Simulate the spatiotemporal development of Ascochyta blight in a chickpea paddock over a growing season. Both host and pathogen activities are simulated in one square metre cells.

```
trace_asco(
weather,
paddock_length,
paddock_width,
sowing_date,
harvest_date,
initial_infection,
seeding_rate = 40,
gp_rr = 0.0065,
max_gp_lim = 5000,
max_new_gp = 350,
latent_period_cdd = 150,
time_zone = "UTC",
primary_infection_foci = "random",
primary_inoculum_intensity = 1,
n_foci = 1,
spores_per_gp_per_wet_hour = 0.22,
splash_cauchy_parameter = 0.5,
wind_cauchy_multiplier = 0.015,
daily_rain_threshold = 2,
hourly_rain_threshold = 0.1,
susceptible_days = 2,
rainfall_multiplier = FALSE
)
```

- weather
weather data for a representative chickpea paddock for a complete chickpea growing season for the model's operation.

- paddock_length
length of a paddock in metres (y).

- paddock_width
width of a paddock in metres (x).

- sowing_date
a character string of a date value indicating sowing date of chickpea seed and the start of the ‘ascotraceR’ model. Preferably in ISO8601 format (YYYY-MM-DD),

*e.g.*“2020-04-26”. Assumes there is sufficient soil moisture to induce germination and start the crop growing season.- harvest_date
a character string of a date value indicating harvest date of chickpea crop, which is also the last day to run the ‘ascotraceR’ model. Preferably in ISO8601 format (YYYY-MM-DD),

*e.g.*, “2020-04-26”.- initial_infection
a character string of a date value referring to the initial or primary infection on seedlings, resulting in the production of infectious growing points.

- seeding_rate
indicate the rate at which chickpea seed is sown per square metre. Defaults to

`40`

.- gp_rr
refers to rate of increase in chickpea growing points per degree Celsius per day. Defaults to

`0.0065`

.- max_gp_lim
maximum number of chickpea growing points (meristems) allowed per square metre. Defaults to

`5000`

.- max_new_gp
Maximum number of new chickpea growing points (meristems), which develop per day, per square metre. Defaults to

`350`

.- latent_period_cdd
latent period in cumulative degree days (sum of daily temperature means) is the period between infection and production of lesions on susceptible growing points. Defaults to

`150`

.- time_zone
refers to time in Coordinated Universal Time (UTC).

- primary_infection_foci
refers to the inoculated coordinates where the infection starts. Accepted inputs are:

`centre`

/`center`

or`random`

(Default) or a`data.frame`

with column names ‘x’, ‘y’ and ‘load’. The`data.frame`

inputs inform the model of specific grid cell/s coordinates where the epidemic should begin. The ‘load’ column is optional and can specify the`primary_inoculum_intensity`

for each coordinate.- primary_inoculum_intensity
Refers to the amount of primary infection as lesions on chickpea plants at the time of

`initial_infection`

. On the date of initial infection in the experiment. The sources of primary inoculum can be infected seed, volunteer chickpea plants or infested stubble from the previous seasons. Defaults to`1`

.- n_foci
Quantifies the number of primary infection foci. The value is

`1`

when`primary_infection_foci = "centre"`

and can be greater than`1`

if`primary_infection_foci = "random`

.- spores_per_gp_per_wet_hour
number of spores produced per infectious growing point during each wet hour. Also known as the

`spore_rate`

. Value is dependent on the susceptibility of the host genotype.- splash_cauchy_parameter
a parameter used in the Cauchy distribution and describes the median distance spores travel due to rain splashes. Default to

`0.5`

.- wind_cauchy_multiplier
a scaling parameter to estimate a Cauchy distribution which resembles the possible distances a conidium travels due to wind driven rain. Defaults to

`0.015`

.- daily_rain_threshold
minimum cumulative rainfall required in a day to allow hourly spore spread events. See also

`hourly_rain_threshold`

. Defaults to`2`

.- hourly_rain_threshold
minimum rainfall in an hour to trigger a spore spread event in the same hour (assuming daily_rain_threshold is already met). Defaults to

`0.1`

.- susceptible_days
the number of days for which conidia remain viable on chickpea after dispersal. Defaults to

`2`

. Conidia remain viable on the plant for at least 48 hours after a spread event- rainfall_multiplier
logical values will turn on or off rainfall multiplier default method. The default method increases the number of spores spread per growing point if the rainfall in the spore spread event hour is greater than one. Numeric values will scale the number of spores spread per growing point against the volume of rainfall in the hour. Defaults to

`FALSE`

.

a nested `list`

object where each sub-list contains daily data for
the day `i_day`

(the model's iteration day) generated by the model
including: * **paddock**, an 'x' 'y' data.table containing: *
**x**, location of quadrat on x-axis in paddock, * **y**, location of
quadrat on y-axis in paddock, * **new_gp**, new growing points produced in
the last 24 hours, * **susceptible_gp**, susceptible growing points in the
last 24 hours, * **exposed_gp**, exposed growing points in the last 24
hours, * **infectious_gp**, infectious growing points in the last 24 hours,

**i_day**, model iteration day, ***cumulative daily weather data**, a data.table containing: ***cdd**, cumulative degree days, ***cwh**, cumulative wet hours, ***cr**, cumulative rainfall in mm, ***gp_standard**, standard growing points assuming growth is not impeded by infection, ***infected_coords**, a data.table of only infectious growing point coordinates, ***new_infections**, a data.table of newly infected growing points, ***exposed_gps**, a data.table of exposed growing points in the latent period phase of infection.

```
if (FALSE) { # interactive()
# First weather data needs to be imported and formatted with `format_weather`
Newmarracarra <-
read.csv(system.file("extdata",
"1998_Newmarracarra_weather_table.csv", package = "ascotraceR"))
station_data <-
system.file("extdata", "stat_dat.csv", package = "ascotraceR")
weather_dat <- format_weather(
x = Newmarracarra,
POSIXct_time = "Local.Time",
temp = "mean_daily_temp",
ws = "ws",
wd_sd = "wd_sd",
rain = "rain_mm",
wd = "wd",
station = "Location",
time_zone = "Australia/Perth",
lonlat_file = station_data)
# Now the `trace_asco` function can be run to simulate disease spread
traced <- trace_asco(
weather = weather_dat,
paddock_length = 100,
paddock_width = 100,
initial_infection = "1998-06-10",
sowing_date = "1998-06-09",
harvest_date = "1998-06-30",
time_zone = "Australia/Perth",
gp_rr = 0.0065,
primary_inoculum_intensity = 40,
spores_per_gp_per_wet_hour = 0.22,
primary_infection_foci = "centre")
traced[[23]] # extracts the model output for day 23
}
```