A - Papers appearing in refereed journals
Hafner, S. D., Pacholski, A., Bittman, S., Carozzi, M., Chantigny, M., Genermont, S., Hani, C., Hansen, M. N., Huijsmans, J., Kupper, T., Misselbrook, T. H., Neftel, A., Nyord, T. and Sommer, S. G. 2019. A flexible semi-empirical model for estimating ammonia volatilization from field-applied slurry. Atmospheric Environment. 199, pp. 474-484.
|Authors||Hafner, S. D., Pacholski, A., Bittman, S., Carozzi, M., Chantigny, M., Genermont, S., Hani, C., Hansen, M. N., Huijsmans, J., Kupper, T., Misselbrook, T. H., Neftel, A., Nyord, T. and Sommer, S. G.|
This work describes a semi-empirical dynamic model for predicting ammonia volatilization from field-applied slurry. Total volatilization is the sum of first-order transfer from two pools: a "fast" pool representing slurry in direct contact with the atmosphere, and a “slow” one representing fractions less available for emission due to infiltration or other processes. This simple structure is sufficient for reproducing the characteristic course of emission over time. Values for parameters that quantify effects of the following predictor variables on partitioning and transfer rates were estimated from a large data set of emission from cattle and pig slurry (490 field plots in 6 countries from the ALFAM2 database): slurry dry matter, application method, application rate, incorporation (shallow or deep), air temperature, wind speed, and rainfall rate. The effects of acidification were estimated using a smaller dataset. Model predictions generally matched the measured course of emission over time in a reserved data subset used for evaluation, although the model over- or under-estimated emission for many individual plots. Mean error was ca. 12% of applied total ammoniacal nitrogen (and as much as 82% of measured emission) for 72 h cumulative emission, and model efficiency (fraction of observed variation explained by the model) was 0.5–0.7. Most of the explanatory power of the model was related to application method. The magnitude and sign of (apparent) model error varied among countries, highlighting the need to understand why measured emission varies among locations. The new model may be a useful tool for predicting fertilizer efficiency of field-applied slurries, assessing emission factors, and quantifying the impact of mitigation. The model can readily be applied or extended, and is available as an R package (ALFAM2, https://github.com/sashahafner/ALFAM2) or a simple spreadsheet (http://www.alfam.dk).
|Keywords||Manure; Slurry; Field application; Ammonia; Model; Software|
|Year of Publication||2019|
|Journal citation||199, pp. 474-484|
|Digital Object Identifier (DOI)||doi:10.1016/j.atmosenv.2018.11.034|
|Open access||Published as non-open access|
|Funder||Biotechnology and Biological Sciences Research Council|
|Funder project or code||S2N - Soil to Nutrition - Work package 2 (WP2) - Adaptive management systems for improved efficiency and nutritional quality|
|Online||19 Nov 2018|
|Publication process dates|
|Accepted||12 Nov 2018|
|Copyright license||CC BY|
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