I'm going to try to develop some information for an article that compares USDA zones, days to maturity, and another measure of the growing season, growing degree days.

A very short explanation is that growing degrees are a measure of time above a certain temperature as it affects the growing season of a plant and its maturity. The difference in growing degrees can explain why one type of tomato matures in 65 days in one location and in 75 days somewhere else. It's why a Texas zone 8b is not the same as an Oregon zone 8b.

To make the article meaningful, I would like to include some example tomato cultivars and the growing degree days for those types. I'd also like to correlate the GDD with days to maturity. Here's where I'm asking for help.

If you have records for any tomato cultivars that includes transplant date, first harvest date, and zip code where planted, I would like to collect that data. I can use weather records in online GDD calculators to estimate the value. If I can get a few different sets of data for common types of tomato then I can calculate average DGG for those varieties. Those growing degree days numbers can be translated to any other area and will give a better estimate of maturity than the standard days to maturity.

To summarize: send me the cultivar name, transplant date, first harvest date, and zip code.

For example: Big Beef, 03/01/2013, 05/18/2013, 78660

David Reaves

Great project. I have often wondered about the diff between my zone 4-5 and that of those outside. Temp certainly, and growing days which pulls my growth the other direction (temp cool, grow days long but growth season short).

I live in ZIP code 98204, just 2 miles from Puget Sound. I only grew tomatoes one year.

A "warm summer day" goes over 75. A "hot summer day" goes over 80F.
Nights almost always go back down under 70 or 65F.
That's just my recollection, not based on checking records.


Sungold cherry tomatoes: ripened and were sweet for 4-6 weeks.

Stupice: ripened but caught a chill after a few weeks and then had no sweetness.

(?Sweet Million?) The plants did not thrive and disappeared before having a chance to ripen. Maybe I planted them too close to the Stupice which shaded them, then choked them.)

>> cultivar name, transplant date, first harvest date, and zip code.

Sorry, I didn't record either date.

All three I bought as seedlings before nights stayed over 50F. I potted them up and then carried them indoors every night for weeks. I planted them out once nights stayed over 50, but nights got cool again pretty early in the Stupice harvest season. Maybe I should have put them in the ground sooner, figuring that the bigger root zone would help more than the cool night would hurt.

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.

I really hope that some gardeners with good records or good memories send you lots of data!I went hunting the Internet once, for similar GDD information, but found almost nothing.

David, best of luck with your project. This is from my 2011 records. Try this and let me know your results. Claud

cultivar name, transplant date, first harvest date, and zip code

Peron Sprayless 04/07/11 05/19/11 38965

Peron Sprayless 08/26/11 12/26/11 38965

Transplant 08/26/2011= First tomato 12/26/2011====These were from the same vines picked on 03/03/12 === Three of the seven plants were still producing tomatoes on 08/2012 almost a year later.
---

Before the first frost on 10/18/11

David, while you're whittlin on the numbers. I was a month later on the fall tomato planting than I wanted to be (life happens). Can you project an estimated fall harvest date using GDD's if I had planted them on 07/26/2011 instead of a month later as happened. Once they started producing at the end of December they continued to produce until I pulled them up in August of 2012. As you can see from the dates I gave you the DTM in the fall and winter tomatoes was about 3 times the DTM in the spring. The spring and fall tomatoes were planted in the exact same spot, just a different time of year. Claud

Well, I know when I planted my tomatoes (although not sure what kind of tomatoes as that is all that it called them on the package -- will research), and I will know when I harvest (or start harvest) and when it ends. I know, by googling, statistics as to degree days and such though am very rusty on it. I know that this information for Alaska is of limited use to you all, but might as well include it for interest value.

I think that data from unusual climates is MOST useful since it will produce data points under circumstances where no one else can do the experiment.

I think the common paradoxical expression "the exception proves the rule" originally meant something more logical. Examining the cases where a general rule does NOT apply tells you a lot about the rule itself, ad how it should have been stated more accurately.

Good old Wikipedia is helpful - that saying came originally from Cicero and meant almost the opposite of how we use it now:
- - "exceptio probat regulam in casibus non exceptis
- - - ("the exception confirms the rule in cases not excepted")

Like, if you say that "Only policemen may beat people up with sticks", you are emphasizing that no one ELSE may beat people up with sticks.

David may be able to figure out that daylength has a big effect, which makes Alaskan Fall an exception.

Or maybe the exception that matters is "and no nights go below 45".

Or maybe what matters most to tomatoes is not exactly the daily mean temperature, but that works out close for most climates. Maybe it is really the # of hours each day spent above 50 that matters. Only an unusual climate or a University growth chamber with heaters and A/C would discover that.

Or, he might find out that Fall in Alaska is not an exception after all, and proves the theory that "degree days over 50F" determines when a crop matures. For example, your Fall may have a lot of days with average temps around 50.

It will be interesting to see how DTM varies in Texas between Spring and Fall crops. Soil temperature? Day length? Or maybe nothing but GDD, pure and simple.

It works the same way in astrophysics: at first they are delighted to discover any kind of rule of thumb like "bigger stars are brighter, bluer and age faster". But then they analyze tons of data with increasing precision and they find some outliers, or classes of exceptions, or the curve is not a straight line. Then they have a grand time over the next 100 years quibbling about all the other things that qualify the basic rule.

In this case, I think the first rule "60 Days To Maturity" is so inaccurate that David has an excellent chance of finding or creating some much-more-useful statistic (perhaps a straight "GDD-above-50F").

David, did you ever use an MS-dos software package called Javelin or Javelin Plus? It was marketed as a spreadsheet competing against Lotus 123, but it was really modeling software and was unlike anything else on the market back then or now for that matter. I still have both in a box somewhere. It would be an ideal tool for what you're trying to do. Claud
Link: http://en.wikipedia.org/wiki/J...

This is a good project.
I am not sure that I have the information that is required,
but will look back in my gardening journals.
The GDD are based on temperature. Hours of sunshine is also a factor?
But your objective to imrove information beyond DTM is good.

Claud, I did use Javelin... years ago when I was in the Army. It was part of their selected software before Lotus 1-2-3, then MS Excel took over. I'm not expecting to do anything beyond basic math, or a little algebra for this project, though.

Rick & Mary Stella, I certainly do want to find extremes in data. That might help to show if there is something else that has a very strong effect other than daily temperatures. I have thought about daylight hours and possibly the moisture as variables. That would take a LOT of information to be able to break down the various influences. The only way I really see that as being possible would be to design an experiment and pay growers across the country to produce specific tomatoes in controlled conditions. What I hope to do, instead, is to average out the influences of day length and moisture by averaging the GDD for a tomato in one area with tomatoes in another area. That will give a generalization for that tomato that, hopefully, is more accurate than the generic DTM. My assumption is that the growing degree days will provide a forecast of harvest that is more accurate simply because it incorporates the temperatures for the growing location. I don't expect it to be perfect, but I do expect it to tell the grower what to look forward to with various maturity tomatoes in their garden.

Also,someone asked why transplant dates? I picked the transplant date because that's what I think I will be able to find. I am hoping to get data from individual growers, but I'm counting on finding data in field trials from universities and commercial growers. Transplant date is a commonly stated value, and it can be used to compare the DTM with the calculated GDD for a tomato. For other vegetables, normally direct seeded, I would try to use germination/emergence.

For tomatoes that get up-potted to fairly large containers, I would think that the date those containers go out would serve as the start date. In those cases I think the final transplant date might produce artificially short GDD values.

Thanks to everyone that has expressed interest! I'm looking forward to any comments or growing data.

DR

I was wondering about that. I guess some growers can put seeds directly in the ground but it sounds like they are the exception. I have the date I planted seeds indoors, then planted out in the greenhouse. That would make a difference too I would think as the greenhouse (really just hoops over my raised beds) changes the equation by changing the exposure of plants to the natural ambient temps, moisture, sunlight. But perhaps inserting variables into the formula would account for that. I tried growing them last year without the top and did get tomatoes so I have a few left over that I could put in pots in the yard uncovered. They would have a slightly different plant out date but would be purely at the whim of the natural environment.

Really interesting project. Love Excel and Access. Use it for my gardens mostly flowers. Now I will have to add a section on vegies with additional fields for such information as you have mentioned. Also as with you all our summers vary greatly. So one summer cannot be compared to another. This one seems to be going to warm and dry, while last summer was rather cool and wet.

Once you have your original goal achieved, a "GDD" number that is more accurate than the generic DTM, you could just add a column for "how close" the GDD number you pick for each variety came to each growers' actual experience.

If those "off by" numbers seem to correlate well with latitude, you have a good hint that growing-season daylength is a factor.

I don't know any way to compensate for "dry summer and I don't water consistently" or "my beds are in partial shade".

As you say, averaging over many growers' experiences should average things like that out.

>> I'm counting on finding data in field trials from universities and commercial growers.

That would be golden!