This function calculates daily chill (with three models) and heat accumulation for every day of an hourly temperature record (best generated with stack_hourly_temps). It includes the option to include calculation of a running mean, which smoothes accumulation curves. Especially for the Dynamic Model, this may be advisable, because it does not accumulate chill smoothly, but rather in steps.
daily_chill(
hourtemps = NULL,
running_mean = 1,
models = list(Chilling_Hours = Chilling_Hours, Utah_Chill_Units = Utah_Model,
Chill_Portions = Dynamic_Model, GDH = GDH),
THourly = NULL
)a daily chill object consisting of the following elements
a character string "daily_chill" indicating that this is a daily_chill object
data frame consisting of the columns YYMMDD, Year, Month, Day and Tmean, plus one column for each model that is evaluated. The latter columns have the name given to the model in the models list and they contain daily total accumulations of the computed metrics.
a dataframe of stacked hourly temperatures (e.g. produced by stack_hourly_temps). This data frame must have a column for Year, a column for JDay (Julian date, or day of the year), a column for Hour and a column for Temp (hourly temperature).
what running mean should be applied to smooth the chill and heat accumulation curves? This should be an odd integer. Use 1 (default) for no smoothing.
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 the set of models provided by the chilling function.
hourtemps was called THourly in an earlier version of this package. So in order to allow function calls written before the 0.57 update to still work, this is included here.
Eike Luedeling
Temperature metrics are calculated according to the specified models. They are computed based on hourly temperature records and then summed to produce daily chill accumulation rates.
Model references for the default models:
Chilling Hours:
Weinberger JH (1950) Chilling requirements of peach varieties. Proc Am Soc Hortic Sci 56, 122-128
Bennett JP (1949) Temperature and bud rest period. Calif Agric 3 (11), 9+12
Utah Model:
Richardson EA, Seeley SD, Walker DR (1974) A model for estimating the completion of rest for Redhaven and Elberta peach trees. HortScience 9(4), 331-332
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
Model comparisons and model equations:
Luedeling E, Zhang M, Luedeling V and Girvetz EH, 2009. Sensitivity of winter chill models for fruit and nut trees to climatic changes expected in California's Central Valley. Agriculture, Ecosystems and Environment 133, 23-31
Luedeling E, Zhang M, McGranahan G and Leslie C, 2009. Validation of winter chill models using historic records of walnut phenology. Agricultural and Forest Meteorology 149, 1854-1864
Luedeling E and Brown PH, 2011. A global analysis of the comparability of winter chill models for fruit and nut trees. International Journal of Biometeorology 55, 411-421
Luedeling E, Kunz A and Blanke M, 2011. Mehr Chilling fuer Obstbaeume in waermeren Wintern? (More winter chill for fruit trees in warmer winters?). Erwerbs-Obstbau 53, 145-155
Review on chilling models in a climate change context:
Luedeling E, 2012. Climate change impacts on winter chill for temperate fruit and nut production: a review. Scientia Horticulturae 144, 218-229
The PLS method is described here:
Luedeling E and Gassner A, 2012. Partial Least Squares Regression for analyzing walnut phenology in California. Agricultural and Forest Meteorology 158, 43-52.
Wold S (1995) PLS for multivariate linear modeling. In: van der Waterbeemd H (ed) Chemometric methods in molecular design: methods and principles in medicinal chemistry, vol 2. Chemie, Weinheim, pp 195-218.
Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab 58(2), 109-130.
Mevik B-H, Wehrens R, Liland KH (2011) PLS: Partial Least Squares and Principal Component Regression. R package version 2.3-0. http://CRAN.R-project.org/package0pls.
Some applications of the PLS procedure:
Luedeling E, Kunz A and Blanke M, 2013. Identification of chilling and heat requirements of cherry trees - a statistical approach. International Journal of Biometeorology 57,679-689.
Yu H, Luedeling E and Xu J, 2010. Stronger winter than spring warming delays spring phenology on the Tibetan Plateau. Proceedings of the National Academy of Sciences (PNAS) 107 (51), 22151-22156.
Yu H, Xu J, Okuto E and Luedeling E, 2012. Seasonal Response of Grasslands to Climate Change on the Tibetan Plateau. PLoS ONE 7(11), e49230.
The exact procedure was used here:
Luedeling E, Guo L, Dai J, Leslie C, Blanke M, 2013. Differential responses of trees to temperature variation during the chilling and forcing phases. Agricultural and Forest Meteorology 181, 33-42.
The chillR package:
Luedeling E, Kunz A and Blanke M, 2013. Identification of chilling and heat requirements of cherry trees - a statistical approach. International Journal of Biometeorology 57,679-689.
models<-list(CP=Dynamic_Model,CU=Utah_Model,GDH=GDH)
dc<-daily_chill(stack_hourly_temps(fix_weather(KA_weather[which(KA_weather$Year>2009),]),
latitude=50.4),11,models)
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