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

Nitrous oxide (NO) is one of the greenhouse gases that can contribute to global warming. Spatial variability of NO can lead to large uncertainties in prediction. However, previous studies have often ignored the spatial dependency to quantify the NO - 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 NO 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 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 NO 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 NO emissions. We found that incorporating spatial dependency in the model not only substantially improved predictions of NO 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 NO emissions across this study region