To explain a little more about what I'm trying to accomplish:
It seems like developing growing degree day (GDD) for some tomatoes would be very useful, more useful than the DTM that we have now. I don't expect to get a ton of data, but if I can get a few tomatoes that are very early, early, mid, late, and very late, then it would be possible to correlate the DTM and GDD for one variety with another.
For example, if Big Beef was measured to have 1650 GDDs (averaged) and a published DTM of 73 days then I could extrapolate that Jet Star, with DTM of 72 days would have about the same GDD. It becomes useful to help different climates translate the DTM into something useful for them.
Forecasts for this year show that a tomato planted in Austin on March 15 can be expected to reach 1650 GDDs by June 2... almost a week over the published DTM for Big Beef. That includes some data for this year plus forecasts based on past years history. The same tomato planted in Seattle, Washington, on April 6 won't be ripe until late August based on the forecast GDD. That is well over the DTM, almost 5 months! I'm not sure that GDD is that rigorous, though. Other variables, like length of daylight could also be relevant. Even though the total GDD is lower, the longer daylight hours might compensate to some degree. I don't think I can build a model to include that variable, too.
If we can collect information for at least a few tomatoes in each general maturity category, it will be possible to compare other tomatoes and their maturity based on the GDD for each person's location rather than rely on a generic days-to-maturity.
