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https://github.com/paulmelloy/ascotracer

This is a clean clone of ascotraceR
https://github.com/paulmelloy/ascotracer

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This is a clean clone of ascotraceR

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README 

        
---

title: "README"

output: github_document

---



```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

```



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# 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)](https://doi.org/10.1094/PHYTO.2002.92.10.1110) 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



```r

install.packages("ascotraceR")

```



Alternatively, you may install the development version from GitHub this way.



```r

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.



```{r, load-libs, message=FALSE}

library("ascotraceR")

library("data.table")

library("lubridate")



set.seed(3)

```



Import the weather data.



```{r load-weather}

# 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)

```



### Wrangle 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.



```{r format-weather}

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.



```{r trace-asco}

# 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()`.



```{r summarise}

summarise_trace(traced)

```



## Code of Conduct



Please note that the ascotraceR project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).

By contributing to this project, you agree to abide by its terms.



## Reference



> Diggle AJ, Salam MU, Thomas GJ, Yang H, O'connell M, Sweetingham M, 2002. AnthracnoseTracer: a spatiotemporal model for simulating the spread of anthracnose in a lupin field. Phytopathology 92, 1110-21.