N2 O emissions also increase (Neill et al., 2005). Microbial degradation of cellulosic feed materials in animals emits CH4 , a 25-fold more potent GHG than CO2 in a 100-year perspective (IPCC, 2007). For ruminants CH4 emissions are substantial, and usually dominate the total CF of beef meat. For pork and chicken, the GHG emissions mostly originate from feed production and manure handling. Energy use in animal houses usually contributes a minor proportion of total GHG emissions from meat production (Steinfeld et al., 2006). An animal’s ability to convert feed to meat, i.e. Feed Conversion Ratio (FCR), is calculated as the energy, protein or mass input in feed divided by the corresponding output in meat. Chickens and pigs generally have lower FCR than ruminants, 3–6 compared with 14–20 (energy in and out) (Smil, 2001). FCR can vary greatly within species depending on animal breed, feed quality and how well the feed matches the specific requirements at different stages of growth, etc. In terms of complete production systems, factors such as mortality, health, feed losses, reproduction and replacement rates, etc. also influence FCR. In principle, lower FCR leads to less feed required per kg meat produced, and therefore less the emissions of GHG per kg meat. Lower FCR in ruminants also leads to less methane emissions from enteric fermentation, since animals that grow faster can be slaughtered at an earlier age. However, the feed composition and nutrient content is also important, so this cannot be taken as the rule in all circumstances (Steinfeld et al., 2006; Strid Eriksson et al., 2005).

2. Materials and methods 2.1. Evaluation method The use of CF as an indicator for other environmental aspects was evaluated by: (1) Examining the biophysical relationship between CF and other environmental impacts based on common LCA calculation methods; and (2) analysing data from existing LCA studies

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2.2. Data collection Data for the evaluation were collected from 23 attributional LCA studies, containing in total 53 scenarios of livestock production systems. Eight studies on conventional pork, chicken and beef production systems described by de Vries and de Boer (2010) (except the study by Ogino et al. (2004) on a very specific type of production system) were complemented with studies on organic livestock production, Swedish studies and some recent studies using the same selection criteria as de Vries and de Boer throughout, although studies on organic production systems were also included (Table 1). Most production systems covered were commonly used, highly industrialised conventional production systems in OECD countries, which are relatively streamlined, and their organic counterparts. The exception was beef production systems, which included intensive meat production from dairy herds and extensive production from grazing animals. The system boundaries were set at the farm gate and hence transport pre- and post-slaughter and slaughtering, packaging, storage and preparation were not included. The environmental impact per kg meat is generally small for these stages compared with the agricultural phase (Cederberg et al., 2009; Peters et al., 2010) and was assumed here to be similar for all types of meat and production systems studied, so its omission did not affect the final evaluation. However, in the case of using CF for consumer communication of food in general it is necessary to include all emission to at least the shop (Jungbluth et al., 2012). All results were recalculated to the functional unit of 1 kg bonefree meat, using a meat yield ratio of 45% for pork, 53% for chicken and 38% for beef, which were estimated means from the studies included. 2.3. Impact categories In most studies included, the LCA results were presented as mid-point indicators for the categories GWP, primary energy use,

140

Primary energy use (MJ/kg bone-free meat)

on livestock production to determine whether the expected correlation was generally found. The relationship between CF and other variables (primary energy use, land use, acidification, eutrophication and toxicity) was tested using the Pearson coefficient of correlation, r (Olsson et al., 2008). The value of r varies between 1 (perfect positive linear relationship) and −1 (perfect negative relationship) (r = 0 means no correlation). The correlation can be subjectively considered strong, medium or weak, depending on the context. The calculation methods used in LCA are based on physical relationships, but are associated with large uncertainties. This is especially true for agricultural systems, due to limited knowledge of underlying processes, subjective choices, lack of data, difficulty in verifying results, and spatial, temporal and methodological variability. Therefore, here we used the interpretation of r suggested by Cohen (1988), whereby a weak correlation is defined as r = 0.1–0.29, a medium correlation as r = 0.30–0.49 and a strong correlation as r = 0.5–1. To determine the significance of the relationship, the probability value, P, of finding the calculated r value when in fact r was zero was also calculated. Results from different LCA studies should always be compared with great caution, as system boundaries, emissions and characterisation factors can differ between studies and the results are associated with great uncertainties (Röös et al., 2011). Therefore, the specific LCA results from the studies included here, although recalculated to the same base of comparison and the same units for the different impact categories, should not be compared in detail, which was not the aim of this study. Instead, the aim was to investigate whether CF is an indicator of other environmental impacts regardless of exactly how the LCA is carried out.

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Chicken, r=0.78, P<0.05 Pork, r=0.78, P<0.05

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Beef, r=0.77, P=0.117 100

80

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Carbon footprint (kg CO2-e/kg bone-free meat) Fig. 1. Correlation between carbon footprint and primary energy use in livestock production, r = 0.78, P < 0.05 across all species.

land use, eutrophication and acidification potential. These results were recalculated into kg CO2 e, MJ primary energy, area in m2 , kg PO4 3 e and kg SO2 e per kg bone-free meat for the different impact categories, respectively, using the conversion factors in Guinée et al. (2002). Primary energy use includes all life cycle steps necessary to obtain energy in a usable form, including e.g. the energy needed for fuel production and construction of energy infrastructure. Agricultural production uses and affects land (in several ways). To make a comprehensive environmental assessment, the impact categories toxicity, biodiversity, soil fertility and water use should be included in LCA, but are more difficult to determine consistently and are therefore often omitted. Toxicity effects and effects on biodiversity were mentioned in some of the studies reviewed, but are mainly discussed qualitatively here. The impacts on soil quality and water use are not discussed, since few or no studies included results or discussions on these. 3. Results 3.1. Primary energy use The literature included in this study (Table 1) showed that production and transport of feed is the main user of energy in livestock production. The GHG emissions from pork and chicken production are usually also dominated by emissions from feed production, i.e. from soils and fossil fuel combustion. As expected, significant relationships (P < 0.05) and strong correlations were found between CF and primary energy use for these species (Fig. 1). Beef production systems can be very energy-efficient if based on grazing natural and semi-natural grasslands. However, pasturebased beef production results in high GHG emissions per kg meat, dominated by CH4 from enteric fermentation and not emissions from feed production as in monogastric species. In the case of Brazilian beef production, primary energy consumption was reported to be only 5 MJ, but CF was 39 kg CO2 e/kg meat due to slow growth rates (Cederberg et al., 2009). However, a study on three beef production systems in the U.S. reported high impacts across all categories for pasture-finished beef (Pelletier et al., 2010b). Across species, the correlation coefficient (r) was 0.78. In several beef scenarios primary energy use was in the same range (20–60 MJ/kg meat) as for chicken and pork, while GHG emissions

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Table 1 Results from studies included in the literature review. Impact categories included were global warming (GWP/CF), acidification potential (AP), eutrophication potential (EP), land use, primary energy use and pesticide use or toxicity. (Numbers as interpreted by the authors, due to lack of transparency in some cases.) Study:

Production system:

Country

Impacts per kg bone-free meat GWP/CF (kg CO2 e)

Primary energy use (MJ)

Land use (m2 )

AP (kg SO2 e)

EP (kg PO4 3 e)

Pesticide use/toxicity 11.7d 10.0d 10.7d 18.8d – – – –

Chicken: da Silva et al. (2010) da Silva et al. (2010) da Silva et al. (2010) da Silva et al. (2010) Katajajuuri et al. (2008) Pelletier (2008) Widheden et al. (2001) Williams et al. (2009)

Large-scale conventional Small-scale conventional Conventional Red labela Conventional National average Conventional National average

BR BR FR FR FI US SE GB

2.3 1.9 2.9 5.2 2.7 2.7 1.8 3.7

35 31 41 73 21 28 17 23

5 5 6 9 7 – 7 9

0.059 0.060 0.055 0.099 0.028 0.030 0.052 0.056

0.023 0.023 0.027 0.045 0.004 0.007 0.019 0.029

Pork: Basset-Mens and Van der Werf (2005) Basset-Mens and Van der Werf (2005) Basset-Mens and Van der Werf (2005) Blonk et al. (1997) Carlsson et al. (2009) Cederberg and Darelius (2001) Cederberg and Flysjö (2004) Cederberg and Flysjö (2004) Cederberg and Flysjö (2004) Cederberg and Nilsson (2004a) Cederberg and Nilsson (2004a) Dalgaard et al. (2007) Halberg et al. (2008) Halberg et al. (2008) Halberg et al. (2008) Pelletier et al. (2010a) Pelletier et al. (2010a) Pelletier et al. (2010a) Pelletier et al. (2010a) Strid Eriksson et al. (2005) Wiedemann et al. (2010) Wiedemann et al. (2010) Williams et al. (2009)

GAPb Red labela Organic, French rules Conventional Organic Conventional Focus on animal welfare Focus on environment Focus on quality and price Organic, sandy soils Organic, clay soils Conventional Organic, traditional Organic, movable huts Organic, climate tents Conv, high profit Conv, low profit Niche, litter, high profit Niche, litter, low profit Conventional Conventional slatted flr Conventional litter Country-average

FR FR FR NL SE SE SE SE SE SE SE DK DK DK DK US US US US SE AU AU GB

5.1 7.7 8.8 8.2 4.6 4.8 4.1 3.6 4.4 4.9 4.8 5.8 6.5 7.4 6.3 5.5 6.8 5.6 7.4 4.3 8.9 5.0 6.6

43 48 59 36 22 26 20 18 22 25 25 – – – – 22 26 25 32 20 40 33 38

12 14 22 – 32 15 13 11 12 36 37 – 15 20 19 – – – – 18 – – 12

0.097 0.050 0.083 0.069 0.120 0.081 0.052 0.026 0.064 0.098 0.091 0.073 0.127 0.136 0.113 – – – – 0.070 – – 0.156

0.046 0.037 0.048 0.040 0.049 0.040 0.023 0.015 0.026 0.078 0.050 0.038 0.060 0.085 0.060 0.035 0.046 0.072 0.102 0.032

0.055

1.37e 1.44e 0.24e – – 1.13e 0.71e 0.62e 1.50e 0.00e 0.17e – – – – – – – – – – – –

Beef: Cederberg and Darelius (2000) Cederberg and Darelius (2000) Cederberg and Darelius (2000) Cederberg and Darelius (2000) Cederberg and Nilsson (2004b) Cederberg et al. (2009) Nguyen et al. (2010) Nguyen et al. (2010) Nguyen et al. (2010) Nguyen et al. (2010) Nguyen et al. (2012) Nguyen et al. (2012) Nguyen et al. (2012) Nguyen et al. (2012) Pelletier et al. (2010b) Pelletier et al. (2010b) Pelletier et al. (2010b) Peters et al. (2010) Peters et al. (2010) Peters et al. (2010) Peters et al. (2010) Williams et al. (2009)

Conv, forage-basedc Conv, grain-basedc Organicc Organic, suckler Organic, ranch Average for export to SE Conv, 12 monthc Conv, 16 monthc Conv, 24 monthc Suckler Ext suckler, maize Ext suckler, maize, O3f Ext suckler, fibre Ext suckler, fibre, O3f Feedlot Backgrounding/feedlot Pasture Organic, year 2002 Organic, year 2004 Feedlot/pasture 2002 Feedlot/pasture 2004 National average

SE SE SE SE SE BR EU EU EU EU FR FR FR FR US US US AU AU AU AU GB

47 48 24 27 9 6 58 58 67 83 90 95 102 99 101 118 127 34 40 41 39 42

33 35 47 71 154 244 23 23 32 60 – – – – – – – – – – – 24

0.13 0.13 0.32 0.40 0.18 – 0.14 0.18 0.24 0.29 0.24 0.24 0.23 0.24 – – – – – – – 0.21

0.068 0.070 0.116 0.146 0.060 – 0.087 0.103 0.159 0.230 0.137 0.137 0.131 0.137 0.274 0.313 0.374 – – – – 0.114

2.21e 3.32e 0.02e 0.01e 0.00e – – – – – – – – – – – – – – – – –

a b c d e f

17 17 17 20 22 39 22 25 28 38 39 39 39 38 39 43 51 11 16 14 14 18

Red label – low intensity production on deep-litter bedding. GAP – Good Agricultural Practice. Calves coming from milk production. Eco-toxicity measured as kg 1.4-DB-equivalents. Pesticide use as g active substance. Diet for maximising omega-3 fatty acids.

from beef were substantially higher in all cases due to the contribution of CH4 from enteric fermentation (Fig. 1). However, due to the exception of low-energy natural pasture based systems with low energy use, CF can only be used as an indicator of primary energy use for production systems with similar feeding strategies.

3.2. Land use In LCA, land use has traditionally been quantified simply as the area (m2 ) used for production of the product. Land use in livestock production is dominated by feed production and pasture. Across all

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300

Acidification pot. (kg SO2e/kg bone-free meat)

Chicken, r=0.70, P=0.081 Pork, r=-0.05, P=0.871

250

Land use (m2/kg bone-free meat)

577

Beef, r=0.56, P=0.075 200

150

100

50

Chicken, r=0.65, P=0.082 0,4

Pork, r=0.28, P=0.268 Beef, r=0.21, P=0.479

0,35 0,3 0,25 0,2 0,15 0,1 0,05 0

0 0

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Carbon footprint (kg CO2-e/kg bone-free meat)

0

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Carbon footprint (kg CO2-e/kg bone-free meat)

Fig. 2. Correlation between carbon footprint and land use in livestock production, r = 0.69, P < 0.05 across all species.

Fig. 3. Correlation between carbon footprint and acidification potential in livestock production, r = 0.78, P < 0.05 across all species.

species, a strong correlation (r = 0.69, P < 0.05) was found between CF and land use, explained by animals with higher FCR needing to eat more feed, production of which requires land and causes GHG emissions. No correlation was found for pork production, owing largely to three organic systems with large free-range areas, which used 32–37 m2 land compared with the average 19 m2 used for pork (Cederberg and Nilsson, 2004a). Excluding these systems gave a strong correlation (r = 0.71, P < 0.05), reflecting that for indoor pork production systems almost all land is used for feed production, which is also the process that contributes most to the CF. For chicken and beef, a strong correlation (P < 0.1) was found for the same reason as for pork (Fig. 2). In general, low FCR, low GHG emissions and low land use are correlated, since less feed requires less land. However, Strid Eriksson et al. (2005) studied the impact of feed choice in pig production and found that CF could be reduced by exchanging soybean meal for (1) peas and rapeseed cake, or (2) rapeseed meal and synthetic amino acids. In the former case the reduction in CF was at the expense of acidification, eutrophication and land use, since more land is needed to grow the lower-yielding pea crop, whereas in the latter case these impacts were simultaneously reduced. This difference demonstrates the importance of CF mitigation strategy. Decreasing CF by using alternative low-CF crops brings a risk of losing feed quality and thus increasing FCR, plus the obvious risk of using more agricultural land. Decreasing CF by using land-efficient feed additives (e.g. synthetic amino acids) improves FCR, thus leading to less use of land. In addition, GHG emissions from manure handling are unrelated to land use and can distort the relationship between land use and CF. GHG emissions from manure handling can be significant, especially in warmer climates (Wiedemann et al., 2010). In such cases the correlation between land use and CF is smaller, as large GHG emissions from manure can lead to high CF without affecting the acreage used.

contribution. Ammonia is released from manure in animal houses, pastures and during manure handling. One important parameter determining the potential for ammonia release is the amount of nitrogen excreted in the manure. A strong correlation between CF and acidification potential was found across species (Fig. 3). Beef production showed higher acidification values than chicken and pork production in 11 of 14 production systems due to the higher FCR of cattle, which results in more manure, and hence nitrogen behind the animal, potentially leading to ammonia emissions. Feed production is the most important source of GHG for monogastric animals and the second most important source for ruminants, so using more feed per kg meat produced gives a higher CF, which explains the strong correlation between CF and acidification potential across species. The benefit of low FCR in decreasing GHG emissions and acidification potential also applies to individual species. However, chicken was the only individual species for which a correlation was found here (r = 0.65, P < 0.1). Two areas of possible conflict between CF and acidification potential were identified; (1) manure management and (2) feeding strategy. The ammonia losses from manure depend on several factors, including temperature and pH. Ammonia gas is highly watersoluble, so liquid manure handling systems emit substantially less ammonia than solid manure handling. However, liquid/slurry storage stimulates CH4 production, due to anaerobe conditions. In temperate and warm regions CH4 evolution from liquid/slurry storage can be substantial, but in cooler climates it is much less (IPCC, 2006; Rodhe et al., 2009). Thus, management aimed at reducing ammonia emissions can lead to increased release of GHG. However, while ammonia emissions from manure are the dominant source of acidification, in cool climates GHG emissions from manure only comprise 10–20% of the total CF of meat for monogastric animals and even less for ruminants. A smaller increase in GHG for a less ammonia-emitting manure system will only lead to a small impact on total CF. Regarding feeding strategy, Strid Eriksson et al. (2005) found that when the type of feed, as opposed to the amount of a particular feed, was optimised for low GHG emissions, acidification potential rose. The low-CF feed (peas in this case) had a more unfavourable protein composition than soy meal, leading to more nitrogen being excreted and less used to build animal tissue.

3.3. Acidification The dominant source of acidifying emissions during animal production is ammonia emissions, while SO2 and NOx emissions from fossil fuel use in feed production and transport make a minor

Eutrophication pot. (kg PO43-/kg bone-free meat)

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scenario with a diet high in grain caused a similar CF value, but increased use of pesticides, compared with a diet with more forage. This is explained by the substantially lower use of pesticides in ley cultivation in Sweden compared with cereal cultivation in these systems. Thus, cattle breeding dominated by pasture or forage might lead to large GHG emissions due to slow growth, but low toxicity impact, in this case even lower toxicity impacts than for chicken and pork systems, which require the production of grain-based feed. The CF can therefore not generally be used as an indicator for toxicity across livestock species.

Chicken, r=0.68, P=0.064 0,35

Pork, r=0.43, P=0.052 Beef, r=0.79, P<0.05

0,3 0,25 0,2 0,15

3.6. Biodiversity

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Carbon footprint (kg CO2-e/kg bone-free meat) Fig. 4. Correlation between carbon footprint and eutrophication potential in livestock production, r = 0.88, P < 0.05 across all species.

3.4. Eutrophication In meat production, nitrate leaching from fields during feed production and ammonia release from manure handling dominate the emissions of eutrophying substances. Efficient handling of nitrogen through low FCR (leaving less nitrogen behind the animal that can potentially leach and requiring less land that can leach nitrate during feed production), manure handling techniques which reduce nitrogen losses, optimised fertiliser use, etc. help reduce both GHG emissions and the most important eutrophying substances. This relationship is evident in the literature as a strong correlation between CF and eutrophication potential (r = 0.88, P < 0.05) across all species. For all individual species, significant relationships (P < 0.1) were found (Fig. 4). Possible explanations for the lack of stronger correlations are methodological differences across studies, varying site-specific conditions determining the calculated leaching of nitrate and the conflict between manure handling systems with reduced ammonia emissions, which could lead to higher GHG (see Section 3.3). 3.5. Toxicity Although several methods for including toxicity impacts have been developed for use in LCA, none has so far achieved widespread use (Finnveden et al., 2009). The release of toxic substances into the environment from meat production is dominated by pesticide use during feed cultivation. The amount of pesticides used, expressed as kg active substance per kg meat, is therefore used as a rough indicator of the potential toxicity effects from agricultural production in some studies (Basset-Mens and Van der Werf, 2005; Cederberg and Darelius, 2000, 2001; Cederberg and Flysjö, 2004). When scenarios with organic production systems, which do not use pesticides or mineral fertilisers at all, were excluded, the correlation coefficient between CF and pesticide use was 0.89 (P < 0.05) across species. This relationship is explained by animals with lower FCR needing less feed. However, if pesticide use is increased to enhance feed yields and lower GHG emissions from feed production there is a risk of conflict, since pesticides contribute very little to total GHG emissions from feed, while higher yields can decrease the emissions per kg output considerably (Röös et al., 2011). For the conventional beef production systems described in Cederberg and Darelius (2000) the relationship was reversed; the

Although several quantitative methods for incorporating biodiversity into LCA have been suggested (Lenzen et al., 2007; Penman et al., 2010), none of the studies reviewed in this study did so. Some studies discussed effects on biodiversity qualitatively (Cederberg et al., 2009; Cederberg and Darelius, 2000, 2001; Cederberg and Flysjö, 2004; Cederberg and Nilsson, 2004b; Widheden et al., 2001). These discussions, in the context of land use, concluded that intensive pork and chicken production with low CF values requires less land for feed production, leaving more land for biodiversity, due to lower FCR. However, the land actually required is used for monoculture of cereals and soy, leading to higher pesticide use and less biodiversity. More extensive production systems, with higher CF values, use more land and thus less land is saved, but lead to higher biodiversity on the land that is used. The consequences for biodiversity of high land use in beef production are reported to range from severe, e.g. when cerrado or tropical forest is converted to pasture (Cederberg et al., 2009), to positive, e.g. when traditional semi-natural pastures, rich in threatened species, are conserved through pasture (Cederberg and Darelius, 2000; Cederberg and Nilsson, 2004b). 4. Summary of results and discussion The results indicate that a unilateral focus on CF for conventional production systems of monogastric species carries little risk of jeopardising other impact categories, since most impact in all categories is related to feed production (Table 2). Hence, if less feed is needed to produce meat, the environmental burden is less. However, pollution swapping may occur for mitigation strategies that do not increase the feed efficiency of the system (Strid Eriksson et al., 2005). For ruminants the situation is more complicated. Beef production relying on grazing on natural and semi-natural grasslands can be very energy-efficient and cause little toxicity impact, since small amounts of energy and pesticides are needed for pasture management, but still cause large GHG emissions due to CH4 emissions from enteric fermentation (Cederberg and Nilsson, 2004b; Cederberg et al., 2009). However in some cases, pasture-finished beef production can require more energy than feedlot-finished systems owing to high impacts from production of winter feed, high throughput volumes and high inputs associated with intensive management of the wheat pasture (Pelletier et al., 2010b). In addition, ruminant systems often require much land for feed production. Although CF proved to be correlated to land use, a simple measure of area (square metres) does not provide a complete picture. Since different types of agricultural land are suitable for different purposes, they should be valued differently. For example, if beef production is carried out on land not suitable for arable farming, or on land that might even require pasture for preserving biodiversity, this might be a land-efficient and environmentally sound way of producing meat, despite the land area use being high compared with a system in which feed is produced on arable land.

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Table 2 CF of meat as an indicator of wider environmental impact. ‘Yes’ = CF can function as an indicator of the environmental impact category, ‘No’ means that it cannot function as an indicator for that category.

All types of meat Meat from monogastric animals Meat from ruminants

Primary energy

Land use

Acidification

Eutrophication

Toxicity

Biodiversity

No Yes No

Yes Yesa Yes

Yes Yes, with restrictionb Yes, with restrictionb

Yes Yes Yes

No Yes, with restrictionc No

Unclear Unclear Unclear

a

Except for systems with free-range (see Section 3.2). Possible conflict across liquid/solid manure handling systems, especially in warm climates, and for feed exchange strategies with differing N efficiency in the animals (see Section 3.3). c Only across conventional systems with regulated pesticide use and not for organic systems that do not use synthetic pesticides or mineral fertilisers. b

Several authors argue that CO2 emissions due to land use change (LUC) should be included in the CF of meat. Negative LUC includes forest clearance to create pasture or cropland, or conversion of permanent grassland to cropland. This releases large amounts of CO2 to the atmosphere from carbon in trees and other vegetation, as well as from carbon bound in soils. Permanent grasslands can sequester carbon, leading to a positive LUC effect as CO2 is removed from the atmosphere (Soussana et al., 2007). Exactly how LUC for meat production should be calculated and allocated across the meat produced in an area requires further research. Initial assessments show that the CF of beef meat could increase by 2- to 25-fold if negative LUC effects such as deforestation were included (Cederberg et al., 2011; Nguyen et al., 2010). However, accounting for C sequestration in grasslands could decrease the CF of meat by 9–43% (Halberg et al., 2010; Nguyen et al., 2012; Pelletier et al., 2010b; Veysset et al., 2011). If inclusion of LUC in CF of meat becomes common practice, the applicability of CF as an indicator for other environmental impacts must be revaluated to include the effects of including LUC according to final calculation rules. A change in consumption from ruminant meat to chicken and pork meat would decrease the acidification and eutrophication potential per kg meat as calculated in LCA (Figs. 3 and 4), the actual acidification and eutrophication effects would not necessarily decrease, as the effects of acidifying and eutrophying substances on the local environment are highly site-specific. Modern pork and chicken production is often large-scale and concentrated geographically, leading to high local concentrations of ammonia and nitrate and potentially causing large local acidifying and eutrophying effects. In contrast, pasture efficiently captures nitrogen, so for beef production systems based on permanent pastures the eutrophying effect could be small even if the eutrophication potential per kg meat calculated in LCA is high (Veysset et al., 2011). The subject of biodiversity is large and complex, and it is unclear how it correlates to CF. Globally, a major threat to biodiversity is the conversion of forests to agricultural land, currently predominately taking place in Southeast Asia, Latin America and Africa (Millennium Ecosystem Assessment, 2005). There are several factors driving deforestation, but one important driver is livestock production, including the need for soy meal as animal feed and pastures for beef production (DeFries et al., 2010; Garnett, 2009; Steinfeld et al., 2006). Therefore, one can argue that producing food on less land will save land and hence biodiversity. For conventional meat production systems, less land means lower CF (see Section 3.2), so on a global scale producing meat and food/feed more CF efficiently would also save biodiversity. Although this line of reasoning is widely used (Green et al., 2005; Steinfeld et al., 2006), some authors claim that arable land should not be considered ‘spoiled’ from a biodiversity perspective and therefore open to ruthless treatment. Including leys in crop rotations, more extensive farming practices such as organic farming, silvopasture and other agroforestry systems can create an agricultural landscape that allows for high production and conserved biodiversity (Fairlie, 2010; Vandermeer and Perfecto, 2006). It is also questionable whether land will ultimately be saved by more intense production

in a globalised and expanding market economy demanding increasing amounts of land for food and energy production (Garnett, 2011). This study used existing LCA studies to examine the correlation between CF and other environmental impacts. Methodological differences between studies risk confounding such evaluations, so the correlation coefficients were also calculated within studies and across studies by the same author. These calculations confirmed the results obtained across all studies, e.g. for primary energy use all individual studies except those with extensive pasture-based systems showed strong correlations between CF and primary energy use (r = 0.9–1). Evaluation of the chicken, pork and beef systems described by Pelletier (2008) and Pelletier et al. (2010a,b) showed very strong correlations between CF and primary energy use, and between CF and eutrophication potential (r = 0.99, P < 0.05 in both cases). 5. Conclusions The carbon footprint of meat can act as an indicator for land use, acidification and eutrophication potential in most cases. For ruminant meat, however, there are apparent possible conflicts with the impact categories energy use and toxicity. To deepen the understanding of the applicability of CF as an indicator of the total environmental impact of meat production, a more sophisticated measure of land use is needed and the impacts on biodiversity and toxicity must be included. Greater knowledge of other highly relevant impact categories such as soil quality and water use, which are seldom included in LCA, is also necessary. However, the risk of damaging other environmental areas when acting on CF must be weighed against the risk of further neglecting to act on global warming by failing to exploit the current market momentum of carbon footprinting. References Bao, H.P., Multani, H.S., 2007. Energy-based life cycle assessment of industrial products. In: Proceedings of the 2007 IEEE International Symposium on Electronics & the Environment, Orlando, USA, May 7–10. Bare, J.C., Hofstetter, P., Pennington, D.W., Udo de Haes, H.A., 2000. Life cycle impact assessment workshop summary midpoints versus endpoints: the sacrifices and benefits. Int. J. LCA 5, 319–326. Basset-Mens, C., Van der Werf, H.M.G., 2005. Scenario-based environmental assessment of farming systems: the case of pig production in France. Agric. Ecosyst. Environ. 105, 127–144. Baumann, H., Tillman, A.-M., 2004. The Hitch Hiker’s Guide to LCA: An Orientation in Life Cycle Assessment Methodology and Application. Studentlitteratur, Lund, Sweden, ISBN-10 9144023642. Berger, M., Finkbeiner, M., 2011. Correlation analysis of life cycle impact assessment indicators measuring resource use. Int. J. Life Cycle Assess. 16, 74–81. Blonk, H., Lafleur, M., Van Zeijts, H., 1997. Towards an environmental infrastructure of the Dutch food industry: exploring the information conversion of five food commodities. Amsterdam, The Netherlands IVAM Environmental Research. University of Amsterdam. Bösch, M., Hellweg, S., Huijbregts, M., Frischknecht, R., 2007. Applying cumulative exergy demand (CExD) indicators to the ecoinvent database. Int. J. Life Cycle Assess. 12, 181–190. BSI, 2008. PAS 2050 Specification for the assessment of the life cycle greenhouse gas emissions of goods and services. BSI British Standards, London, U.K. Carlsson, B., Sonesson, U., Cederberg, C., Sund, V., 2009. Livscykelanalys (LCA) av svenskt ekologiskt griskött. (Life cycle assessment of Swedish organic pork

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