Comparing proportions of plants that died (two-way analysis with interest in interaction effect) I want to compare the proportions of plants in an experiment that had died by the end of the growth period.  I am not interested in how long it took for them to die, although I suspect some people will not like it if this isn't considered in the statistical tests.
Seeds were planted in one of two soils (soil treatments) and were supplied with one of three amendments (amendment treatments).  22 individual seed replicates were established under each treatment combination (e.g., Soil A with Amendment C). As to be expected, not all seeds germinated/emerged, however the majority did. Thus, throughout the experiment, there was an 'unbalanced design' or 'missing data' (?) for subsequent analyses related to growth variables and death.
One question I wish to explore is, "Are the death rates consistent between amendment treatments, and is this affected by soil type?".  Hence I wish to include an interaction term.
I believe I have found a valid statistical test but would like others' opinions please.
Would a binomial (e.g., logistic) regression apply here? That is, per each seedling (remember, these numbers are different per treatment groups due to differing emergence rates), can I treat death as a yes/no determination and compare the numbers using a binomial regression.
For those who are interested, using R, I have constructed this model already with the following code: glm(DIED ~  SOIL * AMENDMENT, data = X, family=binomial).  I then ran this through Anova {car}.
"DIED" is scored as 0 for no, and 1 for yes. The data for this code are restricted to seeds that did emergence (no NA scored for death)
Thanks for reading.

As the comments point out, you have to deal with the possibility that the treatment combinations themselves affected germination. If you can rule that out adequately, then you might be OK with your approach.
It would be more reliable to model the germination process directly. One possibility related to your proposed logistic regression would be an ordinal model that takes into account the necessary progression through germination before you can have a death. Then you would have 3 ordered outcomes: no germination, germination without death, and germination followed by death. Sal Mangiafico's R Companion outlines how such an ordered outcome can be modeled.
That said, a multi-state survival model that takes time into account would probably make more efficient and informative use of your data.
