fruclimadapt (version 0.4.5)

wind_scab: Estimation of the risk for wind scab on fruit skin

Description

The function estimates the risk of wind-induced abrasion injuries (wind scab) on fruit skin during the sensitive periods of the species. This function estimates as risky the daily hours with 'moderate breeze' wind (equal or above 5.5 m s-1 in the Beaufort scale) or stronger, estimated from a dataset with daily wind speeds.

Usage

wind_scab(climdata, fendata)

Value

data frame with the columns Year, Day_s, Day_e, WA_efg (accumulated hours with u2>5.5 m s-1 on early fruit growth stage), Day_h, WA_bh (accumulated hours with u2>5.5 m s-1 on the month before harvest).

Arguments

climdata

a dataframe with daily wind speed data. Required columns are Year, Month, Day, u2med. u2max is optional.

fendata

a dataframe which can contain early fruit growth and harvest dates. Start of initial growth (Start_ing) and end of initial growth (End_ing) dates are used to assess for early wind-induced abrasion risk and harvest dates (Harvest) are used for late (pre-harvest) abrasion risks. Must contain the column Year, and can contain either Start_ing and End_ing or Harvest columns, or the three of them.

Author

Carlos Miranda,

Details

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.

Sensitive periods for wind scab in plums or nectarines correspond to the early stages of fruit growth, usually the first three weeks after full bloom (Michailides et al 1992, Michailides and Morgan 1992), mainly due to persistent leaf brushing on fruit skin between the stages '8-mm fruit' and '20-mm fruit'. A second sensitive period for cherries, plums, peaches and nectarines is pre-harvest (30 days prior to that), due to persistent friction against branches. The function allows to set both periods or only one of them.

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

Michailides TJ, Morgan DP, Ramirez HT and Giacolini EL, 1992. Determination of the period when prunes are prone to development of russet scab and elucidation of the mechanism by which captan controls russet scab. California dried plum board research reports 1992, 157-175.

Michailides TJ and Morgan DP, 1992. Development of wind scab and predisposition of french prune fruits to preharvest and postharvest fungal decay by wind scab and russet scab.California dried plum board research reports 1992, 149-156.

Examples

# Select the appropiate columns from the example Dates_BT dataset
# and estimate wind scab risk for Big Top nectarine in Tudela using
# the example weather dataset Tudela_DW
library(magrittr)
library(dplyr)
library(lubridate)
Bloom <- Dates_BT %>%
   select(Year, sbloom) %>%
   rename(Fday=sbloom) %>%
   filter(Year==2003)

Growth_BT <- Dates_BT %>% select(Year, Start_ing, End_ing, Harvest) %>% 
   filter(Year==2003)
Weather <- Tudela_DW %>%
   filter (Tudela_DW$Year==2003)
WindRisk_BT <- wind_scab(Weather, Growth_BT)