bloom_prediction2: Bloom prediction from chilling and forcing requirements,...

Description Usage Arguments Details Value Author(s) References Examples

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

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bloom_prediction2(
  HourChillTable,
  Chill_req,
  Heat_req,
  permutations = FALSE,
  Chill_model = "Chill_Portions",
  Heat_model = "GDH",
  Start_JDay = 305,
  infocol = NULL
)

Arguments

HourChillTable

a data frame resulting from the chilling_hourtable function.

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)

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

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.

Details

This function is an update to the bloom_prediction function, which was quite slow and didn't allow testing multiple chilling and heat requirements. In this updated version, chilling and heat requirements can be supplied as vectors, which are interpreted in sequence, with each pair of Chill_req and Heat_req values matched according to their position in the vectors. Through the permutations argument, it is also possible to compute stage occurrence dates for all possible combinations of the requirements specified by the Chill_req and Heat_req vectors.

The model allows specifying any numeric column as the chill and heat columns, indicated by the Chill_model and Heat_model parameters.

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).

Author(s)

Eike Luedeling

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

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hourtemps <- stack_hourly_temps(fix_weather(KA_weather[which(KA_weather$Year > 2008), ]),
                                latitude = 50.4)

CT <- chilling_hourtable(hourtemps, Start_JDay = 305)

bloom_prediction2(CT, c(30, 40, 50), c(1000, 1500, 2000))
bloom_prediction2(CT, c(30, 40, 50), c(1000, 1500, 2000), permutations = TRUE)

chillR documentation built on Jan. 11, 2022, 5:07 p.m.