chilling_hourtable: Add chilling and heat accumulation to table of hourly...
In chillR: Statistical Methods for Phenology Analysis in Temperate Fruit Trees
chilling_hourtable R Documentation
Add chilling and heat accumulation to table of hourly temperatures
Description
This function calculates cumulative values for three chill metrics and one
heat metric for every hour of an hourly temperature record. The count is
restarted on a specified date each year.
Usage
chilling_hourtable(hourtemps, Start_JDay)
Arguments
hourtemps
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).
Start_JDay
the start date (in Julian date, or day of the year) of the
calculation for the four metrics. The count is restarted on this date every
year.
Details
Chill metrics are calculated as given in the references below. Chilling
Hours are all hours with temperatures between 0 and 7.2 degrees C. Units of
the Utah Model are calculated as suggested by Richardson et al. (1974)
(different weights for different temperature ranges, and negation of
chilling by warm temperatures). Chill Portions are calculated according to
Fishman et al. (1987a,b). More honestly, they are calculated according to an
Excel sheet produced by Amnon Erez and colleagues, which converts the
complex equations in the Fishman papers into relatively simple Excel
functions. These were translated into R. References to papers that include
the full functions are given below. Growing Degree Hours are calculated
according to Anderson et al. (1986), using the default values they suggest.
Value
data frame consisting of all the columns of the THourly input data
frame, plus the following additional columns: Chilling_Hours (cumulative
number of Chilling Hours since the last Start_JDay), Chill_Portions (same
for units of the Dynamic Models), Chill_Units (same for units of the Utah
Model) and GDH (same for Growing Degree Hours).
Note
After doing extensive model comparisons, and reviewing a lot of
relevant literature, I do not recommend using the Chilling Hours or Utah
Models, especially in warm climates! The Dynamic Model (Chill Portions),
though far from perfect, seems much more reliable.
Author(s)
Eike Luedeling
References
Model references:
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.
Examples
weather<-fix_weather(KA_weather[which(KA_weather$Year>2008),])
hourtemps<-stack_hourly_temps(weather,latitude=50.4)
cht<-chilling_hourtable(hourtemps,20)
chillR documentation built on Nov. 28, 2023, 1:09 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| chilling_hourtable | R Documentation |
Add chilling and heat accumulation to table of hourly temperatures
Description
This function calculates cumulative values for three chill metrics and one heat metric for every hour of an hourly temperature record. The count is restarted on a specified date each year.
Usage
chilling_hourtable(hourtemps, Start_JDay)
Arguments
hourtemps |
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). |
Start_JDay |
the start date (in Julian date, or day of the year) of the calculation for the four metrics. The count is restarted on this date every year. |
Details
Chill metrics are calculated as given in the references below. Chilling Hours are all hours with temperatures between 0 and 7.2 degrees C. Units of the Utah Model are calculated as suggested by Richardson et al. (1974) (different weights for different temperature ranges, and negation of chilling by warm temperatures). Chill Portions are calculated according to Fishman et al. (1987a,b). More honestly, they are calculated according to an Excel sheet produced by Amnon Erez and colleagues, which converts the complex equations in the Fishman papers into relatively simple Excel functions. These were translated into R. References to papers that include the full functions are given below. Growing Degree Hours are calculated according to Anderson et al. (1986), using the default values they suggest.
Value
data frame consisting of all the columns of the THourly input data frame, plus the following additional columns: Chilling_Hours (cumulative number of Chilling Hours since the last Start_JDay), Chill_Portions (same for units of the Dynamic Models), Chill_Units (same for units of the Utah Model) and GDH (same for Growing Degree Hours).
Note
After doing extensive model comparisons, and reviewing a lot of relevant literature, I do not recommend using the Chilling Hours or Utah Models, especially in warm climates! The Dynamic Model (Chill Portions), though far from perfect, seems much more reliable.
Author(s)
Eike Luedeling
References
Model references:
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.
Examples
weather<-fix_weather(KA_weather[which(KA_weather$Year>2008),])
hourtemps<-stack_hourly_temps(weather,latitude=50.4)
cht<-chilling_hourtable(hourtemps,20)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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