ascotracer

This is a clean clone of ascotraceR

https://github.com/paulmelloy/ascotracer

Science Score: 59.0%

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Last synced: 7 months ago · JSON representation

Repository

This is a clean clone of ascotraceR

Basic Info

Host: GitHub
Owner: PaulMelloy
License: other
Language: R
Default Branch: main
Size: 2.01 MB

Statistics

Stars: 1
Watchers: 1
Forks: 0
Open Issues: 7
Releases: 0

Created over 3 years ago · Last pushed over 2 years ago

Metadata Files

Readme Changelog License Code of conduct Codemeta

README.Rmd

---
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.

Owner

Name: Paul Melloy
Login: PaulMelloy
Kind: user
Location: Gatton
Company: The University of Queensland

Website: https://paulmelloy.github.io/
Twitter: PaulMelloy
Repositories: 31
Profile: https://github.com/PaulMelloy

Plant disease modeller for the University of Queensland

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GitHub Events

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Last synced: over 1 year ago

All Time

Total Commits: 991
Total Committers: 5
Avg Commits per committer: 198.2
Development Distribution Score (DDS): 0.642

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Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Paul Melloy	p**y@u**u	355
Adam Sparks	a**s@g**m	186
IhsanKhaliq	5****q	172
PaulMelloy	p**l@m**u	144
IhsanKhaliq	i**q@u**u	134

Committer Domains (Top 20 + Academic)

usq.edu.au: 1 melloy.com.au: 1 usq.edu.u: 1

Issues and Pull Requests

Last synced: 10 months ago

All Time

Total issues: 10
Total pull requests: 3
Average time to close issues: about 13 hours
Average time to close pull requests: about 1 month
Total issue authors: 2
Total pull request authors: 2
Average comments per issue: 0.4
Average comments per pull request: 1.67
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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PaulMelloy (7)
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enhancement (2) bug (1)

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DESCRIPTION cran

R >= 3.5.0 depends
circular >= 0.4 imports
data.table >= 1.13.0 imports
lubridate >= 1.7.9.2 imports
lutz >= 0.3.1 imports
sf * imports
stats * imports
terra * imports
ggplot2 * suggests
knitr * suggests
rmarkdown * suggests
spelling * suggests
testthat >= 3.0.1 suggests

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ascotracer

Science Score: 59.0%

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README.Rmd

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CodeMeta (codemeta.json)

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