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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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Related in: MedlinePlus

Maps of the posterior means of spatial variation in Ln(N2O) (ug N2O-N m−2 hr−1) emission using two spatial models and Bayesian model averaging in pasture.
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pone-0065039-g002: Maps of the posterior means of spatial variation in Ln(N2O) (ug N2O-N m−2 hr−1) emission using two spatial models and Bayesian model averaging in pasture.

Mentions: Figure 2 shows the distributions of the posterior means of the spatial variation in N2O emissions which were obtained using the two spatial models and BMA model. The three maps of posterior spatial variation show similar patterns. However, the CAR model displayed slightly larger areas for high or low spatial variation in N2O than those from the EXP model (Figure 2).


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 the posterior means of spatial variation in Ln(N2O) (ug N2O-N m−2 hr−1) emission using two spatial models and Bayesian model averaging in pasture.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0065039-g002: Maps of the posterior means of spatial variation in Ln(N2O) (ug N2O-N m−2 hr−1) emission using two spatial models and Bayesian model averaging in pasture.
Mentions: Figure 2 shows the distributions of the posterior means of the spatial variation in N2O emissions which were obtained using the two spatial models and BMA model. The three maps of posterior spatial variation show similar patterns. However, the CAR model displayed slightly larger areas for high or low spatial variation in N2O than those from the EXP model (Figure 2).

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