Meta-analysis of methane yields from anaerobic digestion of dairy cattle manure. (March 2016) - Miranda, Nicole D.
Abstract: This work presents a novel meta-analysis for methane yields from anaerobically digesting dairy cattle manure. A database is compiled from 115 articles (2181 cases) by systematically assessing the academic literature. Y C H 4 and secondary variables collected in the database describe performance, operation conditions of digester and manure composition. Inputs to the database are normalised to enable inter-study uni- and multi-variate analysis, according to different types of digesters. Most cases (N = 1299) are reported for batch digesters and meta-analysis results in a mean Y C H 4 of 0.230±0.016 m 3 kg −1 . CSTR (N = 241), results in mean of 0.190±0.016 m 3 kg −1 (random-effect model). Other continuous digesters, such as fixed-film, plug-flow and UASB require further cases for the application of meta-analysis. For non-specified semi-continuous digesters (N = 347), the mean calculated by the random-effect model is 0.204±0.032 m 3 kg −1 (N = 27). With respect to multi-variate analysis of operation conditions, batch digesters form four clusters, while two clusters are identified for CSTR and three for semi-continuous digesters. For variables describing manure composition, batch digesters present eight clusters, while CSTR and semi-continuous digesters have insufficient cases. The strongest correlations found for these secondary variables (Spearman's rho: −0.685 and −0.696) result for pH and Y C H 4 in batch and CSTR, respectively. Maximum Y C H 4 estimated by meta-analysis are proposed to replace the default IPCC values because they are based on a larger sample size and integrate updated literature. In particular, markedly higher Y C H 4 are obtained for Asia, Middle East and Indian Subcontinent than those in the IPCC guidelines. Highlights: A new database of biogas from dairy cattle manure is systematically compiled. CH4 yields are examined with respect to system operation and slurry composition. Underlying structure in the database is analyzed by uni-/multi-variate statistics. Robust and up-to-date yields are presented according to digester type. Applying these yields to the IPCC methodologies can improve GHG estimations.