ALFAM2 (version 4.2.14)

alfam2pars: Default Parameter Sets for the ALFAM2 Model

Description

These default parameter sets can be used to generate average predictions for the alfam2 function. They are described in the papers cited below.

Usage

alfam2pars01
alfam2pars02
alfam2pars03

Author

Sasha D. Hafner, Christoph Haeni, Roland Fuss

Details

In general, the latest default parameter set (highest version number) should be used. Set 1 was presented in Hafner et al. (2019), 2 in Hafner et al. (2021, 2024), and 3 in Hafner et al. (2025). See vignette for more details.

References

Hafner S, Pedersen J, Fuss R, Kamp J, Dalby F, Amon B, Pacholski A, Adamsen A, Sommer S., 2025. Improved tools for estimation of ammonia emission from field-applied animal slurry: refinement of the ALFAM2 model and database. Atmospheric Environment. tools:::Rd_expr_doi("10.1016/j.atmosenv.2024.120910")

Hafner, S.D., Pacholski, A., Bittman, S., Carozzi, M., Chantigny, M., Genermont, S., Haeni, C., Hansen, M., Huijsmans, J., Kupper, T., Misselbrook, T., Neftel, A., Nyord, T., Sommer, S. 2019. A flexible semi-empirical model for estimating ammonia volatilization from field-applied slurry. Atmospheric Environment 199 474-484. tools:::Rd_expr_doi("10.1016/j.atmosenv.2018.11.034")

Hafner, S.D., Nyord, T., Sommer, S.G., Adamsen, A.P.S. 2021. Estimation of Danish emission factors for ammonia from field-applied liquid manure for 1980 to 2019. Danish Centre for Food and Agriculture, Aarhus University, Aarhus, Denmark. Report no. 2021-0251862.

Hafner, S.D., Kamp, J.N., Pedersen, J., 2024. Experimental and model-based comparison of wind tunnel and inverse dispersion model measurement of ammonia emission from field-applied animal slurry. Agricultural and Forest Meteorology 344, 109790. tools:::Rd_expr_doi("10.1016/j.agrformet.2023.109790")

The AlFAM2 project website. https://projects.au.dk/alfam/

Examples

# To view parameter values
alfam2pars03

# One possible way to facilitate comparison of different sets
nms <- unique(c(names(alfam2pars01), names(alfam2pars02), names(alfam2pars03)))
pars <- matrix(rep(NA, length(nms) * 3), 
	       ncol = 3,
	       dimnames = list(nms, c('alfam2pars01', 'alfam2pars02', 'alfam2pars03')))
pars[names(alfam2pars01), 1] <- alfam2pars01
pars[names(alfam2pars02), 2] <- alfam2pars02
pars[names(alfam2pars03), 3] <- alfam2pars03
pars

# See vignette for more details on parameters and predictor variables
# vignette("ALFAM2-start")