Influence of key factors on ammonia and nitrous oxide emission factors for excreta deposited by livestock and land-applied manure.

Ammonia (NH) and nitrous oxide (NO) emissions from livestock manure management have a significant impact on air quality and climate change. There is an increasing urgency to improve our understanding of drivers influencing these emissions. We analysed the DATAMAN ("DATAbase for MANaging greenhouse gas and ammonia emissions factors") database to identify key factors influencing (i) NH emission factors (EFs) for cattle and swine manure applied to land and (ii) NO EFs for cattle and swine manure applied to land, and (iii) cattle urine, dung and sheep urine deposited during grazing. Slurry dry matter (DM) content, total ammoniacal nitrogen (TAN) concentration and method of application were significant drivers of NH EFs from cattle and swine slurry. Mixed effect models explained 14-59 % of the variance in NH EFs. Apart from the method of application, the significant influence of manure DM, manure TAN concentration or pH on NH EFs suggests mitigation strategies should focus on these. Identifying key factors influencing NO EFs from manures and livestock grazing was more challenging, likely because of the complexities associated with microbial processes and soil physical properties impacting NO production and emissions. Generally, significant factors were soil-related e.g. soil water content, pH, clay content, suggesting mitigations may need to consider the conditions of the receiving environment for manure spreading and grazing deposition. Total variability explained by terms in mixed effect model was on average 66 %, with the random effect 'experiment identification number' explaining, on average, 41 % of the total variability in the models. We suspect this term captured the effect of non-measured manure, soil and climate factors and any biases in application and measurement technique effects associated with individual experiments. This analysis has helped to improve our understanding of key factors of NH and NO EFs for inclusion within models. With more studies over time, insights into the underlying processes influencing emissions will be further improved.