Version 0.4.4

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: fruclimadapt
 Type: Package
 Title: Evaluation Tools for Assessing Climate Adaptation of Fruit Tree Species
-Version: 0.4.3.9000
+Version: 0.4.4
 Author: Carlos Miranda
 Maintainer: Carlos Miranda <carlos.miranda@unavarra.es>
 Description: Climate is a critical component limiting growing range of plant species, which

NAMESPACE

@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+export(DTR)
 export(ET_penman)
 export(ET_penman_monteith)
 export(GDD_linear)

NEWS.md

@@ -1,3 +1,10 @@
+# fruclimadapt 0.4.4
+## New features
+* New function (DTR) to calculate the mean diurnal temperature range
+* color_potential now allows to define the evaluation period
+* russet now allows to define the critical relative humidity and the evaluation period
+* coolness_index now allows to define the evaluation period
+
 # fruclimadapt 0.4.3
 ## Bug fixes 
 * Changed a reference in DESCRIPTION with a wrong doi for other referencing the same method.

R/DTR.R

@@ -0,0 +1,62 @@
+#' Calculation of the diurnal temperature range (DTR)
+#'
+#' This function calculates the mean diurnal temperature range (DTR) for
+#' a custom period. Mean DTR is obtained by subtracting the daily minimum
+#' temperature (Tmin) from daily maximum temperature (Tmax) and then
+#' averaged for the period defined by the user, provided as the initial
+#' (init) and end (end) date expressed as days of the year. The function
+#' requires the initial and end dates to be in the same year.
+#'
+#' @param climdata a dataframe with daily maximum and minimum temperatures.
+#'  Must contain the columns Year, Month, Day, Tmax, Tmin.
+#' @param init_d the initial date (as day of the year) of the evaluation period
+#' @param end_d the end date (as day of the year) of the evaluation period
+#' @return dataframe with the value of DTR for each year in the series. 
+#' It contains the columns Year, First_d, Last_d, DTR 
+#' @author Carlos Miranda, \email{carlos.miranda@@unavarra.es}
+#' @examples
+#'
+#' # Select the appropiate columns from the Tudela_DW example dataset,
+#' # and estimate the mean DTR for July on each year in the dataset.
+#' library(tidyverse)
+#' Weather <- Tudela_DW %>%
+#'    select(Year, Month, Day, Tmax, Tmin)
+#' DTR_July <- DTR(Weather, 182, 212)
+#'
+#' @export DTR
+#' @import data.table tidyverse zoo 
+#' @importFrom lubridate make_date
+
+DTR <- function(climdata, init_d, end_d)
+{
+  climdata <- select(climdata,"Year","Month","Day","Tmax","Tmin") %>%
+    mutate(Date = make_date(Year, Month, Day),
+           DOY = yday(Date), DTR_d=Tmax-Tmin)
+
+  seasons <- unique(climdata$Year)
+
+  indices_cn <- c("Year","First_d","Last_d","DTR")
+  indices.df <-data.frame(matrix(ncol=4, nrow=0, byrow=FALSE))
+  colnames(indices.df) <- indices_cn
+
+  for (sea in 1:length(seasons)){
+    Anno <- as.numeric(seasons[sea])
+    climdata_fil <- climdata %>%
+      filter(climdata$Year==Anno)
+
+      DTR <- climdata_fil %>%
+        filter(climdata_fil$DOY>=(init_d) & climdata_fil$DOY<=end_d) %>%
+        summarise(DTR_a=mean(DTR_d)) %>%
+        select(DTR_a) %>%
+        unlist(use.names=FALSE)
+
+      new.row.df <- data.frame(Anno,init_d,end_d) %>%
+        cbind(DTR)
+
+      indices.df <-rbind(indices.df,new.row.df)
+
+  }
+  indices.df <- indices.df %>% rename(Year=Anno)
+  return(indices.df)
+}
+

R/color_potential.R

@@ -3,19 +3,21 @@
 #'
 #' This function estimates the number of days that can be 
 #' considered as highly favorable or unfavorable for anthocyanin 
-#' accumulation in the skin of red apple cultivars during the 
-#' month (30 days) before harvest. A highly favorable day (Cool
-#' day) is considered when the daily maximum temperature is below
-#' 26ºC, a highly unfavorable day (Hot day) when the minimum 
-#' temperature is above 20ºC (Lin-Wang et al, 2011). The average
-#' of maximum and minimum temperatures during the same period is 
-#' also provided. The function allows testing for several harvest 
-#' dates.
+#' accumulation in the skin of red apple cultivars during a
+#' user defined pre-harvest period (30 days  by default). 
+#' A highly favorable day (Cool day) is considered when the daily 
+#' maximum temperature is below 26ºC, a highly unfavorable day (Hot day)
+#' when the minimum temperature is above 20ºC (Lin-Wang et al, 2011). 
+#' The average of maximum and minimum temperatures during the same 
+#' period is also provided. The function allows testing for several 
+#' harvest dates.
 #'
 #' @param climdata a dataframe with daily maximum and minimum temperatures.
 #'  Must contain the columns Year, Month, Day, Tmax, Tmin.
 #' @param harvest a vector with expected harvest days
 #' (expressed as day of the year)
+#' @param span the period (in days) before harvest that will be analyzed. By 
+#' default, this parameter is set in 30 days.
 #' @return dataframe with the number of highly favorable (Cool_d) 
 #' and unfavorable (Hot_d) days for apple red color, as well as the 
 #' average of the maximum (Tmax_avg) and minimum (Tmin_avg) 
@@ -43,7 +45,7 @@
 #' @import data.table tidyverse zoo 
 #' @importFrom lubridate make_date
 
-color_potential <- function(climdata, harvest)
+color_potential <- function(climdata, harvest, span=30)
 {
   climdata <- select(climdata,"Year","Month","Day","Tmax","Tmin") %>%
     mutate(Date = make_date(Year, Month, Day),
@@ -63,7 +65,7 @@ color_potential <- function(climdata, harvest)
     for (nharv in 1:length(harvest)){
       Day_h <- as.numeric(harvest[nharv])
       evacol_fil <- climdata_fil %>%
-        filter(climdata_fil$DOY>(Day_h-30) & climdata_fil$DOY<=Day_h) %>%
+        filter(climdata_fil$DOY>(Day_h-span) & climdata_fil$DOY<=Day_h) %>%
         summarise(Cool_d=sum(Cool),Hot_d=sum(Hot),Tmax_avg=mean(Tmax),Tmin_avg=mean(Tmin)) %>%
         select(Cool_d,Hot_d,Tmax_avg,Tmin_avg)
       new.row.df <- data.frame(Anno,Day_h) %>%

R/cool_night_index.R

@@ -4,14 +4,17 @@
 #' Cool Night index of Tonietto (1999). Instead of calculating 
 #' the mean of minimum temperatures in September/March (Northern
 #' or Southern hemispheres, respectively), this function allows 
-#' to define the harvest date, and calculates the mean of minimum
-#' temperatures in the previous 30 days. The function allows 
-#' testing for several harvest dates simultaneously.
+#' to define the harvest date and the number of days that will be
+#' analyzed (by default, 30 days), and calculates the mean of minimum
+#' temperatures in the in the specified period of days before harvest.
+#' The function allows testing for several harvest dates simultaneously.
 #'
 #' @param climdata a dataframe with daily maximum and minimum temperatures.
 #'  Must contain the columns Year, Month, Day, Tmax, Tmin.
 #' @param harvest a vector with expected harvest days
 #' (expressed as day of the year)
+#' @param span the period (in days) before harvest that will be analyzed. By 
+#' default, this parameter is set in 30 days.
 #' @return dataframe with the values of the indices. It contains the
 #' columns Year, Harvest, Coolness 
 #' @author Carlos Miranda, \email{carlos.miranda@@unavarra.es}
@@ -26,7 +29,7 @@
 #'
 #' # Select the appropiate columns from the Tudela_DW example dataset,
 #' # create a vector or harvest dates and estimate the coolness index 
-#' # for each year in the dataset.
+#' # for the 30 days prior to harvest on each year in the dataset.
 #' library(tidyverse)
 #' Weather <- Tudela_DW %>%
 #'    select(Year, Month, Day, Tmax, Tmin)
@@ -37,7 +40,7 @@
 #' @import data.table tidyverse zoo 
 #' @importFrom lubridate make_date
 
-coolness_index <- function(climdata, harvest)
+coolness_index <- function(climdata, harvest, span=30)
 {
   climdata <- select(climdata,"Year","Month","Day","Tmax","Tmin") %>%
     mutate(Date = make_date(Year, Month, Day),
@@ -56,7 +59,7 @@ coolness_index <- function(climdata, harvest)
      for (nharv in 1:length(harvest)){
         Day_h <- as.numeric(harvest[nharv])
         coolnight <- climdata_fil %>%
-          filter(climdata_fil$DOY>(Day_h-30) & climdata_fil$DOY<=Day_h) %>%
+          filter(climdata_fil$DOY>(Day_h-span) & climdata_fil$DOY<=Day_h) %>%
           summarise(Cool_n=mean(Tmin)) %>%
           select(Cool_n) %>%
           unlist(use.names=FALSE)

R/globals.R

@@ -10,4 +10,4 @@ globalVariables(c("%>%","BEDD_day","Chill","Cool","Cool_d","Cool_n","DOY","DTR",
                          "bind_cols","bind_rows","day","delt","everything","filter",
                          "ftmprt","group_by","h_russ","h_wind","h_wpol","if_else","m_lat",
                          "make_datetime","mutate","rename","right_join","select","slice",
-                         "sr","summarise","summarise_all","tail","windh","wpol","case_when"))
+                         "sr","summarise","summarise_all","tail","windh","wpol","case_when","DTR_a","DTR_d"))

R/russet.R

@@ -1,21 +1,37 @@
 #' Estimation of the russet risk for apple and pear fruits
 #'
-#' This function assesses the risk of russet in apple and pear 
-#' fruits by estimating the number of hours with relative 
-#' humidity above 75\% in the critical period (between 12 
-#' and 30 days after full bloom, DAFB). The function requires
-#' hourly temperatures and humidity, if only daily data is 
-#' available, the function hourly_RH can be used to estimate them.
+#' 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.
 #'
 #' @param climdata a dataframe with hourly temperature and RH
 #' data. Required columns are Date, Year, Month, Day, DOY (julian day),
 #' Hour and RH.
 #' @param 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.
-#' @return data frame with the number of hours with RH>75\%
-#' between 12 and 30 DAFB (Russet_h) each year in the series.
+#' @param RH_crit the relative humidity threshold
+#' @param init_d the initial date (as days after full bloom) of the sensitive period
+#' @param end_d the end date (as days after full bloom) of the sensitive period
+#' @return data frame with the number of risk hours (Russet_hours)
+#' in the sensitive period for each year in the series.
 #' @author Carlos Miranda, \email{carlos.miranda@@unavarra.es}
+#' @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
@@ -28,22 +44,23 @@
 #'    rename(Fday=sbloom)
 #' # Obtain estimated hourly RH from the example dataset Tudela_DW
 #' RH_h <- hourly_RH(Tudela_DW, 42.13132)
-#' # Estimate the number of russet-inducing days for each season
-#' Russet_Risk <-russet(RH_h,Bloom)
+#' # 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)
 #' 
 #' @export russet
 #' @import data.table tidyverse zoo 
 #' @importFrom lubridate make_date
 
-russet <- function(climdata, fendata)
+russet <- function(climdata, fendata, RH_crit, init_d, end_d)
 {
   climdata <- climdata %>%
     group_by(Date) %>%
-    summarise(h_russ= sum(RH>=75)) %>%
+    summarise(h_russ= sum(RH>=RH_crit)) %>%
     mutate(Year=year(Date),Month=month(Date),Day=day(Date), DOY=yday(Date)) %>%
     select(-h_russ,h_russ)
   Seasons <- unique(fendata$Year)
-  russrisks_cn <- c("Year","Russ_h")
+  russrisks_cn <- c("Year","Russ_hours")
   russrisk.df <-data.frame(matrix(ncol=2, nrow=0, byrow=FALSE))
   colnames(russrisk.df) <- russrisks_cn
   for (sea in 1:(length(Seasons))){
@@ -52,14 +69,14 @@ russet <- function(climdata, fendata)
     Fday <- as.numeric(Fdate[2])
     fendata_fil <- fendata %>% filter(fendata$Year==Anno)
     russhours_fil <- climdata %>%
-      filter(climdata$Year==Anno & climdata$DOY>=Fday+12 & climdata$DOY<=Fday+30)
+      filter(climdata$Year==Anno & climdata$DOY>=Fday+init_d & climdata$DOY<=Fday+end_d)
     russhours_fil <- select(russhours_fil, h_russ)
     russhours<- sum(unlist(russhours_fil, use.names=FALSE))
     new.row.df <- data.frame(Anno,russhours)
     russrisk.df <-rbind(russrisk.df,new.row.df)
   }
   russrisk.df <- russrisk.df %>%
-    rename(Year=Anno, Russet_h=russhours)
+    rename(Year=Anno, Russet_hours=russhours)
   return(russrisk.df)
 }
 

cran-comments.md

@@ -1,6 +1,10 @@
 ## Resubmission
-This is a resubmission. In this version I have:
-* Modified a reference with a problematic DOI encoding for other describing the same methodology.
+This is a resubmission. In this version I have added some new features. 
+* New function (DTR) to calculate the mean diurnal temperature range
+* color_potential now allows to define the evaluation period
+* russet now allows to define the critical relative humidity and the evaluation period
+* coolness_index now allows to define the evaluation period
+
 ## Test environments
 * local OS X install, R 4.0.0
 * ubuntu 16.04 (on travis-ci, devel and release), R 4.0.0