HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Hooks, T. Articles by Gaussoin, R. E. Search for Related Content PubMed Articles by Hooks, T. Articles by Gaussoin, R. E. Agricola Articles by Hooks, T. Articles by Gaussoin, R. E. Related Collections Spatial Distribution Statistics

CROP BREEDING & GENETICS

Changing the Support of a Spatial Covariate: A Simulation Study

^a Dep. of Statistics, Univ. of Nebraska–Lincoln, Lincoln, NE 68583-0963
^b USDA-ARS, NPA Grains, Bioenergy, and Forage Research, Univ. of Nebraska–Lincoln, Lincoln, NE 68583-0897
^c Dep. of Agronomy and Horticulture, Univ. of Nebraska–Lincoln, Lincoln, NE 68583-0915

^* Corresponding author (THooks{at}winona.edu).

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 may be used to analyze such data. However, very 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 most 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 to obtain the most accurate experimental results.

Abbreviations: AICC, corrected Akaike information criteria