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This package is a compilation of functions for the assessment of climate adaptation and the identification of potential risks for grapevines and fruit trees. Procedures in the package allow to:
You can install the released version of fruclimadapt from CRAN with:
install.packages("fruclimadapt")And the development version from GitHub with:
install.packages("devtools")
library(devtools)
devtools::install_github("Carm1r/fruclimadapt")install.packages("data.table")
install.packages("lubridate")
install.packages("tidyverse")
install.packages("zoo")
library(fruclimadapt)
library(data.table)
library(tidyverse)
library(zoo)This example shows how to use the functions hourly_temps, chill_portions, GDH_linear and phenology_sequential to estimate the date of occurrence of the phenological stages for a nectarine cultivar, using daily weather data.
library(fruclimadapt)
# Generate a dataset with hourly temperatures from the dataset with daily values (Tudela_DW, included in the package)
data(Tudela_DW)
Tudela_HT <- hourly_temps(Tudela_DW,42.13132)
# Use the hourly dataset to calculate chill as chill portions and growing degree hours
# Calculate chill as chill portions, starting on DOY 305
Chill <- chill_portions(Tudela_HT,305)
# Calculate forcing heat as growing degree hours (GDH) with the linear model using base temperature 4.7 C and no upper thresholds
GDH <- GDH_linear(Tudela_HT,4.7,999,999)
# Combine the datasets Chill and GDH in a dataframe with a format compatible with the function phenology_sequential
Tudela_CH <- merge(Chill,GDH) %>%
select(Date, Year, Month, Day, DOY, Chill,GDH) %>%
arrange(Date) %>%
rename(GD=GDH)
# Obtain the predicted dates for the cultivar "Big Top" using the requirement dataset included in the package (Bigtop_reqs)
data(Bigtop_reqs)
Phenology_BT <- phenology_sequential(Tudela_CH, Bigtop_reqs, 305)This example shows how to use the function spring_frost to estimate the number and accumulated damage caused by spring frosts from budbreaking for the same nectarine cultivar used to estimate the phenology in the previous example.
library(fruclimadapt)
# Use the dataframe with the phenological dates obtained with phenology_sequential to generate a new one with the format required by the function spring_frost
Phenology_frost <- Phenology_BT %>%
select(Freq_Year,Freq_DOY) %>%
rename(Year=Freq_Year,Pheno_date=Freq_DOY)
# Extract a dataframe with daily minimum temperatures from the daily climate example dataset with the format required by spring_frost
Tmin_Tudela <- Tudela_DW %>%
mutate(Date=make_date(Year,Month,Day), DOY=yday(Date)) %>%
select(Year, DOY, Tmin)
# Predict the number and accumulated damage of the spring frosts using the critical values contained in the example dataset Tcrits_peach and extract the dataframe with the total results for each year
data(Tcrits_peach)
Frost_BT <- spring_frost(Tmin_Tudela, Phenology_frost, Tcrits_peach, 181)
Frost_results <- as.data.frame(Frost_BT[['Damage_frosts']])The R/fruclimadapt package as a whole is distributed under GPL-3 (GNU General Public License version 3).
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.