A dynamic mechanistic simulation of bacterial blight disease of rice, causal agent Xanthomonas oryzae pv. oryzae. The model is driven by daily weather data, which can easily be accessed using get_wth() to download weather data from NASA POWER using nasapower.

predict_bacterial_blight(wth, emergence)

predict_bb(wth, emergence)



Weather data with a daily time-step, normally NASA POWER data from get_wth(), but anybase::data.frame() object that has the following properly named columns in them will work.

Field NameValue
YYYYMMDDDate as Year Month Day (ISO8601)
DOYConsecutive day of year, commonly called "Julian date"
TEMPMean daily temperature (°C)
RHUMMean daily relative humidity (%)
RAINMean daily rainfall (mm)
LATOptional latitude of weather observation. See LAT/LON Note.
LONOptional longitude of weather observation. See LAT/LON Note.

Expected date of crop emergence


A data.table::data.table() of disease intensity and infection sites. See SEIR() for a full description of the column values.


The model represents site size as 1 rice plant's leaf.

Default values for this disease model are derived from Table 2 (Savary et al. 2012).

predict_bb() is a shorthand alias for predict_bacterial_blight().


Adapted from cropsim package version 0.2.0-5 by Adam H. Sparks, Department of Primary Industries and Regional Development, WA, AU. Original model development: Serge Savary & Rene Pangga (IRRI). Original R implementation by Robert J. Hijmans, Rene Pangga, & Jorrel Aunario (IRRI).

If the wth object provides LAT and LON columns, these will be included in the output for mapping purposes. Both values must be present. These columns are provided by default when using get_wth().


Adhikari, T.B., 1991. Effects of rice genotype and environment on bacterial blight progression. PhD Thesis, University of the Philippines at Los Baños, 143 p.

Baw A. and Mew, T.W., 1988. Scoring systems for evaluating rice varietal resistance to bacterial blight (BB): lesion size by growth stage. International Rice Research Newsletter 13:10-11.

Horino, O., Mew, T.W., Yamada, T., 1982. The effect of temperature on the development of bacterial leaf blight on rice. Annals of the Phytopathological Society of Japan 48: 72-75.

Luo Wei-Hong, 1996. Simulation and measurement of leaf wetness formation in paddy rice crops. PhD, Wageningen Agricultural University, 87 p.

Medalla, E. 1992. Characterization of resistance of IR cultivars to two races of Xanthomonas oryzae pv. oryzae. Unpublished M.S. Thesis, University of the Philippines at Los Baños, 81 p.

Nayak, P., Suriya Rao, A.V., Chakrabarti, N.K., 1987. Components of resistance to bacterial blight disease of rice. Journal of Phytopathology 119:312-318. DOI: doi:10.1111/j.1439-0434.1987.tb04402.x .

Savary, S., Nelson, A., Willocquet, L., Pangga, I., and Aunario, J. Modeling and mapping potential epidemics of rice diseases globally. Crop Protection, Volume 34, 2012, Pages 6-17, ISSN 0261-2194 DOI: doi:10.1016/j.cropro.2011.11.009 .


Serge Savary, Ireneo Pangga, Robert Hijmans, Jorrel Khalil Aunario, and Adam H. Sparks


if (FALSE) { # interactive()
# get weather for IRRI Zeigler Experiment Station in wet season 2000
wth <- get_wth(
  lonlat = c(121.25562, 14.6774),
  dates = c("2000-06-30", "2000-12-31")
bb <- predict_bacterial_blight(wth, emergence = "2000-07-01")
plot(x = bb$dates, y = bb$intensity, type = "l")
if (FALSE) { # interactive()
# use shorthand function
bb <- predict_bb(wth, emergence = "2000-07-01")
plot(x = bb$dates, y = bb$intensity, type = "l")