| bioclim_thermal {fruclimadapt} | R Documentation |
Calculation of bioclimatic viticultural indices focusing on temperature
Description
This function calculates the Growing Season Average Temperature (GST), the Heliothermal Index (HI) of Huglin, the Winkler (WI) index, the Biologically Effective Degree Day (BEDD) index and the Cool Night (CI) index.
Usage
bioclim_thermal(climdata, lat)
Arguments
climdata |
a dataframe with daily maximum and minimum temperatures. Must contain the columns Year, Month, Day, Tmax, Tmin. |
lat |
the latitude of the site, in decimal degrees. Use positive values for Northern latitudes and negatives for Southern. |
Details
GST index correlates broadly to the maturity potential for grape cultivars grown across many wine regions and provides the basis for zoning viticultural areas in both hemispheres (Hall and Jones, 2009). It is calculated by taking the average of the growing season (April-October in Northern hemisphere, October -April in Southern hemisphere).
HI (Huglin, 1978) is a bioclimatic heat index for viticulture regions using heliothermic potential, which calculates the temperature sum above 10ºC from April until September (Northern hemisphere) or from October until March (Southern hem.). The index takes into consideration daily maximum and average temperature, and slightly modifies the calculated total using the latitude of the location.
WI index (Amerine and Winkler, 1944), also known as growing degree days (GDD) classifies regions based on the accumulation of heat summation units by adding up hours above 10ºC during the growing season.
BEDD index (Gladstones, 1992) is another variant on calculating heat summation which incorporates upper and lower temperature thresholds (accounts for heat accumulation between 10 and 19ºC) and a day length correction similar to HI.
CI index (Tonietto, 1999) takes into account the minimum temperature during grape maturation, which is normally the average minimum air temperature in September/March (Northern or Southern hemispheres, respectively).
Value
data frame with the values of the indices. It contains the columns Year, CI, GST, BEDD, HI, WI
Author(s)
Carlos Miranda, carlos.miranda@unavarra.es
References
Amerine MA and Winkler AJ. 1944. Composition and quality of musts and wines of California grapes. Hilgardia 15: 493-675.
Gladstones J. 1992. Viticulture and environment. Winetitles, Adelaide, Australia
Hall A., Jones GV. 2009. Effect of potential atmospheric warming on temperature-based indices describing Australian winegrape growing conditions. Aust J Grape Wine Res 15. 97-119.
Huglin P. 1978. Noveau mode d'evaluation des possibilites héliothermiques d'un milieu viticole. In: Proceedings of the Symposium International sur l'ecologie de la Vigne. Ministére de l'Agriculture et de l'Industrie Alimentaire, Contança pp 89-98.
Tonietto J. 1999. Les macroclimats viticoles mondiaux et l'influence du mésoclimat sur la typicité de la Syrah et du Muscat de Hambourg dans le sud de la France: methodologie de carácterisation. Thése Doctorat. Ecole Nationale Supérieure Agronomique, Montpellier, 233pp.
Examples
# Select the appropiate columns from a larger dataset with date information
# in Year, Month, Day format, and estimate indices on each year in the series.
library(magrittr)
library(dplyr)
Weather <- Tudela_DW %>%
select(Year, Month, Day, Tmax, Tmin)
latitude <- 42.13132
Tudela_BTI <- bioclim_thermal(Weather, latitude)