HTnorm {drcSeedGerm}

Hydrotime model with normal distribution of base water potential (Bradford, 2002)

Description

This model relates the time-course of the proportion of germinated seeds to the water potential in the substrate. It is based on a normal distribution of base water potential within the seed lot. The equation is:

P(t) = \Phi \left\{ \frac{\Psi - (\theta_H / t) -\Psi_b }{\sigma_{\Psi_b}} \right\}

where \Phi is a gaussian cumulative distribution function for base water potential. In contrast to other hydrotime models (e.g., 'HTE1()', 'HTE2()', 'HTE3()', describing the distribution of germination time), this models describes the distribution of base water potential within the population, while the distribution of germination times is indirectly modelled, but it is not, in itself, gaussian (you see that 't' is at the denominator). The 'HTNorm.fun()' is a generic function, which can be used for plotting or other applications, while the 'HTNorm()' function is meant to be used for model fitting with the 'drmte()' function in the 'drcte()' package.

Usage

HTnorm()
HTnorm.fun(time, Psi, ThetaH, Psib50, sigmaPsib)

Arguments

The 'HTNorm()' function has no arguments. The 'HTNorm.fun()' function has the following arguments:

time

time

Psi

water potential in the substrate

ThetaH

hydro-time constant

Psib50

median base water potential

sigmaPsib

standard deviation for the base water potential within the seed lot

Details

The detail of this time-to-event model and the meaning of parameters are described in Bradford (2002).

Value

The 'HTNorm.fun()' function returns the proportion of germinated seeds, for any given values of time and water potential in the substrate. The 'HTNorm()' function returns a list containing the nonlinear function, the self-starter function, the parameter names and other items which are internally used by the 'drmte()' function.

Note

This function is for use with the R function 'drmte()'

Author(s)

Andrea Onofri

References

Bradford, K.J., 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Science 50, 248–260.

Examples

data(rape)
modg <- drm( propCum ~ timeAf + Psi, fct=HTnorm(), data=rape)
summary(modg)