Predict Rice Sheath Blight Intensity

A dynamic mechanistic simulation of sheath blight disease of rice, causal agent Rhizoctonia solani AG1-1A Kühn. The model is driven by daily weather data, which can easily be accessed usingget_wth() to download weather data from NASA POWER using nasapower.

Usage

predict_sheath_blight(wth, emergence)

predict_sb(wth, emergence)

Arguments

wth

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.

emergence

Expected date of crop emergence

Value

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

Details

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

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

predict_sb() is a shorthand alias for predict_sheath_blight().

Note

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().

References

Castilla, N.P., Leano, R.M., Elazegui, F.A., Teng, P.S., Savary, S., 1996. Effects of plant contacts, inoculation pattern, leaf wetness regime, and nitrogen supply on inoculum efficiency in rice sheath blight. Journal of Phytopathology 144:187-192.

Gross, M.K., Santini, J.B., Tikhonova, I. and Latin, R. 1998. The influence of temperature and leaf wetness duration on infection of perennial ryegrass by Rhizoctonia solani. Plant Disease 82:1012-1016. DOI: doi:10.1094/PDIS.1998.82.9.1012 .

Hashiba, T. and Ijiri, T., 1989. Estimation of yield loss and computerized forecasting system (BLIGHTAS) for rice sheath blight disease. International Symposium on Tropical Agricultural Research: Crop losses due to disease outbreaks in the tropics and countermeasures. Tropical Agricultural Research Series (Japan) No. 22 pp. 163-171.

Savary, S., Willocquet, L., Teng, P.S., 1997. Modelling sheath blight epidemics on rice tillers. Agricultural Systems 55:359-384. DOI: doi:10.1016/S0308-521X(97)00014-0 .

Savary, S., Castilla, N.P., Willocquet, L. 2001. Analysis of the spatio- temporal structure of rice sheath blight epidemics in a farmer's field. Plant Pathology 50:53-68. DOI: doi:10.1046/j.1365-3059.2001.00531.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 .

Sharma, N.R., Teng, P.S., Olivares, F.M., 1990. Effect of rice growth stage on sheath blight (ShB) development and yield loss. International Rice Research Newsletter 15:19-20.

Tu, C.C., Chang, Y.C., Wang, C.W., 1979. Studies on the ecology of Rhizoctonia solani, the causal organism of rice sheath blight. National Science Council Monthly, ROC 7:1208-1219.

See also

SEIR()

Other predict functions: predict_bacterial_blight(), predict_brown_spot(), predict_leaf_blast(), predict_tungro()

Author

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

Examples

# 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")
)
sb <- predict_sheath_blight(wth, emergence = "2000-07-01")
plot(x = sb$dates, y = sb$intensity, type = "l")

# use shorthand function
sb <- predict_sb(wth, emergence = "2000-07-01")
plot(x = sb$dates, y = sb$intensity, type = "l")