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

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 CO 2 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 m 2 cropland and 0.1 to 0.2 m 2 /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.

Section: Introductionmentioning

confidence: 99%

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

Henriksson

Cederberg

Swensson

2014

“…The uncertainty in estimates of GHG emissions for milk production results mainly from uncertainty in emission factors used to estimate N 2 O and enteric CH 4 emissions, DM intake and milk yield (Basset-Mens et al, 2009;Flysjö et al, 2011;Henriksson et al, 2011). We can assume this holds true for our study, if one substitutes 'milk yield' with 'animal growth'.…”

Section: Discussionmentioning

confidence: 99%

Effect of farming practices for greenhouse gas mitigation and subsequent alternative land use on environmental impacts of beef cattle production systems

Nguyen

Doreau

Eugène

et al. 2013

This study evaluated effects of farming practice scenarios aiming to reduce greenhouse gas (GHG) emissions and subsequent alternative land use on environmental impacts of a beef cattle production system using the life cycle assessment approach. The baseline scenario includes a standard cow-calf herd with finishing heifers based on grazing, and a standard bull-fattening herd using a diet mainly based on maize silage, corresponding to current farm characteristics and management by beef farmers in France. Alternative scenarios were developed with changes in farming practices. Some scenarios modified grassland management (S1: decreasing mineral N fertiliser on permanent grassland; S2: decreasing grass losses during grazing) or herd management (S3: underfeeding of heifers in winter; S4: fattening female calves instead of being reared at a moderate growth rate; S5: increasing longevity of cows from 7 to 9 years; S6: advancing first calving age from 3 to 2 years). Other scenarios replaced protein sources (S7: partially replacing a protein supplement by lucerne hay for the cow-calf herd; S8: replacing soya bean meal with rapeseed meal for the fattening herd) or increased n-3 fatty acid content using extruded linseed (S9). The combination of compatible scenarios S1, S2, S5, S6 and S8 was also studied (S10). The impacts, such as climate change (CC, not including CO 2 emissions/sequestration of land use and land-use change, LULUC), CC/LULUC (including CO 2 emissions of LULUC), cumulative energy demand, eutrophication (EP), acidification and land occupation (LO) were expressed per kg of carcass mass and per ha of land occupied. Compared with the baseline, the most promising practice to reduce impacts per kg carcass mass was S10 (all reduced by 13% to 28%), followed by S6 (by 8% to 10%). For other scenarios, impact reduction did not exceed 5%, except for EP (up to 11%) and LO (up to 10%). Effects of changes in farming practices (the scenarios) on environmental impacts varied according to impact category and functional unit. For some scenarios (S2, S4, S6 and S10), permanent grassland area and LO per kg of carcass decreased by 12% to 23% and 9% to 19%, respectively. If the 'excess' permanent grassland was converted to fast-growing conifer forest to sequester carbon in tree and soil biomass, CC/LULUC per kg of carcass could be reduced by 20%, 25%, 27% and 48% for scenarios S2, S4, S6 and S10, respectively. These results illustrate the potential of farming practices and forest as an alternative land use to contribute to short-and mid-term GHG mitigation of beef cattle production systems.

“…Thus, similar to previous studies, total GHG emissions per unit of product and LU were higher for the confinement system than the grass-based system (Rotz et al, 2009;Flysjö et al, 2011). However, per total (on-farm and off-farm) ha, total GHG emissions were lower for the confinement system than the grass-based system, because the stocking rate per total ha of the confinement system was lower than the grass-based system.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, dairy farm GHG emissions are frequently assessed in isolation from dairy production for instance, per unit area or per LU (Casey and Holden, 2005;Capper et al, 2009). In contrast, the LCA approach is typically applied to quantify GHG emissions per unit of product for dairy production systems, for example, Arsenault et al (2009) andFlysjö et al (2011). Previous studies have shown that expressing GHG emissions per unit area, per LU or per unit of product can change the ranking of dairy systems' GHG emissions (Casey and Holden, 2005;Capper et al, 2009;O'Brien et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

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

O’Brien

Shalloo

Patton

et al. 2012

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.