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Application of Bayesian statistics to estimate nitrous oxide emission factors of the nitrogen fertilisers in UK grasslands

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dc.contributor.authorCowan, N.
dc.contributor.authorLevy, P.
dc.contributor.authorDrewer, J.
dc.contributor.authorCarswell, A.
dc.contributor.authorShaw, R.
dc.contributor.authorSimmons, I.
dc.contributor.authorBache, C.
dc.contributor.authorMarinheiro, J.
dc.contributor.authorBrichet, J.
dc.contributor.authorSanchez-Rodriguez, A.R.
dc.contributor.authorCotton, J.
dc.contributor.authorHill, P.W.
dc.contributor.authorChadwick, D.R.
dc.contributor.authorJones, D.L.
dc.contributor.authorMisselbrook, T.H.
dc.contributor.authorShiba, U.
dc.date.accessioned2019-05-17T11:24:16Z
dc.date.available2019-05-17T11:24:16Z
dc.date.issued2019
dc.description.abstractTrapezoidal integration by linear interpolation of data points is by far the most commonly used method of cumulative flux calculations of nitrous oxide (N2O) in studies that use flux chambers; however, this method is incapable of providing accurate uncertainty estimates. A Bayesian approach was used to calculate N2O emission factors (EFs) and their associated uncertainties from flux chamber measurements made after the application of nitrogen fertilisers, in the form of ammonium nitrate (AN), urea (Ur) and urea treated with Agrotain® urease inhibitor (UI) at four grassland sites in the UK. The comparison between the cumulative fluxes estimated using the Bayesian and linear interpolation methods were broadly similar (R2=0.79); however, the Bayesian method was capable of providing realistic uncertainties when a limited number of data points is available. The study reports mean EF values (and 95% confidence intervals) of 0.60 ± 0.63, 0.29 ± 0.22 and 0.26 ± 0.17% of applied N emitted as N2O for the AN, Ur and UI treatments, respectively. There was no significant difference between N2O emissions from the Ur and UI treatments. In the case of the automatic chamber data collected at one site in this study, the data did not fit the log-normal model, implying that more complex models may be needed, particularly for measurement data with high temporal resolutionpt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationEnvironment International 128 (2019) 362–370pt_PT
dc.identifier.doihttps://doi.org/10.1016/j.envint.2019.04.054pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/17860
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionwww.elsevier.com/locate/envintpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectagriculturept_PT
dc.subjectN2Opt_PT
dc.subjecturease inhibitorpt_PT
dc.subjectureapt_PT
dc.subjectuncertaintypt_PT
dc.titleApplication of Bayesian statistics to estimate nitrous oxide emission factors of the nitrogen fertilisers in UK grasslandspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleEnvironment Internationalpt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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