Dairy farms can apply anaerobic digestion (AD) as a manure management system, while producing renewable energy. Ultimately, this can reduce greenhouse gas (GHG) emissions. There is much research work that has quantified the changes in emissions due to AD. However, important methodologies such as the Intergovernmental Panel on Climate Change (IPCC) Guidelines, rely only on a small sample of the accumulated scientific findings in the field. This thesis improves the robustness of these methodologies by applying data-driven techniques to estimate values of the energy output of AD systems and their consequent effect in GHG emissions. For this purpose, meta-analyses techniques are applied to mathematically combine metrics from 155 non-standardised research publications (i.e. with different boundaries, scopes and functional units). As a first step, a novel database is created by systematically searching for relevant articles and assessing them against defined criteria. The database is divided in two parts. Given that the offset of GHGs is highly dependent on the energy output of AD system, empirical methane yields (i.e. key metric of AD performance) are collected in Part I of the database. GHG released from different farm activities are input to Part II of the database. To quantify the change in emissions from these activities, standard baseline and AD scenarios are defined. The second step of the meta-analysis consists of applying uni- and multi-variate tests to the database. For Part I, methane yields are analysed in terms of type of digesters. From the batch digesters, new maximum methane yields are proposed based on the combined results of 42 peer reviewed articles. These results offer better estimates than default values of methane yields from the 2007 Guidelines of the IPCC, which only consider two studies. For continuously stirred tank digesters and semi-continuous digesters lower methane yields are revealed. Multi-variate analysis of methane yields together with operating conditions and manure composition, enable the identification of clusters. These groups of variables can be useful to build potential AD scenarios in dairy farms. For Part II of the database, relative changes in emissions between the activities in the standardised baseline and AD scenarios, are examined. It is found, through meta-analysis, that replacing raw manure by anaerobically-treated manure (i.e. digestate) in storage tanks and for field- application, mitigates baseline emissions by 38.7% and 6.9%, respectively. These relative changes can be used to estimate emissions from digestate, being more specific and evidence-based than the current methodology from the IPCC. In addition, relative changes found for offset of fossil fuels by biogas generated in the AD scenarios indicate a reduction of baseline emissions by 9.0%. Only methane leaks from digesters significantly increase the baseline emissions (by 7.4%). Finally, results found by meta-analyses of methane yields and changes in emissions are applied to four dairy farm case studies. The work presented in the case studies demonstrates the benefits of enhancing the robustness of methods to estimate the effect of AD on GHG emissions from dairy farms.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:730283 |
| Date | January 2016 |
| Creators | Miranda, Nicole |
| Contributors | McCulloch, Malcolm |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ora.ox.ac.uk/objects/uuid:d613759f-8f42-494e-8e95-e977a5fc41b4 |
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