predADD: Prediction Function of the Accumulated Degree Days Method...

In spphpr: Spring Phenological Prediction

Prediction Function of the Accumulated Degree Days Method Using Mean Daily Temperatures

Description

Predicts the occurrence times using the accumulated degree days method based on observed or predicted mean daily air temperatures (Aono, 1993; Shi et al., 2017a, b).

Usage

predADD(S, T0, AADD, Year2, DOY, Temp, DOY.ul = 120)

Arguments

S	the starting date for thermal accumulation (in day-of-year)
T0	the base temperature (in {}^{\circ}C)
AADD	the expected annual accumulated degree days
Year2	the vector of the years recording the climate data for predicting the occurrence times
DOY	the vector of the dates (in day-of-year) for which climate data exist
Temp	the mean daily air temperature data (in {}^{\circ}C) corresponding to DOY
DOY.ul	the upper limit of DOY used to predict the occurrence time

Details

In the accumulated degree days (ADD) method (Shi et al., 2017a, b), the starting date (S), the base temperature (T_{0}), and the annual accumulated degree days (AADD, which is denoted by k) are assumed to be constants across different years. Let k_{i} denote the AADD of the ith year, which equals

k_{i} = \sum_{j=S}^{E_{i}}\left(T_{ij}-T_{0}\right),

where E_{i} represents the ending date (in day-of-year), i.e., the occurrence time of a particular phenological event in the ith year, and T_{ij} represents the mean daily temperature of the jth day of the ith year (in {}^{\circ}C). If T_{ij} \le T_{0}, T_{ij} - T_{0} is defined to be zero. In theory, k_{i} = k, i.e., the AADD values of different years are a constant. However, in practice, there is a certain deviation of k_{i} from k that is estimated by \overline{k} (i.e., the mean of the k_{i} values). The following approach is used to determine the predicted occurrence time. When \sum_{j=S}^{F}\left(T_{ij}-T_{0}\right) = \overline{k} (where F \geq S), it follows that F is the predicted occurrence time; when \sum_{j=S}^{F}\left(T_{ij}-T_{0}\right) < \overline{k} and \sum_{j=S}^{F+1}\left(T_{ij}-T_{0}\right) > \overline{k}, the trapezoid method (Ring and Harris, 1983) is used to determine the predicted occurrence time.

Value

Year	the years with climate data
Time.pred	the predicted occurrence times (day-of-year) in different years

Note

The entire mean daily temperature data set for the spring of each year should be provided.

Author(s)

Peijian Shi [e-mail], Zhenghong Chen [e-mail], Jing Tan [e-mail], Brady K. Quinn [e-mail].

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).

Ring, D.R., Harris, M.K. (1983) Predicting pecan nut casebearer (Lepidoptera: Pyralidae) activity at College Station, Texas. Environmental Entomology 12, 482-486. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/ee/12.2.482")}

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. \Sexpr[results=rd]{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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/aesa/sax063")}

See Also

ADD

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
Temp.val   <- X2$MDT
DOY.ul.val <- 120
S.val      <- 65
T0.val     <- -0.5 
AADD.val   <- 235.5282

res2 <- predADD( S = S.val, T0 = T0.val, AADD = AADD.val, 
                 Year2 = Year2.val, DOY = DOY.val, Temp = Temp.val, 
                 DOY.ul = DOY.ul.val )
res2


ind1  <- res2$Year %in% intersect(res2$Year, Year1.val)
ind2  <- Year1.val %in% intersect(res2$Year, Year1.val)
RMSE1 <- sqrt( sum((Time.val[ind2]-res2$Time.pred[ind1])^2) / length(Time.val[ind2]) ) 
RMSE1 

spphpr documentation built on April 11, 2025, 6:11 p.m.