fruclimadapt (version 0.4.5)

russet: Estimation of the russet risk for apple and pear fruits

Description

This function assesses the risk of russet in fruit skins. The risk is defined by the number of hours with the relative humidity (RH) above a threshold during a given period. For reference, in 'Conference' pear the risk is defined by the number of hours with RH> 75% from 12 to 30 days after full bloom (Alegre, 2013). In 'Golden' apple, the risk is defined by the number of hours with RH> 55% from 30 to 34 days after full bloom (Barcelo-Vidal et al., 2013). The function requires hourly temperatures and humidity, if only daily data is available, the function hourly_RH can be used to estimate them.

Usage

russet(climdata, fendata, RH_crit, init_d, end_d)

Value

data frame with the number of risk hours (Russet_hours) in the sensitive period for each year in the series.

Arguments

climdata

a dataframe with hourly temperature and RH data. Required columns are Date, Year, Month, Day, DOY (julian day), Hour and RH.

fendata

a dataframe with julian day of occurrence of the full bloom (F2) phenological stage. Must contain the columns Year and Fday in that order.

RH_crit

the relative humidity threshold

init_d

the initial date (as days after full bloom) of the sensitive period

end_d

the end date (as days after full bloom) of the sensitive period

Author

Carlos Miranda,

References

Alegre S. 2013. Tecnicas de cultivo. In. VII Foro INIA "adaptacion a cambio climatico en la produccion fruticola de hueso y pepita". Madrid, Spain, pp 1-18 Barcelo-Vidal C, Bonany J, Martin-Fernandez JA and Carbo J. 2013. Modelling of weather parameters to predict russet on 'Golden Delicious' apple. J. Hort. Sci. Biotech. 88: 624-630.

Examples

# Select the appropiate columns from the example dataset
# Dates_BT and rename column names to make the file compatible
# with the function
library(magrittr)
library(dplyr)
library(lubridate)
Bloom <- Dates_BT %>%
   select(Year, sbloom) %>%
   rename(Fday=sbloom) %>%
   filter(Year==2003)
# Obtain estimated hourly RH from the example dataset Tudela_DW
Weather <- Tudela_DW %>%
        filter (Tudela_DW$Year==2003)
RH_h <- hourly_RH(Weather, 42.13132)
# Estimate the number of russet-inducing days for a RH>55\% 
# between 30 to 34 days after full bloom for each season
Russet_Risk <-russet(RH_h,Bloom,55,30,34)