predADD {spphpr} | R Documentation |
Prediction Function of the Accumulated Degree Days Method
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, 2017b).
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 |
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) when the climate data exist |
Temp |
the mean daily air temperature data (in |
DOY.ul |
the upper limit of |
Details
In the accumulated degree days (ADD) method (Shi et al., 2017a, 2017b), the starting date
(S
) and 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 i
th 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 pariticular
phenological event in the i
th year, and T_{ij}
represents the mean daily temperature of the
j
th day of the i
th year (in {}^{\circ}
C). 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
. The following approach
is used to determine the predicted occurrence time.
When \sum_{j=S}^{F}\left(T_{ij}-T_{0}\right) = 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) < k
and
\sum_{j=S}^{F+1}\left(T_{ij}-T_{0}\right) > 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 occurence times (day of year) in different years |
Note
The entire mean daily temperature data in the spring of each year should be provided.
Author(s)
Peijian Shi pjshi@njfu.edu.cn, Zhenghong Chen chenzh64@126.com, Brady K. Quinn Brady.Quinn@dfo-mpo.gc.ca.
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. 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. 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. doi:10.1093/aesa/sax063
See Also
Examples
data(apricotFFD)
data(BJMDT)
X1 <- apricotFFD
X2 <- BJMDT
Year1.val <- X1$Year
Time.val <- X1$Time
Year2.val <- X2$Year
DOY.val <- X2$DOY
Temp.val <- X2$MDT
DOY.ul.val <- 120
S.val <- 65
T0.val <- -0.5
AADD.val <- 235.6447
res2 <- predADD( S = S.val, T0 = T0.val, AADD = AADD.val,
Year2 = Year2.val, DOY = DOY.val, Temp = Temp.val,
DOY.ul = DOY.ul.val )
res2
ind1 <- res2$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