Carbon Footprint Of Milk Production Research Articles

Assessing the impact of climate action mitigation strategies from livestock production: A review – Solution for greenhouse gases mitigation

Abstract The main sources of greenhouse gas (GHG) emissions are enteric fermentation from ruminants and manure management. An integrated strategy-combination approach incorporating nutrition and manure management is necessary to dramatically reduce GHGs and the carbon footprint of milk production. The aim of this review was to assess the effect of climate action mitigation strategies on the GHG production of ruminant animals. This review was based on the different references, which are textbooks, journals, proceedings, conferences, and abstracts accessed through Agricola, Agris, CAB Abstracts, Embase, MEDLINE, and the Web of Science. The research findings were done by using dairy cattle, sheep, and goat; the numbers of animals used in each experiment were given the majority of attention, and decisions were also made based on the method used to quantify methane (CH 4 ) and nitrous oxide. Enteric CH 4 per unit of milk was reduced by 6–8% when higher-starch corn silage was used in place of barley or alfalfa silage. Cows fed seaweed (1.77–2%, on dry matter [DM] basis) experienced a linear drop in enteric CH 4 emission from 383 to 400 g/d. Supplementation of vegetable fats (6%) and coconut oil (12 g/day) to ruminant diets can decrease CH 4 emissions by 21–40% and 50–61.2%, respectively. Similarly, the addition of 21.5 g nitrate/kg DM reduces CH 4 yield (17%) in sheep. Bovaer can reduce methane emissions (30%) for dairy cows. Feed processing and additives are alternative strategies to minimize GHG production in ruminants, but further studies on additives and their residual effect on livestock output is needed.

Factors influencing the carbon footprint of milk production on dairy farms with different feeding strategies in western Europe

Greenhouse gas emissions from dairy production represent a major source of emissions especially in Western Europe where the sector has grown over the past decade. Different feeding strategies have evolved and there is a need to identify effective mitigation measures. Life cycle assessment (LCA) was used to examine carbon footprints (CFs) of milk production across 71 commercial dairy farms in Ireland, Northern Ireland, England, Spain (Galicia and Basque regions), Portugal and France based on monthly data collection over two years. Emissions up to the farm gate were calculated within a global boundary with both higher tier emission factors (HTEF) applicable in respective countries, and default emission factors (DEF). The global warming potential (GWP) used was the GWP100 metric, however results were also calculated using GWP20 for comparison. Functional units were: (i) one tonne fat and protein corrected milk (FPCM); (ii) 1 ha of on-farm agricultural area (FAA); (iii) 1 ha of global agricultural area (GAA). Farms were categorised based on the proportion of time that cows spent grazing. Mean CF per tonne FPCM (FPCM-CF) were 1,129, 1237 and 1519 kg CO2e for grazing (>220d grazing; n = 16), mixed (up to 219d grazing; n = 17) and housed farms (0d grazing; n = 38), respectively. housed had the widest range, from 884 to 2494 kg CO2e/tonne FPCM, and included the farm with the overall lowest FPCM-CF. housed also had the highest mean CF per ha FAA: 44.1 tonne CO2e, followed by mixed (15.2 tonne) and grazing (11.6 tonne). CF (tonne CO2e) per ha GAA followed the same ranking: housed (15.1), mixed (9.8) and grazing (9.2). There was no difference in ranking of the feeding strategies using DEF in comparison with HTEF. A stepwise regression analysis identified feed efficiency and age at first calving as important factors in determining FPCM-CF for all farms. Furthermore, N surplus was important for grazing &mixed farms. The proportion of uncovered slurry storage, milk yield per cow and the amount of bought in concentrate per cow were important for housed farms. Wide variation in CFs implies considerable potential for lowering emissions per tonne FPCM and per ha FAA and GAA, but it is imperative that mitigation measures are tailored to feeding strategy.

Assessment of carbon footprint of milk production and identification of its major determinants in smallholder dairy farms in Karnataka, India

Indian dairy enterprise is dominated by smallholder dairy farms that contribute 72% of the country's total milk production. These smallholder dairy farms are often considered to emit substantial greenhouse gases (GHG) but are poor in productive performances. Therefore, it is crucial to estimate the carbon footprint (CF) of milk production of the smallholder Indian dairy farms. The primary objectives of the study were (1) Assessing the CF of milk production of smallholder dairy farms through life cycle analysis in south-interior Karnataka, India; (2) Identifying the hotspots of GHG emissions and significant factors influencing the CF of milk production in smallholder dairy production system. The study accounted GHG emissions from different sources and considered multiple functions of the smallholder production system. Estimations were made based on primary data collected from 47 farms and associated secondary data. For estimating the CF of milk production, the emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) on a CO2-equivalent (CO2-eq) basis from feed production, enteric fermentation, manure management, transport and energy usage were allocated to fat- and protein-corrected milk (FPCM) based on mass balance, price (crop byproducts and residues) and feed digestibility. Principal component analysis and stepwise linear regression analysis were performed to identify the major factors influencing the CF. The average total GHG emissions (kg CO2-eq yr-1 farm-1) attributable to milk production based on mass, economic, and digestibility allocations were 8,936, 8,641, and 8,759, respectively. The contributions of CH4, N2O, and CO2 to the total farm GHG emission were 70.6%, 20.5%, and 7.69%, respectively. The major emission hotspots were CH4 emission from enteric fermentation (66.8%) and GHG emission from feed production (23.0%). The average CF of cradle-to-dairy cooperative milk production varied from 1.45 to 1.81 kg CO2-eq kg FPCM-1. The CF of milk production was more than 2-fold greater, when milk yield was below 3,500 kg lactating cow-1 yr-1. The FPCM yield 100 kg body weight-1, dry matter intake, and CH4 emission from manure management were the strongest determinants of the CF and explained 83.4% of the observed variation. The study emphasized the importance of considering multiple functions of a mixed crop-livestock-based dairy production system for estimating CF per unit of product. The results suggest that maintaining high-yielding dairy animals and adopting appropriate feeding strategies for better feed utilization are the possible effective interventions for reducing the CF of milk production.

Perspective: Could dairy cow nutrition meaningfully reduce the carbon footprint of milk production?

Perspective: Could dairy cow nutrition meaningfully reduce the carbon footprint of milk production?

Carbon footprint of cows’ milk: a case study of peri-urban and urban dairy farms within Mekelle milk-shed, Ethiopia

In Ethiopia, there is an urge to enhance milk production where urban and peri-urban farms play important role. Therefore, the role of such production system in Greenhouse gas (GHG) emissions should be known. This study was conducted with the objective of estimating the carbon footprint (CF) of milk within Mekelle milkshed, Ethiopia. 50 urban and 42 peri-urban dairy farms were selected randomly and both primary and secondary data was collected. Lifecycle Assessment (LCA) approach was employed to quantify GHG emissions using cradle to- farm gate approach. The mean GHG emissions per cattle unit (000 kg CO2-e y-1) were 2.84 ± 1.23 and 3.19 ± 1.99 for peri-urban and urban farms, respectively. The share of enteric fermentation was 75.5% and 73.6% for peri-urban and urban farms, respectively. Milk contributed for 88.5% and 90.8% to the economic value of peri-urban and urban farms, respectively. Overall, the CF of milk production in urban and peri-urban farms was 2.2 kg CO2-e/kg and 3.2 kg CO2-e/kg without economic allocation, and 2.0 kg CO2-e/kg and 2.8 kg CO2-e/kg with economic allocation. In order to reduce GHG emission intensity from dairy farms it is important to adopt climate smart dairy practices.

Limitations of using simple indicators for evaluating agricultural emission reductions at farm level — evidence from Kenyan smallholder dairy production

National-scale carbon footprints of livestock production are commonly computed from a set of production system characteristics that serve as inputs for greenhouse gas (GHG) emission models. We evaluated the feasibility of using such equations at a finer scale to derive a simple farm-scale indicator of emission intensity (milk yield per head). Using probabilistic simulations, we quantified the impact of input variable uncertainty on emission estimates for smallholder dairy farms in Kenya. We simulated emissions for farm-scale scenarios generated from a survey of 414 households and published or expert-estimated uncertainty bounds. We simulated the impacts of five interventions: changing breeds, retiring unproductive males, keeping fewer replacement males, feeding forage supplements, and balancing animal diets. Impacts were assessed against a true counterfactual and against a more realistic scenario affected by random effects. We estimated errors incurred in classifying farms into adopters and non-adopters of the innovations based on changes in milk yield per animal. Given the current uncertainty, such classification would either miss a large percentage of adopters or misclassify many non-adopters as adopters. As a critical uncertainty, we identified the milk yield of dairy cows. Added precision on this metric reduced but did not eliminate classification errors. We remain cautiously optimistic about using milk yield per head to proxy emission intensity, but its effective use will require further reduction of critical uncertainties. Replacing generic recommendations of parameter uncertainties with context-specific error estimates might lead to a more efficient quantification of the carbon footprint of milk production on smallholder farms.

Carbon and fossil resource depletion footprints of milk production from Canterbury dairy farms

Among the different sustainability metrics, the carbon footprint is the most commonly reported, but the depletion of non-renewable resources such as fossil fuels, is also important. This study aimed to calculate the “cradle to farm-gate” carbon and fossil resource depletion footprint of milk production in Canterbury, New Zealand, comparing two different farm systems: one pasture-based with a relatively small amount of brought-in feed (Lincoln University Dairy Farm, LUDF) and an average Canterbury farm. The estimate of the carbon footprint of milk production was 0.68 and 0.80 kg CO2-eq/kg of fat and protein-corrected milk (FPCM) for the LUDF and Canterbury average, respectively. The main contributor to the carbon footprint for both farms was enteric fermentation, but differences were found mainly in the emissions from the supplementary feed. Conversely, the Canterbury average farm showed lower fossil resource depletion footprint (1.05 MJ/kg FPCM) when compared with the LUDF farm (1.13 MJ/kg FPCM). The differences were mainly related to fertiliser use. However, this difference is small if compared with farms overseas. The study shows that it is important to look at more than one environmental metric when proposing mitigation practices.

Effect of Nutritional Variation and LCA Methodology on the Carbon Footprint of Milk Production From Holstein Friesian Dairy Cows

The UK livestock industry urgently needs to reduce greenhouse gas (GHG) emissions to contribute to ambitious climate change policy commitments. Achieving this requires an improved understanding of emission sources across a range of production systems to lower the burden associated with livestock products. Life cycle assessment (LCA) methods are used in this study to model milk production from two genetic merits of Holstein Friesian cows managed in two novel and two conventional UK dairy systems. Select merit cows sired by bulls with high predicted transmission for fat plus protein yield are compared with Control merit animals sired from UK average merit bulls. Cows were managed in conventional housed and grazed dairy systems with novel Byproduct and Homegrown feeding regimes. A LCA was used to quantify the effect of allocation and management of feed components on the carbon footprint of milk production. Natural variation in nutritional quality of dairy system rations was investigated to quantify uncertainty in the carbon footprint results. Novel production system data are used to assess the effect of introducing home grown legumes and co-product feeds. Control merit footprints across each of the management regimes were significantly higher (p<0.001) in comparison with a high production Select merit, on average by 15%. Livestock emissions (enteric, manure management and deposition) and embedded emissions (purchased feeds, fertiliser, and pesticides) were also significantly higher from control merit cows (p<0.01). Mass and economic allocation methods, and land use functional units, resulted in differences in performance ranking of the dairy systems, with larger footprints resulting from mass allocation. Pairwise comparisons showed GHG's from the systems to be significantly different in total and source category emissions, with significant differences in mean embedded emissions found between most management systems (p<0.05). Monte Carlo simulated system footprints considering the effect of variation in feed digestibility and crude protein also differed significantly from system footprints using standard methods (p < 0.001). Dairy system carbon footprint results should be expressed using multiple units and where possible calculations should incorporate variation in diet digestibility and crude protein content.

Sustainable utilization of Aloe vera waste in the diet of lactating cows for improvement of milk production performance and reduction of carbon footprint

Aloe vera is a medicinal plant cultivated for various applications in medical, food and health products. During the extraction of aloe vera gel from leaves of this plant, a substantial proportion of Aloe vera waste (AVW) is produced. The AVW is a great environmental concern for its disposal; however, it is rich in various bioactive compounds, which could be valorised as an ingredient of feeds in dairy cattle to reduce enteric methane production and consequently carbon footprint of milk production. Therefore, this study investigated the effects of dietary supplementation of AVW on ruminal fermentation, methane production, nutrient utilization, and milk production performance in lactating cows. First, an in vitro experiment was performed using 0, 10, 20, 30 and 40 g/kg of AVW in substrate (five treatments) to select an optimum dose for feeding of dairy cows. Inclusion of AVW at 20 g/kg substrate decreased methane production, increased feed digestibility and total volatile fatty acid concentration in vitro. In the in vivo study, 16 lactating cows were randomly allotted into control and AVW groups (n = 8/group). The control group was fed a total mixed ration, whereas the AVW group was fed the control ration along with AVW at 20 g/kg of dry matter intake for 90 days. Feed intake was slightly greater in the AVW group than that of the control group. Milk production increased due to feeding of AVW. In vivo nutrient digestibility and concentrations of fat, protein, and lactose in milk were not affected, but yields of these milk components increased due to AVW feeding. Supplementation of AVW significantly decreased methane production (g/day), methane yield (g/kg dry matter intake or g/kg digestible organic matter intake), methane intensity (g/kg milk production) or methane conversion ratio. Feeding of AVW improved delayed type of hypersensitivity without affecting other blood variables adversely. This study demonstrates that feeding of AVW at 20 g/kg dry matter intake to dairy cows increased milk production and decreased methane production, which combinedly decreased methane production per unit of milk production substantially without health effects. Thus, feeding of AVW could be beneficial for sustainable and cleaner milk production decreasing environmental burdens of residue disposal problems and ruminal methane production.

Variation in the carbon footprint of milk production on smallholder dairy farms in central Kenya

Milk production by smallholders in Africa has a high carbon footprint (CF) and is predicted to increase significantly in the coming decades. This study, based on data from a sample of 382 farms in central Kenya, is the first assessment of the CF of milk production in Sub-Saharan Africa based on a large dataset of actual farm management practices. The aims of the study were (1) to determine whether there are significant differences in the CF of farms with different feeding systems (i.e., zero-grazing, grazing and mixed systems), and (2) to identify factors associated with variability in CF between farms. This analysis is used to identify options for mitigating GHG emissions from Kenya’s growing dairy production. Average CF ranged between 2.19 and 3.13 kg CO2e/kg fat and protein corrected milk (FPCM), depending on the GWPs and allocation method used. Analysis based on variability in farm management showed that CF was similar between farms with zero-grazing and mixed feeding systems, and significantly higher on farms with grazing only feeding systems, but no difference was detected when input parameter uncertainty was considered. At individual cow level, variation in milk yields explained more than 70% of the variation in GHG intensity. At farm level, milk yield explained less than half of variation in CF. CF was correlated with feed characteristics, manure management practices and herd size and composition. In particular, the level of concentrate use was positively correlated with CF, and was the most important factor explaining variation in CF not attributable to variation in milk yield. Our findings suggest that promoting balanced feed rations and feeding concentrate according to cows’ needs across the lactation cycle could provide opportunities to both increase milk production and reduce the CF of milk production on smallholder farms in central Kenya. Supporting smallholder farmers to implement these mitigation options will require interventions at several levels in feed supply chains in the dairy sector.

Improving farm profitability also reduces the carbon footprint of milk production in intensive dairy production systems

Standard life cycle assessment and economic analysis methods were used to determine the carbon footprint (CF) of milk production and financial performance of a representative sample of dairy farms in Ontario, Canada to assess if there is a trade-off between these two different sustainability measures. Across the 142 dairy farms, CF of milk varied by about 4-fold, from 0.441 to 1.732 CO2eq kg−1 FPCM, a much larger variation than estimated using spatially disaggregated statistical data. Emissions from enteric fermentation and those resulting from the production and supply of feed were the largest contributors to the CF of milk (44% and 36%, respectively). Dairy profits per cow averaged CA$4848 per year but ranged from CA$2530 to $7151 across sample farms. These financial returns were inversely correlated with the CF of milk production, suggesting that rather than a trade-off between GHG emissions intensity and economic performance, there is instead a synergy of the two sustainability indicators. Using a linear regression approach we find that a reduction in the CF of milk production can be achieved while simultaneously improving the profitability of dairy farms. This synergy was largely determined by farm characteristics related to livestock productivity (e.g. milk production per cow) and feeding practices (e.g. herd level total feed use and reliance on purchased feed). Our results suggest that in the absence of explicit GHG reduction policies targeting dairy farms, the main incentive for reducing farm-level GHG emissions could result from the economic pressure on farmers to increase their profitability per cow or per ha.

Discrimination of milk carbon footprints from different dairy farms when using IPCC Tier 1 methodology for calculation of GHG emissions from managed soils

Quantification of the environmental performance of dairy farms should allow comparisons between farms. We assess whether IPCC Tier 1 methodology for emissions from soil management is sufficiently precise to analyse and differentiate the carbon footprint of milk production between practical dairy farms and whether we can correctly identify which farms have the lowest and the highest GHG emissions per product unit, respectively. We used data from 20 Norwegian dairy farms which are very similar in structure, but differ in organic/non-organic management and the share of peat soil of their farmland.We assessed the uncertainty of the carbon footprint by running Monte Carlo simulations with the uncertainty ranges given in Tier 1 of the IPCC guidelines. The carbon footprint is considered different when 95% of all Monte Carlo iterations assume that one farm has higher product-related GHG emissions than the farm in comparison. The uncertainty of results in the single farms, expressed as two-times the standard deviation divided by the median result, ranges between 4.2% and 15.3%. This means that 95% of values in the resulting distribution of one farm are within a range of 4e16% of the median of that farm. Farms can be differentiated when the variation of the carbon footprint is higher than the uncertainty of farm-related emissions. From all 190 direct comparisons of two farms in the tudy, 78% are significantly different.For this uncertainty assessment, it must be established that background processes, especially the datasets for import feed, can be judged covariant in order to prohibit them from influencing the comparison between farms. Secondly, the uncertainty ranges used for the calculation must be appropriate for the assessed systems. We were able to confirm the hypothesis that a significant differentiation of the milk carbon footprint between farms is possible with an IPCC Tier 1 approach for a majority of our comparisons, and found a difference of above 8.7% sufficient to establish significance.

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.

Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions

Efforts to reduce the carbon footprint of milk production through selection and management of low-emitting cows require accurate and large-scale measurements of methane (CH4) emissions from individual cows. Several techniques have been developed to measure CH4 in a research setting but most are not suitable for large-scale recording on farm. Several groups have explored proxies (i.e., indicators or indirect traits) for CH4; ideally these should be accurate, inexpensive, and amenable to being recorded individually on a large scale. This review (1) systematically describes the biological basis of current potential CH4 proxies for dairy cattle; (2) assesses the accuracy and predictive power of single proxies and determines the added value of combining proxies; (3) provides a critical evaluation of the relative merit of the main proxies in terms of their simplicity, cost, accuracy, invasiveness, and throughput; and (4) discusses their suitability as selection traits. The proxies range from simple and low-cost measurements such as body weight and high-throughput milk mid-infrared spectroscopy (MIR) to more challenging measures such as rumen morphology, rumen metabolites, or microbiome profiling. Proxies based on rumen samples are generally poor to moderately accurate predictors of CH4, and are costly and difficult to measure routinely on-farm. Proxies related to body weight or milk yield and composition, on the other hand, are relatively simple, inexpensive, and high throughput, and are easier to implement in practice. In particular, milk MIR, along with covariates such as lactation stage, are a promising option for prediction of CH4 emission in dairy cows. No single proxy was found to accurately predict CH4, and combinations of 2 or more proxies are likely to be a better solution. Combining proxies can increase the accuracy of predictions by 15 to 35%, mainly because different proxies describe independent sources of variation in CH4 and one proxy can correct for shortcomings in the other(s). The most important applications of CH4 proxies are in dairy cattle management and breeding for lower environmental impact. When breeding for traits of lower environmental impact, single or multiple proxies can be used as indirect criteria for the breeding objective, but care should be taken to avoid unfavorable correlated responses. Finally, although combinations of proxies appear to provide the most accurate estimates of CH4, the greatest limitation today is the lack of robustness in their general applicability. Future efforts should therefore be directed toward developing combinations of proxies that are robust and applicable across diverse production systems and environments.

Intensification to Reduce the Carbon Footprint of Smallholder Milk Production: Fact or Fiction?

Will the intensification of cattle-keeping lower the carbon footprint of milk production in resource-poor environments? The authors included the multiple functions of cattle in carbon footprint estimates of milk production in farming systems with different degrees of intensification in Kenya. The carbon footprints (measured in kg CO2 equivalents per kg of milk) of free-grazing with 2.6 cows (1.8 kg) and zero-grazing with 1.5 cows (1.3 kg) on smallholder farms were only slightly higher or at the same level as on large farms with 13.6 cows (1.1 kg) and on a very large farm with 107 cows (1.3 kg). These carbon footprints were comparable with those of milk producers in developed regions. Better feeding is often suggested as a climate change mitigation option; however, only small-step feed improvements can be made. In the debate on intensification as a major strategy to reduce the carbon footprint of milk production, the opportunities are overestimated and constraints for changes in smallholder farming are underestimated.

Carbon footprint of milk production under smallholder dairying in Anand district of Western India: a cradle-to-farm gate life cycle assessment

In recent years, the concept of life cycle assessment (LCA) has proven to be useful because of its potential to assess the integral environmental impacts of agricultural products. Developing countries such as India are good candidates for LCA research because of the large contribution of smallholder dairy system to the production of agricultural products such as milk. Therefore, the aim of the present study was to explore the carbon footprint of milk production under the multi-functional smallholder dairy system in Anand district of Gujarat state, western India. A cradle-to-farm gate LCA was performed by covering 60 smallholder dairy farms within 12 geographically distinct villages of the district. The average farm size was 4.0 animals per farm, and the average number of each category of animal was 2.5 lactating cows, 1.4 lactating buffaloes, 1.8 replacement cows, 1.6 replacement buffaloes, 2.0 retired cows, 1.3 retired buffaloes and 1.0 ox per farm. The emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) on CO2-equivalent (CO2-eq) basis from feed production, enteric fermentation and manure management were allocated to fat- and protein-corrected milk (FPCM) on the basis of mass balance, price and digestibility. Emissions of CO2, CH4 and N2O from cattle contributed 11.0%, 75.4% and 13.6%, respectively, to the total greenhouse gas (GHG) emissions. The contribution of CO2, CH4 and N2O from buffalo was 8.2%, 80.5% and 11.3%, respectively, to the total GHG emissions of farms. The average carbon footprint (CF) of cow milk was 2.3, 1.9 and 2.0 kg CO2-eq/kg FPCM on mass, economic and digestibility basis, respectively, whereas for buffalo, milk CF was 3.0, 2.5 and 2.7 kg CO2-eq/kg FPCM, respectively. On the basis of digestibility allocation, emissions from retired (>10 years of age and incapable of or ceased producing milk) cows and buffaloes were 1571.3 and 2556.1 kg CO2-eq/retirement year, respectively. Overall, the CF of milk production under the smallholder dairy system in Anand district was 2.2 kg CO2-eq/kg FPCM, which reduced to 1.7 kg CO2-eq/kg FPCM when milk, manure, finance and insurance were considered as economic functions of the smallholder system. The CF was lower by 65% and 22% for cow and buffalo milk, respectively, than were the estimates of FAO for southern Asia, and this was mainly attributed to difference in the sources of GHG emissions, manure management systems, feed digestibility and milk production data used by FAO.

Ration Balancing: A Practical Strategy for Reducing Enteric Methane Emission and Carbon Footprint of Milk in India: A Review

Enteric methane is the largest contributor to the total greenhouse gas emissions from agricultural sector. It is closely related to the feed regime and the productivity of dairy animals. Life cycle assessment study indicated that the extensive dairy production system with low milk yields has higher carbon footprint of milk than more intensive system with higher milk yields. In this paper, various methane mitigation strategies have been discussed under Indian feeding and management conditions. These strategies are mainly focused on feeding, breeding and rumen manipulation which can be broadly grouped into those that increase animal productivity and those that directly modify rumen fermentation so that less methane is produced in total. However, most of these strategies are not practically and commercially applicable for reducing methane emissions in large ruminants under field conditions owing to their various limitations. Further, many of these strategies require years of research before practical application and commercially viable products or technologies are available. For India, enteric methane mitigation strategy should be cost-effective and address socio-economic issues. Ration balancing helps in improving productivity and reducing enteric methane emissions in cows and buffaloes at farmers’ doorstep. A life cycle assessment study of milk production in India indicated that a significant reduction in carbon footprint of milk has been observed after feeding a balanced ration. Thus, improving animal productivity through ration balancing with available feed resources could be a most practical strategy for reducing enteric methane emissions and carbon footprint of milk production in India.

Carbon footprint of milk production in Brazil: a comparative case study

Livestock production is a recognized source of environmental impact, and this sector indirectly involves approximately 5 million people in Brazil. Livestock production includes nearly 1.5 million milk producers that use several different production systems. We chose the southern region of Brazil to evaluate the carbon footprint (CF) per 1 kg of energy-corrected milk (ECM) at the farm gate for different dairy production systems with the use of a good level of technology. The dairy production systems were confined feedlot system, semi-confined feedlot system (including some grazing), and pasture-based grazing system. A sensitivity analysis of the dry matter intake (DMI) in each farming system and an uncertainty analysis based on a Monte Carlo (MC) simulation were performed to complement the discussion. The standards ISO 14040: 2006 and ISO 14044: 2006 were used for the comparative life cycle assessment (LCA) focused on the CF. The LCA software tool SimaPro 7.3.3 was used. Sensitivity analyses were conducted on input data for total digestible nutrients (TDN) and crude protein (CP) based on values from the literature. The comparative LCA showed that the confined feedlot system had a lower CF than the other systems studied. Total greenhouse gas emissions were 0.535 kg CO2e kg ECM−1 for the confined feedlot system, 0.778 kg CO2e kg ECM−1 for the semi-confined feedlot system, and 0.738 kg CO2e kg ECM−1 for the pasture-based system without considering the impact from direct land use change (dLUC). When considering these emissions, the CFs for grain and cottonseed production showed CF increases of 45.0, 36.9, and 37.3 % for the confined feedlot, semi-confined feedlot, and pasture-based systems, respectively. The results from the MC simulations showed low uncertainty through variations in TDN and CP. The coefficient of variation was 1.1 % for the confined feedlot, 0.7 % for the semi-confined feedlot, and 1.0 % for the pasture systems. The uncertainties were due mainly to variations in N2O emissions from manure for the three systems. The CF in Brazilian systems was lower than almost all the results found in the literature, even when impacts from the dLUC were considered. The lowest CF in this case study was due mainly to the emission factor used for enteric fermentation.

Carbon footprint of milk production from dairy cows in Australia

This paper explores the carbon footprint of milk produced by dairy cows in Australia and follows the common carbon footprint approach guidelines of the International Dairy Federation and the International Standard for Life Cycle Assessment. Primary data were collected and analysed from 139 farms representing the main dairy regions in Australia. The scope of the study covers all inputs and outputs from cradle to farm gate for the twelve month period from July 2009 to June 2010 (2009/2010). The carbon footprint of average Australian milk for 2009/2010 was calculated to be 1.11 kg carbon dioxide equivalents kg−1 fat and protein corrected milk. Differences in GHG emission per kg of milk produced could be traced back to differences in production practices. The feed conversion efficiency was identified as the most important driver for the reduction of enteric methane emissions. Reduction potentials were also identified for manure management practices.

The impact of various parameters on the carbon footprint of milk production in New Zealand and Sweden

The carbon footprint (CF) of milk production was analysed at the farm gate for two contrasting production systems; an outdoor pasture grazing system in New Zealand (NZ) and a mainly indoor housing system with pronounced use of concentrate feed in Sweden (SE). The method used is based on the conceptual framework of lifecycle assessment (LCA), but only for greenhouse gas (GHG) emissions. National average data were used to model the dairy system in each country. Collection of inventory data and calculations of emissions were harmonised to the greatest extent possible for the two systems. The calculated CF for 1 kg of energy corrected milk (ECM), including related by-products (surplus calves and culled cows), was 1.00 kg carbon dioxide equivalents (CO 2e) for NZ and 1.16 kg CO 2e for SE. Methane from enteric fermentation and nitrous oxide emissions from application of nitrogen (as fertiliser and as excreta dropped directly on the field) were the main contributors to the CF in both countries. The most important parameters to consider when calculating the GHG emissions were dry matter intake (DMI), emission factor (EF) for methane from enteric fermentation, amount of nitrogen applied and EF for direct nitrous oxide emissions from soils. By changing one parameter at a time within ‘reasonable’ limits (i.e. no extreme values assumed), the impact on the total CF was assessed and showed changes of up to 15%. In addition, the uncertainty in CF estimates due to uncertainty in EF for methane from enteric fermentation and nitrous oxide emissions (from soil and due to ammonia volatilisation) were analysed through Monte Carlo simulation. This resulted in an uncertainty distribution corresponding to 0.60–1.52 kg CO 2e kg −1 ECM for NZ and 0.83–1.56 kg CO 2e kg −1 ECM for SE (in the prediction interval 2.5–97.5%). Hence, the variation within the systems based on the main EF is relatively large compared with the difference in CF between the countries.