fruclimadapt (version 0.4.5)

pollination_weather: Evaluation of weather conditions for pollination on a daily series

Description

This function estimates the number of days with conditions favorable, unfavorable and moderately favorable for insect pollination of fruit trees during the flowering period using daily weather data.

Usage

pollination_weather(climdata, fendata, lat)

Value

a data frame with the columns Year, Sbloom (bloom start, DOY) , Ebloom (end of bloom, DOY), Bloom_length (in days), Fav_d (number of favorable days), Modfav_d (number of moderately favorable days) and Unfav_d (number of unfavorable days).

Arguments

climdata

a dataframe with daily maximum and minimum temperatures, wind speed and precipitation. Required columns are Year, Month, Day, Tmax, Tmin, u2med (daily mean wind speed) and Prec (precipitation). u2max (daily maximum wind speed) is optional.

fendata

a dataframe with julian day of the beginning (sbloom) and end (ebloom) of the flowering season. Must contain the columns Year, sbloom and ebloom in that order.

lat

the latitude of the site, in decimal degrees, used to estimate hourly temperatures.

Author

Carlos Miranda,

Details

Days are classified considering the classification proposed by Williams and Sims (1977), by accounting the number of favorable hours for pollination within a day. One hour is considered favorable if the temperature is above 12.5 C, the speed of the wind below 4.5 m s-1 and no rainfall occurs (Williams and Sims, 1977; Ramirez and Davenport, 2013). Hourly wind speeds from daily values are computed using the formulas proposed by Guo et al (2016), using mean daily values (u2med, required) and maximum ones (u2max, optional). If only mean wind values are available, the function uses a modified version of the Guo formula, so that the maximum values are obtained in daytime hours. No hourly downscaling of rainfall is performed, the function allow daily rainfall below 2.0 mm when estimating if a day is favorable for pollination or not.

References

Guo Z, Chang C, Wang R, 2016. A novel method to downscale daily wind statistics to hourly wind data for wind erosion modelling. In: Bian F., Xie Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2015. Communications in Computer and Information Science, vol 569. Springer, Berlin, Heidelberg

Ramirez F and Davenport TL, 2013. Apple pollination: A review. Scientia Horticulturae 162, 188-203.

Williams RR, Sims FP, 1977. The importance of weather and variability in flowering time when deciding pollination schemes for Cox's Orange Pippin. Experimental Horticulture 29, 15-26.

Examples

# Estimate weather conditions during blooming season using the example
# datasets included in the package
library(magrittr)
library(dplyr)
library(lubridate)
Bloom_BT <- Dates_BT %>% 
   select(Year, sbloom, ebloom) %>% 
   filter(Dates_BT$Year<=2002)
Weather <- Tudela_DW %>%
   filter (Tudela_DW$Year<=2002)
Pol_weather_BT <- pollination_weather(Weather,Bloom_BT,42.13132)