cropgrowdays

Version: 0.2.1

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.

Usage

There are currently four key agrometeorological calculation functions in cropgrowdays. These are:

• cumulative calculates cumulative weather data between between two dates, and
• daily_mean calculates daily mean of a weather variable between two dates, and
• growing_degree_days calculates the growing degree days between two dates as the sum of the difference between daily average temp and a baseline value where the daily average temp is capped, and
• stress_days_over calculates the number of days that the maximum temperature is over a baseline value between between two dates.

In addition, several functions are available to calculate the day of year or convert this to a date, namely:

• day_of_year calculates day of year from a date, and
• date_from_day_year calculates a date from the day of the year and the year, and
• day_of_harvest returns day of harvest in the year of sowing which, of course, may be a different year to the year of harvest.

Currently, two functions are available to retrieve SILO weather data from Queensland Government DES longpaddock website.

• get_silodata retrieves weather data for one location from the longpaddock website, and
• get_multi_silodata retrieves weather data for several locations from the longpaddock website.

SILO (Scientific Information for Land Owners) is a database of Australian climate data hosted by the Science and Technology Division of the Queensland Government’s Department of Environment and Science (DES). SILO datasets are constructed from Australian Bureau of Meteorology observations and may change due to data correction or improved interpolation or imputation.

Agrometeorological calculations

The boonah dataset contains meteorological SILO data for the period 1 Jan 2019 to 31 May 2020 obtained from the Longpaddock Queensland Government DES web site https://www.longpaddock.qld.gov.au for Boonah which is located at -27.9927 S, 152.6906 E. The data is in APSIM format and contains temperature, rainfall, evaporation and radiation variables and the rows are consecutive days during the period. The weather data set was obtained using

boonah <-
   get_silodata(latitude = "-27.9927", longitude = "152.6906",
                email = "MY_EMAIL_ADDRESS", START = "20190101", FINISH = "20200531")

To obtain gridded data, which is what get_silodata assumes, you need to supply at least the site latitude and longitude as well as your email address by replacing MY_EMAIL_ADDRESS with your email address. The data is freely available under the Creative Commons 4.0 License. Note that SILO may be unavailable between 11am and 1pm (Brisbane time) each Wednesday and Thursday to allow for essential system maintenance. Also please note that, by default, apsim data are retrieved. Most, but not all, of the other formats are also available. See the help for get_silodata for details. You can obtain this help using ?cropgrowdays::get_silodata at the R Console prompt or using your favourite help system.

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

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.