Disease gradient information provides the spatial distribution of the epidemics at distance from the focus. If you are interested in learning how to calculate the area under the disease gradient curve to make comparison of several scenarios or treatments, this would be a good resource.
I am using Steven Worthington’s ipak function to install and load several packages in R.
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.
I would like to share some R codes that I wrote for creating weather predictors which I used in predictive modeling of sugarcane rusts. I have also created several complex weather variables during my research work but I am going to share some with averages/sums of hourly, weekly, monthly, and also specific time in a day. I will also share the R codes to calculate more complex weather variables in separate posts.
Are you a plant disease epidemiologist? If yes, you probably need to calculate leaf wetness duration using empirical methods. One way to estimate leaf wetness duration is with classification and regression tree method (CART). Learn how to calculate in R!
Let’s generate the dummy weather data and work through it. You can find the equation and procedure of CART method of leaf wetness estimation in this link: https://doi.org/10.1094/PDIS.2002.86.2.179
Average_temperature <- sample(2:25, 20, replace=TRUE)
Dew_Point_Depression <- sample(0.