Structural equation modeling in the plant sciences: An example using yield components in oat
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les 1 000 travaux triés →2 modèles sur 3 ont qualifié ce travail de métarecherche. Ce travail est contesté : il se situe à la frontière empirique du domaine, et son statut dépend du modèle interrogé. C'est l'un des 51 travaux du dossier des désaccords.
Tutorial introducing plant scientists to structural equation modeling, reviewing its principles and offering guidelines on when it is appropriate; the oat field trial is an illustrative vehicle and the object is statistical practice in a research community.
Structural equation modeling is used to analyze oat yield, so the method is not the object of study.
Primary aim is introducing SEM as statistical practice for plant scientists, with guidelines on when SEM is appropriate.
Résumé
Lamb, E. G., Shirtliffe, S. J. and May, W. E. 2011. Structural equation modeling in the plant sciences: An example using yield components in oat. Can. J. Plant Sci. 91: 603-619. Structural equation modeling (SEM) is a powerful statistical approach for the analysis of complex intercorrelated data with a wide range of potential applications in the plant sciences. In this paper we introduce plant scientists to the principles and practice of SEM using as an example an agronomic field trial. We briefly review the history of SEM and path analysis and introduce the statistical concepts underlying SEM. We demonstrate the use of observed and latent variable structural equation models using a multi-site multi-year field trial examining the effects of seed size and seeding density on the plant density and yield of oat in Saskatchewan. Using SEM allowed for insights that a standard univariate analysis would not have revealed. We show that seeding density has strong effects on plant and panicle density, but has very limited effects on final yield. Plant density has a consistent non-linear effect on panicle density across location that was not affected by precipitation. In contrast, the implicit effect of precipitation on seed number appears to be the main driver for final yield. Incorporating precipitation data into the model demonstrates how mechanistic models can be developed by including in the path diagram variables that would normally treated as random factors in a mixed model analysis. Finally, we provide a guideline to assist plant scientists in determining whether SEM is an appropriate method to be used for the analysis of their data.
Conservé avec la notice de tri, où il sert de preuve aux étiquettes ci-dessus.
La notice
- Revue
- BioOne Complete (BioOne)
- Thématique
- Genetics and Plant Breeding
- Domaine
- Agricultural and Biological Sciences
- Établissements canadiens
- —
- Organismes subventionnaires
- —
- Mots-clés
- Structural equation modelingPath analysis (statistics)SeedingUnivariateLatent variablePlant densityPanicleYield (engineering)Variable (mathematics)
- Résumé présent dans OpenAlex
- oui