epicrop
Simulation modelling of crop diseases using a Susceptible-Exposed-Infectious-Removed (SEIR) model in R.
https://codeberg.org/adamhsparks/epicrop
Category: Consumption
Sub Category: Agriculture and Nutrition
Keywords
agricultural-modeling agricultural-modelling agricultural-research botanical-epidemiology crop-protection disease epirice-model model modeling modelling r rice rice-diseases rstats seir seir-model
Keywords from Contributors
agriculture agriculture-research weather weather-data climate rainfall weather-forecast
Last synced: about 19 hours ago
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Simulation modelling of crop diseases using a Susceptible-Exposed-Infectious-Removed (SEIR) model in R
- Host: codeberg.org
- URL: https://codeberg.org/adamhsparks/epicrop
- Owner: adamhsparks
- Created: 2023-11-19T12:16:49.000Z (over 1 year ago)
- Default Branch: main
- Last Synced: 2025-02-13T20:59:19.584Z (3 months ago)
- Topics: agricultural-modeling, agricultural-modelling, agricultural-research, botanical-epidemiology, crop-protection, disease, epirice-model, model, modeling, modelling, r, rice, rice-diseases, rstats, seir, seir-model
- Language:
- Homepage: https://adamhsparks.codeberg.page/epicrop/
- Size: 14.4 MB
- Stars: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
https://codeberg.org/adamhsparks/epicrop/blob/main/
---
output: github_document
---
# Simulation Modelling of Crop Diseases Using a Susceptible-Exposed-Infectious-Removed (seir()) Model 
[](https://www.repostatus.org/#active) [](https://lifecycle.r-lib.org/articles/stages.html#stable) [](https://zenodo.org/badge/latestdoi/58613738) [](https://CRAN.R-project.org/package=epicrop) [](#code-coverage)
A fork of [{cropsim}](https://github.com/r-forge/cropsim/tree/master/pkg/cropsim) (Hijmans *et al.* 2009) designed to make using the EPIRICE model (Savary *et al.* 2012) for rice diseases easier to use.
This version provides easy to use functions to fetch weather data from NASA POWER, via the [{nasapower}](https://cran.r-project.org/package=nasapower) package (Sparks 2018) and predict disease intensity of five rice diseases using a generic Susceptible-Exposed-Infectious-Removed (SEIR) model (Zadoks 1971) function, `seir()`.
The original EPIRICE manuscript, Savary *et al.* (2012), which details the model and results of its use to model global epidemics of rice diseases, was published in *Crop Protection* detailing global unmanaged disease risk of bacterial blight, brown spot, leaf blast, sheath blight and rice tungro, which are included in this package.
# Quick start
You can easily simulate any of the five diseases for rice grown anywhere in the world for years from 1983 to near current using `get_wth()` to fetch data from the [NASA POWER web API](https://power.larc.nasa.gov).
Alternatively, you can supply your own weather data for any time period as long as it fits the model’s requirements.
{epicrop} is not yet on CRAN.
You can install it this way.
The preferred method is using {remotes} for simplicity.
```r
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_git("https://codeberg.org/adamhsparks/epicrop.git",
dependencies = TRUE,
build_manual = TRUE,
build_vignettes = TRUE)
```
However, if you prefer to use {pak} you can do it this way.
It may be faster to install and for Linux users, it can often help by installing system-level packages necessary to build some of the dependencies.
``` r
o <- options() # capture default options
options(pkg.build_vignettes = TRUE)
if (!require("pak")) {
install.packages("pak",
repos = sprintf(
"https://r-lib.github.io/p/pak/stable/%s/%s/%s",
.Platform$pkgType, R.Version()$os, R.Version()$arch
)
)
}
pak::pkg_install("git::https://codeberg.org/adamhsparks/epicrop",
dependencies = TRUE)
options(o) # reset options
```
## Get weather data
First you need to provide weather data for the model; {epicrop} provides the `get_wth()` function to do this.
Using it you can fetch weather data for any place in the world from 1983 to near present by providing the and latitude and dates or length of rice growing season as shown below.
``` r
library(epicrop)
# Fetch weather for year 2000 wet season for a 120 day rice variety at the IRRI
# Zeigler Experiment Station
wth <- get_wth(
lonlat = c(121.25562, 14.6774),
dates = "2000-07-01",
duration = 120
)
#> No encoding supplied: defaulting to UTF-8.
#> No encoding supplied: defaulting to UTF-8.
wth
#> YYYYMMDD DOY TEMP TMIN TMAX RHUM RAIN LAT LON
#>
#> 1: 2000-07-01 183 25.29 23.86 27.78 92.20 23.12 14.6774 121.2556
#> 2: 2000-07-02 184 26.13 23.54 29.90 86.01 17.34 14.6774 121.2556
#> 3: 2000-07-03 185 25.50 24.28 27.23 94.16 29.08 14.6774 121.2556
#> 4: 2000-07-04 186 25.81 24.50 27.56 92.42 13.01 14.6774 121.2556
#> 5: 2000-07-05 187 25.97 25.13 27.40 92.34 32.20 14.6774 121.2556
#> ---
#> 117: 2000-10-25 299 25.82 23.44 29.54 89.76 12.04 14.6774 121.2556
#> 118: 2000-10-26 300 25.44 24.14 26.99 94.93 13.03 14.6774 121.2556
#> 119: 2000-10-27 301 25.74 24.54 27.69 91.43 11.54 14.6774 121.2556
#> 120: 2000-10-28 302 25.44 24.72 26.62 91.90 74.20 14.6774 121.2556
#> 121: 2000-10-29 303 24.97 24.15 26.34 94.15 29.11 14.6774 121.2556
```
## Modelling bacterial blight disease intensity
Once you have the weather data, run the model for any of the five rice diseases by providing the emergence or crop establishment date for transplanted rice.
``` r
bb_sim <- bacterial_blight(wth, emergence = "2000-07-01")
bb_sim
#> simday dates sites latent infectious removed senesced
#>
#> 1: 1 2000-07-01 100.0000 0.00000 0.0000 0.0000 0.000000
#> 2: 2 2000-07-02 108.6875 0.00000 0.0000 0.0000 1.000000
#> 3: 3 2000-07-03 118.1002 0.00000 0.0000 0.0000 2.086875
#> 4: 4 2000-07-04 128.2934 0.00000 0.0000 0.0000 3.267877
#> 5: 5 2000-07-05 139.3254 0.00000 0.0000 0.0000 4.550811
#> ---
#> 116: 116 2000-10-24 1442.4836 105.87923 954.9354 223.8352 2097.385647
#> 117: 117 2000-10-25 1418.0978 76.02332 953.5148 255.1118 2143.087067
#> 118: 118 2000-10-26 1369.3763 72.41827 946.9419 288.4890 2190.645270
#> 119: 119 2000-10-27 1321.9560 67.86296 937.0147 323.7791 2239.629140
#> 120: 120 2000-10-28 1275.5526 62.61810 923.6023 361.0475 2290.117140
#> rateinf rtransfer rgrowth rsenesced diseased intensity AUDPC lat
#>
#> 1: 0.00000 0.00000 9.68750 1.000000 0.000 0.0000000 12.53946 14.6774
#> 2: 0.00000 0.00000 10.49959 1.086875 0.000 0.0000000 12.53946 14.6774
#> 3: 0.00000 0.00000 11.37416 1.181002 0.000 0.0000000 12.53946 14.6774
#> 4: 0.00000 0.00000 12.31499 1.282934 0.000 0.0000000 12.53946 14.6774
#> 5: 0.00000 0.00000 13.32593 1.393254 0.000 0.0000000 12.53946 14.6774
#> ---
#> 116: 0.00000 29.85591 21.31570 45.701419 1284.650 0.4237668 12.53946 14.6774
#> 117: 23.19935 26.80440 22.03601 47.558203 1284.650 0.4206255 12.53946 14.6774
#> 118: 20.80752 25.36284 22.37109 48.983870 1307.849 0.4267361 12.53946 14.6774
#> 119: 18.61122 23.85609 22.69581 50.488000 1328.657 0.4318648 12.53946 14.6774
#> 120: 16.58631 0.00000 23.00696 47.881728 1347.268 0.4360386 12.53946 14.6774
#> lon
#>
#> 1: 121.2556
#> 2: 121.2556
#> 3: 121.2556
#> 4: 121.2556
#> 5: 121.2556
#> ---
#> 116: 121.2556
#> 117: 121.2556
#> 118: 121.2556
#> 119: 121.2556
#> 120: 121.2556
```
Lastly, you can visualise the result of the model run.
``` r
library(ggplot2)
ggplot(
data = bb_sim,
aes(
x = dates,
y = intensity
)
) +
labs(
y = "Intensity",
x = "Date"
) +
geom_line() +
geom_point() +
theme_classic()
```
Bacterial blight disease progress over time. Results for wet season year 2000 at IRRI Zeigler Experiment Station shown. Weather data used to run the model were obtained from the NASA Langley Research Center POWER Project funded through the NASA Earth Science Directorate Applied Science Program.
# Meta
- Please [report any issues or bugs](https://codeberg.org/adamhsparks/epicrop/issues).
- License: GPL-3
- To cite {epicrop}, please use the output from `citation(package = "epicrop")`.
## Code Coverage
```
#> epicrop Coverage: 98.84%
#> R/internal_functions.R: 92.86%
#> R/calculate_audpc.R: 100.00%
#> R/format_wth.R: 100.00%
#> R/get_wth.R: 100.00%
#> R/helper_bacterial_blight.R: 100.00%
#> R/helper_brown_spot.R: 100.00%
#> R/helper_leaf_blast.R: 100.00%
#> R/helper_modified_kim_leaf_blast.R: 100.00%
#> R/helper_modified_kim_sheath_blight.R: 100.00%
#> R/helper_sheath_blight.R: 100.00%
#> R/helper_tungro.R: 100.00%
#> R/leaf_wet.R: 100.00%
#> R/seir.R: 100.00%
```
## Code of Conduct
Please note that the epicrop project is released with a [Contributor Code of Conduct](https://codeberg.org/adamhsparks/epicrop/src/branch/main/CODE_OF_CONDUCT.md).
By contributing to this project, you agree to abide by its terms.
## Other Implementations in R
- The EPIRICE model was originally written in R as a part of the [{cropsim}](https://github.com/r-forge/cropsim/tree/master/pkg/cropsim) package (Hijmans _et al._ 2009).
- The EPIRICE model is also available from CRAN in the [{ZeBook}](https://cran.r-project.org/web/packages/ZeBook/index.html) package (Brun _et al._ 2018) to accompany, "Working with dynamic crop models : methods, tools and examples for agriculture and environment" (Wallach _et al._ 2018).
# References
Brun François, David Makowski, Daniel Wallach, and James W Jones.
(2018).
ZeBook: Working with Dynamic Models for Agriculture and Environment.
DOI: [10.32614/CRAN.package.ZeBook](https://doi.org/10.32614/CRAN.package.ZeBook)
R package version 1.1, .
Robert J. Hijmans, Serge Savary, Rene Pangga and Jorrel Aunario.
(2009).
Simulation modeling of crops and their diseases.
R package version 0.2-6.
Serge Savary, Andrew Nelson, Laetitia Willocquet, Ireneo Pangga and Jorrel Aunario.
(2012).
Modeling and mapping potential epidemics of rice diseases globally.
*Crop Protection*, Volume 34, Pages 6-17, ISSN 0261-2194 DOI: [10.1016/j.cropro.2011.11.009](https://doi.org/10.1016/j.cropro.2011.11.009).
Serge Savary, Stacia Stetkiewicz, François Brun, and Laetitia Willocquet.
Modelling and Mapping Potential Epidemics of Wheat Diseases-Examples on Leaf Rust and Septoria Tritici Blotch Using EPIWHEAT.
(2015).
*European Journal of Plant Pathology* 142, no. 4:771–90.
DOI: [10.1007/s10658-015-0650-7](https://doi.org/10.1007/s10658-015-0650-7).
Adam Sparks.
(2018).
nasapower: A NASA POWER Global Meteorology, Surface Solar Energy and Climatology Data Client for R.
*Journal of Open Source Software*, 3(30), 1035, DOI: [10.21105/joss.01035](https://doi.org/10.21105/joss.01035).
Jan C. Zadoks.
(1971).
Systems Analysis and the Dynamics of Epidemics.
*Phytopathology* 61:600.
DOI: [10.1094/Phyto-61-600](https://doi.org/10.1094/Phyto-61-600).
Wallach, Daniel, David Makowski, James W. Jones, and François Brun.
(2018)
_Working with dynamic crop models: methods, tools and examples for agriculture and environment_. Academic Press.
Owner metadata
- Name: Adam H. Sparks
- Login: adamhsparks
- Email: [email protected]
- Kind: user
- Description: Senior Research Scientist Systems Modelling at DPIRD, Adjunct Associate Professor with University of Southern Queensland and co-founder of Open Plant Pathology
- Website: https://adamhsparks.netlify.app
- Location: Boorloo/Perth, WA
- Twitter:
- Company:
- Icon url: https://codeberg.org/avatars/f0ef9c35b421be2a1226de2f45d10e59e9ca12319fc65691cdbc7080e0d0902c
- Repositories: 1
- Last ynced at: 2024-01-08T02:09:19.312Z
- Profile URL: https://codeberg.org/adamhsparks
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