Fitting threshold models to seed germination data

Published at March 13, 2023 ·  19 min read

In previous posts we have shown that we can use time-to-event curves to describe the germination pattern of a seed population (see here). We have also shown that these curves can be modified to include the effects of external/internal factors/covariates, such as the genotype, the species, the humidity content and temperature in the substrate (see here and here). These modified time-to-event curves can be fitted in ‘one-step’, i.e., we start from the germination data with the appropriate shape (see here), fit the model and retrieve the estimates of model parameters ( go to here for an example )....

Fitting hydro-thermal-time-models to seed germination data

Published at February 10, 2023 ·  15 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Germination assay This dataset was obtained from previously published work (Mesgaran et al., 2017) with Hordeum spontaneum [C. Koch] Thell. The germination assay was conducted using four replicates of 20 seeds tested at six different water potential levels (0, −0....

Fitting thermal-time-models to seed germination data

Published at February 10, 2023 ·  7 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. A motivating examples In recent times, we wanted to model the effect of temperature on seed germination for Hordeum vulgare and we made an assay with three replicated Petri dishes (50 seeds each) at 9 constant temperature levels (1, 3, 7, 10, 15, 20, 25, 30, 35, 40 °C)....

Analysing seed germination and emergence data with R (a tutorial). Part 6

Published at January 18, 2022 ·  13 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Fitting time-to-event models with environmental covariates In the previous post we have shown that time-to-event curves (e.g., germination or emergence curves) can be used to describe the time course of germinations/emergences for a seed lot (this post)....

Analysing seed germination and emergence data with R (a tutorial). Part 7

Published at January 18, 2022 ·  4 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts expand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Exploring the results of a time-to-event fit: model parameters In the previous post we have shown that time-to-event curves (e.g., germination or emergence curves) can be used to describe the time course of germinations/emergences for a seed lot (this post)....

Analysing seed germination and emergence data with R (a tutorial). Part 8

Published at January 18, 2022 ·  8 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts expand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Predictions from a parametric time-to-event model In previous posts we have shown that time-to-event models (e.g., germination or emergence models) can be used to describe the time course of germinations/emergences for a seed lot (this post) or for several seed lots, submitted to different experimental treatments (this post)....

Analysing seed germination and emergence data with R (a tutorial). Part 9

Published at January 18, 2022 ·  10 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts expand on a manuscript that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. In order to work throughout this post, you need to install the ‘drcte’ and ‘drcSeedGerm’ packages, by using the code provided in this page. Quantiles from time-to-event models We have previously shown that time-to-event models (e....

Analysing seed germination and emergence data with R: a tutorial. Part 5

Published at December 23, 2021 ·  14 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Comparing germination/emergence for several seed lots Very often, seed scientists need to compare the germination behavior of different seed populations, e.g., different plant species, or one single plant species submitted to different temperatures, light conditions, priming treatments and so on....

Analysing seed germination and emergence data with R: a tutorial. Part 4

Published at December 6, 2021 ·  9 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Time-to-event models for seed germination/emergence The individual seeds within a population do not germinate/emerge altogether at the same moment; this is an undisputed fact, resulting from seed-to-seed variability in germination/emergence time....

Analysing seed germination and emergence data with R: a tutorial. Part 3

Published at October 19, 2021 ·  12 min read

This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Reshaping time-to-event data The first thing we should consider before working through this tutorial is the structure of germination/emergence data. To our experience, seed scientists are used to storing their datasets in several formats, that may not be immediately usable with the ‘drcte’ and ‘drc’ packages, which this tutorial is built upon....

Analysing seed germination and emergence data with R (a tutorial). Part 2

Published at October 9, 2021 ·  17 min read

This is a follow-up post: if you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Survival analysis and germination/emergence data: an overlooked connection Seed germination and emergence data describe the time until the event of interest occurs and, therefore, they can be put together in the wide group of time-to-event data....

Analysing seed germination and emergence data with R (a tutorial). Part 1

Published at October 7, 2021 ·  4 min read

Introduction to the tutorial Germination/emergence assays are relatively easy to perform, by following standardised procedures, as described, e.g., by the International Seed Testing Association (see here ). In short, we take a sample of seeds and we put them in an appropriate container. We put the container in the right environmental conditions (e.g., relating to humidity content and temperature) and we inspect the seeds according to a regular schedule (e....

Other useful functions for nonlinear regression: threshold models and all that

Published at May 1, 2021 ·  13 min read

In a recent post I presented several equations and just as many self-starting functions for nonlinear regression analyses in R. Today, I would like to build upon that post and present some further equations, relating to the so-called threshold models. But, … what are threshold models? In some instances, we need to describe relationships where the response variable changes abruptly, following a small change in the predictor. A typical threshold model looks like that in the Figure below, where we see three threshold levels:...

Seed germination: fitting hydro-time models with R

Published at March 23, 2020 ·  16 min read

THE CODE IN THIS POST WAS UPDATED ON JANUARY 2022 I am locked at home, due to the COVID-19 emergency in Italy. Luckily I am healthy, but there is not much to do, inside. I thought it might be nice to spend some time to talk about seed germination models and the connections with survival analysis. We all know that seeds need water to germinate. Indeed, the absorption of water activates the hydrolytic enzymes, which break down food resources stored in seeds and provide energy for germination....

Germination data and time-to-event methods: comparing germination curves

Published at July 20, 2019 ·  11 min read

Very often, seed scientists need to compare the germination behaviour of different seed populations, e.g., different plant species, or one single plant species submitted to different temperatures, light conditions, priming treatments and so on. How should such a comparison be performed? Let’s take a practical approach and start from an appropriate example: a few years ago, some collegues studied the germination behaviour for seeds of a plant species (Verbascum arcturus, BTW…), in different conditions....

Companion R Packages

Published at May 30, 2019 ·  1 min read

This blog is supported by a few R packages containing all functions, datasets and other utilities, which are necessary to work through posts, tutorials and books. The packages are: aomisc AgriCensData drcSeedGerm drcte lmDiallel The three packages ‘drcSeedGerm’, ‘drcte’ and ‘lmDiallel’ are already hosted on CRAN and they can be installed from within RStudio, by using the usual ‘install.package()’ function. For all packages, the development versions are hosted on gitHub and they can be installed from there....