/epicrop

Simulation modelling of crop diseases using a Susceptible-Exposed-Infectious-Removed (SEIR) model in R

Primary LanguageRGNU General Public License v3.0GPL-3.0

ATTENTION!

All active development has ceased in this repository due to GitHub's continued pivot towards AI. It has moved to https://codeberg.org/adamhsparks/epicrop. This repository will be deleted in the future.

{epicrop} Simulation Modelling of Crop Diseases Using a Susceptible-Exposed-Infectious-Removed (SEIR) Model

tic codecov Project Status: Active – The project has reached a stable, usable state and is being actively developed. Lifecycle: stable DOI

A fork of {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} package (Sparks 2018, Sparks 2020) and predict disease intensity of five rice diseases using a generic 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. 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.

if (!require("remotes"))
  install.packages("remotes")
remotes::install_github("adamhsparks/epicrop",
                        build_vignettes = TRUE
)

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.

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
)

wth
#>        YYYYMMDD DOY  TEMP  RHUM  RAIN     LAT      LON
#>   1: 2000-07-01 183 25.30 92.19 23.12 14.6774 121.2556
#>   2: 2000-07-02 184 26.13 86.00 17.34 14.6774 121.2556
#>   3: 2000-07-03 185 25.51 94.19 29.08 14.6774 121.2556
#>   4: 2000-07-04 186 25.81 92.44 13.00 14.6774 121.2556
#>   5: 2000-07-05 187 25.97 92.31 32.20 14.6774 121.2556
#>  ---                                                  
#> 117: 2000-10-25 299 25.82 89.75 12.04 14.6774 121.2556
#> 118: 2000-10-26 300 25.44 94.94 13.03 14.6774 121.2556
#> 119: 2000-10-27 301 25.74 91.44 11.54 14.6774 121.2556
#> 120: 2000-10-28 302 25.44 91.88 74.20 14.6774 121.2556
#> 121: 2000-10-29 303 24.97 94.12 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.

bb <- predict_bacterial_blight(wth, emergence = "2000-07-01")

bb
#>      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 1453.0383 107.60598   953.2163 217.8993 2095.744599
#> 117:    117 2000-10-25 1429.1891  77.30207   952.9399 248.4797 2140.855357
#> 118:    118 2000-10-26 1380.4038  73.81997   947.4256 281.2134 2187.881013
#> 119:    119 2000-10-27 1332.8691  69.34615   938.4942 315.9354 2236.407049
#> 120:    120 2000-10-28 1286.3824  64.14358   926.0857 352.6359 2286.436253
#>       rateinf rtransfer  rgrowth rsenesced diseased intensity    AUDPC     lat
#>   1:  0.00000   0.00000  9.68750  1.000000    0.000 0.0000000 12.39699 14.6774
#>   2:  0.00000   0.00000 10.49959  1.086875    0.000 0.0000000 12.39699 14.6774
#>   3:  0.00000   0.00000 11.37416  1.181002    0.000 0.0000000 12.39699 14.6774
#>   4:  0.00000   0.00000 12.31499  1.282934    0.000 0.0000000 12.39699 14.6774
#>   5:  0.00000   0.00000 13.32593  1.393254    0.000 0.0000000 12.39699 14.6774
#>  ---                                                                          
#> 116:  0.00000  30.30391 21.26159 45.110758 1278.722 0.4219893 12.39699 14.6774
#> 117: 23.73737  27.21946 21.97777 47.025655 1278.722 0.4188944 12.39699 14.6774
#> 118: 21.31678  25.79060 22.30807 48.526036 1302.459 0.4252267 12.39699 14.6774
#> 119: 19.08944  24.29201 22.63192 50.029205 1323.776 0.4305705 12.39699 14.6774
#> 120: 17.03536   0.00000 22.94393 47.492428 1342.865 0.4349575 12.39699 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.

library("ggplot2")

ggplot(data = bb,
       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.

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.

  • License: GPL-3

  • To cite {epicrop}, please use the output from citation(package = "epicrop").

Code of Conduct

Please note that the epicrop project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

References

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.

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.

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.

Adam Sparks (2021). nasapower: NASA-POWER Data from R. DOI: 10.5281/zenodo.1040727, R package version 4.0.0, URL: https://CRAN.R-project.org/package=nasapower.

Jan C. Zadoks. (1971). Systems Analysis and the Dynamics of Epidemics. Phytopathology 61:600. DOI: 10.1094/Phyto-61-600.