Animal board invited review: Specialising and intensifying cattle production for better efficiency and less global warming: contrasting results for milk and meat co-production at different scales

Cattle are the world’s largest consumers of plant biomass. Digestion of this biomass by ruminants generates high methane emissions that affect global warming. In the last decades, the specialisation of cattle breeds and livestock systems towards either milk or meat has increased the milk production of dairy cows and the carcass weight of slaughtered cattle. At the animal level and farm level, improved animal performance decreases feed use and greenhouse gas emissions per kg of milk or carcass weight, mainly through a dilution of maintenance requirements per unit of product. However, increasing milk production per dairy cow reduces meat production from the dairy sector, as there are fewer dairy cows. More beef cows are then required if one wants to maintain the same meat production level at country scale. Meat produced from the dairy herd has a better feed efficiency (less feed required per kg of carcass weight) and emits less methane than the meat produced by the cow-calf systems, because the intake of lactating cows is largely for milk production and marginally for meat, whereas the intake of beef cows is entirely for meat. Consequently, the benefits of breed specialisation assessed at the animal level and farm level may not hold when milk and meat productions are considered together. Any change in the milk-to-meat production ratio at the country level affects the numbers of beef cows required to produce meat. At the world scale, a broad diversity in feed efficiencies of cattle products is observed. Where both productions of milk per dairy cow and meat per head of cattle are low, the relationship between milk and meat efficiencies is positive. Improved management practices (feed, reproduction, health) increase the feed efficiency of both products. Where milk and meat productivities are high, a trade-off between feed efficiencies of milk and meat can be observed in relation to the share of meat produced in either the dairy sector or the beef sector. As a result, in developing countries, increasing productivities of both dairy and beef cattle herds will increase milk and meat efficiencies, reduce land use and decrease methane emissions. In other regions of the world, increasing meat production from young animals produced by dairy cows is probably a better option to reduce feed use for an unchanged milk-to-meat production ratio.     

Evaluation of the effect of accounting method,IPCC v. LCA,on grass-based and confinement dairy systems’ greenhouse gas emissions

Life cycle assessment (LCA) and the Intergovernmental Panel on Climate Change (IPCC) guideline methodology, which are the principal greenhouse gas (GHG) quantification methods, were evaluated in this study using a dairy farm GHG model. The model was applied to estimate GHG emissions from two contrasting dairy systems: a seasonal calving pasture-based dairy farm and a total confinement dairy system. Data used to quantify emissions from these systems originated from a research study carried out over a 1-year period in Ireland. The genetic merit of cows modelled was similar for both systems. Total mixed ration was fed in the Confinement system, whereas grazed grass was mainly fed in the grass-based system. GHG emissions from these systems were quantified per unit of product and area. The results of both methods showed that the dairy system that emitted the lowest GHG emissions per unit area did not necessarily emit the lowest GHG emissions possible for a given level of product. Consequently, a recommendation from this study is that GHG emissions be evaluated per unit of product given the growing affluent human population and increasing demand for dairy products. The IPCC and LCA methods ranked dairy systems’ GHG emissions differently. For instance, the IPCC method quantified that the Confinement system reduced GHG emissions per unit of product by 8% compared with the grass-based system, but the LCA approach calculated that the Confinement system increased emissions by 16% when off-farm emissions associated with primary dairy production were included. Thus, GHG emissions should be quantified using approaches that quantify the total GHG emissions associated with the production system, so as to determine whether the dairy system was causing emissions displacement. The IPCC and LCA methods were also used in this study to simulate, through a dairy farm GHG model, what effect management changes within both production systems have on GHG emissions. The findings suggest that single changes have a small mitigating effect on GHG emissions (<5%), except for strategies used to control emissions from manure storage in the Confinement system (14% to 24%). However, when several management strategies were combined, GHG emissions per unit of product could be reduced significantly (15% to 30%). The LCA method was identified as the preferred approach to assess the effect of management changes on GHG emissions, but the analysis indicated that further standardisation of the approach is needed given the sensitivity of the approach to allocation decisions regarding milk and meat.     

Variation in carbon footprint of milk due to management differences between Swedish dairy farms

To identify mitigation options to reduce greenhouse gas (GHG) emissions from milk production (i.e. the carbon footprint (CF) of milk), this study examined the variation in GHG emissions among dairy farms using data from previous CF studies on Swedish milk. Variations between farms in these production data, which were found to have a strong influence on milk CF, were obtained from existing databases of 1051 dairy farms in Sweden in 2005. Monte Carlo (MC) analysis was used to analyse the impact of variations in seven important parameters on milk CF concerning milk yield (energy-corrected milk (ECM) produced and delivered), feed dry matter intake (DMI), enteric CH4 emissions, N content in feed DMI, N-fertiliser rate and diesel used on farm. The largest between-farm variations among the analysed production data were N-fertiliser rate (kg/ha) and diesel used (l/ha) on farm (CV = 31% to 38%). For the parameters concerning milk yield and feed DMI, the CV was approximately 11% and 8%, respectively. The smallest variation in production data was found for N content in feed DMI. According to the MC analysis, these variations in production data led to a variation in milk CF of between 0.94 and 1.33 kg CO2 equivalents (CO2e)/kg ECM, with an average value of 1.13 kg CO2e/kg ECM. We consider that this variation of ±17%, which was found to be based on the used farm data, would be even greater if all Swedish dairy farms were included, as the sample of farms in this study was not totally unbiased. The variation identified in milk CF indicates that a potential exists to reduce GHG emissions from milk production on both the national and farm levels through changes in management. As milk yield and feed DMI are two of the most influential parameters for milk CF, feed conversion efficiency (i.e. units ECM produced/unit DMI) can be used as a rough key performance indicator for predicting CF reductions. However, it must be borne in mind that feeds have different CF due to where and how they are produced.     

System expansion and allocation in life cycle assessment of milk and beef production

Background, Goal and Scope  System expansion is a method used to avoid co-product allocation. Up to this point in time it has seldom been used in LCA studies of food products, although food production systems often are characterised by closely interlinked sub-systems. One of the most important allocation problems that occurs in LCAs of agricultural products is the question of how to handle the co-product beef from milk production since almost half of the beef production in the EU is derived from co-products from the dairy sector. The purpose of this paper is to compare different methods of handling co-products when dividing the environmental burden of the milk production system between milk and the co-products meat and surplus calves. Main Features  This article presents results from an LCA of organic milk production in which different methods of handling the co-products are examined. The comparison of different methods of co-product handling is based on a Swedish LCA case study of milk production where economic allocation between milk and meat was initially used. Allocation of the co-products meat and surplus calves was avoided by expanding the milk system. LCA data were collected from another case study where the alternative way of producing meat was analysed, i.e. using a beef cow that produces one calf per annum to be raised for one and a half year. The LCA of beef production was included in the milk system. A discussion is conducted focussing on the importance of modelling and analysing milk and beef production in an integrated way when foreseeing and planning the environmental consequences of manipulating milk and beef production systems. Results  This study shows that economic allocation between milk and beef favours the product beef. When system expansion is performed, the environmental benefits of milk production due to its co-products of surplus calves and meat become obvious. This is especially connected to the impact categories that describe the potential environmental burden of biogenic emissions such as methane and ammonia and nitrogen losses due to land use and its fertilising. The reason for this is that beef production in combination with milk can be carried out with fewer animals than in sole beef production systems. Conclusion, Recommendation and Perspective  Milk and beef production systems are closely connected. Changes in milk production systems will cause alterations in beef production systems. It is concluded that in prospective LCA studies, system expansion should be performed to obtain adequate information of the environmental consequences of manipulating production systems that are interlinked to each other.     

Climate mitigation by dairy intensification depends on intensive use of spared grassland

Milk and beef production cause 9% of global greenhouse gas (GHG) emissions. Previous life cycle assessment (LCA) studies have shown that dairy intensification reduces the carbon footprint of milk by increasing animal productivity and feed conversion efficiency. None of these studies simultaneously evaluated indirect GHG effects incurred via teleconnections with expansion of feed crop production and replacement suckler‐beef production. We applied consequential LCA to incorporate these effects into GHG mitigation calculations for intensification scenarios among grazing‐based dairy farms in an industrialized country (UK), in which milk production shifts from average to intensive farm typologies, involving higher milk yields per cow and more maize and concentrate feed in cattle diets. Attributional LCA indicated a reduction of up to 0.10 kg CO₂e kg^?1 milk following intensification, reflecting improved feed conversion efficiency. However, consequential LCA indicated that land use change associated with increased demand for maize and concentrate feed, plus additional suckler‐beef production to replace reduced dairy‐beef output, significantly increased GHG emissions following intensification. International displacement of replacement suckler‐beef production to the “global beef frontier” in Brazil resulted in small GHG savings for the UK GHG inventory, but contributed to a net increase in international GHG emissions equivalent to 0.63 kg CO₂e kg^?1 milk. Use of spared dairy grassland for intensive beef production can lead to net GHG mitigation by replacing extensive beef production, enabling afforestation on larger areas of lower quality grassland, or by avoiding expansion of international (Brazilian) beef production. We recommend that LCA boundaries are expanded when evaluating livestock intensification pathways, to avoid potentially misleading conclusions being drawn from “snapshot” carbon footprints. We conclude that dairy intensification in industrialized countries can lead to significant international carbon leakage, and only achieves GHG mitigation when spared dairy grassland is used to intensify beef production, freeing up larger areas for afforestation.     

Sensitivity of the carbon footprint of New Zealand milk to greenhouse gas metrics

Milk production is responsible for emitting a range of greenhouse gases (GHGs), mainly carbon dioxide (CO₂), nitrous oxide (N₂O) and methane (CH₄). In Life Cycle Assessments (LCA), the Global Warming Potential with a time horizon of 100 years (GWP100) is used almost universally to aggregate emissions of individual gases into so-called CO₂-equivalent emissions that are used to calculate the overall carbon footprint of milk production. However, there is growing awareness that, depending on the purpose of the LCA, metrics other than GWP100 could be justified and some would give a very different weighting for the short-lived gas CH₄ relative to the long-lived gases CO₂ and N₂O when calculating the carbon footprint. Pastoral dairy production systems at different levels of intensification differ in the balance of short- and long-lived GHGs associated with on- and off-farm emissions. Differences in the carbon footprint of different production systems could therefore be highly sensitive to the choice of GHG metric. Here we explore the extent to which alternative GHG metric choices would alter the carbon footprint of New Zealand milk production at different levels of intensification at national, regional and individual farm scales and compared to the carbon footprint of milk of selected European countries. We find that the ranking of different production systems and individual farms in terms of their carbon footprint is relatively robust against the choice of GHG metric, despite significant differences in their utilisation of pastures versus supplementary off-farm feed, fertiliser use and energy consumption at various stages of farm operations. However, there are instances where alternative GHG metric choices would fundamentally change the conclusions of LCA of different production systems, including whether a move towards higher or lower input systems would increase or decrease the average carbon footprint of milk production in New Zealand. Greater transparency about the implications of alternative GHG metrics for LCA, and the often inadvertent and implicit value judgements embedded in these metrics, would help ensure that policy decisions and consumer choices based on LCA indeed deliver the climate outcomes intended by end-users.     

Does increasing milk yield per cow reduce greenhouse gas emissions? A system approach

Milk yield per cow has continuously increased in many countries over the last few decades. In addition to potential economic advantages, this is often considered an important strategy to decrease greenhouse gas (GHG) emissions per kg of milk produced. However, it should be considered that milk and beef production systems are closely interlinked, as fattening of surplus calves from dairy farming and culled dairy cows play an important role in beef production in many countries. The main objective of this study was to quantify the effect of increasing milk yield per cow on GHG emissions and on other side effects. Two scenarios were modelled: constant milk production at the farm level and decreasing beef production (as co-product; Scenario 1); and both milk and beef production kept constant by compensating the decline in beef production with beef from suckler cow production (Scenario 2). Model calculations considered two types of production unit (PU): dairy cow PU and suckler cow PU. A dairy cow PU comprises not only milk output from the dairy cow, but also beef output from culled cows and the fattening system for surplus calves. The modelled dairy cow PU differed in milk yield per cow per year (6000, 8000 and 10 000 kg) and breed. Scenario 1 resulted in lower GHG emissions with increasing milk yield per cow. However, when milk and beef outputs were kept constant (Scenario 2), GHG emissions remained approximately constant with increasing milk yield from 6000 to 8000 kg/cow per year, whereas further increases in milk yield (10 000 kg milk/cow per year) resulted in slightly higher (8%) total GHG emissions. Within Scenario 2, two different allocation methods to handle co-products (surplus calves and beef from culled cows) from dairy cow production were evaluated. Results showed that using the 'economic allocation method', GHG emissions per kg milk decreased with increasing milk yield per cow per year, from 1.06 kg CO2 equivalents (CO2eq) to 0.89 kg CO2eq for the 6000 and 10 000 kg yielding dairy cow, respectively. However, emissions per kg of beef increased from 10.75 kg CO2eq to 16.24 kg CO2eq due to the inclusion of suckler cows. This study shows that the environmental impact (GHG emissions) of increasing milk yield per cow in dairy farming differs, depending upon the considered system boundaries, handling and value of co-products and the assumed ratio of milk to beef demand to be satisfied.     

Global environmental costs of China's thirst for milk

China has an ever‐increasing thirst for milk, with a predicted 3.2‐fold increase in demand by 2050 compared to the production level in 2010. What are the environmental implications of meeting this demand, and what is the preferred pathway? We addressed these questions by using a nexus approach, to examine the interdependencies of increasing milk consumption in China by 2050 and its global impacts, under different scenarios of domestic milk production and importation. Meeting China's milk demand in a business as usual scenario will increase global dairy‐related (China and the leading milk exporting regions) greenhouse gas (GHG) emissions by 35% (from 565 to 764 Tg CO_2eq) and land use for dairy feed production by 32% (from 84 to 111 million ha) compared to 2010, while reactive nitrogen losses from the dairy sector will increase by 48% (from 3.6 to 5.4 Tg nitrogen). Producing all additional milk in China with current technology will greatly increase animal feed import; from 1.9 to 8.5 Tg for concentrates and from 1.0 to 6.2 Tg for forage (alfalfa). In addition, it will increase domestic dairy related GHG emissions by 2.2 times compared to 2010 levels. Importing the extra milk will transfer the environmental burden from China to milk exporting countries; current dairy exporting countries may be unable to produce all additional milk due to physical limitations or environmental preferences/legislation. For example, the farmland area for cattle‐feed production in New Zealand would have to increase by more than 57% (1.3 million ha) and that in Europe by more than 39% (15 million ha), while GHG emissions and nitrogen losses would increase roughly proportionally with the increase of farmland in both regions. We propose that a more sustainable dairy future will rely on high milk demanding regions (such as China) improving their domestic milk and feed production efficiencies up to the level of leading milk producing countries. This will decrease the global dairy related GHG emissions and land use by 12% (90 Tg CO_2eq reduction) and 30% (34 million ha land reduction) compared to the business as usual scenario, respectively. However, this still represents an increase in total GHG emissions of 19% whereas land use will decrease by 8% when compared with 2010 levels, respectively.     

Impact of longevity on greenhouse gas emissions and profitability of individual dairy cows analysed with different system boundaries

Dairy production systems are often criticized as being major emitters of greenhouse gases (GHG). In this context, the extension of the length of the productive life of dairy cows is gaining interest as a potential GHG mitigation option. In the present study, we investigated cow and system GHG emission intensity and profitability based on data from 30 dairy cows of different productive lifetime fed either no or limited amounts of concentrate. Detailed information concerning productivity, feeding and individual enteric methane emissions of the individuals was available from a controlled experiment and herd book databases. A simplified GHG balance was calculated for each animal based on the milk produced at the time of the experiment and for their entire lifetime milk production. For the lifetime production, we also included the emissions arising from potential beef produced by fattening the offspring of the dairy cows. This accounted for the effect that changes in the length of productive life will affect the replacement rate and thus the number of calves that can be used for beef production. Profitability was assessed by calculating revenues and full economic costs for the cows in the data set. Both emission intensity and profitability were most favourable in cows with long productive life, whereas cows that had not finished their first lactation performed particularly unfavourably with regard to their emissions per unit of product and rearing costs were mostly not repaid. Including the potential beef production, GHG emissions in relation to total production of animal protein also decreased with age, but the overall variability was greater, as the individual cow history (lifetime milk yield, twin births, stillbirths, etc.) added further sources of variation. The present results show that increasing the length of productive life of dairy cows is a viable way to reduce the climate impact and to improve profitability of dairy production.     

Intensification of dairy production can increase the GHG mitigation potential of the land use sector in East Africa

Sub‐Saharan Africa (SSA) could face food shortages in the future because of its growing population. Agricultural expansion causes forest degradation in SSA through livestock grazing, reducing forest carbon (C) sinks and increasing greenhouse gas (GHG) emissions. Therefore, intensification should produce more food while reducing pressure on forests. This study assessed the potential for the dairy sector in Kenya to contribute to low‐emissions development by exploring three feeding scenarios. The analyses used empirical spatially explicit data, and a simulation model to quantify milk production, agricultural emissions and forest C loss due to grazing. The scenarios explored improvements in forage quality (Fo), feed conservation (Fe) and concentrate supplementation (Co): FoCo fed high‐quality Napier grass (Pennisetum purpureum), FeCo supplemented maize silage and FoFeCo a combination of Napier, silage and concentrates. Land shortages and forest C loss due to grazing were quantified with land requirements and feed availability around forests. All scenarios increased milk yields by 44%–51%, FoCo reduced GHG emission intensity from 2.4 ± 0.1 to 1.6 ± 0.1 kg CO₂eq per kg milk, FeCo reduced it to 2.2 ± 0.1, whereas FoFeCo increased it to 2.7 ± 0.2 kg CO₂eq per kg milk because of land use change emissions. Closing the yield gap of maize by increasing N fertilizer use reduced emission intensities by 17% due to reduced emissions from conversion of grazing land. FoCo was the only scenario that mitigated agricultural and forest emissions by reducing emission intensity by 33% and overall emissions by 2.5% showing that intensification of dairy in a low‐income country can increase milk yields without increasing emissions. There are, however, risks of C leakage if agricultural and forest policies are not aligned leading to loss of forest to produce concentrates. This approach will aid the assessment of the climate‐smartness of livestock production practices at the national level in East Africa.     

Consequential LCA of switching from maize silage-based to grass-based dairy systems

Purpose

This study aimed to investigate the environmental consequences (on climate change and land use) of an increase in preference for grass-based milk in France using a consequential life cycle assessment (CLCA) approach. This increase in preference was assumed to be satisfied domestically, by converting maize silage-based dairy farms (MS farm) to grass-based dairy farms (G farm) while keeping on-farm usable agricultural area and total milk production of farm constant.

Methods

The possible consequences of an increase in preference for grass-based milk were identified based on cause and effect relationships. The conversion from MS to G farm reduced the use of soybean meal, changed the on-farm cropping pattern and produced more animals but less wheat and no rapeseed. Effects on on-farm soil C were predicted with the RothC model and on global land use change (LUC) with models of global agricultural markets (Global Trade Analysis Project (GTAP) and Landbouw Economisch Instituut Trade Analysis Project (LEITAP)). System expansion using animals from a suckler beef production system was applied to estimate the impacts of milk and animal co-products from the dairy system. Land occupation and climate change impacts were estimated. The consequences of farm conversion were attributed only to the milk, as preference for grass-based milk drove the conversion process.

Results and discussion

The conversion from the MS to G farm increases land occupation and climate change impacts for the G farm, respectively, by 9 and 7 % according to GTAP and 14 and 51 % according to LEITAP. Land occupation and climate change impacts of milk produced by the G farm after conversion increased, respectively, by 82 and 13 % with GTAP and 123 and 97 % with LEITAP relative to those for the MS farm (before conversion). The production of additional wheat and rapeseed outside the G farm increased impacts of the G farm (by 29–69 % depending on impacts and model used). Results indicate that the farm conversion would probably have consequences on global LUC and that it is important to account for this in a LCA approach.

Conclusions

Land use and land use change (LULUC) contributed to the impacts of grass-based milk, and results were highly sensitive to the LULUC model used. The many possible chain-of-event pathways that follow a change in preference for a given product yield high uncertainty in CLCA results. This study only assessed one possible way to meet the increase in preference for grass-based milk; it is necessary to perform a sensitivity analysis to investigate other possible scenarios resulting from this increase in preference.     

Localising livestock protein feed production and the impact on land use and greenhouse gas emissions

Livestock farmers in Sweden usually grow feed grains for livestock but import protein feed from outside Sweden. Aside from the economic implications, some environmental issues are associated with this practice. We used life cycle assessment to evaluate the impact of local protein feed production on land use and greenhouse gas emissions, compared with the use of imported protein feed, for pig meat and dairy milk produced in Sweden. Our results showed that local production reduced greenhouse gas emissions by 4.5% and 12%, respectively, for pigs and dairy cows. Land use for feed production in Sweden increased by 11% for pigs and 25% for dairy cows, but total land use decreased for pig production and increased for dairy milk production. Increased protein feed cultivation in Sweden decreased inputs needed for animal production and improved some ecological processes (e.g. nutrient recycling) of the farm systems. However, the differences in results between scenarios are relatively small and influenced to an extent by methodological choices such as co-product allocation. Moreover, it was difficult to assess the contribution of greenhouse emissions from land use change. The available accounting methods we applied did not adequately account for the potential land use changes and in some cases provided conflicting results. We conclude that local protein feed production presents an opportunity to reduce greenhouse gas emissions but at a cost of increasing land occupation in Sweden for feed production.     

An appraisal of carbon footprint of milk from commercial grass-based dairy farms in Ireland according to a certified life cycle assessment methodology

Purpose

Life cycle assessment (LCA) studies of carbon footprint (CF) of milk from grass-based farms are usually limited to small numbers of farms (<30) and rarely certified to international standards, e.g. British Standards Institute publicly available specification 2050 (PAS 2050). The goals of this study were to quantify CF of milk from a large sample of grass-based farms using an accredited PAS 2050 method and to assess the relationships between farm characteristics and CF of milk.

Materials and methods

Data was collected annually using on-farm surveys, milk processor records and national livestock databases for 171 grass-based Irish dairy farms with information successfully obtained electronically from 124 farms and fed into a cradle to farm-gate LCA model. Greenhouse gas (GHG) emissions were estimated with the LCA model in CO₂ equivalents (CO₂-eq) and allocated economically between dairy farm products, except exported crops. Carbon footprint of milk was estimated by expressing GHG emissions attributed to milk per kilogram of fat and protein-corrected milk (FPCM). The Carbon Trust tested the LCA model for non-conformities with PAS 2050. PAS 2050 certification was achieved when non-conformities were fixed or where the effect of all unresolved non-conformities on CF of milk was?<?±5 %.

Results and discussion

The combined effect of LCA model non-conformities with PAS 2050 on CF of milk was <1 %. Consequently, PAS 2050 accreditation was granted. The mean certified CF of milk from grass-based farms was 1.11 kg of CO₂-eq/kg of FPCM, but varied from 0.87 to 1.72 kg of CO₂-eq/kg of FPCM. Although some farm attributes had stronger relationships with CF of milk than the others, no attribute accounted for the majority of variation between farms. However, CF of milk could be reasonably predicted using N efficiency, the length of the grazing season, milk yield/cow and annual replacement rate (R ²?=?0.75). Management changes can be applied simultaneously to improve each of these traits. Thus, grass-based farmers can potentially significantly reduce CF of milk.

Conclusions

The certification of an LCA model to PAS 2050 standards for grass-based dairy farms provides a verifiable approach to quantify CF of milk at a farm or national level. The application of the certified model highlighted a wide range between the CF of milk of commercial farms. However, differences between farms’ CF of milk were explained by variation in various aspects of farm performance. This implies that improving farm efficiency can mitigate CF of milk.     

Implications of Uncertainty and Variability in the Life Cycle Assessment of Pig Production Systems(7 pp)

Goal, Scope and Background Calculating LCA outcomes implies the use of parameters, models, choices and scenarios which introduce uncertainty, as they imperfectly account for the variability of both human and environmental systems. The analysis of the uncertainty of LCA results, and its reduction by an improved estimation of key parameters and through the improvement of the models used to convert emissions into regional impacts, such as eutrophication, are major issues for LCA. Methods In a case study of pig production systems, we propose a simple quantification of the uncertainty of LCA results (intra-system variability) and we explore the inter-system variability to produce more robust LCA outcomes. The quantification of the intra-system uncertainty takes into account the variability of the technical performance (crop yield, feed efficiency) and of emission factors (for NH3, N2O and NO3) and the influence of the functional unit (FU) (kg of pig versus hectare used). For farming systems, the inter-system variability is investigated through differentiating the production mode (conventional, quality label, organic (OA)), and the farmer practices (Good Agricultural Practice (GAP) versus Over Fertilised (OF)), while for natural systems, variability due to physical and climatic characteristics of catchments expected to modify nitrate fate is explored. Results and Conclusion For the eutrophication and climate change impact categories, the uncertainty associated with field emissions contributes more to the overall uncertainty than the uncertainty associated with emissions from livestock buildings, with crop yield and with feed efficiency. For acidification, the uncertainty of emissions from livestock buildings is the single most important contributor to the overall uncertainty. The influence of the FU on eutrophication results is very important when comparing systems with different degrees of intensification such as GAP and OA. Concerning the inter-system variability, differences in farmer practices have a larger effect on eutrophication than differences between production modes. Finally, the physical characteristics of the catchment and the climate strongly affect the results for eutrophication. In conclusion, in this case study, the main sources of uncertainty are in the estimation of emission factors, due both to the variability of environmental conditions and to lack of knowledge (emissions of N2O at the field level), but also in the model used for assessing regional impacts such as eutrophication. Recommendation and Perspective Suitable deterministic simulation models integrating the main controlling variables (environmental conditions, farmer practices, technology used) should be used to predict the emissions of a given system as well as their probabilistic distribution allowing the use of stochastic modelling. Finally, our simulations on eutrophication illustrate the necessity of integrating the fate of pollutants in models of impact assessment and highlight the important margin of improvement existing for the eutrophication impact assessment model.     

Life cycle assessment framework in agriculture on the farm level

Life Cycle Assessment (LCA) is a method that can be used to assess the environmental impact of agriculture, but impact categories and the functional unit of classical LCA’s must be adapted to the specific agricultural production process. Serving as an example, the framework of a LCA of 18 grassland dairy farms covering three farming intensity levels and carried out in the Allgäu region in southern Germany is presented. By focussing on the chosen impact categories and the respective, suitable functional units, the specific needs and backgrounds of conducting an agricultural LCA are discussed in general.     

Developing a predictive model for the energy content of goat milk as the basis for a functional unit formulation to be used in the life cycle assessment of dairy goat production systems

Recent reports on livestock environmental impact based on life cycle assessment (LCA) did not fully consider the case of the dairy goat. Assignment of an environmental impact (e.g. global warming potential) to a specific product needs to be related to the appropriate ‘unitary amount’ or functional unit (FU). For milk, the energy content may provide a common basis for a definition of the FU. To date, no ad hoc formulations for the FU of goat milk have been proposed. For these reasons, this study aimed to develop and test one or more predictive models (DPMs) for the gross energy (GE) content of goat milk, based on published compositional data, such as fat (F), protein, total solids (TS), solid non-fat matter (SNF), lactose (Lac) and ash. The DPMs were developed, selected and tested using a linear regression approach, as a meta-analysis (i.e. meta-regression) was not applicable. However, in the final stage, a control procedure for spurious findings was carried out using a Monte Carlo permutation test. Because several published predictive models (PPMs) for GE in cow milk and goat milk were found in the literature, they were tested on the same data set with which the DPMs were developed. The best-performing DPMs and PPMs were compared directly with a subset of the individual data retrieved from the literature. Overall, the paucity of direct measurements of the GE in goat milk was a limiting factor in collecting data from the literature; thus, only a small data set (n=26) was established, even though it was considered sufficiently representative of milks from different goat breeds. The three best PPMs based on F alone gave more biased estimates of the GE content of the goat milk than the three new DPMs based on F, F and SNF and F and TS, respectively. Accordingly, three different formulations of FU are proposed, depending on the availability of data including both F and TS (or F and SNF) or F alone. Even though several metrics can be used in defining the FU for milk to be used in LCAs of goat farming systems, the proposed FU formulations should be adopted in place of the similar energy-based ones developed for other dairy species.     

Reducing methane on-farm by feeding diets high in fat may not always reduce life cycle greenhouse gas emissions

Purpose

To consider whether feed supplements that reduce methane emissions from dairy cows result in a net reduction in greenhouse gas (GHG) intensity when productivity changes and emissions associated with extra manufacturing and management are included.

Methods

A life cycle assessment was undertaken using a model farm based on dairy farms in Victoria, Australia. The system boundary included the creation of farm inputs and on-farm activities up to the farm gate where the functional unit was 1 L of fat and protein corrected milk (FPCM). Electricity and diesel (scaled per cow), and fertiliser inputs (scaled on farm size) to the model farm were based on average data from a survey of farms. Fertiliser applied to crops was calculated per area of crop. Animal characteristics were based on available data from farms and literature. Three methane-reducing diets (containing brewers grain, hominy or whole cotton seed) and a control diet (cereal grain) were modelled as being fed during summer, with the control diet being fed for the remainder of the year in all cases.

Results and discussion

Greenhouse gas intensity (kg CO₂-eq/L FPCM) was lower than the control diet when the hominy (97 % compared with control) and brewers grain (98 %) diets were used but increased when the whole cottonseed diet was used (104 %). On-farm GHG emissions (kg CO₂-eq) were lower than the control diet when any of the methane-reducing diets were used (98 to 99.5 % of emissions when control diet fed). Diesel use in production and transport of feed supplements accounted for a large portion (63 to 93 %) of their GHG intensity (kg CO₂-eq/t dry matter). Adjusting fertiliser application, changing transport method, changing transport fuel, and using nitrification inhibitors all had little effect on GHG emissions or GHG intensity.

Conclusions

Although feeding strategies that reduce methane emissions from dairy cows can lower the GHG emissions up to the farm gate, they may not result in lower GHG intensities (g CO₂-eq/L FPCM) when pre-farm emissions are included. Both transport distance and the effect of the feed on milk production have important impacts on the outcomes.     

Carbon footprint and land requirement for dairy herd rations: impacts of feed production practices and regional climate variations

Feed production is a significant source of greenhouse gas (GHG) emissions from dairy production and demands large arable and pasture acreage. This study analysed how regional conditions influence GHG emissions of dairy feed rations in a life cycle perspective, that is the carbon footprint (CF) and the land area required. Factors assessed included regional climate variations, grass/clover silage nutrient quality, feedstuff availability, crop yield and feed losses. Using the Nordic feed evaluation model NorFor, rations were optimised for different phases of lactation, dry and growing periods for older cows, first calvers and heifers by regional feed advisors and combined to annual herd rations. Feed production data at farm level were based on national statistics and studies. CF estimates followed standards for life cycle assessment and used emissions factors provided by IPCC. The functional unit was ‘feed consumption to produce 1 kg energy corrected milk (ECM) from a cow with annual milk yield of 9 900 kg ECM including replacement animals and feed losses’. Feed ration CF varied from 417 to 531 g CO₂ e/kg ECM. Grass/clover silage contributed more than 50% of total GHG emissions. Use of higher quality silage increased ration CF by up to 5% as a result of an additional cut and increased rates of synthetic N-fertiliser. Domestically produced horse bean (Vicia faba), by-products from the sugar industry and maize silage were included in the rations with the lowest CF, but horse bean significantly increased ration land requirement. Rations required between 1.4 to 2 m² cropland and 0.1 to 0.2 m²/kg semi-natural grassland per kg ECM and year. Higher yield levels reduced ration total CF. Inclusion of GHG emissions from land use change associated with Brazilian soya feed significantly increased ration CF. Ration CF and land use depended on ration composition, which was highly influenced by the regional availability and production of feedstuffs. The impact of individual feedstuffs on ration CF varies due to, for example, cultivation practices and climate conditions and feedstuffs should therefore be assessed in a ration and regional perspective before being used to decrease milk CF. Land use efficiency should be considered together with ration CF, as these can generate goal conflicts.     

Estimating the environmental impact of dairy cattle breeding programs through emission intensity

A recently developed methodological approach for determining the greenhouse gas emissions impact of national breeding programs was applied to measure the effects of current and future breeding goals on the emission intensity (EI) of the Canadian dairy industry. Emission intensity is the ratio of greenhouse gas outputted in comparison to the product generated. Traits under investigation affected EI by either decreasing the direct emissions yield (i.e. increasing feed performance), changing herd structure (i.e. prolonging herd life) or through the dilution effect of increased production (i.e. increasing fat yield). The intensity value (IV) of each trait, defined as the change in emissions’ intensity per unit change in each trait, was calculated for each of the investigated traits. The IV trend of these traits was compared for the current and prospective selection index, as well as for a system with and without quota (the supply management policy designed to prevent overproduction). The overall EI of the average genetic merit Canadian dairy herd per breeding female was 5.07 kg CO₂eq/kg protein equivalent output. The annual reduction in EI due to the improvement of production traits was −0.027, −0.018 and −0.006 for fat, protein and milk other solids, respectively. The functional traits, herd life and mastitis resistance, had more modest effects (−0.008 and −0.001, respectively). These results are consistent with international studies that identified traits related to production, survival, health and fertility as having the largest impact on the environmental footprint of dairy cattle. Overall, the dairy industry is becoming more efficient by reducing its EI through selection of environmentally favorable traits, with a 1% annual reduction of EI in Canada.     

Comparison of performance and fitness traits in German Angler,Swedish Red and Swedish Polled with Holstein dairy cattle breeds under organic production

Although the use of local breeds is recommended by organic regulations, breed comparisons performed under organic production conditions with similar production intensities are scarce. Therefore, we compared data of local and widely used Holstein dairy cattle breeds from 2011 to 2015 regarding production, fertility and health from German and Swedish organic farms with similar management intensities within country. In Germany, the energy-corrected total milk yield tended to be lower in the local breed Original Angler Cattle (AAZ, 5193 kg) compared to the modern German Holstein Friesian breed (HO, 5620 kg), but AAZ showed higher milk fat and protein contents (AAZ v. HO: 5.09% v. 4.18% and 3.61% v. 3.31%, respectively). In Sweden, the widely used modern Swedish Holstein (SH) breed had the highest milk yield (9209 kg, fat: 4.10%, protein: 3.31%), while the local Swedish Polled (SKB) showed highest milk yield, fat and protein contents (6169 kg, 4.47%, 3.50%, respectively), followed by the local breed Swedish Red (SRB, 8283 kg, 4.33%, 3.46%, respectively). With regard to fertility characteristics, the German breeds showed no differences, but AAZ tended to have less days open compared to HO (−17 days). In Sweden, breeds did not differ with regard to calving interval, but both local breeds showed a lower number of days open (−10.4 in SRB and −24.1 in SKB compared to SH), and SKB needed fewer inseminations until conception (−0.5 inseminations) compared to SH. Proportion of test day records with a somatic cell count content of ≥100 000 cells per ml milk did not reveal breed differences in any of the two countries. German breeds did not differ regarding the proportion of cows with veterinary treatments. In Sweden, SRB showed the lowest proportion of cows with general veterinary treatment as well as specific treatment due to udder problems (22.8 ± 6.42 and 8.05 ± 2.18, respectively), but the local breed SKB did not differ from SH in either of the two traits. In Sweden, we found no breed differences regarding veterinary treatments due to fertility problems or diagnosis of claw or leg problems during claw trimming. Our results indicate a stronger expression of the antagonism between production and functional traits with increasing production intensity. Future breed comparisons, therefore, need to consider different production intensities within organic farming in order to derive practical recommendations as to how to implement European organic regulations with regard to a suitable choice of breeds.