Hello all,

First, thanks to this forum. For some time, i am following and learned a lot from reading.

I am parameterizing a model on weed population dynamics based on data from a long term field experiment.

Structure:
- Three strips containing the three different crops of the crop rotation.
- Three plots within each strip, with the same treatment
- Weeds were sampled in the plots
- Sampling differed in sample size and area sampled.

If possible, the model should take into account the variance on the level of samples.

Weeds were counted several times a year, for instance once in spring and once just before harvest.
While trying to get parameters (for instance reduction in weeds abundance between spring and harvest) i am struggling with 1) the different sampling size (To go back to the example: 24*1/8 m² sampled in spring, 4 times 1/2 m² before harvest) and 2) the distribution of data (very right skewed, high number of zeros)

- I applied boots trapping to even out different sample size, but that is blurring the data a lot (it is not appropriate to enhance sample size)
- I tried to apply lm after log-transformation of data, using lm(rnorm()~rnorm()) to count for different sample sizes, but log transformation is not enough (did not lead to normal distribution).

- One could apply zero inflated modelling, but then, how to get it into the model? I would say, not useful. The high number of zeros are mainly due to the relatively small area sampled. Plots without weeds were very seldom and sampling structure was thought to give an overview of weeds abundance of the whole plot.

Do you know a way to avoid pooling at plot level?

Any suggestions are highly appreciated.
Thanks in advance


Daniel Daedlow (M. sc. agr.)
Working Group Crop Health
Faculty of Agricultural and Environmental Sciences
University of Rostock
Satower Str. 48
D-18051 Rostock
Phone 0049-(0)381-498 3170

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