A biophysical approach to allocation of life cycle environmental burdens for fluid milk supply chain analysis

Life cycle assessment of greenhouse gas emission from the dairy production system - review

Climate change is altering ecological systems and poses a serious threat to human life. Climate change also seriously influences on livestock production by interfering with growth, reproduction, and production. Livestock, on the other hand, is blamed for being a significant contributor to climate change, emitting 8.1 gigatonnes of CO2-eq per year and accounting for two-thirds of global ammonia emissions. Methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) are three major greenhouse gases (GHG) that are primarily produced by enteric fermentation, feed production, diet management, and total product output. Ruminants account for three-quarters of total CO2-equivalent (CO2-eq) emissions from the livestock sector. The global dairy sector alone emits 4.0% of global anthropogenic GHG emissions. Hence, dairy farming needs to engage in environmental impact assessment. Public concern for a sustainable and environmentally friendly farming system is growing, resulting in the significant importance of food-based life cycle assessment (LCA). Over the last decade, LCA has been used in agriculture to assess total GHG emissions associated with products such as milk and manure. It includes the production of farm inputs, farm emissions, milk processing, transportation, consumer use, and waste. LCA studies on milk production would assist us in identifying the specific production processes/areas that contribute to excessive greenhouse gas emissions when producing milk and recommending appropriate mitigation strategies to be implemented for a clean, green, and resilient environment.

Are single global warming potential impact assessments adequate for carbon footprints of agri-food systems?

The vast majority of agri-food climate-based sustainability analyses use global warming potential (GWP100) as an impact assessment, usually in isolation; however, in recent years, discussions have criticised the 'across-the-board' application of GWP100 in Life Cycle Assessments (LCAs), particularly of food systems which generate large amounts of methane (CH4) and considered whether reporting additional and/or alternative metrics may be more applicable to certain circumstances or research questions (e.g. Global Temperature Change Potential (GTP)). This paper reports a largescale sensitivity analysis using a pasture-based beef production system (a high producer of CH4 emissions) as an exemplar to compare various climatatic impact assessments: CO2-equivalents using GWP100 and GTP100, and 'CO2-warming-equivalents' using 'GWP Star', or GWP*. The inventory for this system was compiled using data from the UK Research and Innovation National Capability, the North Wyke Farm Platform, in Devon, SW England. LCAs can have an important bearing on: (i) policymakers' decisions; (ii) farmer management decisions; (iii) consumers' purchasing habits; and (iv) wider perceptions of whether certain activities can be considered 'sustainable' or not; it is, therefore, the responsibility of LCA practitioners and scientists to ensure that subjective decisions are tested as robustly as possible through appropriate sensitivity and uncertainty analyses. We demonstrate herein that the choice of climate impact assessment has dramatic effects on interpretation, with GWP100 and GTP100 producing substantially different results due to their different treatments of CH4 in the context of carbon dioxide (CO2) equivalents. Given its dynamic nature and previously proven strong correspondence with climate models, out of the three assessments covered, GWP* provides the most complete coverage of the temporal evolution of temperature change for different greenhouse gas emissions. We extend previous discussions on the limitations of static emission metrics and encourage LCA practitioners to consider due care and attention where additional information or dynamic approaches may prove superior, scientifically speaking, particularly in cases of decision support.

Milk carbon footprint of silvopastoral dairy systems in the Northern Peruvian Amazon

The objective of this study was to estimate the carbon footprint (CF) of milk production (in kg of CO₂ equivalents (CO₂e) per kg of fat and protein corrected milk (FPCM)) in dairy farms of the San Martín region, in the Peruvian Amazon. A cradle-to-farm gate characterization and analysis were carried out on eight representative dairy farms. Greenhouse gas (GHG) emissions were estimated using equations, following the 2019 refinement of the 2006 IPCC Guidelines. The results showed an average milk production of 9.7 ± 0.82 L milk/cow/day, Gyr x Holstein crosses as the predominant breed, use of cultivated grasses such as Brachiaria brizantha, living fences (Guazuma ulmifolia Lam) as the predominant silvopastoral arrangement, and low level of external inputs such as feed or grain additives. In relation to CF, an average value of 2.26 ± 0.49 kg CO₂e/kg FPCM was obtained, with enteric fermentation being the most important source (1.81 ± 0.51 kg CO₂e/kg FPCM), followed by manure management, land use, and energy/transport (0.26 ± 0.06, 0.14 ± 0.04, and 0.05 ± 0.04 kg CO₂e/kg FPCM, respectively). Differences were found between farmers, obtaining lower CF values (1.76 vs 3.09 kg CO₂e/kg FPCM) on farms with better feed quality, higher production levels, and a higher percentage of lactating animals compared to dry cows. It is concluded that dairy farms in the Peruvian Amazon region can reduce their emissions if they improve their current feeding practices.

Internal causality in agri-food Life Cycle Assessments: Solving allocation problems based on feed energy utilization in egg production

Physical allocation in Life Cycle Assessment (LCA) should, ideally, be based on underlying causal relationships. While both cause-oriented and effect-oriented causality referred to in LCA literature are forms of external causality, internal causality addresses the actual flow of materials and inputs in a system - in other words, the real behaviour of the system under study. While a number or examples of allocation based on physical causality have been used in poultry LCAs, none of these represent the internal causality (the actual biological processes) in egg production. The current study remedies that gap by proposing such a method. Agri-food LCAs, in particular LCAs of livestock production, were used to identify existing physical allocation approaches consistent with internal causality. The most commonly used approach was found to be based on the allocation of feed energy to support the various physiological functions of the livestock species. A feed energy - Metabolizable Energy (ME) - utilization model for allocation in egg production LCAs is hence similarly proposed. Using the inventory of a previous LCA study of egg production in Canada, allocation ratios for eggs and spent hens were developed. Feed utilization models specific to each unit process were identified. The overall differences between ME utilization (∼95% eggs, 5% spent hens) and gross chemical energy content (92% eggs, 8% spent hens) for allocation were relatively small. Scenario analysis, however, showed that the allocation ratios can be considerably different if the causal relationship is interpreted differently. Differences over ∼20% was seen in a scenario which did not allocate between the co-products of each unit process in the system, but rather to the products at the end of a biological causal chain straddling multiple unit processes. The proposed approach is consistent with the interpretation of LCA as a natural sciences framework, and with the ISO 14044 multi-functionality hierarchy, because it reflects actual biological causality in egg production systems. The study results also underscore that practitioners should not only clearly justify their choice of allocation strategy, but also describe its application in detail, since small differences in methods can result in divergent outcomes.

The simulated environmental impact of incorporating white clover into pasture-based dairy production systems

White clover (WC) offers an alternative source of nitrogen (N) for pasture-based systems. Substituting energy- and carbon-intensive synthetic N fertilizers with N derived from biological fixation by WC has been highlighted as a promising environmental mitigation strategy through the omission of emissions, pollutants, and energy usage during the production and application of synthetic fertilizer. Therefore, the objective was to investigate the effect of the inclusion of WC in perennial ryegrass (PRG) swards on the environmental impact of pasture-based dairy systems. Cradle-to-farm gate life cycle assessment of 3 pasture-based dairy systems were conducted: (1) a PRG-WC sward receiving 150 kg of N/ha per year (CL150), (2) a PRG-WC sward receiving 250 kg of N/ha per year (CL250), and (3) a PRG-only sward receiving 250 kg of N/ha per year (GR250). A dairy environmental model was updated with country-specific N excretion equations and recently developed N₂O, NH₃, and NO₃^- emission factors. The environmental impact categories assessed were global warming potential, nonrenewable energy, acidification potential, and eutrophication potential (marine and freshwater). Impact categories were expressed using 2 functional units: per hectare and per metric tonne of fat- and protein-corrected milk. The GR250 system had the lowest milk production and highest global warming potential, nonrenewable energy, and acidification potential per tonne of fat- and protein-corrected milk for all systems. The CL250 system produced the most milk and had the highest environmental impact across all categories when expressed on an area basis. It also had the highest marine eutrophication potential for both functional units. The impact category freshwater eutrophication potential did not differ across the 3 systems. The CL150 system had the lowest environmental impact across all categories and functional units. This life cycle assessment study demonstrates that the substitution of synthetic N fertilizer with atmospheric N fixed by WC has potential to reduce the environmental impact of intensive pasture-based dairy systems in temperate regions, not only through improvement in animal performance but also through the reduction in total emissions and pollutants contributing to the environmental indicators assessed.

Potential to reduce greenhouse gas emissions through different dairy cattle systems in subtropical regions

Carbon (C) footprint of dairy production, expressed in kg C dioxide (CO2) equivalents (CO2e) (kg energy-corrected milk (ECM))-1, encompasses emissions from feed production, diet management and total product output. The proportion of pasture on diets may affect all these factors, mainly in subtropical climate zones, where cows may access tropical and temperate pastures during warm and cold seasons, respectively. The aim of the study was to assess the C footprint of a dairy system with annual tropical and temperate pastures in a subtropical region. The system boundary included all processes up to the animal farm gate. Feed requirement during the entire life of each cow was based on data recorded from Holstein × Jersey cow herds producing an average of 7,000 kg ECM lactation-1. The milk production response as consequence of feed strategies (scenarios) was based on results from two experiments (warm and cold seasons) using lactating cows from the same herd. Three scenarios were evaluated: total mixed ration (TMR) ad libitum intake, 75, and 50% of ad libitum TMR intake with access to grazing either a tropical or temperate pasture during lactation periods. Considering IPCC and international literature values to estimate emissions from urine/dung, feed production and electricity, the C footprint was similar between scenarios, averaging 1.06 kg CO2e (kg ECM)-1. Considering factors from studies conducted in subtropical conditions and actual inputs for on-farm feed production, the C footprint decreased 0.04 kg CO2e (kg ECM)-1 in scenarios including pastures compared to ad libitum TMR. Regardless of factors considered, emissions from feed production decreased as the proportion of pasture went up. In conclusion, decreasing TMR intake and including pastures in dairy cow diets in subtropical conditions have the potential to maintain or reduce the C footprint to a small extent.

Diversification not specialization reduces global and local environmental burdens from livestock production

Milk and beef production generates environmental burdens globally and locally. Across many regions a typical dairy intensification pathway is for dairy farms to specialize on milk production and reduce the co-production of beef (i.e. 'dairy-beef'). Dairy-beef thus reduces and beef needs to be produced elsewhere if beef production is to be maintained. Life Cycle Assessment (LCA) studies quantifying the environmental implications of dairy and beef production have largely focused on the farm level and not captured system connections. Further LCA work has generally represented the 'average' farm of a region, consequently ignoring the range in farm management observed in practice and few studies consider a range of LCA environmental footprints other than carbon footprints. For the first time, we present comprehensive LCA results for multiple environmental burdens based on a large panel dataset for commercial dairy and suckler-beef farms. We present a 15-year LCA assessment of a total of 738 dairy (3624 data points in 15 years) and 1887 suckler-beef (10,340 data points in 15 years) UK farms for five major LCA footprints. We also explore the footprint implications of compensating for reduced dairy-beef through producing this 'displaced' beef on suckler-beef farms. We found a substantial variation in farm footprints not captured in 'average farm' studies. Dairy-beef was much more efficient than beef produced on suckler-beef farms in terms of footprints per unit of beef output. Reducing dairy-beef and replacing it on a suckler-beef farm generally significantly increased environmental burdens. A reduction in carbon footprint was also associated with a reduction in other burdens suggesting no trade-off between local and global emissions. Increasing dairy farm diversification via higher dairy-beef output per unit of milk reduced burdens by up to 11-56%, on average, depending on burden and sensitivity run. We conclude that overspecialization of dairy farms in milk production increases the combined burdens from beef and milk, and that more intensive beef systems that make more efficient use of forage land play a crucial role in mitigating these burdens.

Coproducts performances in biorefineries: Development of Claiming-based allocation models for environmental policy

This study revisited the fundamentals of allocation to joint products and proposed new models for allocating common greenhouse gases emissions among coproducts of biorefineries. These emissions may account for more than 80% of the total emissions of greenhouse gases of the biorefineries. The proposed models optimize the reward of coproducts for their compliance to environmental requirements. They were illustrated by a case study of wheat straw biorefinery built on the literature. Several scenarios were considered with regard to the grain yield, field emissions of greenhouse gases, allocation between grain and straw and policy requirements. The results conform to the expectations and are sensitive to the policy targets and to the environmental performance of the counterpart system. Further research works are necessary to achieve a full application to complex processes. However, the proposed models are promising towards assessing the simultaneous compliance of coproducts of a biorefinery to environment policy requirements.

Evaluation of allocation methods for calculation of carbon footprint of grass-based dairy production

A major methodological issue for life cycle assessment, commonly used to quantify greenhouse gas emissions from livestock systems, is allocation from multifunctional processes. When a process produces more than one output, the environmental burden has to be assigned between the outputs, such as milk and meat from a dairy cow. In the absence of an objective function for choosing an allocation method, a decision must be made considering a range of factors, one of which is the availability and quality of necessary data. The objective of this study was to evaluate allocation methods to calculate the climate change impact of the economically average (€/ha) dairy farm in Ireland considering both milk and meat outputs, focusing specifically on the pedigree of the available data for each method. The methods were: economic, energy, protein, emergy, mass of liveweight, mass of carcass weight and physical causality. The data quality for each method was expressed using a pedigree score based on reliability of the source, completeness, temporal applicability, geographical alignment and technological appropriateness. Scenario analysis was used to compare the normalised impact per functional unit (FU) from the different allocation methods, between the best and worst third of farms (in economic terms, €/ha) in the national farm survey. For the average farm, the allocation factors for milk ranged from 75% (physical causality) to 89% (mass of carcass weight), which in turn resulted in an impact per FU, from 1.04 to 1.22 kg CO₂-eq/kg (fat and protein corrected milk). Pedigree scores ranged from 6.0 to 17.1 with protein and economic allocation having the best pedigree. It was concluded that when making the choice of allocation method, the quality of the data available (pedigree) should be given greater emphasis during the decision making process because the effect of allocation on the results. A range of allocation methods could be deployed to understand the uncertainty associated with the decision.

Variation of greenhouse gas emissions and identification of their drivers during the fattening of Belgian Blue White bulls based on a LCA approach

Greenhouse gas emission intensity (GHGI; kilograms carbon dioxide equivalents/kilograms liveweight gain) have to be reduced so as to limit the impact of human activities on global warming while furnishing food to human. In this respect, performances of 654 Belgian Blue double-muscled bulls (BBdm) during their fattening phase were recorded. On this basis, their greenhouse gas emissions were modelled to estimate variation in GHGI and investigate mitigation options at that level. The relevance of theses option is discussed, taking into account the whole life and production system scales. Large variations (mean (s.d.)) were observed (from 7.2 (0.4) to 10.0 (0.7) kg carbon dioxide equivalents/kg liveweight gain) for, respectively, the 1st- and 4th-quantile groups defined for GHGI. Early culling, low liveweight and age at start of the fattening phase of the bulls would lead to a reduction of GHGI. Nevertheless, more than 32% of the variation remained unexplained. However, decision leading to reduction of GHG intensity at this stage of the life may be compensated in the early stage of BBdm. Attention is drawn on the necessity to encompass the whole life of BBdm for investigating mitigation options and on the sensitivity of the results on models and methodological choices.