FM and milk production: herd ventilation systems in hot climates

The interaction between milk and beef production and emissions from land use change – critical considerations in life cycle assessment and carbon footprint studies of milk

Methods to determine the relative value of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain

A whole farm systems analysis of greenhouse gas emissions of 60 Tasmanian dairy farms

Effect of feed-related farm characteristics on relative values of genetic traits in dairy cows to reduce greenhouse gas emissions along the chain

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

One of the problematic sectors according to GHG (greenhouse gas) and ammonia (NH3) emission quantities is agriculture. Without endangering food production (and intensifying), GHG emissions come from all sources in animal husbandry. The aim of this study was to comprehensively reduce GHG emissions by applying a holistic process management model to one of the most popular cowsheds in Lithuania (260-seat boxing cowshed, cows are milked on site, computerized management of technological processes, productivity of 8600 kg of milk, barn system, and liquid manure). Considering the cow keeping technology applied on the farm, the equipment used, and the feed production and ration system, a model for the management of technological parameters of production processes was prepared for the farm. This model balanced trade-offs among animal welfare, cow productivity, production costs, and GHG and NH3 emissions. The aim of the research was the adaptation of the integrated model to fully control, manage, and optimize milk production processes through bio- and engineering innovations to implement climate-friendly feed production and feeding and feed rationing systems, to improve animal housing and working conditions, and to reduce GHG and NH3 emissions without increasing production costs. The environmental impact assessment was performed with SimaPro 9.1 process modeling software. Data from milk production, biomass cultivation, and feed preparation, transportation, and equipment were used from the Ecoinvent v3 database. Based on the LML-I calculation methodology, the effect of processes was determined. To quantify the potential emissions in the dairy farm, the emission factors were estimated using a life cycle assessment method per functional unit—1000 kg—of standardized milk. Grass silage, maize silage, and feed concentrate were found to account for the largest share of gas emissions—26.09% (107.39 kg CO2 eq. FU−1), 22.70% (93.44 kg CO2 eq. FU−1), and 21.85% (89.92 kg CO2 eq. FU−1) of the total CO2 emissions during the process, respectively. Considering the critical points of the classic SC scenario, the cultivation technology was adjusted, where 50% of N fertilizers were replaced by bioproducts (biological preparations). Both scenarios—classic SC (control variant) and Bio SC (variants using bioproducts)—were evaluated for comparison. The use of biopreparations in the categories reduced the environmental impact from 0.1% to 45.7% in dairy production technology grass silage, barley grain, hay production, and corn silage stocks. The carbon footprint of the sustainable bio-based milk production (0.393 kg CO2 eq. kg−1 FPCM (fat- and protein-adjusted milk)) was lower by 4.6% compared to the average Lithuanian classic dairy farm (0.412 kg CO2 eq. kg−1 FPCM). Based on this methodology, it is possible to assess many dairy farms and address critical points in an integrated way, which can help to improve the quality of dairy production and the environment.

Smallholder dairy farms face enormous challenges in increasing milk production while mitigating greenhouse gas (GHG) emissions, thereby enhancing climate resilience. The carbon footprint (CF) of smallholder milk production is expected to increase with increasing demand for dairy products under the business-as-usual scenario. This study estimates the carbon footprint of smallholder milk production and examines variation across farms using data from 480 households to identify viable options for mitigating GHG emissions. We applied a cradle to farm-gate life cycle assessment (LCA) approach to examine the effects of farming systems on GHG emission intensities across intensification gradients of smallholder farms (SHF) from four potential dairy districts in the central highlands of Ethiopia. According to our findings, enteric fermentation was the primary source of GHG emissions, and methane(CH4) emissions from enteric fermentation and manure management accounted for the majority of total emissions across farms. The estimated average CF varies depending on farming systems, global warming potential (GWP), and allocation methods used. When GHG emissions were allocated to multiple products using economic allocation and based on IPCC (2007)and IPCC (2014)GWPs, the overall average CF of milk production was 1.91 and 2.35kg CO2e/kg fat and protein-corrected milk (FPCM), respectively. On average, milk accounted for 72% of total greenhouse gas emissions. In terms of farm typology, rural SHF systems produced significantly more CF per kg of milk than urban and peri-urban SHF systems. Variations in milk yield explained more than half of the variation in GHG emissions intensity at the farm level. Feed digestibility and feed efficiency had a negative and significant (P < 0.01) association with CF of SHF. Our findings suggested that improving feed digestibility and feed efficiency by increasing the proportion of concentrate and improved forage as well as chemically upgrading straw and crop residue could provide an opportunity to both increase milk yield and reduce the CF of milk production of SHF in the study area. Supporting SHF to realize strategies contributing to climate-resilient dairy development require interventions at several levels in the dairy value chain.

Impacts of future climate scenarios on the balance between productivity and total greenhouse gas emissions from pasture based dairy systems in south-eastern Australia

The impact of uncertainties on predicted greenhouse gas emissions of dairy cow production systems

Estimating the impact of clinical mastitis in dairy cows on greenhouse gas emissions using a dynamic stochastic simulation model: a case study

In order to respond to increasing global food demand and provide for national economic growth, the Estonian Dairy Strategy for 2012−2020 aims to achieve a 30% growth in milk production. At the same time, there is a global attempt to reduce greenhouse gas (GHG) emissions. This paper analyses the medium-term (2015−2020) projections for milk production and associated GHG emissions from dairy cows in Estonia. The FAPRI-GOLD type market model of Estonian agriculture, which is used for projections of agricultural production, was supplemented with a module that helps project GHG emissions. The paper demonstrates the endogenisation of GHG emission factors in a relatively general agricultural market model context. The results imply that increasing milk production by 30% by 2020 would jeopardise Estonia’s commitments with regard to agricultural GHG emissions. However, the average GHG emission per tonne of produced milk will decline, thus reducing the “carbon footprint” of milk production.

The world population is expected to grow to about 10 billion in 2050. To supply the future human population with food while sustaining a liveable planet, food should be produced sustainably. One of the most urgent environmental issues is climate change, induced by greenhouse gas (GHG) emissions. The dairy sector is a large contributor to GHG emissions. Important GHGs related to milk production are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), mainly emitted during feed production, enteric fermentation, and manure management. Diseases in dairy cows can reduce milk production, reproduction performance and longevity, and increase the amount of discarded milk. The objectives of this thesis were to estimate the impact of diseases (subclinical ketosis, clinical mastitis, and foot lesions) on GHG emissions, and to understand the relation between impact of diseases on GHG emissions and economic performance. First, a dynamic stochastic simulation model was developed to simulate the dynamics of the diseases and the associated production losses (reduced milk production, discarded milk, a prolonged calving interval, and removal (culling or dying on the farm)) per cow during one lactation. This model was combined with a life cycle assessment to quantify the impact of diseases on GHG emissions per ton fat-and-protein-corrected milk (kg CO2equivalents/t FPCM) from cradle to farm gate. Processes included were feed production, enteric fermentation, and manure management. The emissions of GHGs of cows with a disease increased on average by 21 (2.3%) kg CO2e/t FPCM per case of subclinical ketosis, by 58 (6.2%) kg CO2e/t FPCM per case of clinical mastitis, by 4 (0.4%) kg CO2e/ t FPCM per case of digital dermatitis, by 39 (4.3%) kg CO2e/ t FPCM per case of white line disease, and by 33 (3.6%) kg CO2e/ t FPCM per case of sole ulcer. An economic analyses was performed to estimate the costs of subclinical ketosis and related diseases. The total costs of subclinical ketosis were 130 per case per year. Comparing the impact of production contributors from a GHG emissions and economic perspective showed that a reduction in milk production had the highest impact on the economic performance, whereas removal and discarded milk had the highest impact on increase in GHG emissions. Prevalence, pathogen type, farm management (e.g. culling, feed, and manure), and prices (e.g. milk and feed) will affect the impact of production contributors on GHG emissions and economic performance. Therefore, specific farm analyses are needed to estimate the impact of diseases for a specific dairy farm. Diseases in dairy cows increase GHG emissions by approximately 0.4 Mton per year, which equals 15% of the Dutch governmental goal of GHG emission reductions in agriculture in 2030. Reducing diseases can decrease GHG emissions, can increase the income of the farmer, and can improve animal welfare. Therefore, reducing diseases can contribute to sustainable development of the dairy sector.

The dairy Carbon Offset Scenario Tool (COST) was developed to explore the influence of various abatement strategies on greenhouse gas (GHG) emissions for Australian dairy farms. COST is a static spreadsheet-based tool that uses Australian GHG inventory methodologies, algorithms and emission factors to estimate carbon dioxide, methane and nitrous oxide emissions of a dairy farm system. One of the key differences between COST and other inventory-based dairy GHG emissions calculators is the ability to explore the effect of reducing total farm emissions on farm income, assuming the strategy was compliant with Kyoto rules for carbon offsets. COST provides ten abatement strategies across the four broad theme areas of diet manipulation, herd and breeding management, feedbase management and waste management. Each abatement strategy contains four sections; two sections for data entry (baseline farm data specific to the strategy explored and strategy-specific variables) and two sections for results (milk production results and GHG/economic-related results). Key sensitive variables for each strategy, identified from prior research, and prices for milk production and carbon offsets are adjusted through up/down buttons, which allows users to quickly explore the impact of these variables on farm emissions and profitability. For example, if the cost to implement an abatement strategy is doubled, what carbon offset income would be required to negate this additional cost? Results are presented as changes in carbon offset income, strategy implementation cost, additional milk production income and net farm income on a per annum and on a per GHG emissions intensity of milk production basis. COST currently contains a comprehensive range of strategies for GHG abatement, although some strategies are still in development. As new technologies or farm management practices leading to a reduction in GHG emission become available, these too will be incorporated into COST. To date, two dairy-specific abatement methodologies have been legislated as part of Australia’s commitment to reducing on-farm GHG emissions through it’s the carbon offset scheme, the Carbon Farming Initiative (CFI) and are incorporated into COST. These are the ‘Destruction of methane generated from dairy manure in covered anaerobic ponds’ and the ‘Methodology for reducing greenhouse gas emissions in milking cows through feeding dietary additives’. As an example, we explored the mitigation option Replace supplements with a source of dietary fats (reflecting the second above-mentioned CFI legislated abatement strategy) as feeding a diet higher in dietary fats has been shown to reduce enteric methane emissions per unit of feed intake. A 400 milking herd was fed a baseline diet of 2.6% dietary fat. By replacing grain with hominy meal, at a rate of 5.0 kg dry matter/ cow per day for 90 days during the 3 summer months, the summer diet fat concentration was increased to 6.4%. Enteric methane emissions were reduced by 40 tonnes of carbon dioxide equivalents (t CO 2 e) per annum for the farm. Waste methane and nitrous oxide emissions were also reduced by 0.5 and 1.6 t CO 2 e/annum, respectively. However, as reductions from these two sources of GHG emissions do not qualify for payment with this CFI methodology, their reduction could not be included as an offset income. At a carbon price of $20/ t CO 2 e, the reduction in enteric methane emissions was valued at $800/farm. The implementation cost of replacing grain with hominy was valued at $18,000/farm due to the hominy meal costing an additional $100/t dry matter compared to the grain. However, the additional milk production achieved due to the higher energy concentration of the diet resulted in an additional 70,200 litres and based on a summer milk price of $0.38/ litre, this equated to an additional income from milk valued at $26,676/farm. The overall result was a net increase in farm profit of $9,476/farm when paid on a reduction in total GHG emissions. COST can quickly allow users to ascertain the level of GHG emission reduction possible with various mitigation options and explore the sensitivity of key variables on GHG emissions and farm profitability.

The Australian dairy industry contributes ~1.6% of the nation’s greenhouse gas (GHG) emissions, emitting an estimated 9.3 million tonnes of carbon dioxide equivalents (CO2e) per annum. This study examined 41 contrasting Australian dairy farms for their GHG emissions using the Dairy Greenhouse Gas Abatement Strategies calculator, which incorporates Intergovernmental Panel on Climate Change and Australian inventory methodologies, algorithms and emission factors. Sources of GHG emissions included were pre-farm embedded emissions associated with key farm inputs (i.e. grains and concentrates, forages and fertilisers), CO2 emissions from electricity and fuel consumption, methane emissions from enteric fermentation and animal waste management, and nitrous oxide emissions from animal waste management and nitrogen fertilisers. The estimated mean (±s.d.) GHG emissions intensity was 1.04 ± 0.17 kg CO2 equivalents/kg of fat and protein-corrected milk (kg CO2e/kg FPCM). Enteric methane emissions were found to be approximately half of total farm emissions. Linear regression analysis showed that 95% of the variation in total farm GHG emissions could be explained by annual milk production. While the results of this study suggest that milk production alone could be a suitable surrogate for estimating GHG emissions for national inventory purposes, the GHG emissions intensity of milk production, on an individual farm basis, was shown to vary by over 100% (0.76–1.68 kg CO2e/kg FPCM). It is clear that using a single emissions factor, such as milk production alone, to estimate any given individual farm’s GHG emissions, has the potential to either substantially under- or overestimate individual farms’ GHG emissions.

The challenge of agricultural sector is to reduce greenhouse gas (GHG) emissions while increasing food, fiber and energy production without jeopardizing environmental integrity. In the Andean zone of Colombia, there is a growing need to develop GHG mitigation techniques associated with milk production. The present study focuses on GHG emissions and potential sinks associated with milk production scenarios in the Andean zone of Colombia. The scenarios considered were as follows: conventional agriculture of Pennisetum clandestinum in rotation with potatoes (PRP), improved pastures of Lolium multiflorum (IP) and silvopastoral system of P. clandestinum in association with Acacia decurrens and Trifolium repens (SPS). Based on the IPCC (Guidelines for national greenhouse gas inventories. The intergovernmental panel on climate change, Institute for Global Environmental Strategies, Kanagawa, 2006. http://www.ipcc-nggip.iges.or.jp/support/Primer_2006GLs.pdf ) methodologies, the annual GHG emissions considering a 6-year production cycle included agricultural sources and gasoline consumption related to the most important agricultural phases in the field, and a potential for soil carbon accumulation and biomass carbon fixation in all the studied scenarios. The lowest GHG emissions were estimated in PRP scenario (3684 kg CO2-eq ha−1 year−1), which also presented additional emissions because of soil carbon losses beyond the lower milk productivity. Highest GHG emissions were observed in IP scenario (7711 kg CO2-eq ha−1 year−1), which exhibited the highest milk productivity and a considerable potential for soil carbon accumulation that could help to offset its emissions. Nevertheless, SPS scenario, which had milk productivity close to that of IP, presented the highest potential to offset the total GHG emission (4878 kg CO2-eq ha−1 year−1) because of soil carbon accumulation and biomass carbon fixation in trees. This study contributed to indicate management strategies that should be prioritized to mitigate the main sources of GHG emission in the extensive and intensive dairy cattle production in the Andean region of Colombia.