Hostname: page-component-89b8bd64d-dvtzq Total loading time: 0 Render date: 2026-05-07T09:40:44.911Z Has data issue: false hasContentIssue false
  • English
  • Français

A note on the analysis of germination data from complex experimental designs

Published online by Cambridge University Press:  18 September 2017

Signe M. Jensen ,
Christian Andreasen ,
Jens C. Streibig ,
Eshagh Keshtkar  and
Christian Ritz
Signe M. Jensen
Affiliation:
Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Højbakkegård Allé 13, DK-2630 Taastrup, Denmark
Christian Andreasen
Affiliation:
Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Højbakkegård Allé 13, DK-2630 Taastrup, Denmark
Jens C. Streibig
Affiliation:
Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Højbakkegård Allé 13, DK-2630 Taastrup, Denmark
Eshagh Keshtkar
Affiliation:
Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, PO Box 14115-336, Tehran, Iran
Christian Ritz*
Affiliation:
Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Rolighedsvej 26, DK-1958 Frederiksberg C, Denmark
*
*Correspondence Email: ritz@nexs.ku.dk
Get access Rights & Permissions [Opens in a new window]

Abstract

In recent years germination experiments have become more and more complex. Typically, they are replicated in time as independent runs and at each time point they involve hierarchical, often factorial experimental designs, which are now commonly analysed by means of linear mixed models. However, in order to characterize germination in response to time elapsed, specific event-time models are needed and mixed model extensions of these models are not readily available, neither in theory nor in practice. As a practical workaround we propose a two-step approach that combines and weighs together results from event-time models fitted separately to data from each germination test by means of meta-analytic random effects models. We show that this approach provides a more appropriate appreciation of the sources of variation in hierarchically structured germination experiments as both between- and within-experiment variation may be recovered from the data.

Keywords

between-experiment variationGerbera hybridalog-logistic functionmeta analysismixed modelrandomized complete block designtime-to-event data

Information

Type
Short Communication
Information
Seed Science Research , Volume 27 , Issue 4 , December 2017 , pp. 321 - 327
Check for updates
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Altop, E.K., Mennan, H., Streibig, J. C., Budak, U. and Ritz, C. (2014) Detecting ALS and ACCase herbicide tolerant accession of Echinochloa oryzoides (Ard.) Fritsch. in rice (Oryza sativa L.) field. Crop Protection 65, 202–206.Google Scholar
Andreasen, C., Kemezys, A.H. and Müller, R. (2014) The effect of fertilizer level and foliar-applied calcium on seed production and germination of Gerbera hybrida . HortScience 49, 538–543.CrossRefGoogle Scholar
Carpenter, W.J., Ostmark, E.R. and Cornell, J.A. (1995) Temperature and seed moisture govern germination and storage of Gerbera seed. HortScience 30, 98–101.CrossRefGoogle Scholar
Chen, D.-G. and Peace, K. E. (2013) Applied Meta-Analysis with R. CRC Press, Boca Raton.Google Scholar
Hothorn, T., Bretz, F. and Westfall, P. (2008) Simultaneous inference in general parametric models. Biometrical Journal 50, 346–363.Google Scholar
Jiang, X. and Kopp-Schneider, A. (2014) Summarizing EC50 estimates from multiple dose-response experiments: A comparison of a meta-analysis strategy to a mixed-effects model approach. Biometrical Journal 56, 493–512.Google Scholar
Konstantopoulos, S. (2011) Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis Methods 2, 61–76.CrossRefGoogle ScholarPubMed
Mennan, H., Streibig, J.C., Ngouajio, M. and Kaya, E. (2012) Tolerance of two Bifora radians Bieb populations to ALS inhibitors in winter wheat. Pest Management Science 68, 116–122.Google Scholar
Pipper, C.B., Ritz, C. and Bisgaard, H. (2012) A versatile method for confirmatory evaluation of the effects of a covariate in multiple models. Applied Statistics 61, 315–326.Google Scholar
R Core Team (2016) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Ritz, C., Pipper, C.B. and Streibig, J.C. (2013) Analysis of germination data from agricultural experiments. European Journal of Agronomy 45, 1–6.Google Scholar
Ritz, C., Baty, F., Streibig, J.C. and Gerhard, D. (2015). Dose-response analysis using R. PLOS ONE 10, e0146021.Google Scholar
Willan, R.L. (1985) A guide to forest seed handling ‒ with special reference to the tropics. FAO Forestry Paper 20/2.Google Scholar
Viechtbauer, W. (2010) Conducting meta-analyses in R with the metafor package. Journal of Statistical Software 36, 1–48.Google Scholar