ascotraceR: An R package resource to simulate the spatiotemporal spread of Ascochyta blight in a chickpea field over a growing season
The goal of of ascotraceR is to develop a weather driven model to simulate the spread of Ascochyta blight disease in a chickpea field over a growing season.
This model is adapted from a model developed by (Diggle et al. 2002) for simulating the spread of anthracnose in a lupin field. The model is run using local weather data. The ascotraceR model simulates the pathogen related processes of conidial production, dispersal, successful deposition and infection on chickpea plants. Host related processes of growth are simulated in terms of development of growing points. The model divides the paddock into 1 square metre cells (observation quadrats/units) and simulates chickpea growth and A. rabiei activities in each cell. Initially, there is one growing point per sown seed when seed are sown. Chickpea growth is then described in terms of increase in the number of growing points. Conidia are dispersed from infested stubble by rain splash or wind driven rain when rainfall threshold is reached. Rainfall threshold refers to the minimum amount of rainfall required to disperse conidia from pycnidia and to provide sufficient duration of moisture for conidia to germinate and penetrate into the host tissues. After penetrating host tissues, conidia produce infected growing points. Infected growing points become sporulating lesions after completion of a latent period. The length of the latent period is a function of temperature, and the number of conidia produced per sporulating growing point depends on the level of resistance of the chickpea cultivar. As the model runs, it keeps a continuous track of non-infected, latent, infected and sporulating growing points (lesions). The ascotraceR’s minimum input requirements are location specific weather data and a list of input variables.
Quick start
ascotraceR is available on CRAN. To install the latest release, just run
install.packages("ascotraceR")Alternatively, you may install the development version from GitHub this way.
if (!require("remotes"))
install.packages("remotes")
remotes::install_github("IhsanKhaliq/ascotraceR",
build_vignettes = TRUE
)Once installed you can simulate disease spread in a chickpea paddock.
Load the library.
Import the weather data.
# weather data
Billa_Billa <- fread(
system.file(
"extdata",
"2020_Billa_Billa_weather_data_ozforecast.csv",
package = "ascotraceR"
)
)
# format time column
Billa_Billa[, local_time := dmy_hm(local_time)]
# specify the station coordinates of the Billa Billa weather station
Billa_Billa[, c("lat", "lon") := .(-28.1011505, 150.3307084)]
head(Billa_Billa)## day local_time assessment_number mean_daily_temp wind_ km_h ws
## 1: 1 2020-06-04 00:00:00 NA 4.1 0.9 0.25
## 2: 1 2020-06-04 00:15:00 NA 3.9 0.6 0.17
## 3: 1 2020-06-04 00:30:00 NA 3.9 2.0 0.56
## 4: 1 2020-06-04 00:45:00 NA 4.3 1.2 0.33
## 5: 1 2020-06-04 01:00:00 NA 3.7 2.4 0.67
## 6: 1 2020-06-04 01:15:00 NA 3.5 1.3 0.36
## ws_sd wd wd_sd cummulative_rain_since_9am rain_mm wet_hours location
## 1: NA 215 NA 0 0 NA Billa_Billa
## 2: NA 215 NA 0 0 NA Billa_Billa
## 3: NA 215 NA 0 0 NA Billa_Billa
## 4: NA 215 NA 0 0 NA Billa_Billa
## 5: NA 215 NA 0 0 NA Billa_Billa
## 6: NA 215 NA 0 0 NA Billa_Billa
## lat lon
## 1: -28.10115 150.3307
## 2: -28.10115 150.3307
## 3: -28.10115 150.3307
## 4: -28.10115 150.3307
## 5: -28.10115 150.3307
## 6: -28.10115 150.3307Wrangle weather data
A function, format_weather(), is provided to convert raw weather data into the format appropriate for the model. It is mandatory to use this function to ensure weather data is properly formatted before running the model.
Billa_Billa <- format_weather(
x = Billa_Billa,
POSIXct_time = "local_time",
temp = "mean_daily_temp",
ws = "ws",
wd_sd = "wd_sd",
rain = "rain_mm",
wd = "wd",
station = "location",
time_zone = "Australia/Brisbane",
lon = "lon",
lat = "lat"
)Simulate Ascochyta blight spread
A function, trace_asco(), is provided to simulate the spread of Ascochyta blight in a chickpea field over a growing season.
# Predict Ascochyta blight spread for the year 2020 at Billa Billa
traced <- trace_asco(
weather = Billa_Billa,
paddock_length = 20,
paddock_width = 20,
initial_infection = "2020-07-17",
sowing_date = "2020-06-04",
harvest_date = "2020-10-27",
time_zone = "Australia/Brisbane",
seeding_rate = 40,
gp_rr = 0.0065,
spores_per_gp_per_wet_hour = 0.6,
latent_period_cdd = 150,
primary_inoculum_intensity = 100,
primary_infection_foci = "centre"
)Summarise the output
You can easily get summary statistics for the whole paddock over the simulated season and area under the disease progress curve, AUDPC, using summarise_trace().
summarise_trace(traced)## i_day new_gp susceptible_gp exposed_gp infectious_gp i_date day
## 1: 1 16000 16000 0 0 2020-06-04 156
## 2: 2 1200 17200 0 0 2020-06-05 157
## 3: 3 1200 18400 0 0 2020-06-06 158
## 4: 4 1600 20000 0 0 2020-06-07 159
## 5: 5 2000 22000 0 0 2020-06-08 160
## ---
## 143: 143 400 1991999 0 734 2020-10-24 298
## 144: 144 0 1991999 28 734 2020-10-25 299
## 145: 145 0 1991999 28 734 2020-10-26 300
## 146: 146 1 1992000 28 734 2020-10-27 301
## 147: 147 0 1992000 28 734 2020-10-28 302
## cdd cwh cr gp_standard AUDPC
## 1: 0.00000 0 0.0 40 49393
## 2: 10.74583 0 0.0 43 49393
## 3: 22.84583 0 0.0 46 49393
## 4: 35.41042 0 0.0 50 49393
## 5: 49.38958 1 0.6 55 49393
## ---
## 143: 2133.81645 65 76.2 4980 49393
## 144: 2154.64770 73 94.4 4980 49393
## 145: 2176.49562 73 94.4 4980 49393
## 146: 2195.63104 73 94.4 4980 49393
## 147: 2215.57687 74 97.0 4980 49393Code of Conduct
Please note that the ascotraceR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
