The objective of this work was to carry out a preliminary assessment of the performance of different models for the simulation of three main phenology stages (budburst, flowering and veraison) of grapevine in China. This work utilised observations from five representative wine regions (Changli, Laixi, ShangriLa, Xiaxian, and Yanqi) and four widely cultivated grape cultivars (CabernetSauvignon, Cabernet franc, Merlot, and Chardonnay) in China. The corresponding daily temperature data were used to simulate the timing of grape phenology stages using the different phenological models. The simulation dates and the actual dates were compared and the performance of the models was assessed for the different cultivars and wine regions. The GDD_{10} model exhibited the best performance for budburst simulation in soilburying regions, irrespective of the cultivar and location. For flowering and veraison, the optimal model varied in performance between cultivars and locations, and nonlinear models exhibited better performance than linear models. In general, the performance of these models was better for the latter two stages than for budburst. The models with relatively good performance were selected for further calibration using the limited Chinese observations. The impact of soilburying management on budburst simulation was also discussed. These results highlight the strengths of some phenological models for use in China. This study also reiterates the strong need for the establishment of a grapevine phenology observation network in China to obtain more comprehensive data.
Phenological models are important tools with a wide range of applications in grapevine cultivation. Models can be applied in the shortterm planning of viticultural practices, with a focus on the timing of treatments for different grape growth and development stages, like pest management (
These general applications highlight the importance of phenological models to properly describe grapevine growth and development. Numerous mechanistic models, or processbased models, have been recently developed to study grapevine phenology with the assumption that temperature is the main regulator of phenological development (
The grapevine developmental cycle is typically described in the three main phenological stages of budburst, flowering, and veraison (
The dormancy period is classically described by the three main phases of paradormancy, endodormancy, and ecodormancy (
For flowering and veraison simulation, only heat units are taken into account, but models vary according to the starting date of the calculation. Models can start with a fixed date, like the Grapevine Flowering Veraison model (GFV) (
In general, the applicability of these models needs to be adapted and evaluated under local conditions. In recent years, some processbased phenological models have been parameterised and validated based on a large phenological database that includes measurements from different vineyards worldwide (
An increasing number of wineries have sprung up all over China, presenting a new framework in the Chinese wine industry (
The main aim of this study was to assess the ability of a set of phenological processbased models to simulate the main grape phenological stages of four cultivars under Chinese climatic conditions. This work is the first study performed using observed phenological data collected from Chinese vineyards.
The different data and models used herein are described in this section.
The observed phenological data of the three main stages of budburst, flowering, and veraison were obtained for four cultivars of CabernetSauvignon, Cabernet franc, Merlot, and Chardonnay grown in five wine regions in China. Each phenological stage was basically judged at 50 % level of appearance. The locations of these five regions are shown in Figure 1. The basic information of the phenological data for each wine region are summarised in Table 1. For Changli, the data were from two wineries covering two different time periods. For Laixi, the data were from one winery; CabernetSauvignon was in two different plots during the same time period, therefore the average values of the two plots were used. For ShangriLa, data were from one winery at an altitude of 2000 to 2200 m. Because of the severe winter, grapevines in 90 % of vineyards in China need to be covered with soil to varying degrees in order to successfully overwinter (
To simulate the timing of different phenological stages during the same period, daily temperature observations were used as input variables. As no meteorological data have been collected in vineyards, meteorological data from nearby weather stations were used in the study. These data were obtained from the China Meteorological Administration, along with daily observation records of temperature (mean, maximum, and minimum) and geographical information. The climate characteristics of each wine region, the location of weather stations, and the distance from the weather station to the corresponding vineyard are described in Table 1.
titre du tableau
Wine region
Climate characteristics ^{a}
Weather station ^{b}
Distance between winery and weather station (km)
Years
FFS (d)
DI
AAT (°C)
Lat (° N)
Lon (° E)
Alt (m)
CabernetSauvignon
Merlot
Chardonnay
Cabernet franc
Yanqi
184
12.5
3469.0
42.08
86.57
1055.3
27.8
20162017
20162017
20162017
20162017
Changli
Winery 1
213
1.0
3722.5
39.72
119.17
17.6
1.4
20092013
20092013
20092013
20092013
Winery 2
(ditto)
(ditto)
7.5
20142016
20142016
20142016
20142016
Laixi
211
1.1
3683.3
36.90
120.55
76.9
22.7
20132017
20132017
20132017
20142017
Xiaxian
208
1.4
4023.7
35.17
111.23
402.9
10.1
20142017
20142017
20152017
NA^{c}
ShangriLa
265
0.6
3048.7
26.85
100.22
2380.9
24.1
20142017
NA
NA
NA
^{a} FrostFree Season (FFS), Dryness Index in growing season (DI), Accumulated Active Temperature (AAT) based 10 °C in growing season. ^{b} latitude (Lat.), longitude (Lon.), altitude (Alt.).^{c} no data available (NA).
Several candidate models were selected, because they are simple enough to ensure parsimony of input parameters, which makes it possible to test these models with limited data. Additionally, the parameter values of these models for the selected cultivars are available from previous studies. Table 2 provides an overview of the candidate models for each phenological phase, as well as the parameter values for available cultivars and the related original references. As it was not possible to obtain the parameter values of every model for all four varieties from literature, we have only listed the available cultivars for each model.
The Growing Degree Day model (GDD) is based on the classical thermal time concept (
:
where
is the starting date in Day of Year (DOY) format, N is the date of a phenological stage (here budburst, flowering, or veraison) in DOY format,
is the temperature for day n, and
is the base temperature above which the thermal summation is calculated.
For the budburst calculation, the
value was set as 1st January,
varied between cultivars, and
was fixed at 5 °C and 10 °C for the GDD_{5 }and GDD_{10 }models respectively (
For the flowering and veraison calculation, we used two models: the GFV (Grapevine Flowering Veraison) model and the GDD_{10} model. The GFV model (
,
, and
have been fitted based on a large database of observations predominantly from vineyards in Western Europe (France, Italy, Switzerland and Germany). This model uses daily mean temperatures, and the parameters
=60 and
=0 °C are the same for all cultivars, while
differs within cultivars (
= 10 °C (García de CortázarAtauri, 2006).
We also explored the linear model proposed by
values for the different phases. The parameter values were fixed according to those obtained by
This model combines several submodels which have been selected and simplified by
). The accumulation of forcing units then starts (using a sigmoid function given as Equation 3) until the threshold (
) is reached, which is calculated using the
value (Equation 4).
where
is the starting date, N is the date of a certain phenological stage (dormancy break or budburst),
is the mean daily temperature, a and c are parameters to describe the temperature response to calculate chilling units,
and
are parameters describing the relationship between the Critical Chilling
) and the forcing units required
to reach the budburst stage.
For flowering and veraison, the calculation only takes into account forcing units, using a sigmoid function (Equation 5), and daily mean temperatures. For these stages, the starting point (
) is the previously calculated stage.
The BRIN model takes into account the dormancy period. This model combines two original models (
Cold action is based on the
concept, where an arithmetic progression of 10 °C in temperature causes an action with a geometric regression of ratio
. Dormancy break (
) reaches a critical state (
) (Equation 6).
With
Where
is the starting date,
is the maximum temperature for day n, and
is the minimum temperature for day n.
The accumulation of
starts on 1st^{August of the previous year, and parameter }
was fixed at 2.17 for all cultivars (
After calculation of the Dormancy Break, the model starts to calculate the growing degree hours using Richardson’s model (Equation 7). Budburst Date (
) is when the sum of growing degree hours reaches a critical amount (
) (Equation 7).
The hourly temperature of day n [T (h, n)] can be calculated by linear interpolation between the maximal and minimal temperatures of day
If
then
If
then
The linear response is limited by two temperatures:
, which is the minimal threshold of the plant response and
, which is the maximal threshold reached once the plant response stays at the maximum value (Equation 9).
If
then
If
then
(9)
If
then
and
parameters are fixed at 5 °C and 25 °C, respectively (
A nonlinear model was proposed by
If
,
If
or
,
with
Where
is the daily rate of thermal summation within the range from 0 to 1,
is the starting date of the forcing,
is the date of a phenological stage when
is reached, and
,
, and
refer to minimum, optimum, and maximum temperature thresholds respectively. Temperature below or above the minimum and maximum thresholds are considered to have no effect on the plants.
Temperatures
and
were fixed at 0 °C and 40 °C respectively (Table 2), and
and
differ for each phenological phases and for each cultivar (García de CortázarAtauri
= March 15th) and can directly calculate veraison (García de CortázarAtauri
Table 2 provides an overview of the parameter values used in the aforementioned models for different phases and cultivars, as well as the corresponding references.
titre du tableau
Phenological phase
model
Parameter
Parameter value
Reference
Budburst
GDD_{5}
1 (1st^{Jan)}
(García de CortázarAtauri
5
CS: 318.6; M: 265.3; CH: 220.1
GDD_{10}
1(1st Jan)
(García de CortázarAtauri
10
CS: 52.5; M: 38.7; CH: 33.3
BRIN
152 (1st Aug)
(García de CortázarAtauri, 2006; García de CortázarAtauri
2.17
CS: 106.8; M: 105.7; CF: 66.8; CH: 101.2
5
25
CS: 9169.4; M: 7595.5; CF: 11548.0; CH: 6576.7
GDD
46 (15th Feb)
(
2
CS: 774.3
Caffarra
121（1st Sep）
(
0.005
2.800
176.260
0.015
78.692
Flowering
WE
0
(García de CortázarAtauri
CS: 30.2; CH: 30.3
40
CS: 20.3; CH: 18.8
GFV
60
(
0
CS: 1299; M: 1269; CF: 1245; CH: 1217
GDD_{10}
10
(
CS: 350; M: 347.7; CF: 292.4; CH: 304.9
GDD
10
(
CS: 619.8
Caffarra
CH: 24.710
(
Veraison
WE
0
(García de CortázarAtauri
CS: 24.3; CH: 24.3
40
CS: 63; CH: 56.2
WE
74 (15th March)
(García de CortázarAtauri
0
CS:25.63; CF: 26.88;
40
CS:111.34; CF: 100.39
GFV
60
(
0
CS: 2689; M: 2636; CF: 2692; CH: 2547
GDD_{10}
10
(
CS: 725; M: 677; CF: 705.6; CH: 646.7
GDD
6
(
CS: 1189.8
Caffarra
CH: 51.146
(
Grape varieties: CabernetSauvignon (CS), Merlot (M), Cabernet franc (CF), Chardonnay (CH).
Three statistical criteria were selected to evaluate the performance of different phenological models: the root mean square error (RMSE), the efficiency of the model (EF), and the mean bias error (MBE) (
The RMSE provides information about the mean error of the prediction of the model (Equation 11):
where
is the simulated date,
is the observed date, and
is the number of observations.
The efficiency of the model (EF) provides an estimation of the variance of the observations explained by the model (Equation 12). If EF = 0 or less, the model does not explain any variation. A negative value indicates that the model performed worse than the null model (the null model is based on the mean value of the observed dataset used to calibrate the model), and a value above zero indicates that the model has explained more variance than the null model (with a maximum value of 1 corresponding to the perfect model).
where
is the simulated date,
is the observed date,
is the number of observations, and
is the mean value of the observations.
The MBE is the average predicted error representing the systematic error of the model (under or above predictions).
Where
is the simulative date,
is the observed date, and
is the number of observations. A systematic overestimation of the model can be indicated by a positive value of MBE, and a negative value of MBE indicates a systematic underestimation of the actual observation.
To calculate the timing of different phenological stages, all these models were run using the Phenological Modelling Platform (PMP) software, version 5.5 (
The models that gave the best results were optimised using PMP 5.5 for their parameter
with the abovementioned Chinese phenological data for each cultivar. PMP 5.5 uses the simulated annealing algorithm of Metropolis (
ShangriLa is the only site in which vines do not require soilburying, and only data for CabernetSauvignon were accessible for this location. Thus, to better compare the performance of models between cultivars, the data from this region were excluded in the analysis presented in Table 3, Table 4, and Table 5.
Five models (BRIN, GDD_{Duchêne}, GDD_{5}, GDD_{10}, and Caffarra) were tested for four cultivars (CabernetSauvignon, Chardonnay, Merlot, and Cabernet franc) to simulate budburst (Table 3).
Only the BRIN model was tested for Cabernet franc, giving the worst result for all the tested cultivars. Although the performances of these models differed for the different cultivars, there were obvious differences between models. Except for GDD_{10}, all the models revealed very poor performance (Table 3), with high RMSE and negative efficiency for all the tested cultivars. Three statistical criteria gave consistent results, where the higher the RMSE, the lower the EF and the higher the absolute value of MBE were. The GDD_{10} model is the only model that performed well for all available cultivars. Except for the Caffarra model, all models showed overestimation with MBE > 0.
titre du tableau
Cultivar
Number of observations
Statistical criteria
BRIN
GDD_{Duchêne}
GDD_{5}
GDD_{10}
Caffarra
CabernetSauvignon
18
RMSE
18.5
11.9
17.4
EF
2.20
0.33
1.81
MBE
17.4
9.9
16.4
Chardonnay
17
RMSE
14.9
14.7
22.8
EF
1.48
1.41
4.79
MBE
14.0
13.9
21.2
Merlot
18
RMSE
16.9
16.4
EF
1.82
1.83
MBE
15.5
15.6
Cabernet franc
12
RMSE
28.1
EF
19.22
MBE
27.7
The values in bold indicate the best result for each variety.
Figure 3 compares all the available observed budburst dates and the corresponding simulated dates. Three statistical criteria and the Pearson’s correlation coefficient (r) have also been added. Except for the Caffarra model, all the models showed a good correlation between the simulated data and observed data. Both the observed and simulated data show that the budburst date is mostly related to the place where the grapevines are grown. For the five regions, the earliest budburst dates occurred in ShangriLa, followed by Xiaxian. The later budburst dates occurred in Yanqi, Changli, and Laixi. Changli and Laixi revealed a similar time range, which was longer than that of Yanqi. Most of the simulations of the BRIN and GDD_{5} models gave results that were more than 10 days later than the observed dates, with high MBE, high RMSE and negative EF. The later the observed budburst, the bigger the difference between the observed and simulated dates for the BRIN model. Different phenological behaviour was observed in the analysis of the only nonsoilburying region, ShangriLa, with a simulated budburst date very similar to, or earlier than, the observed budburst date for the BRIN and GDD_{5} models. For the Caffarra model, all the simulations were earlier than the observations, and showed the worst performance. For the GDD_{Duchêne} model, 45.5 % of the data points fell within the range of
. For the GDD_{10} model, 82.5 % of the points were located within
, showing the best performance, but one of the ShangriLa data points is obviously isolated from the others.
The dashed lines represent
The dashed lines represent
Five models (GFV, GDD_{10}, WE, GDD_{Duchêne}, and Caffarra) were used to simulate flowering for four cultivars (Table 4).
In general, the performance of the models was good when simulating flowering. Except for the GFV model, which did not simulate the timing of flowering well (RMSE > 10 and EF < 0) for all cultivars, all models performed relatively well with certain differences between cultivars. The GDD_{10} model gave the lowest RMSE (4.4) for Cabernet franc and the highest RMSE (8.1) with a negative EF value for Merlot. The WE, GDD_{Duchêne}, and Caffarra models were only available for one or two cultivars, but they all showed relatively good performance, with the WE model being the best for its two available cultivars (CabernetSauvignon and Chardonnay). There are only two models (GFV and GDD_{10}) available for Merlot and Cabernet franc. Neither model was reasonable for Merlot, but Cabernet franc was well simulated by GDD_{10}.
titre du tableau
Cultivar
Number of observations
Statistical criteria
GFV
GDD_{10}
WE
GDD_{Duchêne}
Caffarra
CabernetSauvignon
19
RMSE
15.0
7.7
7.0
EF
2.52
0.11
0.26
MBE
14.6
7.2
6.2
Chardonnay
18
RMSE
15.5
6.0
6.7
EF
2.51
0.51
0.38
MBE
15.1
5.4
6.1
Merlot
19
RMSE
15.2
EF
3.32
MBE
14.9
Cabernet franc
14
RMSE
13.2
EF
5.88
MBE
12.8
The values in bold indicate the best result for each variety.
In figure 4 the observed and simulated flowering dates are compared. All models showed a good correlation between observations and simulations according to the correlation coefficient (r). Flowering was earliest in Xiaxian, followed by ShangriLa. Changli and Laixi showed similar flowering times. The flowering dates were overestimated in all tested models. For the GFV model, 90.5 % of the simulated dates were more than ten days later than the observed dates, showing the worst performance. For the WE, GDD_{Duchêne }and GDD_{10} models, 87.2 %, 81.8 % and 81.4 % respectively of the simulated dates were less than ten days later than the observed dates. For the Caffarra model, almost all the simulations were less than ten days later than the observations. Except for GFV, all models showed a relatively good performance. For all available models, two ShangriLa data points were isolated from the other data.
The dashed lines represent y = x ± 10, and the full line represents y = x.
The dashed lines represent y = x ± 10, and the full line represents y = x.
Table 5 illustrates the performance of six different models (GFV, GDD_{10}, WE, WE with constant
, GDD_{Duchêne}, and Caffarra) in the simulation of veraison.
Although the GDD_{10} and GDD_{Duchêne }models performed well in the flowering simulation, they showed poor performance with high RMSE and negative EF for all available cultivars in the veraison simulation. The GFV model showed a better performance at this stage with positive EF values for all cultivars, except for Cabernet franc. The WE, GDD_{Duchêne}, and Caffarra models could only be applied to one or two cultivars, but they all preformed relatively well. No one model was best for all cultivars. The GFV model performed best for CabernetSauvignon, while the Caffarra model was best for Chardonnay. We only tested two models for Merlot, out of which the GFV model performed better. Three models (GFV, GDD_{10}, WE with constant
_{gave acceptable results.}
titre du tableau
Cultivar
Number of observations
Statistical criteria
GFV
GDD_{10}
WE
WE (with t_{0} constant)
GDD_{Duchêne}
Caffarra
CabernetSauvignon
19
RMSE
19.0
9.9
9.8
20.8
EF
1.36
0.36
0.37
1.83
MBE
17.3
6.2
2.5
19.6
Chardonnay
18
RMSE
8.4
13.8
6.8
EF
0.21
1.11
0.48
MBE
6.0
13.0
5.3
Merlot
19
RMSE
14.6
EF
1.35
MBE
13.6
Cabernet franc
14
RMSE
8.7
18.5
EF
0.03
3.69
MBE
1.8
17.0
The values in bold indicate the best result for each variety.
Figure 5 compares the simulated and observed veraison dates. In contrast to other phenological stages, the veraison dates were obviously underestimated by several models. In particular, the GDD_{Duchêne} model gave the highest RMSE, the highest negative MBE and a negative EF, showing 90.5 % of the simulated dates more than ten days earlier than the observed dates. The GDD_{10} model also showed bad performance, with 72.2 % of the simulated veraison dates earlier than observations by more than ten days. For the GFV model and the WE model with constant
, 75 % and 65.7 % of the data were located within
In the simulation of budburst, although the models performed differently for the different wine regions, the GDD_{10} model showed the best performance for all regions except ShangriLa, with RMSE lower than 10 d (Figure 6a). GDD_{5 }performed_{best for ShangriLa. Laixi was quite well simulated by all the models. }
In the simulation of flowering, the GDD_{Duchêne} model performed relatively consistently for the five wine regions, and performed best in the Xiaxian region (Figure 6b). In contrast, the GFV model showed the worst performance for all the wine regions. The performance of the WE model differed for the different wine regions, showing its worst performance for ShangriLa and its best for Changli. The GDD_{10} model gave the most similar results, but with higher RMSE values. All models performed least well for Yanqi and ShangriLa.
In the simulation of veraison, the performance of the different models differed greatly for a single location, especially for ShangriLa, with an RMSE value of 1.16 d for the GDD_{Duchêne} model, and 27.1 d for the GFV model (Figure 6c). The opposite result was observed in Laixi, with an RMSE value of 26.7 d for the GDD_{Duchêne} model, and 10.1 d for the GFV model. The GDD_{Duchêne} , GFV, and WE models with constant
only performed well for one or two regions, while the WE model showed relatively good performance for all regions with an average RMSE of 6.98 d.
Some models with good performance were selected in this part, and we tried to calibrate these models using limited Chinese phenological data to obtain new values for the parameter
titre du tableau
Phenological phase
Cultivar
Model
Number
Number
Using previous Fcrit
Using new Fcrit
RMSE
EF
MBE
RMSE
EF
MBE
Budburst
CabernetSauvignon
GDD_{10}
5
22
52.5
7.7
0.59
0.7
48.8
7.5
0.61
0.3
Merlot
GDD_{10}
4
17
38.7
6.2
0.60
3.2
27.9
5.8
0.63
0.2
Chardonnay
GDD_{10}
4
18
33.3
7.7
0.35
5.1
18.9
5.8
0.64
1.0
Cabernet franc
GDD_{10}
3
12




33.2
5.9
0.11
2.1
Flowering
CabernetSauvignon
WE
5
22
20.3
7.7
0.18
6.7
16.5
3.2
0.86
0.0
GDD_{10}
5
22
350
10.1
0.40
8.9
258
3.5
0.83
0.2
GDD
5
22
619.8
7.3
0.26
6.5
529.6
3.2
0.85
0.8
Chardonnay
WE
4
17
18.8
5.9
0.49
5.3
15.7
2.4
0.92
0.1
GDD_{10}
4
17
304.9
6.0
0.51
5.4
250.8
2.5
0.91
0.1
Caffarra
4
17
24.710
6.7
0.38
6.2
20.381
2.4
0.92
0.2
Merlot
WE
4
18




17.9
3.0
0.84
0.6
GDD_{10}
4
18
347.7
8.1
0.18
7.6
274.2
3.0
0.84
0.6
Cabernet franc
WE
3
13




18.9
3.8
0.47
0.0
GDD_{10}
3
13
292.4
4.4
0.30
2.0
271.6
3.7
0.51
0.0
Veraison
CabernetSauvignon
GFV
5
21
2689
11.9
0.07
2.1
2610
11.5
0.14
1.0
WE
5
21
63
9.5
0.41
5.4
68
7.9
0.59
0.2
WEconstant
5
21
111.34
11.6
0.10
4.4
106.67
10.7
0.25
0.29
Chardonnay
Caffarra
4
18
51.146
5.3
0.68
2.8
53.805
4.6
0.77
0.1
WE
4
18
56.2
6.8
0.48
5.3
61.4
4.3
0.79
0.0
GFV
4
18
2547
8.4
0.21
6.0
2388
5.8
0.62
0.0
Merlot
GFV
4
19
2636
8.3
0.24
5.4
2495
6.4
0.54
0.1
WE
4
19




53.24
5.5
0.66
0.0
Cabernet Franc
WEconstant
3
14
100.39
8.1
0.11
0.1
100.37
8.0
0.12
0.0
WE




45.2
4.97
0.66
0.23
In the simulation of budburst, the new
Several models performed similarly in predicting the timing of flowering and veraison for CabernetSauvignon and Chardonnay. In order to avoid arbitrary judgment, three models were selected and separately calibrated for each cultivar for flowering and veraison.
In the simulation of flowering, the new
In the simulation of veraison, although the fitting result of the models was not as good as in flowering, there was still an improvement for all cultivars, with the best analysis being for Chardonnay. For both CabernetSauvignon and Chardonnay, the curve models performed better than linear models, and the WE model gave the best results for the two cultivars.
This is the first assessment of grape phenological models in China, which utilised limited phenological data.
While the Caffarra model contains the most parameters and was predicted to better explain the budburst process, it was the worst model for growth data for grapes in China. However, it performed well for growth data in northern Italy, with an average EF value of 0.33 and MBE value of 5.1 when using an external dataset (
There was an overall improvement of the performance of models for veraison and flowering, which is consistent with the study by
In accordance with previous studies (
We tried to obtain the largest possible dataset of phenological stages from different varieties and regions of China, but the amount of data was quite limited. Most wineries in China do not record observations of grapevine phenology. We did not have sufficient data for both calibration and validation, so we relied on parameters calibrated in previous studies.
Most wine regions in China are in semiarid and arid areas, therefore irrigation is necessary. In western Europe, where the models were originally tested and calibrated, irrigation is not allowed or very strictly used in most vineyards. Different water conditions may change the crop phenology to some degree (
Soilburying is indispensable in most Chinese wine regions where the extreme low temperature is less than 15 °C (
Here is an example to illustrate the difference between air temperature and soil temperature at several depths (Figure S1). Soil temperature data were not available for the regions studied here, therefore data from Fangshan, another winegrowing site with temperate monsoon climate, were used instead. Air temperature was lower than soil temperature from October to February, but from March to September, air temperature was usually higher than soil temperature at deep depth and lower than soil temperature at shallow depth. Thus, the relationship between air temperature and soil temperature changes with the season at different depths, which directly leads to the inaccuracy of models that only take into account air temperatures during the soilburying period.
The actual accumulation of temperature by GDD_{5} and GDD_{10} models, which take into account the process of soilburying, was also illustrated in Fangshan for CabernetSauvignon (Figure S2). The observed budburst date is for grapevine covered with about 30 cm of soil. Both GDD_{5} and GDD_{10} showed overestimation. More importantly, the simulated budburst that was calculated using air temperature was earlier than that calculated using actual temperatures; furthermore, the deeper the soil depth, the later the simulated budburst. The temperature accumulation by GDD_{5} starts earlier than GDD_{10}. This difference generates more accumulated temperature during the soilburying period, which directly increases the uncertainty caused by the difference between soil temperature and air temperature. Therefore, soilburying had less effect on the accuracy of GDD_{10}. However, the accuracy of the results may be impacted by the depth of soilburying and the time of soiluncovering, which can vary between different regions.
The climate data used for each vineyard were obtained from nearby meteorological stations.
According to a study by
Being at the beginning of the grapevine growth cycle, the accuracy of budburst directly determines the accuracy of the simulation of subsequent phases. Therefore, the sitespecific estimation of model parameters would be required to increase the accuracy of these models for budburst (
According to
for flowering and veraison with a minimum number of 20 observations from three sites. Sufficient phenological data combined with associated weather data would allow us to evaluate the performance of current models, as well as to calibrate and validate new models for more cultivars under additional climate conditions, thus facilitating modeling in China. A more complete phenological observation network for grapevine is therefore required for China, similar to those in other countries, such as PEP725 (
This study assessed the performance of different phenological models to simulate the different grape phenological phases in five grapegrowing regions in China.
For budburst, most models exhibited poor performance. The GDD_{10 }was the only model to perform well, irrespective of the cultivar and location in the soilburying zones.
For flowering and veraison, most models provide relatively good performance, which varied between cultivars and regions. In general, nonlinear models performed better than linear models, especially for veraison, but not all models can be applied to all varieties.
The models with relatively good results were optimised for their
parameter using these limited Chinese observations. The impact of the difference between air temperature (calculated temperature) and soil temperature (actual temperature) during the soilburying period on the inaccuracy of models in the budburst simulation was also discussed. As this study was only based on limited observed data, the establishment of a grapevine phenology observation network to obtain more data would facilitate regionspecific modelling and allow model application for more varieties. Our results illustrate the potential for the use of models to simulate grape growth in China, which can facilitate the development of improved cultivation strategies.
The authors would like to warmly thank all the relevant personnel for providing the phenological data, especially the independent winemaker Mr. Kexi Ma. The authors are also thankful to China Meteorological Administration for providing the meteorological data. Xueqiu Wang was supported by a grant from China Scholarship Council (CSC).