Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.

Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few resea...

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Autores principales:	Xiaodong Huang, Peter Grace, Wenbiao Hu, David Rowlings, Kerrie Mengersen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b53f9a6627214433a9f874cc4f212dab
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spelling	oai:doaj.org-article:b53f9a6627214433a9f874cc4f212dab2021-11-18T07:43:07ZSpatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.1932-620310.1371/journal.pone.0065039https://doaj.org/article/b53f9a6627214433a9f874cc4f212dab2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23750227/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2) field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.Xiaodong HuangPeter GraceWenbiao HuDavid RowlingsKerrie MengersenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 6, p e65039 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaodong Huang Peter Grace Wenbiao Hu David Rowlings Kerrie Mengersen Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.
description	Nitrous oxide (N2O) is one of the greenhouse gases that can contribute to global warming. Spatial variability of N2O can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the N2O - environmental factors relationships. Few researches have examined the impacts of various spatial correlation structures (e.g. independence, distance-based and neighbourhood based) on spatial prediction of N2O emissions. This study aimed to assess the impact of three spatial correlation structures on spatial predictions and calibrate the spatial prediction using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data. The data were measured in 17 chambers randomly placed across a 271 m(2) field between October 2007 and September 2008 in the southeast of Australia. We used a Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. We compared these with a Bayesian regression model with independent errors. The three approaches resulted in different derived maps of spatial prediction of N2O emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of N2O emission from soil, but also better quantified uncertainties of soil parameters in the study. The hybrid model structure obtained by BMA improved the accuracy of spatial prediction of N2O emissions across this study region.
format	article
author	Xiaodong Huang Peter Grace Wenbiao Hu David Rowlings Kerrie Mengersen
author_facet	Xiaodong Huang Peter Grace Wenbiao Hu David Rowlings Kerrie Mengersen
author_sort	Xiaodong Huang
title	Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.
title_short	Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.
title_full	Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.
title_fullStr	Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.
title_full_unstemmed	Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.
title_sort	spatial prediction of n2o emissions in pasture: a bayesian model averaging analysis.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/b53f9a6627214433a9f874cc4f212dab
work_keys_str_mv	AT xiaodonghuang spatialpredictionofn2oemissionsinpastureabayesianmodelaveraginganalysis AT petergrace spatialpredictionofn2oemissionsinpastureabayesianmodelaveraginganalysis AT wenbiaohu spatialpredictionofn2oemissionsinpastureabayesianmodelaveraginganalysis AT davidrowlings spatialpredictionofn2oemissionsinpastureabayesianmodelaveraginganalysis AT kerriemengersen spatialpredictionofn2oemissionsinpastureabayesianmodelaveraginganalysis
_version_	1718423057428643840

Spatial prediction of N2O emissions in pasture: a Bayesian model averaging analysis.

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