In cropgrowdays: Crop Growing Degree Days and Agrometeorological Calculations

Example R syntax is provided for calculating daily mean radiation, total rainfall, growing degree dates and the number of stress days between two dates. Alternatively, a number of days before or after a certain date may be specified.

Note that employing mapping functions to add agrometeorological variables to large data frames can take a substantial amount of computational time. We describe how to employ the furrr package, which provides a relatively simple way to apply mapping functions in parallel, to speed up these calculations.

Growing Degree Days

The growing_degree_days function calculates the sum of degree days for each day $i = 1 \ldots n$. The growing degree days $GDD$ summed over $n$ days are

$$GDD = \sum_i^n (Tmax_{i} + Tmin_{i}) / 2 - T_{base}$$

during specified dates for a tibble/data frame of daily weather data. For each day $i$, the maximum temperature is $Tmax_{i}$ and minimum is $Tmin_{i}$. Note that the maximum temperature $Tmax$ is capped at maxt_cap degrees when calculating average temperature. The defaults are $T_{base} = 5^{\circ}C$ and $Tmax$ is capped at $Tmax_{cap} = 30^{\circ}C$. (See @mcmaster1997 or https://farmwest.com/climate/calculator-information/gdd/ [@anonGDD])

The gdd functions in the pollen package [@pollen2019] and in agroclim [@agroclim2020] also calculate growing degree days. While these functions do not allow for a fixed number of days, and in the case of agroclim::gdd assume a more limited growing season since the function appears to be tailored to grapes, further variations on the formula above as outlined in @baskerville1969 are available.

To calculate the growing degree days at Boonah using weather data from the boonah object between flowering and harvest use:

## Growing Degree Days between two dates
crop$flower_date[4]     # flowering date for 4th field or farm in 'crop'
crop$harvest_date[4]    # harvest date for 4th field or farm in 'crop'
growing_degree_days(boonah, startdate = crop$flower_date[4],
                         enddate = crop$harvest_date[4]) #, monitor = TRUE)

Stress days

stress_days_over calculates the number of days when the maximum temperature exceeded a base line stress_temp during specified dates for a tibble/data frame of daily weather data. The default stress_temp is set at $30^{\circ}C$.

To calculate the number of stress days at Boonah between flowering and harvest, use:

## Stress days  between two dates
stress_days_over(boonah, startdate = crop$flower_date[4],
                 enddate = crop$harvest_date[4]) # , monitor = TRUE)

Cumulative calculations

cumulative calculates the sum total of daily values between two dates from a tibble/data frame of daily weather data. Typically this is used for solar radiation or rainfall.

To calculate the total rainfall at Boonah between flowering and harvest, use:

## cumulative rainfall between two dates (flowering and harvest)
cumulative(boonah, var = rain, startdate = crop$flower_date[4],
           enddate = crop$harvest_date[4])

Daily means

daily_mean calculates the daily average of a variable between two dates from a tibble/data frame of daily weather data. Typically this would be for temperature, rainfall or solar radiation.

To calculate daily mean radiation in the 3 day period from day of flowering onwards (which also includes day of flowering), use:

## daily mean radiation for the three days ending on crop$flower_date[4]
crop$flower_date[4] # a particular flowering date
daily_mean(boonah, enddate = crop$flower_date[4], ndays = 3,
           monitor = TRUE)

Extracting weather data from a tibble or dataframe

To extract column(s) from a tibble/data frame of daily weather data between two specified dates we use weather_extract. Either specify the start and end dates or specify one of these dates and also the number of days after or before, respectively.

## Extract daily rainfall & maximum temperature data using %>% pipe operator
boonah |>
  weather_extract(c(rain, maxt), date = date_met, startdate = ymd("2019-08-16"),
                  enddate = ymd("2019-08-21"))

Adding agrometeorological variables to dataframes

We can add agrometeorological variables to the crop tibble using the tidyverse functions map_dbl, map_dbl2 and pmap to calculate new columns employing the weather data from the boonah object. Use map_dbl for one varying date and map_dbl2 for varying start and end dates. For more than two varying parameters, which may be necessary if for instance our weather object contained multiple locations or sites, then we can use pmap. These functions are from the purrr library. Alternatively, we could use functions from the apply family such as mapply from the base package.

To add growing degree days 7 days post sowing and the number of stress days above $30^\circ C$ from flowering to harvest to the crop tibble, then we employ the following mutate syntax to extract the appropriate weather data from the boonah weather data object.

## Growing degree and stress days
crop2 <- crop |>
  dplyr::mutate(gddays_post_sow_7d =
           purrr::map_dbl(sowing_date, function(x)
             growing_degree_days(boonah, startdate = x, ndays = 7)),
         stressdays_flower_harvest =
           purrr::map2_dbl(flower_date, harvest_date, function(x, y)
             stress_days_over(boonah, startdate = x, enddate = y)))
print(crop2, n=5)

Similarly, to add total rainfall for the 7 days post sowing and the mean daily radiation from flowering to harvest we use:

## Totals and daily means
crop3 <- crop |>
  dplyr::mutate(totalrain_post_sow_7d =
           purrr::map_dbl(sowing_date, function(x)
             cumulative(boonah, var = rain, startdate = x, ndays = 7)),
         meanrad_flower_harvest =
           purrr::map2_dbl(flower_date, harvest_date, function(x, y)
             daily_mean(boonah, var = radn, startdate = x, enddate = y)))
print(crop3, n=5)

Speeding up calculations using `furrr`

For large datasets these calculations can be time consuming. One approach that may prove useful is to use the furrr package which is a bridge between purrr‘s family of mapping functions and future‘s parallel processing capabilities. If speed is an issue, then it is worth trying because it is simple to implement. While some tweaking may prove useful, it seems that the defaults work pretty well (see ?future::plan). After setting the number of workers, then simply replace mapping functions by putting future_ at the front of the name of the mapping function. For instance, map2_dbl is replaced with future_map2_dbl. While the results are not shown here, to add total rain and mean radiation as before, use something like:

ptm <- proc.time() # Start the clock!
## set number of 'furrr' workers
library(furrr)
plan(multisession, workers = 2)
## Totals and daily means
crop3 <- crop |>
  dplyr::mutate(totalrain_post_sow_7d =
           future_map_dbl(sowing_date, function(x)
             cumulative(boonah, var = rain, startdate = x, ndays = 7)),
         meanrad_flower_harvest =
           future_map2_dbl(flower_date, harvest_date, function(x, y)
             daily_mean(boonah, var = radn, startdate = x, enddate = y)))
print(crop3, n=5)
proc.time() - ptm # Stop the clock!