spphpr (version 1.1.4)

ADD2: Function for Implementing the Accumulated Degree Days Method Using Minimum and Maximum Daily Temperatures

Description

Estimates the starting date (\(S\), in day-of-year) and base tempeature (\(T_{0}\), in \({}^{\circ}\)C) in the accumulated degree days method using minimum and maximum daily air temperatures (Aono, 1993; Shi et al., 2017a, b).

Usage

ADD2(S.pd = NULL, T0.arr, Year1, Time, Year2, DOY, Tmin, Tmax,  
     DOY.ul = 120, fig.opt = TRUE, S.def = 54, verbose = TRUE)

Value

S.arr

the candidate starting dates (in day-of-year), whose default ranges from the minimum DOY to min(DOY.ul, the maximum DOY)

cor.coef.arr

the candidate correlation coefficients between the mean of the (Tmin + Tmax)/2 values (from a candidate starting date to the observed occurrence time) and the observed occurrence time

cor.coef

the minimum correlation coefficient, i.e., min(cor.coef.arr)

search.failure

a value of 0 or 1 of showing whether the starting date is successfully determined by the minimum correlation coefficient method when S.pd = NULL, where 0 represents success and 1 represents failure

mAADD.arr

an vector saving the interannual mean of the annual acccumulated degree days (AADD) values for each of the candidate base temperatures

RMSE.arr

a vector saving the candidate root-mean-square errors (in days) between the observed and predicted occurrence times for each of the candidate base temperatures

AADD.arr

the annual accumulated degree days (AADD) values in different years

Year

The overlapping years between Year1 and Year2

Time

The observed occurrence times (day-of-year) in the overlapping years between Year1 and Year2

Time.pred

the predicted occurrence times in different years

S

the determined starting date (day-of-year)

T0

the determined base temperature (in \({}^{\circ}\)C)

AADD

the expected annual accumulated degree days

RMSE

the smallest RMSE (in days) from the different candidate base temperatures

unused.years

the years that have phenological records but lack climate data

Arguments

S.pd

the pre-determined starting date for thermal accumulation (in day-of-year)

T0.arr

the candidate base temperatures (in \({}^{\circ}\)C)

Year1

the vector of the years in which a particular phenological event was recorded

Time

the vector of the occurrence times (in day-of-year) of a particular phenological event across many years

Year2

the vector of the years recording the climate data corresponding to the occurrence times

DOY

the vector of the dates (in day-of-year) for which climate data exist

Tmin

the minimum daily air temperature data (in \({}^{\circ}\)C) corresponding to DOY

Tmax

the maximum daily air temperature data (in \({}^{\circ}\)C) corresponding to DOY

DOY.ul

the upper limit of DOY used to predict the occurrence time

fig.opt

an optional argument to draw the figures associated with the determinations of the starting date and base temperature, and a comparison between the predicted and observed occurrence times

S.def

a mandatory defintion of the starting date when (i) S.pd is NULL and (ii) the minimum correlation coefficient method fails to find a suitable starting date

verbose

an optional argument allowing users to suppress the printing of computation progress

Author

Peijian Shi , Zhenghong Chen , Jing Tan , Brady K. Quinn .

Details

The default of S.pd is NULL. In this case, the date associated with the minimum correlation coefficient [between the mean of the (Tmin + Tmax)/2 values (from a candidate starting date to the observed occurrence time) and the observed occurrence time] will be determined to be the starting date on the condition that it is smaller than the mininum phenological occurrence time. If the determined date associated with the minimum correlation coefficient is greater than the mininum phenological occurrence time, S.def will be used as the starting date. If S.pd is not NULL, the starting date will be directly set as S.pd irrespective of the minimum correlation coefficient method and the value of S.def. This means that S.pd is superior to S.def in determining the starting date.

\(\qquad\)The function does not require that Year1 is the same as unique(Year2), and the intersection of the two vectors of years will be kept. The unused years that have phenological records but lack climate data will be showed in unused.years in the returned list.

\(\qquad\)The numerical value of DOY.ul should be greater than or equal to the maximum Time.

References

Aono, Y. (1993) Climatological studies on blooming of cherry tree (Prunus yedoensis) by means of DTS method. Bulletin of the University of Osaka Prefecture. Ser. B, Agriculture and life sciences 45, 155\(-\)192 (in Japanese with English abstract).

Shi, P., Chen, Z., Reddy, G.V.P., Hui, C., Huang, J., Xiao, M. (2017a) Timing of cherry tree blooming: Contrasting effects of rising winter low temperatures and early spring temperatures. Agricultural and Forest Meteorology 240\(-\)241, 78\(-\)89. tools:::Rd_expr_doi("10.1016/j.agrformet.2017.04.001")

Shi, P., Fan, M., Reddy, G.V.P. (2017b) Comparison of thermal performance equations in describing temperature-dependent developmental rates of insects: (III) Phenological applications. Annals of the Entomological Society of America 110, 558\(-\)564. tools:::Rd_expr_doi("10.1093/aesa/sax063")

See Also

predADD2

Examples

data(apricotFFD)
data(BJDAT)
X1 <- apricotFFD
X2 <- BJDAT

Year1.val  <- X1$Year
Time.val   <- X1$Time
Year2.val  <- X2$Year
DOY.val    <- X2$DOY
Tmin.val   <- X2$MinDT
Tmax.val   <- X2$MaxDT
DOY.ul.val <- 120
T0.arr0    <- seq(3.5, 4, by = 0.1) 
S.pd0      <- NULL

# \donttest{
  cand.res1 <- ADD2( S.pd = S.pd0, T0.arr = T0.arr0, Year1 = Year1.val, Time = Time.val, 
                     Year2 = Year2.val, DOY = DOY.val, Tmin = Tmin.val, Tmax = Tmax.val, 
                     DOY.ul = DOY.ul.val, fig.opt = TRUE, S.def=54, verbose = TRUE )
  cand.res1

  S0 <- cand.res1$S.arr 
  r0 <- cand.res1$cor.coef.arr
  
  dev.new()
  par1 <- par(family="serif")
  par2 <- par(mar=c(5, 5, 2, 2))
  par3 <- par(mgp=c(3, 1, 0))
  plot( S0, r0, cex.lab = 1.5, cex.axis = 1.5, xlab = "Candidate starting date (day-of-year)", 
        ylab="Correlation coefficient between the mean temperature and FFD", type="l" )
  ind <- which.min(r0)
  points(S0[ind], r0[ind], cex = 1.5, pch = 16)
  text(S0[ind], r0[ind] + 0.1, bquote(paste(italic(S), " = ", .(S0[ind]), sep = "")), cex = 1.5)
  par(par1)
  par(par2)
  par(par3)

  # graphics.off()

# }