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

Huang X, Grace P, Hu W, Rowlings D, Mengersen K - PLoS ONE (2013)

Bottom Line: We compared these with a Bayesian regression model with independent errors.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.

View Article: PubMed Central - PubMed

Affiliation: Mathematical Sciences, Queensland University of Technology, Brisbane, Australia. xiaodong.huang@student.qut.edu.au

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.

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Maps of observed and posterior mean Ln(N2O) (ug N2O-N m −2 hr −1) from the CAR, EXP, BMA and linear regression models across the study site in pasture.
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pone-0065039-g001: Maps of observed and posterior mean Ln(N2O) (ug N2O-N m −2 hr −1) from the CAR, EXP, BMA and linear regression models across the study site in pasture.

Mentions: The spatial patterns of predicted N2O using the CAR and EXP models were similar to the observed spatial pattern, particularly the CAR model (Figure 1). However, there were slight errors for classifications of areas into different emission level groups for the two spatial models, particularly the EXP model. The results of the independent model could not match the observed spatial distribution of N2O emission.


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

Huang X, Grace P, Hu W, Rowlings D, Mengersen K - PLoS ONE (2013)

Maps of observed and posterior mean Ln(N2O) (ug N2O-N m −2 hr −1) from the CAR, EXP, BMA and linear regression models across the study site in pasture.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3672208&req=5

pone-0065039-g001: Maps of observed and posterior mean Ln(N2O) (ug N2O-N m −2 hr −1) from the CAR, EXP, BMA and linear regression models across the study site in pasture.
Mentions: The spatial patterns of predicted N2O using the CAR and EXP models were similar to the observed spatial pattern, particularly the CAR model (Figure 1). However, there were slight errors for classifications of areas into different emission level groups for the two spatial models, particularly the EXP model. The results of the independent model could not match the observed spatial distribution of N2O emission.

Bottom Line: We compared these with a Bayesian regression model with independent errors.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.

View Article: PubMed Central - PubMed

Affiliation: Mathematical Sciences, Queensland University of Technology, Brisbane, Australia. xiaodong.huang@student.qut.edu.au

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.

Show MeSH
Related in: MedlinePlus