Advances in Animal Biosciences (2013), 4:s1, pp 2–8 & The Animal Consortium 2013 doi:10.1017/S2040470013000241
advances in animal biosciences
Making sense of methods to audit emissions – various audit methods to estimate dairy production carbon footprint D. O’Brien-, C. Grainger and L. Shalloo Livestock Systems Research Department, Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland
A dairy farm greenhouse gas (GHG) model was applied in this study to compare the Intergovernmental Panel on Climate Change (IPCC) method and the life cycle assessment (LCA) procedure, which are the principal methods for quantifying the carbon footprint of dairy production. The objectives of this paper were to compare the auditing methods in estimating the carbon footprint of grass and confinement dairy systems and to assess the methods in estimating the footprint of grass-based dairy farms varying in cow genetic potential, stocking rate and level of concentrate feeding. The input data used to operate the model was based on published research studies. The results of the study showed that the IPCC and LCA methods ranked the carbon footprint of dairy systems differently. For example, the IPCC method found that the carbon footprint of the confinement dairy system was 8% lower than the grass system, but the LCA results show that the confinement system increased the carbon footprint by 16%. The comparison of grass-based dairy systems, differing in cow genotype, stocking rate and concentrate fed per cow also showed that the methods did not agree on the ranking of dairy systems carbon footprint. The re-ranking of dairy systems carbon footprint occurred because the IPCC method excludes emissions associated with imported goods, for example, concentrate. Thus, it is incorrect to consider only components of the dairy system relevant for policy reporting such as that used by IPCC when estimating the carbon footprint of dairy produce. Instead, holistic approaches, such as LCA, which consider on and off-farm GHG emissions should be used. Therefore, reform of the present policy framework is required to enable quantification of the impact of mitigation strategies on global emissions. The evaluation of the carbon footprint from grass-based systems differing in cow genotype also demonstrated that selecting cows solely for milk production will increase the carbon footprint of grass-based dairy systems relative to cows selected on a combination of traits, because of reduced cow fertility and thus higher emissions from replacement heifers. Keywords: carbon footprint, grass, confinement
Introduction Internationally, dairy producers are faced with the challenge of reducing greenhouse gas (GHG) emissions, which are the primary cause of global warming (Fisher et al., 2007), while increasing production to satisfy growing demand (FAO, 2006). This issue has led to an increasing interest in reducing the carbon footprint (kg of GHG per unit of product) of dairy production. However, there are substantial differences associated with systems of dairy production and the methodologies used to calculate or audit their emissions. The most frequently applied method to assess the carbon footprint of dairy production is life cycle assessment (LCA). LCA is a holistic systems approach that aims to quantify the potential environmental impacts, for example, GHG emissions generated throughout a product’s life cycle, from raw-material acquisition through production, use, recycling and final disposal (ISO, 2006). Thus, for primary dairy production, LCA -
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E-mail: donal.o’
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quantifies GHG emissions from all processes associated with the dairy farm up to the point milk is sold from the farm. The general framework and requirements of LCA are internationally standardised (ISO, 2006). In addition, specific guidelines based on international standards have recently been developed for applying LCA to assess the carbon footprint of dairy production (International Dairy Federation (IDF), 2010). However, LCA is not the recognised method for reporting the dairy sectors contribution to national GHG emissions. The standard method for reporting GHG emissions is the Intergovernmental Panel on Climate Change (IPCC) guidelines (IPCC, 1996 and 2006). The IPCC method, unlike LCA, quantifies GHG emissions using a national sector-based approach (Schils et al., 2005). The approach estimates emissions from the production and consumption of goods within defined national boundaries and emissions from the production of goods exported from a nation, but does not consider emissions from the production of imported goods (Peters, 2008). The objectives of this paper were to compare the IPCC and LCA auditing methods to estimate the carbon footprint of
Making sense of methods to audit emissions grass and confinement dairy systems and evaluate the methodologies in estimating the footprint of pastoral dairy farms varying in cow genetic potential, stocking rate and level of concentrate feeding. Material and methods
Dairy farm systems This study first evaluated the carbon footprint of a grassbased and a confinement dairy system (Table 1) based on a research trial conducted at Moorepark dairy research centre, Fermoy, Co Cork, Ireland. The design and results of the research trial have previously reported by Olmos et al. (2009). Briefly, spring-calving Holstein–Friesian cows were blocked based on genetic merit, parity, expected calving date, body condition score and predicted milk yield and assigned randomly from within pairs to either the grassbased or confinement system. The aim of the grass-based system was to maximise the use of grazed grass in the feed budget of lactating dairy cows by targeting calving to commence in early spring (14 February) which is generally the onset of the grass-growing season. Grass silage was harvested on-farm when grass growth exceeded feed demand. The system was self-sufficient for forage. The fertiliser input (N) was 260 kg/ha. Grass silage and concentrate were offered during periods when pasture growth was unable to meet the nutritional requirements of the herd. The annual concentrate offered was 370 kg of dry matter (DM) per cow. Concentrate was mainly offered in early and late lactation. Within the confinement system, cows were housed full time and fed a total mixed ration (TMR). However, replacement
animals were only indoors during the winter period and grazed pasture during the growing season. Grass silage was harvested on-farm and any other feed used was imported. Holstein–Friesian cows were offered specific TMR diets during the dry period and lactation. The TMR offered was comparable in nutrient composition to TMR fed in other studies (Grainger et al., 2009), apart from DM content, which was lower than other studies (Bargo et al., 2002). The carbon footprint of six contrasting pastoral dairy production systems, differing in genetic potential and type of pasture-based feed system were also assessed in this study (Table 2). The design of the study and production and reproduction data used in the analysis was previously reported by Horan et al. (2004 and 2005). In short, two Holstein–Friesian genotypes were compared: high economic breeding index potential (HEBI) and low EBI potential (LEBI). The HEBI genotypes represented a breeding program where selection was based on improving a number of traits, for example, milk production, fertility and durability. The LEBI represented a breeding program where animals were selected solely for milk production. Cow genotypes were blocked and randomised across three grass-based feed systems; high grass allowance system (HG, control); high concentrate supplementation system (HC) and a high stocking rate system (HS). The HG system had an overall stocking rate of 2.47 cow/ha, N fertiliser input of 290 kg of N/ha and received 325 kg of DM concentrate per cow in early lactation. The HC system had a similar overall stocking rate and N fertiliser input as the HG system, but 1445 kg of DM concentrate was offered per cow per lactation, with a greater proportion in early spring. The HS system had a similar concentrate and
Table 1 Technical description of the seasonal grass-based dairy system (Grass) and the confinement dairy system (confinement)a Item Farm size Milking cows Total FPCMb production Delivered FPCM Milk fat Milk protein Length of lactation Replacement rate Cull rate Average BW Stocking rate Concentrate Grass Grass silage Maize silage Barley straw N fertilizer Annual manure exported
Unit
Grass
Confinement
ha # kg FPCM/cow per year kg FPCM/cow per year % % Days % % kg LUc/ha kg DM/cow per year kg DM/cow per year kg DM/cow per year kg DM/cow per year kg DM/cow per year kg N/ha per year %
40 90 6639 6538 4.31 3.49 305 18 18 567 2.54 370 4093 1063 0 0 260 0
20 90 8040 7942 4.15 3.37 305 18 18 609 5.10 2865 0 1497 1746 499 85 23
FPCM 5 fat- and protein-corrected milk; DM 5 dry matter. a Data based on research of Patton et al. (2009) and Olmos et al. (2009). b FPCM 5 (0.337 1 0.116 3 %fat 1 0.06 3 %protein) 3 kg milk (Thomassen and de Boer, 2005). c LU 5 livestock unit equivalent to 1 dairy cow.
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O’Brien, Grainger and Shalloo Table 2 Milk production, BW and reproductive performance of two Holstein–Friesian genotypes (LEBI; HEBI) within the HG, HS and HC feed systemsa HG
HS
HC
Item
LEBI
HEBI
LEBI
HEBI
NZ
HP
No. of lactation records Milk production Milk (kg/cow) Fat (g/kg) Protein (g/kg) Lactose (g/kg) Average BW (kg) Reproductionb Gestation length (days) 42-day in-calf rate (%) Overall pregnancy rate (%) Total services per cow
65
65
65
65
65
65
6748 40.6 34.5 46.3 558
6335 43.9 36.5 46.7 552
6531 41 34.8 46.6 551
6255 45.6 36.1 46.6 542
6597 44.5 37.2 47.5 541
7724 40 35.4 47.7 564
284 54 74 2.07
278 74 93 1.61
284 54 74 2.07
278 74 93 1.61
278 74 93 1.61
284 54 74 2.07
BW 5 body weight; LEBI 5 low economic breeding index potential; HEBI 5 high economic breeding index potential; HG 5 high grass allowance; HC 5 high concentrate; HS 5 high stocking rate. a McCarthy et al. (2007). b Breeding was initiated at, on average, 60 days in milk.
N input as the HG system, but had an overall stocking rate of 2.74 cows/ha.
GHG modelling GHG emissions were calculated using a dairy farm GHG model (O’Brien et al., 2011). The model estimates the dominant GHG emissions from dairy production: carbon dioxide (CO2), nitrous oxide (N2O), and CH4. The GHG model operates in combination with the Moorepark Dairy System Model (MDSM; Shalloo et al., 2004), which defines key parameters (e.g. feed budgets) required for the GHG model to quantify emissions. The model uses two methods, LCA and the IPCC method, to quantify GHG emissions from dairy systems. The subsequent sections summarise how the LCA and IPCC methodologies were applied in the GHG model. LCA methodology. The LCA method was applied to quantify emissions from all processes associated with dairy production up to the point milk is sold from the farm. The approach was implemented in the GHG model by combining input data from the MDSM with specific LCA GHG emission factors in Microsoft Excel. The emission factors used in this study have previously reported by O’Brien et al. (2011 and 2012). In brief, the LCA emission factors for on-farm sources were obtained from the agricultural section of the Irish GHG national inventory (Duffy et al., 2011). On-farm sources of CO2 were estimated using emission factors from the IPCC (2006) guidelines and data from Nemecek and Ka¨gi (2007). The LCA emission factors for off-farm GHG sources (GHG emissions from the production and supply of purchased farm inputs, e.g. synthetic fertiliser) were obtained mainly from literature and databases included with the LCA software, SimaPro (Pre´ Consultants, 2010). The LCA approach did not consider GHG removals, because farm sinks were assumed to be in equilibrium. 4
The LCA method allocates GHG emissions between multifunctional processes (O’Brien et al., 2011), including the production of concentrate co-products, such as barley grain and straw, and the production of milk and meat on-farm. The procedures used to allocate GHG emissions between co-products were based on recently published LCA guidelines (IDF, 2010). For concentrate co-products, GHG emissions were allocated based on their relative economic values using data from Nemecek and Ka¨gi (2007), Central Statistics Office (CSO, 2011) and Ecoinvent (2010). For, milk and meat, GHG emissions were allocated based on the physiological feed requirements of the cow to produce milk and meat. The following equation from the (IDF, 2010) was used to calculate the allocation factor for milk and meat: AF ¼ 1 5:7717 Mmeat =Mmilk ð1Þ where AF is the allocation factor for milk, Mmeat the sum of BW of all animals including bull calves and culled mature animals and Mmilk the sum of mass of milk sold, corrected to 4% fat and 3.3% protein. GHG emissions calculated using LCA were estimated in terms of their 100-year global warming potentials (CO2 equivalents; CO2-eq), which on a weight basis relative to CO2 was set to a factor of 25 for 1 kg of CH4 and 298 for 1 kg of N2O (IPCC, 2007). The main outputs of the LCA approach are a static account of dairy systems annual on-farm and total (on and off-farm) GHG emission in CO2-eq and the carbon footprint of dairy production expressed as kg of CO2-eq per kg of milk fat and protein (MS).
IPCC methodology. The sources of GHG attributed to dairy farming using the IPCC method are enteric fermentation, manure management and agricultural soils (N2O emissions from synthetic and organic fertiliser use, manure excreted by
Making sense of methods to audit emissions
Sensitivity analysis. GHG emissions reported as part of a countries agricultural sector are only relevant for primary dairy production using the IPCC method, but national emissions from dairy production are also reported in the energy, industrial and waste sectors (Crosson et al., 2011). Thus, to completely assess the effect of applying different GHG auditing methods, the IPCC method was also applied to consider all national sources of GHG emissions associated with dairy production. This was achieved using emission factors from the relevant sectors of the Irish GHG national inventory (Duffy et al., 2011) and data from the research study of Olmos et al. (2009) previously described, which compared a grass-based and a confinement dairy system. Results
Carbon footprint of grass-based and confinement dairy systems The comparison of the grass-based and confinement dairy systems showed that the carbon footprint was 8% lower for the confinement system using the IPCC method (Figure 1), but the LCA comparison showed that the carbon footprint of the confinement system was 16% higher than the grassbased system (Figure 2). The LCA results showed that the main contributors to total (on and off-farm) GHG emissions in the grass-based system were CH4 from enteric fermentation (44%), N2O emission from excreta deposited during grazing (13%), GHG emissions associated with the production of fertiliser (10%) and N2O from synthetic fertiliser application (9%). Enteric fermentation was the greatest contributor to on-farm GHG emissions (54%) in the grassbased system followed by N2O from excreta deposited during grazing (16%), and N2O from fertiliser application (11%). Off-farm GHG emissions consisted mainly of GHG
kg CO2-eq/kg milk solids
12
Fertiliser Manure Enteric fermentation
8
4
0 Grass
Confinement
Figure 1 The carbon footprint of a grass-based and a confinement dairy system calculated using the Intergovernmental Panel on Climate Change (IPCC) method.
20 kg CO2-eq/kg milk solids
grazing cattle and N2O from ammonia re-deposition and nitrate leaching). The IPCC method was implemented in the GHG model using IPCC emission factors from the agriculture section of the Irish GHG national inventory (Duffy et al., 2011) and the parameters generated by the MDSM. The IPCC emission factors are categorised into different tiers, with each successive tier having an increased level of detail and accuracy. The Irish GHG inventory uses IPCC emission factors from different tiers to calculate emissions from dairy production. Thus, the IPCC method in the GHG model uses tier-1 emission factors to calculate N2O from agricultural soils and tier-2 emission factors to estimate CH4 from enteric fermentation and manure management (Duffy et al., 2011). GHG emissions quantified using the IPCC method were estimated in terms of their 100-year global warming potential using the same procedure as described for the LCA method. The IDF (2010) physical causality approach was used to allocate GHG emission between milk and meat. The outputs of the IPCC approach are a static account of dairy systems annual GHG emission in CO2-eq and the carbon footprint of dairy production expressed as kg of CO2-eq per kg of MS.
Other inputs Concentrate Fertiliser Manure Enteric fermentation
16 12 8 4 0 Grass
Confinement
Figure 2 The carbon footprint of a grass-based and a confinement dairy system calculated using life cycle assessment (LCA).
emissions from the production of fertiliser (57%) and concentrate feed (26%). The main contributors to total GHG emissions in the confinement system, based on the LCA approach, were CH4 from enteric fermentation (36%), GHG emissions from the production of concentrate (25%) and CH4 from manure storage (20%; Figure 1). On-farm GHG emissions in the confinement system were mainly from enteric CH4 (57%). Methane from manure storage was the next largest source of on-farm GHG emissions (31%). The production of concentrate feed was the main source of off-farm GHG emissions (69%) followed by CO2 from electricity, fuel and lime production (15%). The main sources of GHG emissions from concentrate production were CO2 from the conversion of rainforest to agricultural land (35%), GHG emissions from energy and fertiliser production (17%), CO2 from agricultural field operations and transport of feed (15%) and N2O from fertiliser application (12%). The dominant sources of GHG emissions in the grassbased system according to the IPCC method were CH4 from enteric fermentation (55%) and manure storage (9%), N2O from manure excreted by grazing cattle (16%) and N2O from synthetic fertiliser application (11%; Figure 2). Methane from enteric fermentation (58%) and N2O and CH4 from manure storage (33%) were the main contributors to GHG emissions in the confinement system (Figure 2). 5
O’Brien, Grainger and Shalloo Table 3 The IPCC method and LCA quantified carbon footprint of two Holstein–Friesian genotypes (LEBI; HEBI) within the HG, HS and HC feed systems HG Carbon footprint kg CO2-eqa/kg MSb
HS
HC
Method LEBI HEBI LEBI HEBI LEBI HEBI IPCC LCA
11.9 11.3 12.1 11.2 10.9 10.7 13.3 12.6 13.4 12.3 12.7 12.5
IPCC 5 Intergovernmental Panel on Climate Change; LCA 5 life cycle assessment; LEBI 5 low economic breeding index; HEBI 5 high economic breeding index; HG 5 high grass allowance; HS 5 high stocking rate; HC 5 high concentrate. a CO2-eq 5 CO2 equivalent. b Milk solids 5 kg of milk fat plus protein.
Effect of genotype, stocking rate and concentrate feeding on grass-based dairy systems carbon footprint The IPCC approach found that on average, the carbon footprint for the HG and HS feed systems was similar, but the HC feed system had a lower carbon footprint (Table 3). In all feed systems, the HEBI genotype produced a lower carbon footprint than the LEBI genotype. For the LEBI genotype, the lowest carbon footprint was produced in the HC feed system and highest in the HS feed system. The HEBI genotype achieved their lowest carbon footprint in the HC feed system and highest carbon footprint in the HG feed system. The LCA method showed that on average the HC feed system produced the lowest carbon footprint followed by the HS and HG feed systems. The HEBI genotype produced a lower carbon footprint than the LEBI genotype in all feed systems. For the LEBI genotype, the lowest carbon footprint was produced in the HC feed system and highest in the HS feed system. The HEBI genotype achieved their lowest carbon footprint in the HS feed system and highest carbon footprint in the HG system. The largest source of GHG emissions for all grass-based systems regardless of method was enteric fermentation (50% to 56%). The LCA method found that the next most significant sources of emissions were from fertiliser production and application, manure excreted by grazing cattle and manure storage and spreading. The LCA approach also found concentrate production was an important source of emissions (10%) in the HC feed system. The IPCC approach found the largest sources of emissions excluding enteric fermentation for all dairy systems were manure deposited on pasture, manure storage and fertiliser use. The LCA method showed that the proportion of GHG emissions from off-farm sources increased by 4% moving from the HG and HS feed system to the HC feed system and that genotype had little effect. The IPCC approach found neither feed system nor genotype had an effect on the proportion of GHG emissions from off-farm sources. Sensitivity analysis of IPCC method The application of the IPCC method to quantify all national sources of GHG emissions associated with dairy production 6
increased the carbon footprint of the grass-based system reported in Figure 1 by 8% to 12.75 kg CO2-eq/kg MS and increased the carbon footprint of the confinement system in Figure 2 by 16.5% to 12.71 kg CO2-eq/kg MS. The main cause of the increase in emissions was the inclusion of national emissions from electricity generation and fuel use. Energy usage per kg of MS was greater for the confinement system compared with the grass-based, which explains the greater increase in emissions for the confinement system. Nevertheless, the ranking of the carbon footprint of dairy systems did not change by applying the IPCC method to consider all national sources of GHG emissions. Discussion Previous studies that have analysed the carbon footprint of dairy systems have generally applied environmental system analysis approaches such as LCA, for example Arsenault et al. (2009) and Rotz et al. (2010). The IPCC method of quantifying agricultural GHG emissions has rarely been used to assess emissions from these different systems of dairy production (Schils et al., 2006; Browne et al., 2011). Thus, the application of the LCA and IPCC methods in this study provides a unique opportunity to determine if these methods agree on their predictions of the carbon footprint of contrasting dairy production systems.
Effect of grass and confinement dairy systems on GHG emissions In agreement with previous studies, the LCA and IPCC methods agreed that CH4 from enteric fermentation was the main cause of on-farm GHG emissions for seasonal grass-based and confinement dairy systems (Flysjo¨ et al., 2011; O’Brien et al., 2011). Both methods also agreed with previous studies that the other main sources of on-farm GHG emissions for the grass-based system were N2O from manure excreted by grazing cattle and N2O emission from fertiliser application (Schils et al., 2006; Basset-Mens et al., 2009). In addition, the IPCC and LCA methods supported previous analysis, that on-farm GHG emissions in the confinement system were emitted mainly from manure storage when enteric CH4 emission was omitted (Arsenault et al., 2009; Rotz et al., 2010). Thus, excluding enteric CH4, the main sources of on-farm GHG emissions differed between dairy systems. The contribution of on-farm sources to GHG emissions varied between systems, because of differences in the length of the grazing season and the proportion of feed produced on-farm. Therefore, specific approaches should be developed to achieve the largest reduction in on-farm GHG emissions from grass-based and confinement dairy systems. Numerous studies have reported that feed intake is the principal driver of enteric CH4 emissions from cattle (Hegarty et al., 2007; O’Neill et al., 2011). Furthermore, feed intake is also a key factor required to quantify CH4 and N2O emissions from manure (Basset-Mens et al., 2009). Therefore, both methods showed, that on-farm GHG emissions per livestock unit (LU) was greater (9%) for the confinement system than
Making sense of methods to audit emissions the grass-based system, because grass-fed cows produced 18% less milk per LU and thus consumed 16% less feed. Nevertheless, per kg of MS, both methods observed that on-farm GHG emissions were greater for the grass-based system than for the confinement system. This was because TMR-fed Holstein–Friesian cows in the confinement system produced about 2.4 times more milk per on-farm ha and 21% more milk per LU than forage-fed cows. The LCA method also quantifies off-farm GHG emissions related to dairy systems. Congruous with previous studies, CO2 and N2O emissions from the cultivation and transport of imported feed and GHG emissions from the manufacture of synthetic N fertiliser were the main contributors to off-farm GHG emissions (Thomassen et al., 2008; Van der Werf et al., 2009). Off-farm GHG emissions from the grass-based system were less than half of those from the confinement system, because the confinement system consumed more imported feedstuffs than the grass-based system. As a consequence of the large difference between dairy systems off-farm GHG emissions, the LCA approach found, that the increase in milk yield per LU moving from the grass-based system to the confinement system (21%) was less than the increase in total GHG emission per LU (38%). Thus, similar to previous studies, the carbon footprint was higher for the confinement system than the grass-based system (Rotz et al., 2009; Flysjo¨ et al., 2011).
Effect of methodology on dairy systems carbon footprint The IPCC method, unlike the LCA approach, does not attribute GHG emissions from the production of farm imports to primary dairy production, because these emissions are outside national boundaries or attributed to another sector, such as energy (Schils et al., 2005). Excluding off-farm GHG sources associated with dairy production would not matter if farming systems always ranked the same whether the IPCC or LCA methods were used. For example, our study found that excluding off-farm GHG sources had no effect on the ranking of dairy systems carbon footprint when assessed per LU. However, when the carbon footprint was expressed per kg of MS, the IPCC method indicated that the grass system had higher emissions, but the LCA approach showed that the grass system had lower emissions. The different ranking of dairy systems carbon footprint using the IPCC and LCA method agrees with previous work and can be explained by the exclusion of emissions associated with upstream production chains (e.g. imported feedstuffs) using the IPCC method (O’Brien et al., 2011 and 2012). The disagreement between the IPCC and LCA methods highlights that it is incorrect to only consider certain on-farm components of the dairy system relevant for policy reporting (Schils et al., 2006; O’Brien et al., 2011 and 2012). Instead, whole-farm approaches such as LCA give a more realistic assessment of GHG emissions from differing dairy production systems. Holistic approaches increase emissions attributed to dairy systems relative to approaches that consider only part of the production system, but are more likely to ensure that GHG emissions are reduced and not transferred to another part of the production system (carbon leakage). The shortfall of the
IPCC method for assessing GHG emissions from different dairy systems does not mean the approach should no longer be used to report national GHG emissions (Schils et al., 2006; Crosson et al., 2011). However, to achieve a reduction in GHG emissions, reform of the IPCC method is needed given the problems identified in our study and previous studies (Peters, 2008; Peters and Hertwich, 2008). Consequently, it has been suggested that countries should quantify GHG emissions associated with national consumption (production and imports minus exports) in addition to estimating GHG emissions produced within a nation (Peters, 2008; Peters and Hertwich, 2008). This approach would overcome some of the problems with the present IPCC method, such as carbon leakage and credit dairy practices that reduce emissions in other sectors or nations. However, consumption-based national GHG accounting would require decisions to extend outside geopolitical boundaries, which is a significant barrier to the implementation of the approach (Peters, 2008). Furthermore, consumption-based national GHG inventories require more assumptions and data, thereby increasing uncertainty and reducing the accuracy of GHG national inventories.
Influence of genotype on grass-based dairy systems carbon footprint Regardless of methodology selecting animals solely for milk production caused the carbon footprint of dairy production to increase through a rise in emissions from non-productive animals and a consequent decrease in farm milk. The increases observed in emissions per kg of MS for animals with higher genetic potential for milk are similar to the findings of Lovett et al. (2006). Therefore, based on these findings, continuing to increase the genetic merit of Holstein–Friesian cows for milk yield alone will cause the carbon footprint of dairy production to increase. However, if the fertility of Holstein–Friesians cows with high genetic merit for milk yield (LEBI genotype) improved (similar to HEBI genotype), this could lead to substantial reductions in the carbon footprint of dairy products. In Ireland, selection indexes for breeding have changed from selecting solely for milk performance to selecting based on combination of milk production, fertility and health traits. It is anticipated, based on these result that this change will contribute to reducing the carbon footprint of dairy production. Conclusion This study highlights that it is incorrect to consider only components of the dairy system relevant for policy reporting such as that used by IPCC when estimating the carbon footprint of dairy produce. Instead, LCA methods which consider on and off-farm GHG emissions should be used. Thus, reform of the present policy framework is needed to enable quantification of the impact of mitigation strategies on global emissions. The findings of our analysis also show that selecting dairy cows only for increased milk production will increase the carbon footprint of dairy production relative to cows selected on a combination of production and reproductive traits. 7
O’Brien, Grainger and Shalloo Further information The Dairy Solutions Symposium is a biennial event that covers a wide variety of themes and topics of relevance and importance to the dairy industry. The aim is to provide high level, up-to-date information and research to dairy professionals, technologists and scientists. In 2012, the theme addressed the biggest challenge facing all those involved in dairy production: optimising production efficiency while lowering environmental impact. For more information, please visit www.dairycowsolutions.com or contact
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