# Calculating Agrometeorological Variables for Crops

## Quick Start

Install the latest stable version of cropgrowdays via CRAN with:

    install.packages("cropgrowdays")

You can install the development version of cropgrowdays from `GitLab` with:

    ## if you don't have 'remotes' installed, automatically install it
    if (!require("remotes")) {
      install.packages("remotes", repos = "http://cran.rstudio.com/")
      library("remotes")
    }
    install_gitlab("petebaker/cropgrowdays", build_vignettes = TRUE)

## Overview

The cropgrowdays package provides functions to calculate agrometeorological quantities of interest for modelling crop data. Currently, functions are provided for calculating growing degree days, stress days, cumulative and daily means of weather data. Australian meteorological data can be obtained from Queensland Government’s Department of Environment and Science (DES) website. In addition, functions are provided to convert days of the year to dates, and vice-versa.

We recommend using the cropgrowdays package in conjunction with the tidyverse and lubridate packages. Additionally, we also recommend using the furrr package to speed up adding agrometeorological variables to large data frames. For this document, we only use the lubridate package as follows.

    suppressMessages(library(lubridate))
    library(cropgrowdays)

Note that if you are not familiar with the `lubridate` package, then in order to see which functions are provided and which functions conflict with other packages, initially it may best not to suppress messages using `suppressMessages`.

## Obtaining Australian meteorological data

The `boonah` dataset was obtained from the Queensland Government DES longpaddock website https://www.longpaddock.qld.gov.au using the `get_silodata` function. Please see getting-weather-data vignette for details.

The data obtained is

    ## weather data object
    print(boonah, n=5)
    #: # A tibble: 517 × 10
    #:    year   day  radn  maxt  mint  rain  evap    vp   code date_met
    #:   <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <int> <date>
    #: 1  2019     1  26.2  33.9  16.3     0   7.8  20.6 222222 2019-01-01
    #: 2  2019     2  28.2  33.4  17.6     0   7.7  19.8 222222 2019-01-02
    #: 3  2019     3  20.5  32.8  16.7     0   6.8  21.9 222222 2019-01-03
    #: 4  2019     4  23    32.5  21       2   7.7  22   222222 2019-01-04
    #: 5  2019     5  27    33.6  16.8     0   6    21.8 222222 2019-01-05
    #: # ℹ 512 more rows

## Agrometeorological calculations

The `crop` dataset consists of dates for a hypothetical crop data set which would also usually contain agronomic traits of interest such as yield, dry matter yield and so on. Each row of data contains sowing, flowering and harvest dates for a typical field or farm in Queensland, Australia.

    ## crop data object
    print(crop, n=5)
    #: # A tibble: 10 × 3
    #:   sowing_date flower_date harvest_date
    #:   <date>      <date>      <date>
    #: 1 2019-08-25  2019-10-14  2019-11-03
    #: 2 2019-09-20  2019-11-09  2019-11-29
    #: 3 2019-12-18  2020-02-06  2020-02-26
    #: 4 2020-01-15  2020-03-06  2020-03-26
    #: 5 2020-02-15  2020-04-06  2020-04-26
    #: # ℹ 5 more rows

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 McMaster and Wilhelm (1997) or https://farmwest.com/climate/calculator-information/gdd/ (Anon 2021))

The gdd functions in the pollen package (Nowosad 2019) and in agroclim (Serrano-Notivoli 2020) 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 Baskerville and Emin (1969) 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$flower_date[4]     # flowering date for 4th field or farm in 'crop'
    #: [1] "2020-03-06"
    crop$harvest_date[4]    # harvest date for 4th field or farm in 'crop'
    #: [1] "2020-03-26"
    growing_degree_days(boonah, startdate = crop$flower_date[4],
                             enddate = crop$harvest_date[4]) #, monitor = TRUE)
    #: [1] 359.05

### 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)
    #: [1] 4

### 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])
    #: [1] 22.8

### 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
    #: [1] "2020-03-06"
    daily_mean(boonah, enddate = crop$flower_date[4], ndays = 3,
               monitor = TRUE)
    #: # A tibble: 3 × 2
    #:   date_met    radn
    #:   <date>     <dbl>
    #: 1 2020-03-04  10.7
    #: 2 2020-03-05  11.8
    #: 3 2020-03-06  11
    #: [1] 11.16667

## 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"))
    #: # A tibble: 6 × 3
    #:   date_met    rain  maxt
    #:   <date>     <dbl> <dbl>
    #: 1 2019-08-16     0  26
    #: 2 2019-08-17     0  28
    #: 3 2019-08-18     0  25.7
    #: 4 2019-08-19     0  26.8
    #: 5 2019-08-20     0  23.1
    #: 6 2019-08-21     0  26.3

## 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)
    #: # A tibble: 10 × 5
    #:   sowing_date flower_date harvest_date gddays_post_sow_7d stressdays_flower_ha…¹
    #:   <date>      <date>      <date>                    <dbl>                  <dbl>
    #: 1 2019-08-25  2019-10-14  2019-11-03                 76.4                     10
    #: 2 2019-09-20  2019-11-09  2019-11-29                104.                      20
    #: 3 2019-12-18  2020-02-06  2020-02-26                132.                      11
    #: 4 2020-01-15  2020-03-06  2020-03-26                142.                       4
    #: 5 2020-02-15  2020-04-06  2020-04-26                145.                       5
    #: # ℹ 5 more rows
    #: # ℹ abbreviated name: ¹​stressdays_flower_harvest

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)
    #: # A tibble: 10 × 5
    #:   sowing_date flower_date harvest_date totalrain_post_sow_7d
    #:   <date>      <date>      <date>                       <dbl>
    #: 1 2019-08-25  2019-10-14  2019-11-03                    10.5
    #: 2 2019-09-20  2019-11-09  2019-11-29                     0
    #: 3 2019-12-18  2020-02-06  2020-02-26                     0
    #: 4 2020-01-15  2020-03-06  2020-03-26                    88.4
    #: 5 2020-02-15  2020-04-06  2020-04-26                    20.7
    #: # ℹ 5 more rows
    #: # ℹ 1 more variable: meanrad_flower_harvest <dbl>

### 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!

For recent work, we have found that setting 4 workers was optimal but this will of course depend on your setup.

## Day of year calculations

When modelling crops, agronomists typically specify dates as the day of year. Several functions are available for day of year calculations and converting these back to dates. In `R`, dates, times and timezone data are easily manipulated using the `lubridate` package.

The `day_of_year` function is used to convert a date to the day of year, which could be based on the calendar year starting on 1 January, the Australian financial year starting on 1 July or an arbitrary starting date.

    ##  Day of Calendar Year
    day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")))
    #: [1] 366 183   1
    day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")), return_year = TRUE)
    #:   day year
    #: 1 366 2020
    #: 2 183 2020
    #: 3   1 2020

    ## Day of Financial Year
    day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")), type = "financial")
    #: [1] 184   1 185
    day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")), type = "fin",
                return_year = TRUE)
    #:   day  fin_year
    #: 1 184 2020/2021
    #: 2   1 2020/2021
    #: 3 185 2019/2020

To convert a day of year to a date, use `date_from_day_year` noting that while the calendar year is the default, we can specify the Australian financial year or an arbitrary starting date.

    ## Convert day of year to a date
    date_from_day_year(21,2021)
    #: [1] "2021-01-21"
    date_from_day_year(21,2021, type = "fina")
    #: [1] "2021-07-21"

Finally, while we can use `day_of_year` to obtain the day of the current year, if a crop is planted near the end of the year then we way wish to know the day of harvest which will fall in the next year. The `day_of_harvest` function provides the day of year in the year of sowing which can be used to calculate other quantities like day of flowering etc. Thus, quantities like the number of days between harvest and sowing are easily calculated taking into account that the crop may grow past the end of the year. Alternatively, these quantities are also easily computed directly on the dates by using the `lubridate` package. For instance the convenience function `cropgrowdays::number_of_days` is essentially a call to `as.numeric(finish_date - start_date) + 1`.

    ## Day of harvest using the first day of the year of sowing as the base day
    day_of_year(ymd("2021-01-05"))
    #: [1] 5
    day_of_harvest(x = ymd("2021-01-05"), sowing = ymd("2020-12-20"))  # > 366
    #: [1] 371

Note that the first calculation simply assumes the first day of the year is 1 January 2021 whereas the second calculation yields a result assuming the first day of the year is 1 January 2020. Hence, since 2020 is a leap year containing 366 days, then the day of harvest is \(366 + 5 = 371\).

## References

Anon. 2021. “GDD.” Farmwest. https://farmwest.com/climate/calculator-information/gdd/.
Baskerville, G. L., and P. Emin. 1969. “Rapid Estimation of Heat Accumulation from Maximum and Minimum Temperatures.” Ecology 50 (3): 514–17. https://doi.org/10.2307/1933912.
McMaster, Gregory S, and W W Wilhelm. 1997. “Growing Degree-Days: One Equation, Two Interpretations.” Agricultural and Forest Meteorology 87 (4): 291–300. https://doi.org/10.1016/S0168-1923(97)00027-0.
Nowosad, Jakub. 2019. Pollen: Analysis of Aerobiological Data. https://CRAN.R-project.org/package=pollen.
Serrano-Notivoli, Roberto. 2020. Agroclim: Climatic Indices for Agriculture. https://CRAN.R-project.org/package=agroclim.