EXAMPLE - DETERMINING NEMATODE DEGREE DAYS:

Let's try an example in which we will calculate the number of degree days in a typical growing season. It may be wise to set a bookmark on your browser so you can easily return to this page as needed. For a Netscape browser, you can also select New Browser from the File menu which will open a second window so you can jump back and forth between these directions and the UC IPM server.

For nematology, the CIMIS(California Irrigation Management Information System) stations, for which weather summaries are available on the UC IPM server, are generally the most useful because they most consistently report soil temperatures. In the UC IPM listing of stations, these stations are denoted by .A for automatic at the end of the station name.

For many weather variables, the UC IPM programs utilize nearby backup stations or data summaries to fill in missing data gaps. However, this information does not seem to be generally available for missing soil temperature data. To deal with this problem, it is usually best to start by retreiving the soil temperature information from the weather station of interest to determine which days have missing values.

If only a day or two are missing, interpolating between the days available will still lead to a useful result. If there are large gaps in available data, or drastic differences between temperatures preceeding and following available days, it may be best to try to find a nearby station with more complete information, or that can be used to help indicate the rate of temperature change in the missing data.

Lets look at phenology model for Columbia Root-knot Nematode available on the UC IPM server to obtain some background information. From this, we see that the lower developmental threshold is 5 C, the first generation required 1,000 degrees days, and subsequent generations 500-600 degree days. Lets see how this information can be used to predict the number of generations of Columbia root-knot nematode in the Tulelake/Klamath Basin.

We begin by retreiving weather records for a CIMIS station at the Tulelake Intermountain Research and Extension Center (Tulelake Field Station). This will allow us to see where there are missing data points which must be taken into account.

There are 5 steps to this process.

• 1. Select a county
• 2. Select a weather station in that county
• 3. Select a time period
• 4. Select variables and backup stations
• 5. Select output file format and units

The UCIPM server will lead you through these steps with a series of menus. For our example:

• 1. Select Siskiyou County and click Submit.
• 2. For station TULELAKE2.A, click on Daily data.
• 3. Select May 15 through October 15, 1995 (this is an average growing season for potatoes in Tulelake)
• 4. Select whatever variables you wish, but be sure to select soil temperature, max and min (note only 1 backup station is available for this option)
• 5. Select Metric Output Units. For now, use the default selection to allow you to view the information on your browser. The option to receive and save the output as a file that can be read by a spreadsheet is also useful, but cannot be easily interpreted on the screen.

Then select retrieve data and watch what happens. Scroll through the data and note that two dates (June 26 and August 21) are followed by an A in the soil temperature column. The data for these dates is from long term average data. Having a few missing days of data is pretty common. The more missing days, the less confidence you will have in your predictions.

Also, note the temperature differential between the maximum and minimum temperatues preceding the missing data. June 25 was 22.2 and 17.8, while June 27 was 26.7 and 18.3. August 20 was 20.6 and 16.1, while August 22 was 22.8 and 18.3.

Next we will proceed to the UC IPM server to retrieve degree day information from this same weather station. This is a 6 step proccess:

• 1. Select units, enter thresholds, and select method of calculation and upper cutoff method.
• 2. Select a county and type of station.
• 3. Click Calculate.
• 4. Select a weather station.
• 5. Select a time period, temperature variable, backup stations, and output file format.
• 6. Click Calculate degree-days.

1. Our lower threshold is 5C and we will not select an upper threshold. Nematology degree day work is typically done on the basis of degrees C rather than F, so select Celsius. Use the defaults of single sine and horizontal cutoff. Click Calculate.

2.Select Siskiyou county.

3. Select the dates May 15 through October 15, 1995. Click Continue.

4. Select Tulelake2.A

5. Select soil temperature max/min.

6. Select the default output Formatted Report to be viewed on the screen

7. Click on Calculate Degree-Days

Note that in the two places where there are missing days, the calculation starts again from 0. Because of the missing data, we will need to add up the 3 sets of data to get the total number of degree-days. We could also interpolate and add an appropriate (although not really significant in this case) number of degree days for the two missing data points.

Without adding any data for the 2 missing days, the total degree days obtained is 1830.2.

Given the phenology model figures of 1,000 degree days for the first generation and 500 to 600 for the second, we can estimate 2.5 nematode generations for our example.

If one were to look at the Tulelake situation over a period of years, the number of generations varies from about 1.5 to 3. The higher number of generations correlates well with years in which grower's have reported the most damage.

Potential limitations to this technique rest with how accurately and how far in advance the development of nematode populations can be predicted. Accuracy will be greater in situations in which soil temperatures do not fluctuate greatly from year to year.

Another problem is that soil temperatures are typically measured only at a single depth by many weather stations while root growth and nematode development occur over a range of depths. The more soil temperatures vary, the more asynchronous nematode populations will be and the less accurate will be predictions based on temperatures taken at a single depth.

Yet another variable would be how great a differential exists between the weather station chosen and the actual field of interest. As technology improves, and becomes less expensive, the ability for grower's to have access to more accurate data from their own fields will increase.

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