Title:	Process Based Epidemiological Model for Cercospora Leaf Spot of Sugar Beet
Version:	0.0.2
Description:	Estimates sugar beet canopy closure with remotely sensed leaf area index and estimates when action might be needed to protect the crop from a Leaf Spot epidemic with a negative prognosis model based on published models.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), remotes, stats, spelling, geosphere, ggplot2
Config/testthat/edition:	3
Imports:	data.table, lubridate, sf, terra, minpack.lm, circular
VignetteBuilder:	knitr
Depends:	R (≥ 4.3.0)
LazyData:	true
URL:	https://paulmelloy.com.au/cercospoRa/, https://github.com/PaulMelloy/cercospoRa, https://paulmelloy.com.au/cercospoRa/
BugReports:	https://github.com/PaulMelloy/cercospoRa/issues
NeedsCompilation:	no
Packaged:	2025-05-23 06:02:33 UTC; paul
Author:	Paul Melloy [aut, cre, trl, cph] (Translated equations from literature and developed package.), Rene Heim [aut, cph] (Code review and development of leaf area index model), Nathan Okole [aut, ctb, cph] (Code review and development of leaf area index model), Facundo Ramón Ispizua Yamati [dtc, ctb] (Code review), Anne-Katrin Mahlein [ctb, cph]
Maintainer:	Paul Melloy <paul@melloy.com.au>
Repository:	CRAN
Date/Publication:	2025-05-23 06:20:02 UTC

Calculate daily infection values

Description

This function calculates the daily infection values for Cercospora beticola on sugar beet. Functions were adapted from Wolf and Verreet (2005) and Wolf et al (2001)

Usage

calc_DIV(date_time, Tm, RH, rain, dat, rh_threshold = 70)

Arguments

Value

data.table, with probability of infection for each day, between 0 and 1 Undertaken with two methods by Wolf and Verreet (2005)

References

Wolf, P. F. J., and J. A. Verreet. “Factors Affecting the Onset of Cercospora Leaf Spot Epidemics in Sugar Beet and Establishment of Disease-Monitoring Thresholds.” Phytopathology® 95, no. 3 (March 2005): 269–74. https://doi.org/10.1094/PHYTO-95-0269.

Wolf, P. F. J., M. Heindl, and J. A. Verreet. “Influence of Sugar Beet Leaf Mass Development on Predisposition of the Crop to Cercospora Beticola (Sacc.).” Journal of Plant Diseases and Protection 108, no. 6 (2001): 578–92.

Examples

date_t <- Sys.time() + seq(0, 179 * 60 * 10, (60 * 10))
Tm <- rnorm(180,20,10)
RH <- runif(180,min = 40,90)
rain <- rbinom(180,1,0.1) * runif(180,0.1,20)

DIV1 <- calc_DIV(
date_time = date_t,
Tm = Tm,
RH = RH,
rain = rain
)

Calculate canopy closure date

Description

Calculate canopy closure date

Usage

calc_c_closure(param_rxt, x1 = 1.3, k = 6)

Arguments

Details

Calculates canopy closure dates from LAI and growth rate parameters

Value

SpatRast where the values represent day where canopy closure occurred as an integer from the time origin "1970-01-01". The canopy closure date is reached when 90% canopy closure has occurred for the specified location, in this case the pixel.

Examples

img_dir <- system.file("extdata", "uav_img",package = "cercospoRa")
epidemic_onset_param <-
  read_sb_growth_parameter(img_files = list.files(img_dir,pattern = "tif",
                                                  full.names = TRUE),
                           img_dates = as.POSIXct(
                             c("2022-06-14","2022-06-28"),tz = "UTC"),
                           target_res = 10)
param_rxt <- calc_r_x0(epidemic_onset_param,
                       min_r = 0.02,
                       max_r = 0.05,
                       k = 6)
canopy_closure <- calc_c_closure(param_rxt,
                                 x1 = 1.3,
                                 k=6 )

Calculate epidemic onset

Description

Calculate epidemic onset

Usage

calc_epidemic_onset(start, end, c_closure, weather, cultivar_sus = 5)

Arguments

Value

If the input weather is conducive for epidemic, the function returns a POSIX_ct date when epidemic commences. If no epidemic occurs, a numeric, proportion indicating the progress an epidemic is returned

Examples

wethr <- read.csv(system.file("extdata", "clean_weather.csv",
                  package = "cercospoRa"))
wethr <- format_weather(wethr,time_zone = "UTC")

calc_epidemic_onset(start = as.POSIXct("2022-04-25",tz = "UTC"),
                    end = as.POSIXct("2022-09-30",tz = "UTC"),
                    c_closure = as.POSIXct("2022-07-01",tz = "UTC"),
                    weather = wethr)

Calculate epidemic onset from image

Description

Calculate epidemic onset from image

Usage

calc_epidemic_onset_from_image(start, end, cc_r, weather, cultivar_sus = 5)

Arguments

Value

If the input weather is conducive for epidemic, the function returns a POSIX_ct date when epidemic commences. If no epidemic occurs, a numeric, proportion indicating the progress an epidemic is returned

Examples

wethr <- read.csv(system.file("extdata", "clean_weather.csv",
                  package = "cercospoRa"))
wethr <- format_weather(wethr,time_zone = "UTC")

img_dir <- system.file("extdata", "uav_img",package = "cercospoRa")

epidemic_onset_param <-
   read_sb_growth_parameter(
      list.files(img_dir,pattern = "tif",
                 full.names = TRUE),
      img_dates = as.POSIXct(c("2022-06-14","2022-06-28"),
                             tz = "UTC"),
      10)
param_rxt <- calc_r_x0(epidemic_onset_param,
                       min_r = 0.02,
                       max_r = 0.05,
                       k = 6)
c_closure_map <- calc_c_closure(param_rxt,
                                x1 = 1.3,
                                k=6 )
 # this takes about 20 sec to run
epidemic_onset_map <- calc_epidemic_onset_from_image(start = as.POSIXct("2022-04-25",tz = "UTC"),
                                                     end = as.POSIXct("2022-09-30",tz = "UTC"),
                                                     cc_r = c_closure_map,
                                                     weather = wethr)

terra::plot(epidemic_onset_map)

Calculate growth rate and LAI at t0

Description

Calculate growth rate and LAI at t0

Usage

calc_r_x0(param_r, min_r = 0.02, max_r = 0.05, k = 6)

Arguments

Details

Fits a non-linear model to remotely sensed LAI values and estimates the leaf area index (LAI) and growth rate r at the start of the time window t0.

Value

param_rxt: list containing parameters that are necessary to calculate canopy closure dates. These parameters are r, the growth rate; x0, the initial LAI value; and t0, the initial date.

Examples

img_dir <- system.file("extdata", "uav_img",package = "cercospoRa")
epidemic_onset_param <-
  read_sb_growth_parameter(img_files = list.files(img_dir,pattern = "tif",
                                                  full.names = TRUE),
                           img_dates = as.POSIXct(
                             c("2022-06-14","2022-06-28"),tz = "UTC"),
                           target_res = 10)
param_rxt <- calc_r_x0(epidemic_onset_param,
                       min_r = 0.02,
                       max_r = 0.05,
                       k = 6)

Calculate cDIV threshold for sugar beet cultivars

Description

This function was written using summary data from Wolf and Verreet (2005), Table 1 and Table 2 to determine the equivalent cumulative daily infection value where epidemic onset begins. The average difference between 'high' and 'low' susceptible cultivars was 1.2 on the variety scale. The average difference between low and highly susceptible cultivars minimum cDIV on each starting time in Table 2 was 4.637.

Usage

calc_susceptibility(var_scale = 4)

Arguments

Value

numeric, cDIV threshold for the start of the epidemic

Format weather data into a standardised format.

Description

Formats raw weather data into an object suitable for use in the affiliated spore dispersal packages such as ascotraceR. This standardised data format ensures that the supplied weather data meet the requirements for functions in the aforementioned packages. Input weather data expects a 'long' data format, where each line is an observation at a point in time.

Usage

format_weather(
  w,
  YYYY = NULL,
  MM = NULL,
  DD = NULL,
  hh = NULL,
  mm = NULL,
  ss = NULL,
  POSIXct_time = NULL,
  time_zone = NULL,
  temp,
  rain,
  rh,
  ws,
  wd,
  wd_sd,
  station,
  lon = NULL,
  lat = NULL,
  lonlat_file = NULL,
  data_check = TRUE
)

Arguments

Details

time_zone The time-zone in which the time was recorded. All weather stations in w must fall within the same time-zone. If the required stations are located in differing time zones, format_weather() should be run separately on each object, then data can be combined after formatting.

wd_sd If weather data is provided in hourly increments, a column with the standard deviation of the wind direction over the hour is required to be provided. If the weather data are sub-hourly, the standard deviation will be calculated and returned automatically.

lon, lat and lonlat_file If w provides longitude and latitude values for station locations, these may be specified in the lon and lat columns. If the coordinates are not relevant to the study location NA can be specified and the function will drop these column variables. If these data are not included, (NULL) a separate file may be provided that contains the longitude, latitude and matching station name to provide station locations in the final epiphy.weather object that is created by specifying the file path to a CSV file using lonlat_file.

Value

A epiphy.weather object (an extension of data.table) containing the supplied weather aggregated to each hour in a suitable format for use with disease models. Depending on the input weather, classes will be given to the output object to indicate which models it meets the data requirements for. Some of the columns returned are as follows:

Examples

# load the weather data to be formatted
weather_station_data <-
   read.csv(system.file("extdata",
                        "scaddan_weather.csv",
                        package = "cercospoRa"))
weather_station_data$Local.Time <-
   as.POSIXct(weather_station_data$Local.Time, format = "%Y-%m-%d %H:%M:%S",
              tz = "UTC")

weather <- format_weather(
   w = weather_station_data,
   POSIXct_time = "Local.Time",
   ws = "meanWindSpeeds",
   wd_sd = "stdDevWindDirections",
   rain = "Rainfall",
   temp = "Temperature",
   wd = "meanWindDirections",
   lon = "Station.Longitude",
   lat = "Station.Latitude",
   station = "StationID",
   time_zone = "UTC"
)

# Reformat saved weather

# Create file path and save data
file_path_name <- paste(tempdir(), "weather_saved.csv", sep = "\\")
write.csv(weather, file = file_path_name,
          row.names = FALSE)

# Read data back in to
weather2 <- read.csv(file_path_name, stringsAsFactors = FALSE)

# reformat the data to have appropriate column classes and data class
weather2 <- format_weather(weather2,
                           time_zone = "UTC")
unlink(file_path_name) # remove temporary weather file

Calculate Cercospora Latent Period

Description

Calculate Cercospora Latent Period

Usage

latent_period(Tm, ref = "wolf")

Arguments

Details

Calculates the latent period for Cercospora beticola infections on sugar beet. Note the published formula in Wolf and Verreet (2005) contains an error in the exponent. e(0.35 x (Tm - 10)) should be e(-0.35 x (Tm - 10)). See issue #22 on Github for additional information.

Value

numeric, Latent period for a given hour. ref = "jarroudi" returns latent period in days.

References

Wolf, P. F. J., and J. A. Verreet. “Factors Affecting the Onset of Cercospora Leaf Spot Epidemics in Sugar Beet and Establishment of Disease-Monitoring Thresholds.” Phytopathology® 95, no. 3 (March 2005): 269–74. https://doi.org/10.1094/PHYTO-95-0269.

El Jarroudi, Moussa, Fadia Chairi, Louis Kouadio, Kathleen Antoons, Abdoul-Hamid Mohamed Sallah, and Xavier Fettweis. “Weather-Based Predictive Modeling of Cercospora Beticola Infection Events in Sugar Beet in Belgium.” Journal of Fungi 7, no. 9 (September 18, 2021): 777. https://doi.org/10.3390/jof7090777.

Read LAI images at several time points

Description

Read LAI images at several time points

Usage

read_sb_growth_parameter(img_files, img_dates, target_res)

Arguments

Value

terra::SpatRast with a layer for each input layer. Each layer is named according to the img_dates. The output SpatRast is suitable for input in calc_r_x0()

Examples

epidemic_onset_param <-
  read_sb_growth_parameter(img_files = list.files(system.file("extdata", "uav_img",
                                                              package = "cercospoRa"),
                                                 pattern = ".tif",
                                                 full.names = TRUE),
                           img_dates = as.POSIXct(c("2022-06-14",
                                                    "2022-06-28"),
                                                  tz = "UTC"),
                           target_res = 10)

Get temperature index

Description

Temperature index is a proportional representation of the latent period. Temperatures at or above the optimum temperature for the disease cycle will yield 1 and the lower the temperature the infinitely longer the latent period.

Usage

temperature_index(Tm, opt_Tm = 21)

Arguments

Details

For equations and original documentation, see Wolf and Verreet (2005) and Wolf et al. (2001)

Value

numeric, proportion representing the speed of the latent period in relation to the temperature optimum

References

Wolf, P. F. J., and J. A. Verreet. “Factors Affecting the Onset of Cercospora Leaf Spot Epidemics in Sugar Beet and Establishment of Disease-Monitoring Thresholds.” Phytopathology® 95, no. 3 (March 2005): 269–74. https://doi.org/10.1094/PHYTO-95-0269.

Wolf, P. F. J., F.-J. Weis, and J.-A. Verreet. “Threshold Values as Indicators of Fungicide Treatments for the Control of Leaf Blotching Caused by Cercospora Beticola (Sacc.) in Sugar Beets.” Journal of Plant Diseases and Protection 108, no. 3 (2001): 244–57.

Weather station data

Description

A unformatted weather station dataset from Germany

Author(s)

Institute for Sugar Beet Research - IFZ

Source

Climavi One + Climavi Soil, Agvolution GmbH, Gottingen, Germany A data frame with 8,016 rows and 15 columns: Weather station that accompanied field trial described in cercospora manuscript

Station

Identifier

Datum

Date

Stunde

hour

Minute

minute

T-005

Temperature in degrees Celcius at 5cm

T-200

Temperature in degrees Celcius at 200cm

T-020

Temperature in degrees Celcius at 20cm

F-200

Percentage Relative humidity at 200cm

GS200

Global radiation (W/m2) at 200cm

WR200

Wind direction degrees at 200cm

N100

Precipitation (mm) at 100cm

WG200

Wind Speed (m/s) at 200cm

...