fruclimadapt: Evaluation tools for assessing climate adaptation of fruit tree species in R.

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:

• Downscale daily meteorological variables to hourly values
• Estimate chilling and forcing heat accumulation
• Estimate plant phenology
• Calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation
• Estimate the indicence of weather-related disorders in fruits
• Estimate plant water requirements.

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Resources

• Installation
• 1. Required packages
• 2. Example: Estimate the phenology of a peach cultivar
• 3. Example: Estimate the number and damage caused by spring frosts

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Installation

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")

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Using fruclimadapt

1. Required packages

• data.table
• lubridate
• tidyverse
• zoo

install.packages("data.table")
install.packages("lubridate")
install.packages("tidyverse")
install.packages("zoo")

library(fruclimadapt)
library(data.table)
library(tidyverse)
library(zoo)

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2. Example. Estimate the phenology of a peach cultivar

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)

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3. Estimate the number and damage caused by spring frosts

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']])

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Licenses

The R/fruclimadapt package as a whole is distributed under GPL-3 (GNU General Public License version 3).

Author

Carlos Miranda