About Degree Days
Degree days (DD) are, essentially, a mathematical way to calculate the accumulation of heating units over time. (Cooling units, i.e. chilling hours, can also be calculated, though this is not currently programmed into NEWA.) A brief description of DDs is available from the University of Massachusetts Extension Service: Growing Degree Days for Management of Insect Pests in the Landscape
Keep in mind…
- NEWA serves many agricultural and horticultural commodities.
- There are several formulas that can be used to calculate degree days.
- Max and Min temperatures are collected during a ‘defined’ 24-hour period.
Because DDs are a way of expressing heating units, entomologists, plant pathologists, horticulturists, and agronomists have utilized DD calculations to model the development (phenology) of arthropod pests, plant diseases, plants, crops, and weeds. For instance, we know that the best fit for explaining the development of ascospores of the apple scab fungus is using degree days calculated with a low cutoff temperature of 32°F. We also know that codling moth development does not progress below 50°F. This is also the case for most plants, thus DDs calculated with a base temperature (or low cutoff) of 50°F are commonly referred to as growing degree days, or GDDs.
NEWA serves many agricultural and horticultural commodities - Several crop, pest, and disease phenology models are programmed into NEWA. Some rely solely on DD tables, some display results directly (DD accumulations are not apparent to the user), and some provide DD ranges when IPM decisions and interventions are needed (hanging traps, spray timings, etc.)
Degree Days (DD) calculated in NEWA and the insect phenology, crop phenology and disease tools for which they were developed.
|apple scab ascospore maturity
|seed corn maggot
|alfalfa and other legumes
|obliquebanded leafroller, spotted tentiform leafminer
|oriental fruit moth
|grape berry moth
|codling moth, plum curculio, apple maggot, San Jose scale, blueberry maggot
|fire blight shoot blight symptom development
|brown marmorated stink bug
Implementation of these models is guided by research and extension faculty at Cornell University, extension educators in Cornell Cooperative Extension, and experts at our partner land grant institutions. NEWA also provides a platform for stakeholder input to improve model performance and webpage results.
For example, the "Results" pages on the Apple Insect Models on NEWA, are generated by accumulating temperature data for the location of interest, generating a DD value using Baskerville-Emin calculations (described below), and then comparing that total against a lookup table of DD ranges and populating messages on the Pest Status, Pest Development, and Pest Management boxes on the screen.
There are several formulas that can be used to calculate degree days – In NEWA, historically, the simple Max-Min average formula is what has been used for DD calculations. This formula can readily be calculated by hand and was also included in many of the Cornell Pest Management Guidelines. The Baskerville-Emin (BE) formula uses a sine wave algorithm and results in more precise DD calculations. This formula was implemented in NEWA in ~2006.
NEWA offers a choice of either the average (or simple) formula or the BE formula for use with the various base temperatures on the NEWA Degree Day Calculator and the NEWA All Weather Data Query webpages.
Currently, the BE formula is being used in all the NEWA apple disease and apple insect phenology models that utilize DD accumulations. Drs. Cox and Agnello have chosen to use BE DDs because of their higher precision. Furthermore, BE DDs have been used in the entomology field observations in Geneva, NY, for the past 25 years or more.
If you are comparing the Scaffolds “Upcoming Pest Events” tabulated DDs with what is tabulated for Geneva in NEWA, make sure you compare these with the BE DDs to get the best match. “Best match” because, having used two calculators to crunch an involved equation and come up with two answers that don’t exactly match, it is true that software programs can differ slightly in the way they handle rounding of decimal places, etc., which can create some differences in the mathematical answer.
Max and Min temperatures are collected during a ‘defined’ 24-hour period - Another area that introduces variability in DD accumulations is how the 24-hour period is defined. For some, the 24-hour day begins at midnight, and for some it ends at midnight. That is, in some systems midnight is 0:00, in some it is 24:00. In NEWA, midnight marks the end of the day. NEWA’s 24-hour period runs from 12:01 AM (= 0:01) until 12 AM (midnight).
Data is logged for NEWA’s database in a variety of ways. Therefore, some NEWA weather stations may miss the true Max and true Min temperature for a given day, because it might have occurred at 2:16 PM and not at a time when data was logged. Hence, this may add another source of variability!
Daylight savings time can be problematic. Essentially, an hour is lost and then gained in the annual time continuum. NEWA will soon begin utilizing the same methodology as the National Weather Service (NWS) for dealing with this 23-hour-long day and 25-hour-long day during the year.
The NWS has Weather Observer sites reporting daily Max and Min temperatures. These sites collect data, once per day, at specified times, which can affect DD value calculations. Consider the time when you look at the values from your Max-Min thermometer and then clear them. If you look at these first thing in the morning and invariably at 5:00 AM, then you are collecting a true 24-hour Max and Min temperature for the period 5:00 AM the previous day until 5:00 AM the current day. If you collect this data in the afternoon, the 24-hour period range would be different. Over time, climatologists have found that “afternoon” observations typically accumulate more DD’s than “morning” ones.
The bottom line - when comparing DD data, keep in mind the sources of variability in DD accumulations. And don't sweat the discrepancies you find too much; like they say, “you can measure it with a micrometer, but what's the sense if you have to cut it out with a hatchet?” Nothing is more accurate than looking outside and seeing if you have green tip or counting the insects in your traps. We certainly don't expect an adult codling moth to pop out on the dot at 489 DD50 from January 1, or plum curculio to stop immigrating into the orchard at 308 DD50 from petal fall; there are simply too many sources of variability (e.g., in whose data one is using, how it was collected, in how representative a site, and at what point in time, etc.) to make this level of tracking practical.
NEWA provides theoretical predictions and forecasts. The theoretical models predicting pest development or disease risk use the weather data collected (or forecasted) from the weather station location. These results should not be substituted for actual observations of plant growth stage, pest presence, and disease occurrence determined through scouting or insect pheromone traps.
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