A dynamic mechanistic simulation of rice brown spot, causal agent
Cochliobolus miyabeanus. The model is driven by daily weather data, which
can easily be accessed usingget_wth() to download weather data from
NASA POWER using nasapower.
predict_brown_spot(wth, emergence)
predict_bs(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 Name | Value |
| YYYYMMDD | Date as Year Month Day (ISO8601) |
| DOY | Consecutive day of year, commonly called "Julian date" |
| TEMP | Mean daily temperature (°C) |
| RHUM | Mean daily relative humidity (%) |
| RAIN | Mean daily rainfall (mm) |
| LAT | Optional latitude of weather observation. See LAT/LON Note. |
| LON | Optional 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 10 mm2 of a rice plant's leaf.
Default values for this disease model are derived from Table 2 (Savary et al. 2012).
predict_bs() is a shorthand alias for predict_brown_spot().
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().
The optimum temperature for brown spot as presented in Table 2 of Savary et al. 2012 has a typo. The optimal value should be 25 °C, not 20 °C as shown. The correct value, 25 °C, is used in this implementation.
Klomp, A.O., 1977. Early senescence of rice and Drechslera oryzae in the Wageningen polder, Surinam. PhD Thesis, 97p.
Levy, Y. and Cohen, Y., 1980. Sporulation of Helminthosporium turcicum on sweet corn: Effects of temperature and dew period. Canadian Journal of Plant Pathology 2:65-69. DOI: doi:10.1080/07060668009501440 .
Luo Wei-Hong, 1996. Simulation and measurement of leaf wetness formation in paddy rice crops. PhD, Wageningen Agricultural University, 87 p.
Padmanabhan, S.Y. and Ganguly, D. 1954. Relation between the age of rice plant and its susceptibility to Helminthosporium and blast disease. Proceedings of the Indian Academy of Sciences B 29:44-50.
Sarkar, A.K. and Sen Gupta, P.K., 1977. Effect of temperature and humidity on disease development and sporulation of Helminthosporium oryzae on rice. Indian Phytopathology 30:258-259.
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 .
Waggoner. P.E., Horsfall, J.G., and Lukens, R.J. 1972. EPIMAY. A Simulator of Southern Corn Leaf Blight. Bulletin of the Connecticut Experiment Station, New Haven, 85 p.
Other predict functions:
predict_bacterial_blight(),
predict_leaf_blast(),
predict_sheath_blight(),
predict_tungro()
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")
)
bs <- predict_brown_spot(wth, emergence = "2000-07-01")
plot(x = bs$dates, y = bs$intensity, type = "l")
}
if (FALSE) { # interactive()
# use shorthand function
bs <- predict_bs(wth, emergence = "2000-07-01")
plot(x = bs$dates, y = bs$intensity, type = "l")
}