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On the Analysis of Combined Experiments

Published online by Cambridge University Press:  20 January 2017

David C. Blouin,
Eric P. Webster and
Jason A. Bond
David C. Blouin*
Affiliation:
Department of Experimental Statistics, 45 Agricultural Administration Building, Louisiana State University Agricultural Center, Baton Rouge, LA 70803
Eric P. Webster
Affiliation:
School of Plant, Environmental, and Soil Sciences, 104 Sturgis Hall, Louisiana State University Agricultural Center, Baton Rouge, LA 70803
Jason A. Bond
Affiliation:
Delta Research and Extension Center, Mississippi State University, Stoneville, MS 38776
*
Corresponding author's E-mail: dblouin@lsu.edu
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Abstract

The replication of experiments over multiple environments such as locations and years is a common practice in field research. A major reason for the practice is to estimate the effects of treatments over a variety of environments. Environments are frequently classed as random effects in the model for statistical analysis, while treatments are almost always classed as fixed effects. Where environments are random and treatments are fixed, it is not always necessary to include all possible interactions between treatments and environments as random effects in the model. The rationale for decisions about the inclusion or exclusion of fixed by random effects in a mixed model is presented. Where the effects of treatments over broad populations of environments are to be estimated, it is often most appropriate to include only those fixed by random effects that reference experimental units.

La repetición de experimentos en múltiples ambientes, como por ejemplo, lugares y años, es una práctica común en investigaciones de campo. Una de las razones fundamentales para esta práctica es la de estimar los efectos de los tratamientos en una variedad de ambientes. Estos últimos, son frecuentemente clasificados como efectos aleatorios en el modelo de análisis estadístico, mientras que los tratamientos son casi siempre clasificados como efectos fijos. Donde los ambientes son aleatorios y los tratamientos son fijos, no siempre es necesario incluir todas las interacciones posibles entre ellos como efectos aleatorios en el modelo. En este trabajo se presenta la justificación para las decisiones en cuanto a incluir o excluir los efectos fijos por aleatorios, en un modelo mixto. En casos en donde se van a estimar los efectos de los tratamientos sobre poblaciones extensas de ambientes, frecuentemente es más apropiado incluir solamente aquellos efectos fijos por aleatorios que hagan referencia a las unidades experimentales.

Keywords

Fixed effectinteractionmixed modelpredictable functionrandom effect

Information

Type
Education/Extension
Information
Weed Technology , Volume 25 , Issue 1 , March 2011, pp. 165 - 169
Copyright
Copyright © Weed Science Society of America 

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