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A Four Dimensional Spatio-Temporal Analysis of an Agricultural Dataset.

Donald MR, Mengersen KL, Young RR - PLoS ONE (2015)

Bottom Line: The proposed models vary in their representation of the spatial correlation in the data, the assumed temporal pattern and choice of conditional autoregressive (CAR) and other priors.In terms of the substantive question, we find that response cropping is generally more effective than long fallow cropping in reducing soil moisture at the depths considered (100 cm to 220 cm).Thus, if we wish to reduce the possibility of deep drainage and increased groundwater salinity, the recommended cropping system is response cropping.

View Article: PubMed Central - PubMed

Affiliation: Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia.

ABSTRACT
While a variety of statistical models now exist for the spatio-temporal analysis of two-dimensional (surface) data collected over time, there are few published examples of analogous models for the spatial analysis of data taken over four dimensions: latitude, longitude, height or depth, and time. When taking account of the autocorrelation of data within and between dimensions, the notion of closeness often differs for each of the dimensions. Here, we consider a number of approaches to the analysis of such a dataset, which arises from an agricultural experiment exploring the impact of different cropping systems on soil moisture. The proposed models vary in their representation of the spatial correlation in the data, the assumed temporal pattern and choice of conditional autoregressive (CAR) and other priors. In terms of the substantive question, we find that response cropping is generally more effective than long fallow cropping in reducing soil moisture at the depths considered (100 cm to 220 cm). Thus, if we wish to reduce the possibility of deep drainage and increased groundwater salinity, the recommended cropping system is response cropping.

No MeSH data available.


Long fallowing vs Response cropping at all depths.Left panel: Point estimates from the MCMC iterates of Method 1. Right panel: Spline curves from BayesX pspline estimation (Method 2, Eq 6).
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pone.0141120.g003: Long fallowing vs Response cropping at all depths.Left panel: Point estimates from the MCMC iterates of Method 1. Right panel: Spline curves from BayesX pspline estimation (Method 2, Eq 6).

Mentions: The left panel of Fig 3 shows the point estimates from Method 1 for the contrasts at depths 100 cm to 220 cm. This graph exhibits an apparent continuity of the contrast estimates across time and depth. The same estimates are graphed again as a contour graph of moisture across day and depth (top panel Fig 2) in order to show the continuity across time and depth.


A Four Dimensional Spatio-Temporal Analysis of an Agricultural Dataset.

Donald MR, Mengersen KL, Young RR - PLoS ONE (2015)

Long fallowing vs Response cropping at all depths.Left panel: Point estimates from the MCMC iterates of Method 1. Right panel: Spline curves from BayesX pspline estimation (Method 2, Eq 6).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4626095&req=5

pone.0141120.g003: Long fallowing vs Response cropping at all depths.Left panel: Point estimates from the MCMC iterates of Method 1. Right panel: Spline curves from BayesX pspline estimation (Method 2, Eq 6).
Mentions: The left panel of Fig 3 shows the point estimates from Method 1 for the contrasts at depths 100 cm to 220 cm. This graph exhibits an apparent continuity of the contrast estimates across time and depth. The same estimates are graphed again as a contour graph of moisture across day and depth (top panel Fig 2) in order to show the continuity across time and depth.

Bottom Line: The proposed models vary in their representation of the spatial correlation in the data, the assumed temporal pattern and choice of conditional autoregressive (CAR) and other priors.In terms of the substantive question, we find that response cropping is generally more effective than long fallow cropping in reducing soil moisture at the depths considered (100 cm to 220 cm).Thus, if we wish to reduce the possibility of deep drainage and increased groundwater salinity, the recommended cropping system is response cropping.

View Article: PubMed Central - PubMed

Affiliation: Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia.

ABSTRACT
While a variety of statistical models now exist for the spatio-temporal analysis of two-dimensional (surface) data collected over time, there are few published examples of analogous models for the spatial analysis of data taken over four dimensions: latitude, longitude, height or depth, and time. When taking account of the autocorrelation of data within and between dimensions, the notion of closeness often differs for each of the dimensions. Here, we consider a number of approaches to the analysis of such a dataset, which arises from an agricultural experiment exploring the impact of different cropping systems on soil moisture. The proposed models vary in their representation of the spatial correlation in the data, the assumed temporal pattern and choice of conditional autoregressive (CAR) and other priors. In terms of the substantive question, we find that response cropping is generally more effective than long fallow cropping in reducing soil moisture at the depths considered (100 cm to 220 cm). Thus, if we wish to reduce the possibility of deep drainage and increased groundwater salinity, the recommended cropping system is response cropping.

No MeSH data available.