predict_leaf_blast: Predict Rice Leaf Blast Intensity
In adamhsparks/epirice: Simulation Modelling of Crop Diseases Using a Susceptible-Exposed-Infectious-Removed (SEIR) Model
View source: R/predict_leaf_blast.R
predict_leaf_blast R Documentation
Predict Rice Leaf Blast Intensity
Description
A dynamic mechanistic simulation of leaf blast disease of rice, causal agent
Magnaporthe oryzae. 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_leaf_blast(wth, emergence)
predict_lb(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
Details
The model represents site size as 45
mm2 of a rice plant's leaf.
Default values for this disease model are derived from Table 2 (Savary
et al. 2012).
predict_lb() is a shorthand alias for predict_leaf_blast().
Value
A data.table::data.table() of disease intensity and infection
sites. See SEIR() for a full description of the column values.
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().
The optimum temperature for leaf blast as presented in Table 2 of
Savary et al. 2012 has a typo. The optimal value should be 20 °C, not
25 °C as shown. The correct value, 20 °C, is used in this implementation.
Author(s)
Serge Savary, Ireneo Pangga, Robert Hijmans, Jorrel Khalil Aunario,
and Adam H. Sparks
References
El Refaei, M.I., 1977. Epidemiology of rice blast disease in the
tropics with special reference to the leaf wetness in relation to disease
development. PhD Thesis, Indian Agricultural Research Institute, New Delhi,
195 p.
Hemmi, T., Abe, T., Ikaya, J., and Inoue, Y. 1936. Studies on the rice blast
disease. IV. Relation of the environment to the development of blast disease
and physiologic specialization in the rice blast fungus. Materials for Rural
Improvement, Department of Agriculture and Forestry, Japan No. 105, 145p.
Hwang, B.K., Koh, Y.J., Chung, H.S., 1987. Effects of adult-plant resistance
on blast severity and yield of rice. Plant Disease 71:1035-1038. DOI:
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1094/PD-71-1035")}.
Kato, H. and Kozaka, T., 1974. Effect of temperature on lesion enlargement
and sporulation of Pyricularia oryzae in rice leaves. Phytopathology
64:828-830. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1094/Phyto-64-828")}.
Wei-Hong, L. 1996. Simulation and measurement of leaf wetness formation in
paddy rice crops. PhD Thesis, Wageningen Agricultural University, 87 p.
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:
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.cropro.2011.11.009")}.
Torres, C.Q., 1986. Effect of plant age on the expression of resistance to
Pyricularia oryzae Cav. in upland rice varieties. PhD Thesis, University of
the Philippines at Los Baños, 82 p.
See Also
SEIR()
Other predict functions:
predict_bacterial_blight(),
predict_brown_spot(),
predict_sheath_blight(),
predict_tungro()
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")
)
lb <- predict_leaf_blast(wth, emergence = "2000-07-01")
plot(x = lb$dates, y = lb$intensity, type = "l")
# use shorthand function
lb <- predict_lb(wth, emergence = "2000-07-01")
plot(x = lb$dates, y = lb$intensity, type = "l")
adamhsparks/epirice documentation built on Nov. 22, 2023, 5:16 p.m.
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View source: R/predict_leaf_blast.R
| predict_leaf_blast | R Documentation |
Predict Rice Leaf Blast Intensity
Description
A dynamic mechanistic simulation of leaf blast disease of rice, causal agent
Magnaporthe oryzae. 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_leaf_blast(wth, emergence)
predict_lb(wth, emergence)
Arguments
wth |
Weather data with a daily time-step, normally NASA
POWER data from
| |||||||||||||||||
emergence |
Expected date of crop emergence |
Details
The model represents site size as 45 mm2 of a rice plant's leaf.
Default values for this disease model are derived from Table 2 (Savary et al. 2012).
predict_lb() is a shorthand alias for predict_leaf_blast().
Value
A data.table::data.table() of disease intensity and infection
sites. See SEIR() for a full description of the column values.
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().
The optimum temperature for leaf blast as presented in Table 2 of Savary et al. 2012 has a typo. The optimal value should be 20 °C, not 25 °C as shown. The correct value, 20 °C, is used in this implementation.
Author(s)
Serge Savary, Ireneo Pangga, Robert Hijmans, Jorrel Khalil Aunario, and Adam H. Sparks
References
El Refaei, M.I., 1977. Epidemiology of rice blast disease in the tropics with special reference to the leaf wetness in relation to disease development. PhD Thesis, Indian Agricultural Research Institute, New Delhi, 195 p.
Hemmi, T., Abe, T., Ikaya, J., and Inoue, Y. 1936. Studies on the rice blast disease. IV. Relation of the environment to the development of blast disease and physiologic specialization in the rice blast fungus. Materials for Rural Improvement, Department of Agriculture and Forestry, Japan No. 105, 145p.
Hwang, B.K., Koh, Y.J., Chung, H.S., 1987. Effects of adult-plant resistance on blast severity and yield of rice. Plant Disease 71:1035-1038. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1094/PD-71-1035")}.
Kato, H. and Kozaka, T., 1974. Effect of temperature on lesion enlargement and sporulation of Pyricularia oryzae in rice leaves. Phytopathology 64:828-830. DOI: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1094/Phyto-64-828")}.
Wei-Hong, L. 1996. Simulation and measurement of leaf wetness formation in paddy rice crops. PhD Thesis, Wageningen Agricultural University, 87 p.
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: \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.cropro.2011.11.009")}.
Torres, C.Q., 1986. Effect of plant age on the expression of resistance to Pyricularia oryzae Cav. in upland rice varieties. PhD Thesis, University of the Philippines at Los Baños, 82 p.
See Also
SEIR()
Other predict functions:
predict_bacterial_blight(),
predict_brown_spot(),
predict_sheath_blight(),
predict_tungro()
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")
)
lb <- predict_leaf_blast(wth, emergence = "2000-07-01")
plot(x = lb$dates, y = lb$intensity, type = "l")
# use shorthand function
lb <- predict_lb(wth, emergence = "2000-07-01")
plot(x = lb$dates, y = lb$intensity, type = "l")
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