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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) or chirps package (de Sousa _et al. 2020), which provides weather data from the Client for the Climate Hazards Center ‘CHIRPS’ and ‘CHIRTS’ 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 or CHIRPS and CHIRTS web APIs. 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 1189.7392 53.98388   880.6517 440.0734 2221.835013
#> 117:    117 2000-10-25 1153.1921 35.83058   850.5259 488.3525 2282.011556
#> 118:    118 2000-10-26 1109.5230 29.63849   824.0101 532.8853 2338.076260
#> 119:    119 2000-10-27 1071.6977 22.15270   801.0138 573.6951 2389.981335
#> 120:    120 2000-10-28 1033.1810 31.28753   757.8526 616.8564 2443.859534
#>        rateinf rtransfer  rgrowth rsenesced diseased intensity     lat      lon
#>   1:  0.000000   0.00000  9.68750  1.000000    0.000 0.0000000 14.6774 121.2556
#>   2:  0.000000   0.00000 10.49959  1.086875    0.000 0.0000000 14.6774 121.2556
#>   3:  0.000000   0.00000 11.37416  1.181002    0.000 0.0000000 14.6774 121.2556
#>   4:  0.000000   0.00000 12.31499  1.282934    0.000 0.0000000 14.6774 121.2556
#>   5:  0.000000   0.00000 13.32593  1.393254    0.000 0.0000000 14.6774 121.2556
#>  ---                                                                           
#> 116:  0.000000  18.15330 23.62940 60.176543 1374.709 0.4399580 14.6774 121.2556
#> 117: 11.824954  18.01704 24.22060 56.064704 1374.709 0.4345846 14.6774 121.2556
#> 118: 10.327744  17.81354 24.40753 51.905074 1386.534 0.4348314 14.6774 121.2556
#> 119:  9.134832   0.00000 24.49635 53.878200 1396.862 0.4344198 14.6774 121.2556
#> 120:  7.831815   0.00000 24.56460 55.340858 1405.996 0.4330412 14.6774 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.

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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. European Journal of Plant Pathology 142, no. 4 (August 1, 2015): 771–90. DOI: 10.1007/s10658-015-0650-7.

Kauê de Sousa and Adam H. Sparks and William Ashmall and Jacob van Etten and Svein Ø. Solberg (2020). chirps: API Client for the CHIRPS Precipitation Data in R. Journal of Open Source Software, 5(51), 2419, DOI: 10.21105/joss.02419

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 (2020). nasapower: NASA-POWER Data from R. R package version 3.0.1, 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.