5.2 Results of phenology and must quality estimation
5.2.1 Budburst event and flowering dates
Budburst date The budburst model is based on the budburst date averaged over the seven studied vine varieties. The cross validation results are shown in Figure 5.2a. The prediction error is minimal at 3 predictors; an increasing number of predictors would increase the prediction error. Taking three predictors the relative model error is 2.4 % and the relative prediction error 3.2 %. The selected predictors are: degree days in March (DD3), maximum temperature in April (TX4), and number of frost days from January to March (FD1-3). The degree days (DD) are calculated with the single triangle method of Zalomet al. (1983):
DD=
⎧⎪
⎪⎪
⎨
⎪⎪
⎪⎩
0, if TL> Tmax , Tmean−TL, if TL< Tmin ,
(Tmax−TL)2
2(Tmax−Tmin), if Tmin < TL< Tmax ,
(5.6)
withTLa threshold temperature where the vine begins to grow,Tmax the maximum temperature, Tmean the mean temperature and Tmin the minimum temperature.
The regression coefficients and the explained variance are presented in Table 5.1.
The model is applied to all vine varieties separately with the same predictors but different coefficients. It performs similarly well for the different vine cultivars: the explained variance ranges between 80 % and 84 % depending on variety. The degree days in March contribute most to the explained variance. High values of degree days in March and maximum temperature in April move budburst date backwards.
A high amount of frost days from January to March delays bud break. This stands in close agreement with Pouget (1964) who concluded: the higher the temperature the faster and earlier the budburst date, provided previous temperatures were not too low. The investigated varieties have similar budburst dates, thus no important differences are expected.
The time series of observed and calculated budburst events are compared in Figure 5.3. The model for budburst event has a correlation of 0.91 with the obser-vations and the root mean squared error is below three days. About 90 % of the residuals are in the range of ± 4.8 days. Beyond this interval a too late budburst date was calculated only for 1991 (7.5 days) and 1974 (5.7 days). During both years, the vegetation period begun very early. In 1974 maximum temperature in April was higher than usual and that year budburst date has been earliest since 1959 (Weinjahr, 1974). During April 1991 a strong frost period damaged many vine stocks (Weinjahr, 1991).
58 Chapter 5. Statistical modelling of phenological events and must quality Table 5.1: Total explained variance (R²), contribution of the selected predictors to R² and regression coefficients of the budburst model for each and averaged (ALL) vine varieties.
Total R² Contribution to R² Regression coefficients
in [%] DD3 TX4 FD1-3 const DD3 TX4 FD1-3
ALL 82.9 64.1 14.0 3.8 142.62 -0.36 -1.69 +0.13
Auxerrois 84.1 66.3 14.2 3.6 142.16 -0.35 -1.62 +0.12
Elbling 80.8 62.8 13.7 4.3 141.27 -0.36 -1.70 +0.14
Pinot blanc 83.2 64.9 15.3 3.0 144.12 -0.36 -1.74 +0.12 Pinot gris 82.2 65.7 12.5 4.0 141.68 -0.36 -1.61 +0.13
Riesling 80.8 63.0 14.6 3.2 144.49 -0.36 -1.73 +0.12
Rivaner 83.3 67.8 12.2 3.3 142.09 -0.38 -1.60 +0.12
Traminer 81.2 59.6 16.4 5.2 143.54 -0.34 -1.89 +0.16
Note: Predictors: DD3 degree days in March, TX4 maximum temperature in April, FD1-3 frost days from January to March
0 1 2 3 4 5 6 7 8 9 10
0 5 10 15 20 25 30 35 40 45 50
number of predictors, k MSE [days2 ]
Budburst
Developmental data Crossvalidation data
(a)
0 1 2 3 4 5 6 7 8 9 10
0 5 10 15 20 25 30 35 40 45 50 55 60 65
number of predictors, k MSE [days2 ]
Flowering date
Developmental data Cross validation data
(b)
Figure 5.2: Residual MSE as a function of the number of the regression predictors for budburst date (a) and flowering date (b)
Flowering date Cross validation for the flowering model does not suggest a clear optimal number of predictors; three or four predictors can be chosen with approximately the same uncertainty (Figure 5.2b). The model error is expected to be 2.6 days and the prediction error 3.8 days.
The results of the flowering model are presented in Table 5.2. This model per-forms slightly better than the budburst model with 87.7 % explained variance for the mean flowering date and between 87 % and 88 % depending on vine variety.
The degree days during April and May describe together about 75 % of the vari-ability; the degree days in May is the dominant predictor. Maximum temperature
59
Table 5.2: Total explained variance (R²), contribution of the selected predictors to R² and regression coefficients of the flowering model for each and averaged (ALL) vine varieties.
Total R² Contribution to R² Regression coefficients
in [%] DD5 DD4 TX6 BB const DD5 DD4 TX6 BB
ALL 87.7 60.9 16.7 8.0 2.1 207.47 -0.13 -0.08 -1.35 +0.19 Auxerrois 88.3 60.5 16.8 9.4 1.6 211.88 -0.13 -0.08 -1.44 +0.17 Elbling 87.1 61.5 16.2 7.3 2.1 205.96 -0.14 -0.07 -1.29 +0.19 Pinot blanc 86.8 58.5 17.2 8.3 2.8 204.93 -0.13 -0.07 -1.43 +0.22 Pinot gris 88.3 61.2 16.4 8.7 2.0 209.43 -0.13 -0.07 -1.42 +0.18 Riesling 88.5 61.6 17.7 7.0 2.2 205.32 -0.13 -0.08 -1.24 +0.19 Rivaner 87.0 60.0 17.3 7.5 2.2 206.25 -0.13 -0.08 -1.30 +0.18 Traminer 86.3 61.3 15.2 7.9 1.9 208.49 -0.14 -0.07 -1.36 +0.18 Note: Predictors: DD5degree days in May,DD4degree days in April,TX6maximum temper-ature in June,BBbudburst date
100 105 110 115 120 125 130 135
1965 1970
1975 1980 1985
1990 1995 2000 2005 Budburst date
DOY
Observ.
Model
−9
−7
−5
−3
−1 1 3 5 7 9
XX X
1965 1970 1975 1980 1985 1990 199 5
2000 2005 Residuals: Obs.−Model
Days
rmse = 2.9
100 105 110 115 120 125 130 100
105 110 115 120 125 130
Correlation
Observation
Model
corr = 0.91
Figure 5.3: Time series of the observed and approximated budburst event date (left), their correlation (centre) and the residuals (right). The red crosses in the residual plot flag the missing data.
145 150 155 160 165 170 175 180 185 190
Flowering date
1965 1970 1975 1980 1985 1990 1995 2000 2005
DOY
Observ.
Model
−7
−5
−3
−1 1 3 5 7
Residuals: Obs.−Model
XX X XX
196519701975198019851990199520002005
Days
rmse = 2.7
155 160 165 170 175 180 185 190 155
160 165 170 175 180 185 190
Correlation
Observation
Model
r = 0.94
Figure 5.4: Time series of the observed and approximated flowering event date (left), their correlation (centre) and the residuals (right). The red crosses in the residual plot flag the missing data.
60 Chapter 5. Statistical modelling of phenological events and must quality in June explains 7 % to 9 % of the observed variance. The last significant pre-dictor is the date of budburst with a contribution of about 2 % to the variance of flowering date. If the estimated budburst is used instead of the observed one, the correlation between the observed and the predicted flowering date is 0.91 and the explained variance is 82.8 %. Again, temperature is the leading factor for the flowering date. Warm periods before flowering move blooming and also fruit set-ting backward. From a late budburst date it can be concluded that winter and/or spring were rather cold, thus a delay in the initiation of the vegetation cycle is expected. Therefore, a late budburst date means also a later flowering date, but this delay is can be caught up by unusual high temperatures during April to June.
The largest difference between the observed and the calculated flowering date is 6.2 days and happened in 1990 (Figure 5.4). That year is marked by a very early start of the vegetation period; bud swelling was 23 days earlier than on average (Weinjahr, 1990). Although late frost damaged the buds and retarded the flowering period, it was still 8 days earlier than usual. In 1969 and 1984 calculated flowering event is 5 days too early. Around 24 June 1969 heavy thunderstorms damaged the vines and the Moselle bursted its banks (Weinjahr, 1969). Vegetation period was very late in 1984 because March and April were colder than normal. May and June were also cool and the flowering date was 14 days too late (8 July). This date is the latest date during the period 1966-2005.
Besides the years 1971, 1972 and 2000, where no phenological data was available, the years 1985 and 1997 were left out for the flowering event estimation. These years were marked by extreme meteorological conditions (Weinjahr, 1985, 1997).
In 1985 flowering event was very late because of a cold winter, especially January and also during a period in June. In June 1997 heavy rainfall occurred before and during flowering; 218.55 mm rain have been observed which is 3.25 times more than the long term average (67.18 mm between 1951-2005).