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

Huang, Xiaodong, Grace, Peter, Hu, Wenbiao, Rowlings, David, & Mengersen, Kerrie (2013) Spatial prediction of N2O emissions in pasture : a Bayesian model averaging analysis. PLoS ONE, 8(6), e65039.

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Abstract

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.

Impact and interest:

1 citations in Scopus
1 citations in Web of Science®
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ID Code: 67157
Item Type: Journal Article
Refereed: Yes
DOI: 10.1371/journal.pone.0065039
ISSN: 1932-6203
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Stochastic Analysis and Modelling (010406)
Divisions: Current > Schools > School of Earth, Environmental & Biological Sciences
Current > QUT Faculties and Divisions > Faculty of Health
Past > Institutes > Institute for Creative Industries and Innovation
Current > Institutes > Institute of Health and Biomedical Innovation
Past > Institutes > Institute for Sustainable Resources
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Current > Schools > School of Public Health & Social Work
Copyright Owner: Copyright 2013 Huang et al.
Copyright Statement: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Deposited On: 12 Feb 2014 05:17
Last Modified: 16 Nov 2016 05:45

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