Suchergebnisse

The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.

Estimates of global greenhouse gases (GHG) emissions attributable to livestock range from 8 to 51%. This variability creates confusion among policy makers and the public as it suggests that there is a lack of consensus among scientists with regard to the contribution of livestock to global GHG emissions. In reality, estimates of international scientific organizations such as the International Governmental Panel on Climate Change (IPCC) and the Food and Agriculture Organization (FAO) are in close agreement, with variation mainly arising on how GHG emissions are allocated to land use and land use change. Other estimates involve major deviations from international protocols, such as estimated global warming potential of CH4 or including respired CO2 in GHG emissions. These approaches also fail to differentiate short-term CO2 arising from oxidation of plant C by ruminants from CO2 released from fixed fossil C through combustion. These deviances from internationally accepted protocols create confusion and direct attention from anthropomorphic practices which have the most important contribution to global GHG emissions. Global estimates of livestock GHG emissions are most reliable when they are generated by internationally recognized scientific panels with expertise across a range of disciplines, and with no preconceived bias to particular outcomes.

Developing efficient policy instruments and incentive schemes to promote the uptake of greenhouse gas mitigation measures requires some kind of prioritisation of the mitigation measures. An important consideration in this process is the estimated cost and costefficiency the measures. The high number of reports done in developed countries show a high variability in the country-level cost-effectiveness estimates, and suggest that approaches providing higher granularity at the spatial and farm type could suit better to the purpose of regional policy development. At the same time, there is still a gap in our understanding of economic mitigation potential of agriculture in developing and newly industrialised countries.To address these questions this report presents three studies. The first is a literature review of the cost-effectiveness estimates of mitigation measures published in the past 15 years, discussing the variability in these estimates. The second study reports on marginal abatement cost curves for beef cattle production in Brazil. Finally, the last report presents the conceptual basis of a tool to assess the financial implications of the mitigation measures to be used in parallel with the FarmAC model, ultimately providing mitigation measure costeffectiveness estimates specific to individual farms. Additionally, it describes the selection of mitigation measures which have been assessed at the farm level in Component 3 of the AnimalChange project.