Help for package cropgrowdays

Title:

Crop Growing Degree Days and Agrometeorological Calculations

Version:

0.2.1

Description:

Calculate agrometeorological variables for crops including growing degree days (McMaster, GS & Wilhelm, WW (1997) <doi:10.1016/S0168-1923(97)00027-0>), cumulative rainfall, number of stress days and cumulative or mean radiation and evaporation. Convert dates to day of year and vice versa. Also, download curated and interpolated Australian weather data from the Queensland Government DES longpaddock website https://www.longpaddock.qld.gov.au/. This data is freely available under the Creative Commons 4.0 licence.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

URL:

https://gitlab.com/petebaker/cropgrowdays/

BugReports:

https://gitlab.com/petebaker/cropgrowdays/-/issues

RoxygenNote:

7.3.1

Depends:

R (≥ 4.2.0)

Imports:

lubridate, dplyr, tibble, purrr, purrrlyr, httr

Suggests:

rmarkdown, knitr, furrr, testthat (≥ 3.0.0)

Config/testthat/edition:

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2024-03-12 14:09:11 UTC; pete

Author:

Peter Baker

[aut, cre], Miranda Mortlock

[aut]

Maintainer:

Peter Baker <drpetebaker@gmail.com>

Repository:

CRAN

Date/Publication:

2024-03-12 14:20:06 UTC

Cropgrowdays: Agrometeorological calculations for crops

Description

Weather related calculations for crops are provided for a specified period including growing degree days, cumulative weather variables, the number of stress days over a baseline temperature and mean daily weather. Additionally, a function is provided to download historical Australian weather data from the Queensland Government DES longpaddock website.

Agrometeorological calculation functions

This package provides the calculation functions cumulative, growing_degree_days, stress_days_over and daily_mean. These functions employ weather_extract which can be used to extract column(s) or variable(s) between two specified dates which can also be employed directly.

Day of year calculations

Day of year can be calculated from a date using day_of_year and the calculation reversed using date_from_day_year. Since a crop may be harvested in a different year than it is sown, then the harvest day of year in which it is sown may be more appropriate and can be obtained with day_of_harvest.

Downloading SILO data

The function get_silodata and is available to retrieve SILO weather data for one location from Queensland Government DES longpaddock website https://www.longpaddock.qld.gov.au/. Use get_multi_silodata to retrieve multiple locations.

Author(s)

Maintainer: Peter Baker drpetebaker@gmail.com (ORCID)

Authors:

Miranda Mortlock miranda.mortlock@qut.edu.au (ORCID)

SILO weather data extracted for Boonah SE Queensland

Description

A dataset containing daily weather data in APSIM format for Boonah at -27.9927 S, 152.6906 E for the period 1 Jan 2019 to 31 May 2020.

Usage

boonah

Format

A data frame with 517 rows and 10 variables:

year: Year
day: Day
radn: Radiation (MJ/m^2)
maxt: Max Temperature (degrees C)
mint: Min Temperature (degrees C)
rain: Rainfall (mm)
evap: Evaporation (mm)
vp: Vapour Pressure (hPa)
code: Source Code is a six digit string describing the source of the meteorological data where each digit is either 0, an actual observation; 1, an actual observation from a composite station; 2, a value interpolated from daily observations; 3, a value interpolated from daily observations using the anomaly interpolation method for CLIMARC data; 6, a synthetic pan value; or 7, an interpolated long term average
date_met: The date on which daily weather was collected

Details

These data were obtained using

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

where “MY_EMAIL_ADDRESS” was replaced with an appropriate address.

Source

Meteorological SILO data obtained from the Longpaddock Qld Government web site https://www.longpaddock.qld.gov.au for Boonah -27.9927 S, 152.6906 E for the period 1 Jan 2019 to 31 May 2020.

Crop data containing hypothetical sowing flowering and harvest dates

Description

A dataset containing typical dates for agronomic data

Usage

crop

Format

A data frame with 10 rows and 3 variables:

sowing_date: Sowing date of crop
flower_date: Flowering date of crop
harvest_date: Harvest date of crop

Details

Typical dates for crop data sets that would typically contain agronomic traits such as yield, dry matter yield and so on. However, here the dataset only contains dates which are used to demonstrate how to add computed weather variables either starting or ending at a specified date or between two dates.

Source

Hypothetical sowing, flowering and harvest dates based on typical horticultural crops in Queensland

Sum of a weather variable between between two dates

Description

Calculates the sum or total of daily values between two dates from a tibble or data frame of daily weather data. Alternatively, a number of days before or after a specific date may be specified. Typically this is used for solar radiation, evaporation or rainfall since the total rainfall, radiation or evaporation during a specified period may prove useful for modelling yield or plant growth.

Usage

cumulative(
  data,
  var = NULL,
  datevar = NULL,
  ndays = 5,
  na.rm = FALSE,
  startdate = NULL,
  enddate = NULL,
  monitor = FALSE,
  warn.consecutive = TRUE,
  ...
)

Arguments

data

Tibble or dataframe of daily weather data

var

Variable(s) to be extracted (Default: radn). Several columns may be specified using column names c(variable1, variable2, ...)

datevar

Date variable specifying day (Default: date_met)

ndays

Number of days after/before the start or end date, respectively. Ignored of both the start and end dates are specified (Default: 5)

na.rm

Used for calculations (Default: FALSE)

startdate

Start date of data to be extracted

enddate

Final date of data to be extracted

monitor

For debugging. Prints data and dates. (Default: FALSE)

warn.consecutive

A logical indicating whether to check that dates are consecutive, that none are missing and provide a warning if not (Default:TRUE)

...

options to be passed to sum calculation

Details

The sum is returned but if there are any missing values, then the sum is set to NA since the default na.rm is TRUE. Note that if there are any missing dates, then a warning is issued but the sum of non-missing values is returned.

If any values are missing, while the calculated sum or total may prove useful, it will not include all the data and so may lead to biased underestimates. Hence, in these cases it may be unlikely that the sum is a good estimate but the appropriateness of the estimate will depend on the exact circumstances of the missing data and so this decision is left to the user.

Value

Numeric variable containing the sum of all values of the weather variable var during the specified period.

Examples

## Selected calculations
## library(tidyverse)   # purrr used here for crop2 example
library(dplyr)
library(purrr)
cumulative(boonah, enddate = crop$flower_date[4], ndays = 3,
                    monitor = TRUE)
cumulative(boonah, enddate = crop$harvest_date[4], ndays = 3,
                    monitor = TRUE)
cumulative(boonah, startdate = crop$flower_date[4],
                    enddate = crop$harvest_date[4], monitor = TRUE)
cumulative(boonah, startdate = crop$flower_date[4],
                    enddate = crop$harvest_date[4])
cumulative(boonah, var = rain, startdate = crop$flower_date[4],
                    enddate = crop$harvest_date[4])

## Add selected totals of weather variables in 'boonah' to 'crop'' tibble
## using 'map2_dbl' from the 'purrr' package
## Note: using equivalent 'furrr' functions can speed up calculations 
crop2 <- crop |>
  dplyr::mutate(totalrain_post_sow_7d =
          purrr::map_dbl(sowing_date, function(x)
            cumulative(boonah, var = rain, startdate = x, ndays = 7)),
          totalrad_flower_harvest =
            purrr::map2_dbl(flower_date, harvest_date, function(x, y)
              cumulative(boonah, var = radn, startdate = x, enddate = y)))
crop2

Mean of daily weather variable values between two dates

Description

Calculates the average of daily values of the variable between two dates from a tibble or data frame of daily weather data. Alternatively, a number of days before or after a specific date may be specified. Typically this would be used for temperature, rainfall or solar radiation.

Usage

daily_mean(
  data,
  var = NULL,
  datevar = NULL,
  ndays = 5,
  na.rm = FALSE,
  startdate = NULL,
  enddate = NULL,
  monitor = FALSE,
  warn.consecutive = TRUE,
  ...
)

Arguments

data

Tibble or dataframe of daily weather data

var

Variable(s) to be extracted (Default: radn). Several columns may be specified using column names c(variable1, variable2, ...)

datevar

Date variable specifying day (Default: date_met)

ndays

Number of days after/before the start or end date, respectively. Ignored of both the start and end dates are specified (Default: 5)

na.rm

Used for calculations (Default: FALSE)

startdate

Start date of data to be extracted

enddate

Final date of data to be extracted

monitor

For debugging. Prints data and dates. (Default: FALSE)

warn.consecutive

A logical indicating whether to check that dates are consecutive, that none are missing and provide a warning if not (Default:TRUE)

...

options to be passed to mean calculation

Details

The mean is returned but if there are any missing values, then the mean is set to NA since the default na.rm is TRUE. Note that if there are any missing dates, then a warning is issued but the mean of non-missing values is returned.

If any values are missing, while the calculated mean may prove useful, it will not include all the data and so may lead to biased estimates. Hence, in these cases, the appropriateness of the estimate will depend on the exact circumstances of the missing data and so this decision is left to the user.

Value

Numeric variable containing the daily mean of the weather variable var during the specified period.

Examples

##  Selected calculations
## library(tidyverse)   # only purrr used here for crop2 example
library(dplyr)
library(purrr)
daily_mean(boonah, enddate = crop$flower_date[4], ndays = 3,
                    monitor = TRUE)
daily_mean(boonah, enddate = crop$harvest_date[4], ndays = 3,
                    monitor = TRUE)
daily_mean(boonah, startdate = crop$flower_date[4],
                    enddate = crop$harvest_date[4], monitor = TRUE)

## Add selected daily means of weather variables in 'boonah' to 'crop'
## tibble using 'map2_dbl' from the 'purrr' package
## Note: using equivalent 'furrr' functions can speed up calculations 
crop2 <- crop |>
  mutate(mean_maxtemp_post_sow_7d =
          purrr::map_dbl(sowing_date, function(x)
            daily_mean(boonah, var = maxt, startdate = x, ndays = 7)),
          mean_rad_flower_harvest =
            purrr::map2_dbl(flower_date, harvest_date, function(x, y)
              daily_mean(boonah, var = radn, startdate = x, enddate = y)))
crop2

Calculate a date from the day of the year and the year

Description

date_from_day_of_year returns the date using the day of the year and a year for a calendar or financial year. The first day of the year is 1 January for the calendar year, 1 July for the Australian financial year or can be specified. Alternatively, the first day of the year can be any day of the year if desired.

Usage

date_from_day_year(
  day,
  year,
  type = c("calendar", "financial", "other"),
  base = NULL
)

Arguments

day

Day as an numeric (integer) used for calculation the date

year

Year as an integer (integer) used for calculation the date

type

A character string specifying the type of year. “calendar” is a calendar year starting on 1 January, “financial” an Australian financial year beginning on 1 July and “other” is for a year starting on another date which is specified in base. Default: “calendar”

base

A list specifying the day and month of the first day to be used in calculations. The name of the day component must be named either “day” or “d”, and the month must be named either “month” or “m”.

Value

A date of class Date calculated from the day of the year and the year

Examples

library(lubridate)
date_from_day_year(day = 366, year = 2020)
date_from_day_year(21,2021)
date_from_day_year(day = c(21, 24, 30), year = rep(2021, 3))
date_from_day_year(21,2021, type = "financial")
date_from_day_year(21,2021, type = "other", base = list(d=1, m=9))

The day of harvest in the year of sowing

Description

day_of_harvest calculates the day of year of harvest (or another quantity such as petal fall) if harvest occurs in the next year after sowing. For instance, if the sowing date is near the end of the year, then the harvest date will fall in the next year. Hence, the harvest day (as a day of year) will be be smaller than the sowing date (as a day of the year). The day_of_harvest function rectifies this situation by calculating the harvest date as the day of year in the previous year. Hence, the resulting day of year will be greater than 366. Of course, this is not necessary if the sowing and harvest dates are in the same year, in which case day_of_year will provide exactly the same result.

Usage

day_of_harvest(
  x,
  sowing,
  type = c("calendar", "financial", "other"),
  base = NULL,
  verbose = FALSE
)

Arguments

x

A harvest Date used for calculation of the day of the year.

sowing

A sowing Date used for calculation.

type

base

verbose

Logical to indicate whether to indicate data errors. (Default:FALSE)

Value

An numeric vector containing the day of harvest in the year of sowing which will differ from the day of harvest if the sowing date is in the previous year

Examples

library(lubridate)
day_of_harvest(x = ymd("2020-06-15"), sowing = ymd("2020-06-01")) # leap year
day_of_harvest(x = ymd("2021-06-15"), sowing = ymd("2021-06-01")) # not
day_of_harvest(x = ymd("2021-06-15"), sowing = ymd("2020-06-01")) # 366+166
day_of_harvest(x = ymd("2021-02-05"), sowing = ymd("2021-01-28"))
day_of_harvest(x = ymd("2021-02-05"), sowing = ymd("2021-01-28"),
               type = "financial")
day_of_harvest(x = ymd("2021-09-05"), sowing = ymd("2021-01-28"),
               type = "financial") # 67 + 365
## number_of_days(ymd("2021-02-05"), ymd("2020-09-01")) + 1
day_of_harvest(x = ymd("2021-02-05"), sowing = ymd("2021-01-28"),
               type = "other", base = list(m = 9, day = 1))

Calculate day of year from a date

Description

day_of_year returns the day of the year as an integer. The first day of the year is 1 January for the calendar year, 1 July for the Australian financial year or can be specified as any day of the year if desired.

Usage

day_of_year(
  x,
  type = c("calendar", "financial", "other"),
  return_year = FALSE,
  base = NULL
)

Arguments

x

A date used for calculation the day of the year.

type

return_year

A logical indicating whether to return the year or not. Default: FALSE

base

Value

A numeric vector containing day of the year. If return_year is TRUE then a data.frame is returned containing two columns day and year. The first column day is always numeric but the class of the year column depends on type. For type “calendar”, “financial”, “other” then year is numeric, character and the base date as a Date, respectively.

Examples

library(lubridate)
## Day of Calendar Year
day_of_year(ymd("2020-01-05"))
day_of_year(ymd("2021-01-05"))
day_of_year(ymd(c("2020-06-05"), "2021-06-05")) # 29 Feb in 2020 only
day_of_year(ymd("2020-12-31"))
day_of_year(ymd("2021-12-31"))
day_of_year(ymd("2020-12-31"), return_year = TRUE)
day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")))
day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")),
            return_year = TRUE)

## Day of Financial Year
day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")),
            type = "financial")
day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")),
            type = "financial", return_year = TRUE)
day_of_year(x = ymd("2021-09-05"), type = "financial") # 67

## Specify the year starts on 1 September
day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")),
            type = "other", base = list(d = 1, m = 9))
day_of_year(ymd(c("2020-12-31", "2020-07-01", "2020-01-01")),
            type = "other", base = list(d = 1, m = 9), return_year = TRUE)

Retrieve SILO data for multiple sites from Qld DES longpaddock website

Description

Uses get_silodata to retrieve SILO (Scientific Information for Land Owners) data for multiple sites. SILO products are provided free of charge to the public for use under the Creative Commons Attribution 4.0 license and appear to be subject to fair use limits. Note that SILO may be unavailable between 11am and 1pm (Brisbane time) each Wednesday and Thursday to allow for essential system maintenance.

Usage

get_multi_silodata(
  latitude,
  longitude,
  Sitename,
  email,
  START = "20201101",
  FINISH = "20201231",
  FORMAT = "apsim",
  PASSWORD = "apitest",
  URL = "https://www.longpaddock.qld.gov.au/cgi-bin/silo/DataDrillDataset.php"
)

Arguments

latitude

A numerical vector containing site latitudes for data retrieval

longitude

A numerical vector containing site longitudes for data retrieval

Sitename

A vector of strings containing site names or labels which provide an extra column in the dataset named Sitename

email

A string containing your email which is required by DES in order to access the data

START

Start date as a character string “YYYYMMDD” with no spaces. Default: “20201101”

FINISH

Last date as a character string “YYYYMMDD” with no spaces. Default: “20201231”

FORMAT

of data file required. While this function was originally constructed to obtain apsim format, other formats are now available but not as tested. rainman is not included since it returns six separate files. See https://www.longpaddock.qld.gov.au/silo/about/file-formats-and-samples/ Default: “apsim”

PASSWORD

Default: “apitest”

URL

is the URL for querying the website and probably will not need to be changed. Default: “https://www.longpaddock.qld.gov.au/cgi-bin/silo/DataDrillDataset.php”

Value

A tibble (dataframe) containing specified or default climate variables. If “APSIM” format is specified then an extra column date_met, containing the date, is returned along with the usual year and day of year day. The Sitename variable contains the name of each site.

Examples

## Not run: 
## Example: Replace MY_EMAIL_ADDRESS with your email address below
two_sites  <-
  get_multi_silodata(latitude = c(-27.00, -28.00),
                     longitude = c(151.00, 152.00),
                     Sitename = c("Site_1", "Site_2"),
                     START = "20201101", FINISH = "20201105",
                     FORMAT = "allmort",
                     email = "MY_EMAIL_ADDRESS")
two_sites

## End(Not run)

Retrieve weather data from Queensland Government DES longpaddock website

Description

SILO (Scientific Information for Land Owners) is a database of Australian climate data from 1889 (current to yesterday). It provides data sets for a range of climate variables. SILO is hosted by the Science and Technology Division of the Queensland Government's Department of Environment and Science (DES). For more information please see https://www.longpaddock.qld.gov.au/silo/about. A number of data formats are available via the FORMAT option, but we have mainly used the “apsim” format. Other formats may be retrieved but these are largely untested. Please lodge an issue if there are problems. For details, please see https://www.longpaddock.qld.gov.au/silo/about/file-formats-and-samples/. SILO products are provided free of charge to the public for use under the Creative Commons Attribution 4.0 license and appear to be subject to fair use limits. Note that SILO may be unavailable between 11am and 1pm (Brisbane time) each Wednesday and Thursday to allow for essential system maintenance.

Usage

get_silodata(
  latitude,
  longitude,
  email,
  START = "20201101",
  FINISH = "20201231",
  FORMAT = c("apsim", "fao56", "standard", "allmort", "ascepm", "evap_span", "span",
    "all2016", "alldata", "p51", "rainonly", "monthly", "cenw"),
  PASSWORD = "apitest",
  extras = NULL,
  URL = "https://www.longpaddock.qld.gov.au/cgi-bin/silo/DataDrillDataset.php"
)

Arguments

latitude

A number or character string of the site latitude for data retrieval

longitude

A number or character string of the site longitude for data retrieval

email

A string containing your email which is required by DES in order to access the data

START

Start date as a character string “YYYYMMDD” with no spaces. Default: “20201101”

FINISH

Last date as a character string “YYYYMMDD” with no spaces. Default: “20201231”

FORMAT

PASSWORD

Default: “apitest”

extras

A list containing variable names and values for extra columns. Note that the new variable(s) will have the same value. Default: NULL

URL

is the URL for querying the website and probably will not need to be changed. Default: “https://www.longpaddock.qld.gov.au/cgi-bin/silo/DataDrillDataset.php”

Details

When extrais specified, then extra columns which could for instance include the site name are added to each row for later calculations. This can also be employed in a loop using for() loops or a tidyverse approach, such as purrrlyr::by_row() in order to manipulate data by site.

Value

Data frame/tibble containing specified or default climate variables. If “APSIM” format is specified then an extra column date_met, containing the date, is returned along with the usual year and day of year day.

Examples

## Not run: 
## Example 1: Replace MY_EMAIL_ADDRESS with your email address below
##            Latitude and Longitude character strings
boonah_data <-
  get_silodata(latitude = "-27.9927", longitude = "152.6906",
               email = "MY_EMAIL_ADDRESS",
               START = "20190101", FINISH = "20200531")
## Example 2: Replace MY_EMAIL_ADDRESS below with yours - adds extra column
##            Latitude and Longitude are numeric
boonah_data2 <-
  get_silodata(latitude = -27.9927, longitude = 152.6906,
               email = "MY_EMAIL_ADDRESS",
               START = "20190101", FINISH = "20200531",
               extras = list(Sitename = "Boonah"))
## Example 3: Replace MY_EMAIL_ADDRESS below with yours - adds extra column
##            Latitude and Longitude are numeric.
##            Retreives all Morton hydrological evapotranspirations
boonah_data3 <-
  get_silodata(latitude = -27.9927, longitude = 152.6906,
               email = "MY_EMAIL_ADDRESS", FORMAT  =  "allmort",
               START = "20190101", FINISH = "20190115",
               extras = list(Sitename = "Boonah"))
## Example 4: Replace MY_EMAIL_ADDRESS below with yours - adds extra column
##            Latitude and Longitude are numeric. Retreives two months of
##            total rainfall and evaporation and means for max/min
##            temperatures, solar radiation and vapour pressure
boonah_data4 <-
  get_silodata(latitude = -27.9927, longitude = 152.6906,
               email = "drpetebaker@gmail.com", FORMAT = "monthly",
               START = "20190101", FINISH = "20190228",
               extras = list(Sitename = "Boonah"))

## End(Not run)

Degree days as the sum of capped average daily temperature above a baseline value

Description

Calculate the sum of of degree days (average temperature - base temperature base_temp for each day) during specified dates for a tibble or data frame of daily weather data. Alternatively, a number of days before or after a specific date may be specified. Note that the maximum temperature is capped at maxt_cap when calculating the average temperature.

Usage

growing_degree_days(
  data,
  varmax = NULL,
  varmin = NULL,
  datevar = NULL,
  maxt_cap = 30,
  base_temp = 5,
  na.rm = FALSE,
  ndays = 5,
  startdate = NULL,
  enddate = NULL,
  monitor = FALSE,
  warn.consecutive = TRUE
)

Arguments

data

Tibble or dataframe of daily weather data

varmax

Name of variable containing max temp (default 'maxt')

varmin

Name of variable containing min temp (default 'mint')

datevar

Date variable specifying day (Default: date_met)

maxt_cap

A numeric value set to the temperature considered to be the maximum necessary for plant growth. Maximum temperature is capped at this value for calculating average daily temp (Default: 30)

base_temp

A numeric value set to the temperature considered to be the minimum necessary for plant growth (Default: 5)

na.rm

Used for calculations (Default: FALSE)

ndays

Number of days after/before the start or end date, respectively. Ignored of both the start and end dates are specified (Default: 5)

startdate

Start date of data to be extracted

enddate

Final date of data to be extracted

monitor

For debugging. Prints data and dates. (Default: FALSE)

warn.consecutive

A logical indicating whether to check that dates are consecutive, that none are missing and provide a warning if not (Default:TRUE)

Details

The value returned is the sum of of degree days GDD = \sum_i (Tmax_i + Tmin_i) / 2 - Tbase during specified dates for a tibble or data frame of daily weather data. The maximum temperature Tmax is capped at maxt_cap degrees when calculating average temp (see https://farmwest.com/climate/calculator-information/gdd/ or McMaster, GS, & Wilhelm, WW (1997)). Baskerville, G & Emin, P (1969) provide variations on this method.

The sum of degree days is returned but if there are any missing values, then the value returned will be NA since the default na.rm is TRUE. Note that if there are any missing dates, then a warning is issued but the sum of non-missing values is returned.

If any values are missing, while the sum of degree days may prove useful, it will not include all the data and so will lead to biased underestimates. Hence, in these cases it is unlikely that the value returned is a good estimate but the appropriateness of the estimate will depend on the exact circumstances of the missing data and so this decision is left to the user.

Value

Numeric variable containing the sum of degree days during the period

References

Baskerville, G., & Emin, P. (1969). Rapid Estimation of Heat Accumulation from Maximum and Minimum Temperatures. Ecology, 50(3), 514-517. doi:10.2307/1933912
McMaster, G. S., & Wilhelm, W. W. (1997). Growing degree-days: One equation, two interpretations. Agricultural and Forest Meteorology, 87(4), 291–300. doi:10.1016/S0168-1923(97)00027-0
Anon. (2021). GDD. Farmwest. Retrieved June 15, 2021, from https://farmwest.com/climate/calculator-information/gdd/

Examples

## Selected calculations
## library(tidyverse)  # only purrr used here for crop2 example
library(dplyr)
library(purrr)
growing_degree_days(boonah, enddate = crop$flower_date[4], ndays = 3,
                    varmax = maxt, varmin = mint,
                    monitor = TRUE)
growing_degree_days(boonah, enddate = crop$harvest_date[4], ndays = 3,
                    varmax = maxt, varmin = mint,
                    monitor = TRUE)
growing_degree_days(boonah, startdate = crop$flower_date[4],
                    varmax = maxt, varmin = mint,
                    enddate = crop$harvest_date[4], monitor = TRUE)

## Add selected growing degree days at 'boonah' to 'crop' tibble
## using 'map2_dbl' from the 'purrr' package
## Note: using equivalent 'furrr' functions can speed up calculations 
crop2 <- crop |>
  mutate(gddays8_post_sow_7d =
          purrr::map_dbl(sowing_date, function(x)
            growing_degree_days(boonah, startdate = x, ndays = 7,
                                base_temp = 8)),
          gddays_flower_harvest =
            purrr::map2_dbl(flower_date, harvest_date, function(x, y)
              growing_degree_days(boonah, startdate = x, enddate = y)))
crop2

The number of days between two dates

Description

A convenience function which is simply as.numeric(end_date - start_date)

Usage

number_of_days(x, start)

Arguments

x

The end date with class Date

start

The start date with class Date

Value

A numeric variable containing the number of days between the two dates

Examples

library(lubridate)
number_of_days(x = ymd("2021-01-05"), start = ymd("2020-12-28"))

The number of days that maximum temp is over a baseline value

Description

Calculate the number of days when the maximum temperature exceeds a base line stress_temp during specified dates for a tibble or data frame of daily weather data. Alternatively, a number of days before or after a specific date may be specified. The default value of stress_temp is 30 degrees C.

Usage

stress_days_over(
  data,
  var = NULL,
  datevar = NULL,
  ndays = 5,
  na.rm = FALSE,
  stress_temp = 30,
  startdate = NULL,
  enddate = NULL,
  monitor = FALSE,
  warn.consecutive = TRUE,
  ...
)

Arguments

data

Tibble or dataframe of daily weather data

var

Variable to be extracted (Default: maxt)

datevar

Date variable specifying day (Default: date_met)

ndays

Number of days after/before the start or end date, respectively. Ignored of both the start and end dates are specified (Default: 5)

na.rm

Used for calculations (Default: FALSE)

stress_temp

A numeric value set to the temperature considered to be stressful if the maximum temperature exceeds (Default: 30)

startdate

Start date of data to be extracted

enddate

Final date of data to be extracted

monitor

For debugging. Prints data and dates. (Default: FALSE)

warn.consecutive

A logical indicating whether to check that dates are consecutive, that none are missing and provide a warning if not (Default:TRUE)

...

options to be passed to sum calculation

Details

The number of days is returned but if there are any missing values, then the sum is set to NA since the default na.rm is TRUE. Note that if there are any missing dates, then a warning is issued but the sum of non-missing values is returned.

If any values are missing, while the number of days over stress_temp total may prove useful, it will not include all the data and so may lead to biased underestimates. Hence, depending on the time of year, it may be unlikely that this is a good estimate but the appropriateness of the estimate will depend on the exact circumstances of the missing data and so this decision is left to the user.

Value

Numeric variable containing the number of days where the maximum temperature was above the specified stress temperature cutoff

Examples

##  Selected calculations
## library(tidyverse) # only purrr used here for crop2 example
library(dplyr)
library(purrr)
stress_days_over(boonah, enddate = crop$flower_date[4], ndays = 3,
                 monitor = TRUE)
stress_days_over(boonah, enddate = crop$harvest_date[4], ndays = 3,
                 monitor = TRUE)
stress_days_over(boonah, startdate = crop$flower_date[4],
                 enddate = crop$harvest_date[4], monitor = TRUE)

## Add selected stress days at 'boonah' to 'crop' tibble
## using 'map2_dbl' from the 'purrr' package
## Note: using equivalent 'furrr' functions can speed up calculations 
crop2 <- crop |>
  mutate(stressdays25_post_sow_7d =
          purrr::map_dbl(sowing_date, function(x)
            stress_days_over(boonah, startdate = x, ndays = 7,
                             stress_temp = 25)),
          stressdays_flower_harvest =
            purrr::map2_dbl(flower_date, harvest_date, function(x, y)
              stress_days_over(boonah, startdate = x, enddate = y)))
crop2

SILO weather data extracted for two arbitrary sites

Description

A dataset containing daily weather data in APSIM format for Site_1: at -27 S, 151 E and Site_2: -28 S, 152 E for the period 1 to 5 Nov 2020

Usage

two_sites

Format

A data frame with 10 rows and 11 variables:

year: Year
day: Day
radn: Radiation (MJ/m^2)
maxt: Max Temperature (degrees C)
mint: Min Temperature (degrees C)
rain: Rainfall (mm)
evap: Evaporation (mm)
vp: Vapour Pressure (hPa)
code: Source Code is a six digit string describing the source of the meteorological data where each digit is either 0, an actual observation; 1, an actual observation from a composite station; 2, a value interpolated from daily observations; 3, a value interpolated from daily observations using the anomaly interpolation method for CLIMARC data; 6, a synthetic pan value; or 7, an interpolated long term average
date_met: The date on which daily weather was collected
Sitename: Sites labelled “Site_1” and “Site_2”

Details

The data were obtained with

two_sites <- get_multi_silodata(latitude = c(-27.00, -28.00), longitude = c(151.00, 152.00), Sitename = c("Site_1", "Site_2"), START = "20201101", FINISH = "20201105", email = "MY_EMAIL_ADDRESS")

where “MY_EMAIL_ADDRESS” was relaced with an appropriate address.

Source

Meteorological SILO data obtained from the Longpaddock Qld Government web site https://www.longpaddock.qld.gov.au for two sites for the period 1 to 5 Nov 2020

Extract one or more columns of weather data between two dates

Description

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

Usage

weather_extract(
  data,
  var,
  datevar = NULL,
  ndays = 5,
  startdate = NULL,
  enddate = NULL,
  monitor = FALSE,
  return.dates = TRUE,
  warn.consecutive = TRUE
)

Arguments

data

Tibble or dataframe of daily weather data

var

Variable(s) to be extracted (Default: radn). Several columns may be specified using column names c(variable1, variable2, ...)

datevar

Date variable specifying day (Default: date_met)

ndays

Number of days after/before the start or end date, respectively. Ignored of both the start and end dates are specified (Default: 5)

startdate

Start date of data to be extracted

enddate

Final date of data to be extracted

monitor

For debugging. Prints data and dates. (Default: FALSE)

return.dates

A logical indicating whether to return the date column (Default: TRUE)

warn.consecutive

A logical indicating whether to check that dates are consecutive, that none are missing and provide a warning if not (Default:TRUE)

Value

A tibble (data frame) of extracted weather data

Examples

library(lubridate)
boonah |>
weather_extract(rain, date = date_met, startdate = ymd("2019-08-16"),
                  enddate = ymd("2019-08-21"))
boonah |>
  weather_extract(rain, startdate = ymd("2019-08-16"),
                  enddate = ymd("2019-08-21"))
boonah |>
  weather_extract(maxt, date = date_met, startdate = ymd("2019-08-16"),
                  ndays = 3, return.dates = FALSE)
boonah |>
  weather_extract(mint, enddate = ymd("2019-08-16"), ndays = 1)
## extract multiple columns
boonah |>
  weather_extract(c(year, day, mint, maxt), enddate = ymd("2019-08-16"),
                  ndays = 3)

Cropgrowdays: Agrometeorological calculations for crops

Description

Agrometeorological calculation functions

Day of year calculations

Downloading SILO data

Author(s)

See Also

SILO weather data extracted for Boonah SE Queensland

Description

Usage

Format

Details

Source

Crop data containing hypothetical sowing flowering and harvest dates

Description

Usage

Format

Details

Source

Sum of a weather variable between between two dates

Description

Usage

Arguments

Details

Value

See Also

Examples

Mean of daily weather variable values between two dates

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculate a date from the day of the year and the year

Description

Usage

Arguments

Value

Examples

The day of harvest in the year of sowing

Description

Usage

Arguments

Value

Examples

Calculate day of year from a date

Description

Usage

Arguments

Value

See Also

Examples

Retrieve SILO data for multiple sites from Qld DES longpaddock website

Description

Usage

Arguments

Value

See Also

Examples

Retrieve weather data from Queensland Government DES longpaddock website

Description

Usage

Arguments

Details

Value

See Also

Examples

Degree days as the sum of capped average daily temperature above a baseline value

Description

Usage

Arguments

Details

Value

References

See Also

Examples

The number of days between two dates

Description