Analysis of germination data from agricultural experiments

Christian Ritz; Christian Bressen Pipper; Jens Carl Streibig

doi:10.1016/j.eja.2012.10.003

Analysis of germination data from agricultural experiments

Christian Ritz, Christian Bressen Pipper, Jens Carl Streibig

Afgrødevidenskab

76 Citationer (Scopus)

Abstract

Expensive agricultural experiments deserve to be analyzed as efficiently as possible by extracting as much information as possible from the data while respecting the experimental design. We consider experiments where germination in response to time elapsed is used to characterize germination performance and vigour. We formulate statistical models in a biologically meaningful framework in such a way that the inherent data structure, which centers around repeatedly monitoring seeds over time, is duly incorporated in the statistical analysis. Consequently, estimation and model checking procedures are based on treating the data as event times, that is, to record the time it takes for germination (the event of interest) to occur. The key result is that the use of appropriate statistical models is important in order to get a better appreciation of the uncertainty that is present in germination experiments.

Originalsprog	Engelsk
Tidsskrift	European Journal of Agronomy
Vol/bind	45
Sider (fra-til)	1-6
Antal sider	6
ISSN	1161-0301
DOI	https://doi.org/10.1016/j.eja.2012.10.003
Status	Udgivet - feb. 2013

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10.1016/j.eja.2012.10.003

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title = "Analysis of germination data from agricultural experiments",

abstract = "Expensive agricultural experiments deserve to be analyzed as efficiently as possible by extracting as much information as possible from the data while respecting the experimental design. We consider experiments where germination in response to time elapsed is used to characterize germination performance and vigour. We formulate statistical models in a biologically meaningful framework in such a way that the inherent data structure, which centers around repeatedly monitoring seeds over time, is duly incorporated in the statistical analysis. Consequently, estimation and model checking procedures are based on treating the data as event times, that is, to record the time it takes for germination (the event of interest) to occur. The key result is that the use of appropriate statistical models is important in order to get a better appreciation of the uncertainty that is present in germination experiments.",

author = "Christian Ritz and Pipper, {Christian Bressen} and Streibig, {Jens Carl}",

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AU - Ritz, Christian

AU - Pipper, Christian Bressen

AU - Streibig, Jens Carl

PY - 2013/2

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N2 - Expensive agricultural experiments deserve to be analyzed as efficiently as possible by extracting as much information as possible from the data while respecting the experimental design. We consider experiments where germination in response to time elapsed is used to characterize germination performance and vigour. We formulate statistical models in a biologically meaningful framework in such a way that the inherent data structure, which centers around repeatedly monitoring seeds over time, is duly incorporated in the statistical analysis. Consequently, estimation and model checking procedures are based on treating the data as event times, that is, to record the time it takes for germination (the event of interest) to occur. The key result is that the use of appropriate statistical models is important in order to get a better appreciation of the uncertainty that is present in germination experiments.

AB - Expensive agricultural experiments deserve to be analyzed as efficiently as possible by extracting as much information as possible from the data while respecting the experimental design. We consider experiments where germination in response to time elapsed is used to characterize germination performance and vigour. We formulate statistical models in a biologically meaningful framework in such a way that the inherent data structure, which centers around repeatedly monitoring seeds over time, is duly incorporated in the statistical analysis. Consequently, estimation and model checking procedures are based on treating the data as event times, that is, to record the time it takes for germination (the event of interest) to occur. The key result is that the use of appropriate statistical models is important in order to get a better appreciation of the uncertainty that is present in germination experiments.

U2 - 10.1016/j.eja.2012.10.003

DO - 10.1016/j.eja.2012.10.003

M3 - Journal article

SN - 1161-0301

VL - 45

SP - 1

EP - 6

JO - European Journal of Agronomy

JF - European Journal of Agronomy

ER -

Analysis of germination data from agricultural experiments

Abstract

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