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United States Department of Agriculture

Agricultural Research Service

Title: Changing the Support of a Spatial Covariate: a Simulation Study

Authors
item Hooks, Tisha - UNINVERSITY OF NEBRASKA
item Pedersen, Jeffrey
item Marx, David - UNIVERSITY OF NEBRASKA
item Gaussoin, Roch - UNIVERSITY OF NEBRASKA

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 6, 2006
Publication Date: March 1, 2007
Citation: Hooks, T., Pedersen, J.F., Marx, D.B., Gaussoin, R.E. 2007. Changing the support of a spatial covariate: a simulation study. Crop Science 47:622-626.

Interpretive Summary: Researchers are increasingly able to capture data referenced to specific geographical coordinates on both the character of interest, and on other variables, called covariates, that can be measured but are not controllable within the constraints of their experiments. A combination of geostatistical models and analysis of covariance methods is used to analyze such data. However, basic questions regarding the effects of using a covariate whose support (frequency or density of measurements) differs from that of the response variable must be addressed to utilize these methods more efficiently. In this experiment, a simulation study was conducted to assess the following: (i) the gain in efficiency when geostatistical models are used, (ii) the gain in efficiency when analyses of covariance methods are used, and (iii) the effects of including a covariate whose support differs from that of the response variable in the analysis. Results from this study suggest that analyses which both account for spatial structure and exploit information from a covariate are most powerful. Also, the results indicate that the support of the covariate should be as close as possible to the support of the response variable in order to obtain the most accurate experimental results.

Technical Abstract: Researchers are increasingly able to capture spatially referenced data on both a response and a covariate more frequently and in more detail. A combination of geostatisical models and analysis of covariance methods is used to analyze such data. However, basic questions regarding the effects of using a covariate whose support differs from that of the response variable must be addressed to utilize these methods more efficiently. In this experiment, a simulation study was conducted to assess the following: (i) the gain in efficiency when geostatistical models are used, (ii) the gain in efficiency when analysis of covariance methods are used, and (iii) the effects of including a covariate whose support differs from that of the response variable in the analysis. This study suggests that analyses which both account for spatial structure and exploit information from a covariate are most powerful. Also, the results indicate that the support of the covariate should be as close as possible to the support of the response variable in order to obtain the most accurate experimental results.

Last Modified: 10/23/2014
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