Evaluation Tools for Assessing Climate Adaptation of Fruit Tree Species

Climate is a critical component limiting growing range of plant species, which also determines cultivar adaptation to a region. The evaluation of climate influence on fruit production is critical for decision-making in the design stage of orchards and vineyards and in the evaluation of the potential consequences of future climate. Bio- climatic indices and plant phenology are commonly used to describe the suitability of climate for growing quality fruit and to provide temporal and spatial information about regarding ongoing and future changes. 'fruclimadapt' streamlines the assessment of climate adaptation and the identification of potential risks for grapevines and fruit trees. Procedures in the package allow to i) downscale daily meteorological variables to hourly values (Forster et al (2016) ), ii) estimate chilling and forcing heat accumulation (Miranda et al (2019) < https://ec.europa.eu/eip/agriculture/sites/default/files/fg30_mp5_phenology_critical_temperatures.pdf>), iii) estimate plant phenology (Schwartz (2012) ), iv) calculate bioclimatic indices to evaluate fruit tree and grapevine adaptation (e.g. Badr et al (2017) ), v) estimate the incidence of weather-related disorders in fruits (e.g. Snyder and de Melo-Abreu (2005, ISBN:92-5-105328-6) and vi) estimate plant water requirements (Allen et al (1998, ISBN:92-5-104219-5)).

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.

install.packages("fruclimadapt")

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)

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)

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']])