Data

Spatial prediction of N2O emissions in a Mooloolah pasture: summary statistics

Queensland University of Technology
Distinguished Professor Kerrie Mengersen (Aggregated by) Dr David Rowlings (Aggregated by) Grace, Peter (Aggregated by) Professor Peter Grace (Aggregated by) Professor Wenbiao Hu (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://goo.gl/iQvNf0&rft.title=Spatial prediction of N2O emissions in a Mooloolah pasture: summary statistics&rft.identifier=10378.3/8085/1018.15726&rft.publisher=Queensland University of Technology&rft.description=The dataset is the product of a study which assessed the impact of three spatial correlation structures on spatial predictions. Spatial predictions were calibrated using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data.The data were measured in 17 chambers randomly placed across a 271 m2 field between October 2007 and September 2008 in Mooloolah, Queensland. A Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model were used to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site. The dataset presents summary statistics of observed variables for the 17 chambers over the sampling period.&rft.creator=Distinguished Professor Kerrie Mengersen&rft.creator=Dr David Rowlings&rft.creator=Grace, Peter&rft.creator=Professor Peter Grace&rft.creator=Professor Wenbiao Hu&rft.date=2015&rft.coverage=152.963267,-26.765970&rft_rights=© 2013 Huang et al. &rft_rights=Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/au/&rft_subject=Spatial autocorrelation &rft_subject=Geography &rft_subject=Interpolation &rft_subject=Global change ecology &rft_subject=BIOLOGICAL SCIENCES&rft_subject=Nitrification &rft_subject=Geoinformatics&rft_subject=Geostatistics&rft_subject=Bayes theorem&rft_subject=Spatial analysis &rft_subject=Environmental chemistry &rft_subject=Environmental geography &rft_subject=Environmental sciences &rft_subject=Soil science &rft_subject=MATHEMATICAL SCIENCES&rft_subject=Spatial distribution&rft_subject=Linear regression analysis&rft_subject=Ecology&rft_subject=Soil chemistry &rft.type=dataset&rft.language=English Access the data
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Creative Commons Attribution 3.0
http://creativecommons.org/licenses/by/3.0/au/

© 2013 Huang et al.

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Postal Address:
Distinguished Professor Kerrie Mengersen

[email protected]

Full description

The dataset is the product of a study which assessed the impact of three spatial correlation structures on spatial predictions. Spatial predictions were calibrated using Bayesian model averaging (BMA) based on replicated, irregular point-referenced data.

The data were measured in 17 chambers randomly placed across a 271 m2 field between October 2007 and September 2008 in Mooloolah, Queensland.

A Bayesian geostatistical model and a Bayesian spatial conditional autoregressive (CAR) model were used to investigate and accommodate spatial dependency, and to estimate the effects of environmental variables on N2O emissions across the study site.

The dataset presents summary statistics of observed variables for the 17 chambers over the sampling period.

Data time period: 10 2007 to 30 09 2008

This dataset is part of a larger collection

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  • Associated with 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).
    eprints.qut.edu.au/67157/
  • Associated with Xiaodong Huang, Former QUT staff, Research collaborator
    goo.gl/pAfKLv

152.96327,-26.76597

152.963267,-26.76597

Subjects
Biological Sciences | Bayes theorem | Ecology | Environmental chemistry | Environmental geography | Environmental sciences | Geography | Geoinformatics | Geostatistics | Global change ecology | Interpolation | Linear regression analysis | Mathematical Sciences | Nitrification | Soil chemistry | Soil science | Spatial analysis | Spatial autocorrelation | Spatial distribution |

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  • Local : 10378.3/8085/1018.15726