Calculate thermal time for a crop
Examples
library(apsimwise)
# Example weather data
mint <- c(0, 10)
maxt <- c(30, 40)
date <- as.Date(c("2024-01-01", "2024-01-02"))
# Use default lupin parameters
thermal_time(
mint,
maxt,
date = date,
lat = -27,
crop = "lupin"
)
#> [1] 16.64386 24.88400
# Modify the global lupin thermal time response for all following calculations
crop("lupin")$set(
"phenology.thermal_time" =
list(
x = c(0, 25, 35),
y = c(0, 25, 0)
)
)
thermal_time(
mint,
maxt,
date = date,
lat = -27,
crop = "lupin"
)
#> [1] 16.64386 24.88400
# Then the parameters can be reset
crop("lupin")$reset()
# Override thermal time parameters for a single call
thermal_time(
mint,
maxt,
date = date,
lat = -27,
crop = "lupin",
x_temp = c(0, 25, 35),
y_temp = c(0, 25, 0)
)
#> [1] 16.64386 24.88400
# Calculate thermal time from a weather file
met_file <- system.file("extdata/ppd_72150.met", package = "tidyweather")
# Read weather records
records <- read_weather(met_file)
# Calculate thermal time for lupin using tidyverse ways
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
records |>
mutate(
tt = thermal_time(
mint = mint,
maxt = maxt,
date = date,
latitude = latitude[1],
crop = "lupin"
)
) |>
select(name, date, mint, maxt, tt)
#> # A tibble: 587 × 5
#> name date mint maxt tt
#> <chr> <date> <dbl> <dbl> <dbl>
#> 1 WAGGA WAGGA AMO 2023-12-01 14.4 28.7 21.9
#> 2 WAGGA WAGGA AMO 2023-12-02 15.1 29.1 21.5
#> 3 WAGGA WAGGA AMO 2023-12-03 10 27.8 19.9
#> 4 WAGGA WAGGA AMO 2023-12-04 14.6 30.4 22.8
#> 5 WAGGA WAGGA AMO 2023-12-05 15.1 37.9 27.3
#> 6 WAGGA WAGGA AMO 2023-12-06 18.5 33.3 25.6
#> 7 WAGGA WAGGA AMO 2023-12-07 14.9 33.4 25.3
#> 8 WAGGA WAGGA AMO 2023-12-08 20.4 36.4 29.0
#> 9 WAGGA WAGGA AMO 2023-12-09 22.6 38.6 27.6
#> 10 WAGGA WAGGA AMO 2023-12-10 22.3 34.6 28.4
#> # ℹ 577 more rows
# Calculate thermal time for lupin grouped by year and site if there are multiple sites
# Create some fake data for a second site
records2 <- records |>
mutate(
name = "site2",
mint = mint + 1,
maxt = maxt + 1,
latitude = latitude - 10
) |>
bind_rows(records)
# Calculate thermal time for lupin grouped by site
records2 |>
group_by(name) |>
mutate(
tt = thermal_time(
mint = mint,
maxt = maxt,
date = date,
latitude = latitude[1],
crop = "lupin"
)
) |>
select(name, date, mint, maxt, tt)
#> # A tibble: 1,174 × 5
#> # Groups: name [2]
#> name date mint maxt tt
#> <chr> <date> <dbl> <dbl> <dbl>
#> 1 site2 2023-12-01 15.4 29.7 23.2
#> 2 site2 2023-12-02 16.1 30.1 22.9
#> 3 site2 2023-12-03 11 28.8 21.3
#> 4 site2 2023-12-04 15.6 31.4 24.2
#> 5 site2 2023-12-05 16.1 38.9 28.9
#> 6 site2 2023-12-06 19.5 34.3 27.1
#> 7 site2 2023-12-07 15.9 34.4 26.8
#> 8 site2 2023-12-08 21.4 37.4 28.8
#> 9 site2 2023-12-09 23.6 39.6 23.3
#> 10 site2 2023-12-10 23.3 35.6 29.8
#> # ℹ 1,164 more rows