,
Miranda Mortlock [aut] | Title: | Crop Growing Degree Days and Agrometeorological Calculations |
|---|---|
| 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. |
| Authors: | Peter Baker [aut, cre] ,
Miranda Mortlock [aut] |
| Maintainer: | Peter Baker <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.2.1.9000 |
| Built: | 2024-11-09 05:09:03 UTC |
| Source: | https://gitlab.com/petebaker/cropgrowdays |
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.
boonahboonah
A data frame with 517 rows and 10 variables:
Year
Day
Radiation (MJ/m^2)
Max Temperature (degrees C)
Min Temperature (degrees C)
Rainfall (mm)
Evaporation (mm)
Vapour Pressure (hPa)
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
The date on which daily weather was collected
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.
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.
A dataset containing typical dates for agronomic data
cropcrop
A data frame with 10 rows and 3 variables:
Sowing date of crop
Flowering date of crop
Harvest date of crop
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.
Hypothetical sowing, flowering and harvest dates based on typical horticultural crops in Queensland
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.
cumulative( data, var = NULL, datevar = NULL, ndays = 5, na.rm = FALSE, startdate = NULL, enddate = NULL, monitor = FALSE, warn.consecutive = TRUE, ... )cumulative( data, var = NULL, datevar = NULL, ndays = 5, na.rm = FALSE, startdate = NULL, enddate = NULL, monitor = FALSE, warn.consecutive = TRUE, ... )
data |
Tibble or dataframe of daily weather data |
var |
Variable(s) to be extracted (Default: radn). Several columns
may be specified using column names |
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 |
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.
Numeric variable containing the sum of all values of the
weather variable var during the specified period.
sum, daily_mean,
growing_degree_days,
stress_days_over, and
weather_extract
## 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## 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
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.
daily_mean( data, var = NULL, datevar = NULL, ndays = 5, na.rm = FALSE, startdate = NULL, enddate = NULL, monitor = FALSE, warn.consecutive = TRUE, ... )daily_mean( data, var = NULL, datevar = NULL, ndays = 5, na.rm = FALSE, startdate = NULL, enddate = NULL, monitor = FALSE, warn.consecutive = TRUE, ... )
data |
Tibble or dataframe of daily weather data |
var |
Variable(s) to be extracted (Default: radn). Several columns
may be specified using column names |
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 |
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.
Numeric variable containing the daily mean of the weather
variable var during the specified period.
mean, cumulative,
growing_degree_days,
stress_days_over, and
weather_extract
## 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## 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
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.
date_from_day_year( day, year, type = c("calendar", "financial", "other"), base = NULL )date_from_day_year( day, year, type = c("calendar", "financial", "other"), base = NULL )
day |
Day as an |
year |
Year as an |
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 |
A |
A date of class Date calculated from
the day of the year and the year
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))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))
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.
day_of_harvest( x, sowing, type = c("calendar", "financial", "other"), base = NULL, verbose = FALSE )day_of_harvest( x, sowing, type = c("calendar", "financial", "other"), base = NULL, verbose = FALSE )
x |
A harvest |
sowing |
A sowing |
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 |
A |
verbose |
Logical to indicate whether to indicate data errors. (Default:FALSE) |
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
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))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))
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.
day_of_year( x, type = c("calendar", "financial", "other"), return_year = FALSE, base = NULL )day_of_year( x, type = c("calendar", "financial", "other"), return_year = FALSE, base = NULL )
x |
A |
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 |
return_year |
A logical indicating whether to return the year or not. Default: FALSE |
base |
A |
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.
date_from_day_year for converting day of
year and year to a Date and
day_of_harvest for calculating the day of harvest
given a sowing date where the start of the year is the first day
of the year which contains the sowing date
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)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)
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.
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" )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" )
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
|
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 |
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” |
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.
## 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)## 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)
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.
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" )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" )
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 |
of data file required. While this function was
originally constructed to obtain |
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: |
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” |
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.
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.
## 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 = "[email protected]", FORMAT = "monthly", START = "20190101", FINISH = "20190228", extras = list(Sitename = "Boonah")) ## End(Not run)## 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 = "[email protected]", FORMAT = "monthly", START = "20190101", FINISH = "20190228", extras = list(Sitename = "Boonah")) ## End(Not run)
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.
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 )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 )
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) |
The value returned is the sum of of degree days 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.
Numeric variable containing the sum of degree days during the period
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/
cumulative, daily_mean,
stress_days_over, and
weather_extract
## 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## 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
A convenience function which is simply as.numeric(end_date -
start_date)
number_of_days(x, start)number_of_days(x, start)
x |
The end date with class |
start |
The start date with class |
A numeric variable containing the number
of days between the two dates
library(lubridate) number_of_days(x = ymd("2021-01-05"), start = ymd("2020-12-28"))library(lubridate) number_of_days(x = ymd("2021-01-05"), start = ymd("2020-12-28"))
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.
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, ... )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, ... )
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 |
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.
Numeric variable containing the number of days where the maximum temperature was above the specified stress temperature cutoff
cumulative, daily_mean,
growing_degree_days, and
weather_extract
## 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## 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
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
two_sitestwo_sites
A data frame with 10 rows and 11 variables:
Year
Day
Radiation (MJ/m^2)
Max Temperature (degrees C)
Min Temperature (degrees C)
Rainfall (mm)
Evaporation (mm)
Vapour Pressure (hPa)
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
The date on which daily weather was collected
Sites labelled “Site_1” and “Site_2”
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.
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 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.
weather_extract( data, var, datevar = NULL, ndays = 5, startdate = NULL, enddate = NULL, monitor = FALSE, return.dates = TRUE, warn.consecutive = TRUE )weather_extract( data, var, datevar = NULL, ndays = 5, startdate = NULL, enddate = NULL, monitor = FALSE, return.dates = TRUE, warn.consecutive = TRUE )
data |
Tibble or dataframe of daily weather data |
var |
Variable(s) to be extracted (Default: radn). Several columns
may be specified using column names |
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) |
A tibble (data frame) of extracted weather data
between filter,
cumulative, daily_mean,
growing_degree_days, and stress_days_over
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)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)