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Abstract: Agriculture contributes significantly to the anthropogenic emissions of non-CO2 greenhouse gases methane and nitrous oxide. In this paper, a review is presented of the agriculture related sources of methane and nitrous oxide, and of the main strategies for mitigation. The rumen is the most important source of methane production, especially in cattle husbandry. Less, but still substantial, amounts of methane are produced from cattle manures. In pig and poultry husbandry, most methane originates from manures. The main sources of nitrous oxide are: nitrogen fertilisers, land applied animal manure, and urine deposited by grazing animals. Most effective mitigation strategies for methane comprise a source approach, i.e. changing animals’ diets towards greater efficiencies. Methane emissions, however, can also be effectively reduced by optimal use of the gas produced from manures, e.g. for energy production. Frequent and complete manure removal from animal housing, combined with on-farm biogas production is an example of an integrated on-farm solution. Reduced fertiliser nitrogen input, optimal fertiliser form, adding nitrification inhibitors, land drainage management, and reduced land compaction by restricted grazing are the best ways to mitigate nitrous oxide emissions from farm land, whereas, management of bedding material and solid manure reduce nitrous oxide emissions from housing and storage. Other than for methane, mitigation measures for nitrous oxide interact with other important environmental issues, like reduction of nitrate leaching and ammonia emission. Mitigation strategies for reduction of the greenhouse gases should also minimize pollution swapping. [Copyright &y& Elsevier]

[Display omitted] • A modeling cascade permits cost-efficient ammonia emission predictions. • Urination and nitrogen excretion patterns and local near-surface wind are crucial. • Alkalizing and cooling of urine puddles in the course of time must be taken into account. • Effective cleaning efficiency modulates the average emission value. • Case study measurements for cleaning efficiency,cooling and alkalizing are needed. One way to estimate ammonia emission rates from naturally ventilated housing systems is to scale-up mechanistic modeling results. However, obtaining the relevant data to set initial and boundary conditions adequately is usually very challenging and for a whole barn barely possible. This study has investigated the potential of coupling different mechanistic modeling approaches towards an overarching barn scale ammonia emission model, which might permit ammonia emission projections for naturally ventilated housing systems with minimal measurement efforts. To this end, we combined an ammonia volatilization model for shallow urine or slurry puddles with a dynamic mechanistic model of digestion and excretion of nitrogen, an empirical model to estimate urination volumes, semi-empirical models for pH and temperature dynamics of the puddles and a mechanistic air flow model. The ammonia volatilization model was integrated with a time step of one second over a period of twenty-four hours, while the relevant boundary conditions were updated on an hourly base (determined by the other mentioned submodels). Projections and uncertainties of the approach were investigated for a farm case with about ten months of on-farm measurements in a naturally ventilated dairy cattle building with scraped solid floor in Northern Germany. The results showed that the nested model was in general capable to reproduce the long-term emission trend and variability, while the short-term variability was damped compared with the emission measurements. A sensitivity study indicated that particularly a refinement of the submodules for urine puddle alkalizing, urination volume and urea concentration distributions as well as for local near-surface wind speeds have a great potential to further improve the overall model accuracy. The cleaning efficiency of the scraper has turned out to be a crucial and sensitive parameter in the modeling, which so far has been described insufficiently by measurements or modeling approaches. [ABSTRACT FROM AUTHOR]