crop_sensitive: Crop sensitive indices

In climatrends: Climate Variability Indices for Ecological Modelling

Crop sensitive indices

Description

Compute crop sensitive indices. These indices are designed to capture the changes in temperature extremes during key phenological stages (e.g. anthesis), but can also be applied to other phenological stages.

Usage

crop_sensitive(object, ...)

## Default S3 method:
crop_sensitive(object, tmin, ...)

## S3 method for class 'data.frame'
crop_sensitive(object, day.one, ...)

## S3 method for class 'array'
crop_sensitive(object, day.one, ...)

## S3 method for class 'sf'
crop_sensitive(object, day.one, ..., as.sf = TRUE)

Arguments

object	a numeric vector with the maximum temperature, or a data.frame with geographical coordinates (lonlat), or an object of class sf with geometry 'POINT' or 'POLYGON', or an array with two dimensions containing the maximum and minimum temperature, in that order. See details
...	additional arguments passed to methods. See details
tmin	a numeric vector with the minimum temperature
day.one	a vector of class Date or any other object that can be coerced to Date (e.g. integer, character YYYY-MM-DD) for the starting day to capture the climate data
as.sf	logical, to return an object of class 'sf'

Details

The function uses pre-defined threshold to compute the indices. For hts_mean (32, 35, 38 Celsius), for hts_max (36, 39, 42 Celsius), for hse (31 Celsius), for cdi_mean (22, 23, 24 Celsius), for cdi_max (27, 28, 29 Celsius) and for lethal (43, 46, 49 Celsius).

Additional arguments:

The thresholds can be adjusted using the arguments hts_mean.threshold, hts_max.threshold, hse.threshold, cdi_mean.threshold, cdi_max.threshold and lethal.threshold which are a numeric (or vector of numeric)

last.day: an object (optional to span) of class Date or any other object that can be coerced to Date (e.g. integer, character YYYY-MM-DD) for the last day of the time series. For data.frame, array and sf methods

span: an integer (optional to last.day) or a vector with integers (optional if last.day is given) for the length of the time series to be captured. For data.frame, array and sf methods

Value

A dataframe with crop sensitive indices with n colunms depending on the number of thresholds passed to each index:

hts_mean	high temperature stress using daily MEAN temperature, and given as percentage number of days a certain threshold is exceeded
hts_max	high temperature stress using daily MAX temperature, and given as percentage number of days a certain threshold is exceeded
hse	heat stress event, and given as percentage number of days a a certain threshold is exceeded for at least two consecutive days
hse_ms	heat stress event, and given the maximum number of days a certain threshold is exceeded for at least two consecutive days
cdi_mean	crop duration index using daily MEAN temperature, and given as max(Tmean - threshold, 0)
cdi_max	crop duration index using daily MAX temperature, and given as max(Tmax - threshold, 0)
lethal	lethal temperatures, defined as percentage of days during the timeseries where daily MEAN temperature exceeds a given threshold

References

Challinor et al. (2016). Nature Climate Change 6(10):6954-958 doi: 10.1038/nclimate3061

Trnka et al. (2014). Nature Climate Change 4(7):637–43. doi: 10.1038/nclimate2242

See Also

Other temperature functions: ETo(), GDD(), temperature()

Examples

# the default method
set.seed(78)
tmax <- runif(50, 37, 47)
set.seed(79)
tmin <- runif(50, 31, 34)

crop_sensitive(tmax, tmin)

###############################################

# the array method
data("temp_dat", package = "climatrends")

# use the default thresholds
crop_sensitive(temp_dat,
               day.one = "2013-10-27",
               last.day = "2013-11-04")

# or change the thresholds based on the crop physiology
crop_sensitive(temp_dat,
               day.one = "2013-10-27",
               last.day = "2013-11-04",
               hts_mean.threshold = c(24),
               hts_max.threshold = c(31, 33))

climatrends documentation built on Jan. 6, 2023, 5:18 p.m.