chillR (version 0.75)

bloom_prediction3: Bloom prediction from chilling and forcing requirements, assumed to be fulfilled strictly in sequence - version 3

Description

This is a pretty rudimentary function to predict phenological dates from chilling and forcing requirements and hourly chilling and forcing data. Note that there are enormous uncertainties in these predictions, which are hardly ever acknowledged. So please use this function with caution.

Usage

bloom_prediction3(
  hourtemps,
  Chill_req,
  Heat_req,
  models = c(Chill_Portions = Dynamic_Model, GDH = GDH_model),
  permutations = FALSE,
  Chill_model = "Chill_Portions",
  Heat_model = "GDH",
  Start_JDay = 305,
  infocol = NULL
)

Value

data frame containing the predicted Julian dates of chilling requirement fulfillment and timing of the phenological stage. Columns are Season, Creq, Hreq, Creq_full (day when the chilling requirement is fulfilled) and Pheno_date (the predicted date of the phenological event).

Arguments

hourtemps

a data frame of hourly temperatures (e.g. resulting from the stack_hourly_temps function - should have columns "Year", "Month", "Day" and "Temp").

Chill_req

numeric vector indicating one or multiple chilling requirements of the particular growth stage (in the unit specified by "Chill_model")

Heat_req

numeric vector indicating one or multiple heat requirements of the particular growth stage (in Growing Degree Hours)

models

named list of models that should be applied to the hourly temperature data. These should be functions that take as input a vector of hourly temperatures. This defaults to c(Chill_Portions = Dynamic_Model, GDH = GDH_model), which refer to the Dynamic chill model and the Growing Degree Hours model functions contained in chillR.

permutations

boolean parameter indicating whether all possible combinations of the supplied chilling and heat requirements should be used. Defaults to FALSE, which means that the function matches chilling and heat requirements according to their positions in the Chill_req and Heat_req vectors and only predicts stage occurrence dates for these combinations.

Chill_model

character string specifying the chill model to use. This has to correspond to the name of the column in HourChillTable that contains the chill accumulation (default is "Chill_Portions" for units of the Dynamic Model).

Heat_model

character string specifying the heat model to use. This has to correspond to the name of the column in HourChillTable that contains the heat accumulation (e.g "GDH").

Start_JDay

numeric parameter indicating the day when chill accumulation is supposed to start. Note that this is also the latest acceptable bloom date.

infocol

a vector of length length(Chill_req) which contains additional information for each element of the vector. This is preserved and included in the output. This only works when permutation=FALSE, and is meant to facilitate recognition of particular phenological events in the output.

Author

Eike Luedeling

Details

This function is an update to the bloom_prediction and bloom_prediction2 functions. This version takes hourly temperatures as input rather than requiring pre-calculated chill and heat records. This functionality is now integrated in the function, so that users can now specify a list of temperature metrics/models to be computed and used in the bloom prediction.

References

Model references:

Dynamic Model:

Erez A, Fishman S, Linsley-Noakes GC, Allan P (1990) The dynamic model for rest completion in peach buds. Acta Hortic 276, 165-174

Fishman S, Erez A, Couvillon GA (1987a) The temperature dependence of dormancy breaking in plants - computer simulation of processes studied under controlled temperatures. J Theor Biol 126(3), 309-321

Fishman S, Erez A, Couvillon GA (1987b) The temperature dependence of dormancy breaking in plants - mathematical analysis of a two-step model involving a cooperative transition. J Theor Biol 124(4), 473-483

Growing Degree Hours:

Anderson JL, Richardson EA, Kesner CD (1986) Validation of chill unit and flower bud phenology models for 'Montmorency' sour cherry. Acta Hortic 184, 71-78

Examples

hourtemps <- stack_hourly_temps(fix_weather(KA_weather[which(KA_weather$Year > 2007), ]),
                                latitude = 50.4)

bloom_prediction3(hourtemps, c(30, 140, 50), c(1000, 1500, 2000))

bloom_prediction3(hourtemps, c(30, 40, 50), c(1000, 1500, 2000), permutations = TRUE,
                  Start_JDay = 1)

bloom_prediction3(hourtemps, c(300, 400, 600), c(100, 150, 200), permutations = TRUE,
                  Start_JDay = 1, models = c(CH = Chilling_Hours, Heat = GDD),
                  Chill_model = "CH", Heat_model = "Heat")