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

Section for Crop Sciences

76 Citations (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.

Original language	English
Journal	European Journal of Agronomy
Volume	45
Pages (from-to)	1-6
Number of pages	6
ISSN	1161-0301
DOIs	https://doi.org/10.1016/j.eja.2012.10.003
Publication status	Published - 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.",

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AU - Ritz, Christian

AU - Pipper, Christian Bressen

AU - Streibig, Jens Carl

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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.

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DO - 10.1016/j.eja.2012.10.003

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SP - 1

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JO - European Journal of Agronomy

JF - European Journal of Agronomy

ER -

Analysis of germination data from agricultural experiments

Abstract

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