Introduction

Phenological models are important tools with a wide range of applications in grapevine cultivation. Models can be applied in the short-term planning of viticultural practices, with a focus on the timing of treatments for different grape growth and development stages, like pest management (Galvan et al., 2009) and irrigation (Basile et al., 2012). Phenological models have also become useful tools for projecting the impacts of future climate change on viticulture (Duchêne et al., 2010). Obvious phenological changes have been observed in different wine regions and varieties (Bock et al., 2011; Duchêne and Schneider, 2005; García de Cortázar-Atauri et al., 2017; Lisek, 2008; Urhausen et al., 2011; Webb et al., 2011). Rising temperature, combined with advancing phenological phases, will profoundly affect many different processes during plant growth, altering both overall quality and yield (van Leeuwen and Darriet, 2016; Webb et al., 2011). Given that grape phenology is a good indicator of a changing climate, phenological models can also be used for climate reconstruction to identify past climate change (Chuine et al., 2004; García de Cortázar-Atauri et al., 2010b; Yiou et al., 2012).

These general applications highlight the importance of phenological models to properly describe grapevine growth and development. Numerous mechanistic models, or process-based models, have been recently developed to study grapevine phenology with the assumption that temperature is the main regulator of phenological development (Duchêne et al., 2010; García de Cortázar-Atauri et al., 2009; Nendel, 2010; Parker et al., 2011). These models are driven by chilling (for autumn and winter conditions) and forcing units (for spring and summer conditions) using various time-steps (e.g., daily or hourly temperature). These data are accumulated from a starting date (usually a phenological stage), and when they reach a critical threshold, the phenological stage (often judged at 50&nbsp% level of appearance) occurs (Parker et al., 2011).

The grapevine developmental cycle is typically described in the three main phenological stages of budburst, flowering, and veraison (Duchêne et al., 2010). “Maturity” is usually not considered a phenological stage because of the difficulty in accurately defining the time of maturity (Duchêne et al., 2010). Although sugar content is considered to be a good indicator of maturity, it can be influenced by many other factors in addition to climate (Jackson and Lombard, 1993).

The dormancy period is classically described by the three main phases of paradormancy, endodormancy, and ecodormancy (Lang, 1987; Sarvas, 1974). In process-based models, the first phase (paradormancy) is not usually included, and they mostly start on a fixed date (e.g., 1st August in García de Cortázar-Atauri et al., 2009). Endodormancy and ecodormancy periods are classically described based on chilling and forcing temperatures respectively. Previous reports have used one of two types of models to simulate budburst. The first does not include the endodormancy phase and only takes into account a forcing model to calculate budburst (Duchêne et al., 2010; Nendel, 2010). These models start from a predefined fixed date (which differs for each model), and they calculate budburst using a linear (e.g., growing degree-days) or non-linear (e.g., sigmoid) function. The second type of model describes both endodormancy and ecodormancy phases, using chilling and forcing functions that can be combined using different hypotheses (Chuine et al., 2013). For example, the BRIN model (García de Cortázar-Atauri et al., 2009) sequentially takes endodormancy and ecodormancy into account, while Caffarra’s model (Caffarra and Eccel, 2010) takes into accont the possibility of an interaction between phases.

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) (Parker et al., 2013), or they can start from the previous phenological date, like the Growing Degree-days model (García de Cortázar-Atauri et al., 2009), the curvilinear WE model (Wang and Engel, 1998), and the Sigmoidal model (Caffarra and Eccel, 2010).

In general, the applicability of these models needs to be adapted and evaluated under local conditions. In recent years, some process-based phenological models have been parameterised and validated based on a large phenological database that includes measurements from different vineyards worldwide (Cuccia et al., 2014; Duchêne et al., 2010; García de Cortázar-Atauri et al., 2009; García de Cortázar-Atauri et al., 2010a; Parker et al., 2011). This validation is required for further use of these models to accurately predict the timing of grapevine development under a changing climate.

An increasing number of wineries have sprung up all over China, presenting a new framework in the Chinese wine industry (Banks and Overton, 2010). Wine grapes have been widely cultivated in 179 counties in China, with a total area of 163,200 ha in 2016 (Wang et al., 2018). Despite this widespread cultivation, to our knowledge, there has been no report of the use of phenological models to study grapevine cultivation in China. Unlike most classical wine regions in the world, which are dominated by a Mediterranean climate or an oceanic climate, China has a totally different climate regime, defined as typical continental monsoon climate characterised by hot and rainy summers, and cold and dry winters (Li et al., 2011). Approximately 90&nbsp% of the vineyards in China have to be covered with soil to ensure plant survival in winter (Li and Wang, 2015). Therefore, it is important to assess the performance of phenological models under Chinese climatic conditions.

The main aim of this study was to assess the ability of a set of phenological process-based 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.

Materials and methods

The different data and models used herein are described in this section.

1. Observed phenological data

The observed phenological data of the three main stages of budburst, flowering, and veraison were obtained for four cultivars of Cabernet-Sauvignon, Cabernet franc, Merlot, and Chardonnay grown in five wine regions in China. Each phenological stage was basically judged at 50&nbsp% 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; Cabernet-Sauvignon was in two different plots during the same time period, therefore the average values of the two plots were used. For Shangri-La, data were from one winery at an altitude of 2000 to 2200 m. Because of the severe winter, grapevines in 90&nbsp% of vineyards in China need to be covered with soil to varying degrees in order to successfully overwinter (Li and Wang, 2015). Shangri-La is the only non-soil-burying region included in this study. The five study locations can be catagorised into three different climatic types. The dates of the phenological stages are expressed as day of year (DOY).

Figure 1. The locations of the five wine regions in China.

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2. Meteorological data

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.

Table 1. Summary of the data used in this study.

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Wine region		Climate characteristics a			Weather station b			Distance between winery and weather station (km)	Years
		FFS (d)	DI	AAT (°C)	Lat (°&nbspN)	Lon (° E)	Alt (m)		Cabernet-Sauvignon	Merlot	Chardonnay	Cabernet franc
Yanqi		184	12.5	3469.0	42.08	86.57	1055.3	27.8	2016-2017	2016-2017	2016-2017	2016-2017
Changli	Winery 1	213	1.0	3722.5	39.72	119.17	17.6	1.4	2009-2013	2009-2013	2009-2013	2009-2013
	Winery 2	(ditto)			(ditto)			7.5	2014-2016	2014-2016	2014-2016	2014-2016
Laixi		211	1.1	3683.3	36.90	120.55	76.9	22.7	2013-2017	2013-2017	2013-2017	2014-2017
Xiaxian		208	1.4	4023.7	35.17	111.23	402.9	10.1	2014-2017	2014-2017	2015-2017	NAc
Shangri-La		265	0.6	3048.7	26.85	100.22	2380.9	24.1	2014-2017	NA	NA	NA

^a Frost-Free 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).

2.1. Phenological models

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.

2.2. Growing Degree Day model

The Growing Degree Day model (GDD) is based on the classical thermal time concept (Bonhomme, 2000). This model calculates the cumulative daily temperatures above a temperature threshold (base temperature), usually starting from a given date (Equation 1). In this model, a phenological stage occurs when the sum of forcing unit reaches a critical state $F_{crit}$:

$F_{crit}={\sum }_{t_0}^{N}Max\left(T\left(n\right)-T_b，0\right)\left(1\right)$

where $t_0$ 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, $T\left(n\right)$is the temperature for day n, and $T_b$ is the base temperature above which the thermal summation is calculated.

For the budburst calculation, the $t_0$ value was set as 1st January, T(n) was the daily mean temperature, $F_{crit}$ varied between cultivars, and $T_b$ was fixed at 5 °C and 10 °C for the GDD₅ and GDD₁₀ models respectively (García de Cortázar-Atauri et al., 2009).

For the flowering and veraison calculation, we used two models: the GFV (Grapevine Flowering Veraison) model and the GDD₁₀ model. The GFV model (Parker et al., 2011) is a version of the GDD model to which parameters $t_0$, $T_b$, and $F_{crit}$ 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 $t_0$=60 and $T_b$=0 °C are the same for all cultivars, while $F_{crit}$ differs within cultivars (Parker et al., 2013). In the GDD₁₀ model, the previous stage is used as the starting date, and the mean daily temperature and $T_b$ = 10 °C (García&nbspde&nbspCortázarAtauri, 2006).

We also explored the linear model proposed by Duchêne et al. (2010) (GDD_Duchêne). This model is similar to the original GDD, but it calculates the accumulation using daily maximum temperatures with different $T_b$ values for the different phases. The parameter values were fixed according to those obtained by Duchêne et al. (2010), but were only available for Cabernet-Sauvignon in this study.

2.3. Caffarra’s model

This model combines several sub-models which have been selected and simplified by Caffarra and Eccel (2010) to simulate the three main phenological stages for the Chardonnay cultivar. For budburst, the calculation was based on a parallel two-phase model (Chuine, 2000). The accumulation of chilling temperatures (using a normal function) started from 1st September (Equation 2) and the Dormancy Break (DB) stage is calculated once the chilling threshold has been reached ($C_{crit}$). The accumulation of forcing units then starts (using a sigmoid function given as Equation 3) until the threshold ($F_{crit}$) is reached, which is calculated using the $C_{crit}$ value (Equation 4).

$C_{crit}={\sum }_{t_0=Sept.1^{st}}^{N=DB}\frac{2}{1+e^{{a\left(Tm-c\right)}^2}}\left(2\right)$


$F_{crit}={\sum }_{t_{0=DOY{C}_{crit}}}^{N=BB}\frac{1}{1+e^{-0.26\left(T_m-16.06\right)}}\left(3\right)$


$F_{crit}=co1\ast e^{co2\ast C_{crit}}\left(4\right)$

where $t_0$ is the starting date, N is the date of a certain phenological stage (dormancy break or budburst),$T_m$ is the mean daily temperature, a and c are parameters to describe the temperature response to calculate chilling units, $co1$ and $co2$ are parameters describing the relationship between the Critical Chilling
($C_{crit}$) and the forcing units required $\left(F_{crit}\right)$ 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 ($t_0$) is the previously calculated stage.

$F_{crit}={\sum }_{t_0}^N\frac{1}{1+e^{-0.26\left(T_m-16.06\right)}}\left(5\right)$

2.4. BRIN model

The BRIN model takes into account the dormancy period. This model combines two original models (García de Cortázar-Atauri et al., 2009): Bidabe’s Cold Action model (Bidabe, 1965a; Bidabe, 1965b) to calculate the dormancy period, and the Richardson model (Richardson, 1974; Richardson et al., 1975) to calculate the post-dormancy period.

Cold action is based on the $Q_{10}$ concept, where an arithmetic progression of 10 °C in temperature causes an action with a geometric regression of ratio $Q_{10}$. Dormancy break (DB) occurs when the amount of daily chilling units ($C_U$) reaches a critical state ($C_{crit}$) (Equation 6).

$C_{crit}={\sum }_{t_0=-152}^{N=DB}{C}_U\left(6\right)$

With $C_U=Q_{10}^{\frac{{-T}_{x\left(n\right)}}{10}}\mp Q_{10}^{\frac{{-T}_{n\left(n\right)}}{10}}$

Where $t_0$ is the starting date, N is the date of dormancy break, $T_{x\left(n\right)}$ is the maximum temperature for day n, and $T_{n\left(n\right)}$ is the minimum temperature for day n.

The accumulation of $C_U$ starts on 1st August of the previous year, and parameter $Q_{10}$ was fixed at 2.17 for all cultivars (García de Cortázar-Atauri et al., 2009).

After calculation of the Dormancy Break, the model starts to calculate the growing degree hours using Richardson’s model (Equation 7). Budburst Date ($N_{BB}$) is when the sum of growing degree hours reaches a critical amount ($G_c$) (Equation 7).

$G_c={\sum }_{t_0=DB}^{BB}{\sum }_{h=1}^{24}T\left(h,n\right)\left(7\right)$

The hourly temperature of day n [T (h, n)] can be calculated by linear interpolation between the maximal and minimal temperatures of day n and day n+1, and by assuming a day length of 12 h (as shown in Figure 2) (Equation 8).

If $h\le 12$ then $T\left(h,n\right)=T_n\left(n\right)+h\times \left(\frac{T_x\left(n\right)-T_n\left(n\right)}{12}\right)$

If $h>12$ then $T\left(h,n\right)=T_x\left(n\right)-\left(h-12\right)\times \left(\frac{T_x\left(n\right)-T_n\left(n+1\right)}{12}\right)$

Figure 2. Illustration of the calculation of the hourly temperature [T (h, n)] from daily minimum and maximum temperatures by the BRIN model.

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The linear response is limited by two temperatures: $T_{low}$, which is the minimal threshold of the plant response and $T_{high}$, which is the maximal threshold reached once the plant response stays at the maximum value (Equation 9).

If $T\left(h,n\right)<T_{low}$ then $T\left(h,n\right)=0$

If $T_{low}<T\left(h,n\right)\le T_{high}$ then $T\left(h,n\right)=T\left(h,n\right)-T_{low}$ (9)

If $T\left(h,n\right)>T_{high}$ then $T\left(h,n\right)=T_{high}-T_{low}$

$T_{low}$
and $T_{high}$ parameters are fixed at 5 °C and 25 °C, respectively (García de Cortázar-Atauri et al., 2009).

2.5. Wang and Engel’s model

A non-linear model was proposed by Wang and Engel (1998) (WE model) to simulate wheat crop development. This model was applied to calculate grapevine flowering and veraison (García de Cortázar-Atauri et al., 2010a). The curvilinear structure of this model can take into account negative effects of high temperatures on grapevine development (Equation 10):

$F_{crit}={\sum }_{t_0}^{N}F\left(T\right)\left(10\right)$

If $T_{min}<T<T_{max}$, $F\left(T\right)={\sum }_{t_0}^N\frac{{2\left(T-T_{min}\right)}^{\alpha }{\left(T_{opt}-T_{min}\right)}^{A\alpha }-{\left(T-T_{min}\right)}^{2\alpha }}{{\left(T_{opt}-T_{min}\right)}^{2\alpha }}$

If $T<T_{min}$ or $T>T_{max}$, $F\left(T\right)=0$

with $\alpha =\frac{log2}{log\left[\frac{T_{max}-T_{min}}{T_{opt}-T_{min}}\right]}$

Where $F\left(T\right)$ is the daily rate of thermal summation within the range from 0 to 1, $t_0$ is the starting date of the forcing, $N$ is the date of a phenological stage when $F_{crit}$ is reached, and $T_{min}$, $T_{opt}$, and $T_{max}$ 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 $T_{min}$ and $T_{max}$ were fixed at 0&nbsp°C and 40&nbsp°C respectively (Table 2), and $T_{opt}$ and $F_{crit}$ differ for each phenological phases and for each cultivar (García de Cortázar-Atauri et al., 2010a). Two versions of this model were tested in this study: one starts at a fixed date ($t_0$ = March 15th) and can directly calculate veraison (García de Cortázar-Atauri et al., 2010b), and the other allows flowering and veraison to be calculated starting from the previous phenological stage (García de Cortázar-Atauri et al., 2010a).

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.

Table 2. The parameter values of the different models used in this paper.

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Phenological phase	model	Parameter	Parameter value	Reference
Budburst	GDD5	t0	1 (1st Jan)	(García de Cortázar-Atauri et al., 2009)
		Tb	5
		Fcrit	CS: 318.6; M: 265.3; CH: 220.1
	GDD10	t0	1(1st Jan)	(García de Cortázar-Atauri et al., 2009)
		Tb	10
		Fcrit	CS: 52.5; M: 38.7; CH: 33.3
	BRIN	t0	-152 (1st Aug)	(García de Cortázar-Atauri, 2006; García de Cortázar-Atauri et al., 2009)
		Q10	2.17
		Cc	CS: 106.8; M: 105.7; CF: 66.8; CH: 101.2
		Tlow	5
		Thigh	25
		Fcrit	CS: 9169.4; M: 7595.5; CF: 11548.0; CH: 6576.7
	GDDDuchêne	t0	46 (15th Feb)	(Duchêne et al., 2010)
		Tb	2
		Fcrit	CS: 774.3
	Caffarra	t0	-121（1st Sep）	(Caffarra and Eccel, 2010)
		a	0.005
		c	2.800
		co1	176.260
		co2	-0.015
		Ccrit	78.692
Flowering	WE	Tmin	0	(García de Cortázar-Atauri et al., 2010a)
		Topt	CS: 30.2; CH: 30.3
		Tmax	40
		Fcrit	CS: 20.3; CH: 18.8
	GFV	t0	60	(Parker et al., 2013; Parker et al., 2011)
		Tb	0
		Fcrit	CS: 1299; M: 1269; CF: 1245; CH: 1217
	GDD10	Tb	10	(García de Cortázar-Atauri, 2006; Valdés-Gómez et al., 2009)
		Fcrit	CS: 350; M: 347.7; CF: 292.4; CH: 304.9
	GDDDuchêne	Tb	10	(Duchêne et al., 2010)
		Fcrit	CS: 619.8
	Caffarra	Fcrit	CH: 24.710	(Caffarra and Eccel, 2010)
Veraison	WE	Tmin	0	(García de Cortázar-Atauri et al., 2010a)
		Topt	CS: 24.3; CH: 24.3
		Tmax	40
		Fcrit	CS: 63; CH: 56.2
	WE (with constant t0)	t0	74 (15th March)	(García de Cortázar-Atauri et al., 2010b; Yiou et al., 2012)
		Tmin	0
		Topt	CS:25.63; CF: 26.88;
		Tmax	40
		Fcrit	CS:111.34; CF: 100.39
	GFV	t0	60	(Parker et al., 2013 ; Parker et al., 2011)
		Tb	0
		Fcrit	CS: 2689; M: 2636; CF: 2692; CH: 2547
	GDD10	Tb	10	(García de Cortázar-Atauri, 2006; Valdés-Gómez et al., 2009)
		Fcrit	CS: 725; M: 677; CF: 705.6; CH: 646.7
	GDDDuchêne	Tb	6	(Duchêne et al., 2010)
		Fcrit	CS: 1189.8
	Caffarra	Fcrit	CH: 51.146	(Caffarra and Eccel, 2010)

Grape varieties: Cabernet-Sauvignon (CS), Merlot (M), Cabernet franc (CF), Chardonnay (CH).

Results

1. General performance of phenological models in the simulation of budburst, flowering and veraison in China

Shangri-La is the only site in which vines do not require soil-burying, and only data for Cabernet-Sauvignon 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&nbsp5.

1.1. Budburst

Five models (BRIN, GDD_Duchêne, GDD₅, GDD₁₀, 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 GDD₁₀, 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&nbspvalue of MBE were. The GDD₁₀ model is the only model that performed well for all available cultivars. Except for the Caffarra model, all models showed overestimation with MBE&nbsp>&nbsp0.

Table 3. The performance of different models in the simulation of budburst for different cultivars.

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Cultivar	Number of observations	Statistical criteria	BRIN	GDDDuchêne	GDD5	GDD10	Caffarra
Cabernet-Sauvignon	18	RMSE	18.5	11.9	17.4	6.2
		EF	-2.20	-0.33	-1.81	0.64
		MBE	17.4	9.9	16.4	2.3
Chardonnay	17	RMSE	14.9		14.7	7.7	22.8
		EF	-1.48		-1.41	0.35	-4.79
		MBE	14.0		13.9	5.1	-21.2
Merlot	18	RMSE	16.9		16.4	6.2
		EF	-1.82		-1.83	0.60
		MBE	15.5		15.6	3.2
Cabernet franc	12	RMSE	28.1
		EF	-19.22
		MBE	27.7	&nbsp	&nbsp	&nbsp	&nbsp

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 Shangri-La, 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 GDD₅ 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 non-soil-burying region, Shangri-La, with a simulated budburst date very similar to, or earlier than, the observed budburst date for the BRIN and GDD₅ 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&nbsp% of the data points fell within the range of $y=x\pm 10$. For the GDD₁₀ model, 82.5&nbsp% of the points were located within $y=x\pm 10$, showing the best performance, but one of the Shangri-La data points is obviously isolated from the others.

Figure 3. Observed versus simulated budburst date (DOY) of five different models (BRIN, Caffarra, GDD10, GDD5, and GDDDuchêne).

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The dashed lines represent $y=x\pm 10$, and the full line represents $y=x$

1.2. Flowering

Five models (GFV, GDD₁₀, 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&nbsp>&nbsp10 and EF&nbsp<&nbsp0) for all cultivars, all models performed relatively well with certain differences between cultivars. The GDD₁₀ 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 (Cabernet-Sauvignon and Chardonnay). There are only two models (GFV and GDD₁₀) available for Merlot and Cabernet franc. Neither model was reasonable for Merlot, but Cabernet franc was well simulated by GDD₁₀.

Table 4. The performance of different models in the simulation of flowering for different cultivars.

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Cultivar	Number of observations	Statistical criteria	GFV	GDD10	WE	GDDDuchêne	Caffarra
Cabernet-Sauvignon	19	RMSE	15.0	7.7	6.3	7.0	&nbsp
		EF	-2.52	0.11	0.41	0.26
		MBE	14.6	7.2	5.6	6.2
Chardonnay	18	RMSE	15.5	6.0	5.9		6.7
		EF	-2.51	0.51	0.49		0.38
		MBE	15.1	5.4	5.3		6.1
Merlot	19	RMSE	15.2	8.1
		EF	-3.32	-0.18
		MBE	14.9	7.6
Cabernet franc	14	RMSE	13.2	4.4
		EF	-5.88	0.30
		MBE	12.8	2.0	&nbsp	&nbsp	&nbsp

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 Shangri-La. Changli and Laixi showed similar flowering times. The flowering dates were overestimated in all tested models. For the GFV model, 90.5&nbsp% of the simulated dates were more than ten days later than the observed dates, showing the worst performance. For the WE, GDD_Duchêne and GDD₁₀ models, 87.2&nbsp%, 81.8&nbsp% and 81.4&nbsp% 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 Shangri-La data points were isolated from the other data.

Figure 4. Observed versus simulated flowering date (DOY) of five different models (Caffarra, GDD10, GDDDuchêne, GFV, and WE).

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The dashed lines represent y = x ± 10, and the full line represents y = x.

1.3. Veraison

Table 5 illustrates the performance of six different models (GFV, GDD₁₀, WE, WE with constant $t_0$, GDD_Duchêne, and Caffarra) in the simulation of veraison.

Although the GDD₁₀ 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 Cabernet-Sauvignon, 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, GDD₁₀, WE with constant t₀) were tested for Cabernet franc, but only the WE model with the constant $t_0$ gave acceptable results.

Table 5. The performance of different models in the simulation of veraison for different cultivars.

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Cultivar	Number of observations	Statistical criteria	GFV	GDD10	WE	WE (with t0 constant)	GDDDuchêne	Caffarra
Cabernet-Sauvignon	19	RMSE	8.9	19.0	9.9	9.8	20.8
		EF	0.49	-1.36	0.36	0.37	-1.83
		MBE	-0.5	-17.3	-6.2	2.5	-19.6
Chardonnay	18	RMSE	8.4	13.8	6.8			5.3
		EF	0.21	-1.11	0.48			0.68
		MBE	6.0	-13.0	-5.3			-2.8
Merlot	19	RMSE	8.3	14.6
		EF	0.24	-1.35
		MBE	5.4	-13.6
Cabernet franc	14	RMSE	8.7	18.5		8.1
		EF	-0.03	-3.69		0.11
		MBE	1.8	-17.0	&nbsp	0.1	&nbsp	&nbsp

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&nbsp% of the simulated dates more than ten days earlier than the observed dates. The GDD₁₀ model also showed bad performance, with 72.2&nbsp% of the simulated veraison dates earlier than observations by more than ten days. For the GFV model and the WE model with constant $t_0$, 75&nbsp% and 65.7&nbsp% of the data were located within y&nbsp=&nbspx&nbsp±&nbsp10 of which 29.6&nbsp% and 39.1&nbsp% showed simulated dates earlier than observed dates. These two models showed an obvious trend in which the later the observed date, the higher the possibility of underestimation. More than half of dates predicted by the Caffarra and WE models were earlier than the observed dates; however, most were within 10&nbspd, thus showing relatively good performance. For most applied models, there were still two Shangri-La data points showing a different regulation.

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The dashed lines represent y&nbsp=&nbspx&nbsp±&nbsp10 and the full line represents y&nbsp=&nbspx. MBE: mean bias error between simulations and observations2. The performance of phenological models in different wine regions.

Figure 5. Observed versus simulated veraison date (DOY) of six different models (Caffarra, GDD10, GDD_Duchêne, GFV, WE, and WE with constant t₀).

In order to explore the impact of local climatic conditions on phenology simulation, the performance of each model available for Cabernet-Sauvignon was compared for different wine regions. Models available for Cabernet-Sauvignon were used for this comparison, because only data for Cabernet-Sauvignon were available for all five regions. There is only a total of 19 observations in all of the regions, and only two are available in Yanqi which makes the calculation of EF impossible sometimes, therefore we just use the RMSE to make a simple comparison.

Figure 6. Comparison of the RMSE of different models in five different wine regions for CabernetSauvignon: a. budburst, b. flowering and c. veraison.

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In the simulation of budburst, although the models performed differently for the different wine regions, the GDD₁₀ model showed the best performance for all regions except Shangri-La, with RMSE lower than 10 d (Figure 6a). GDD₅ performed best for Shangri-La. 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 Shangri-La and its best for Changli. The GDD₁₀ model gave the most similar results, but with higher RMSE values. All models performed least well for Yanqi and Shangri-La.

In the simulation of veraison, the performance of the different models differed greatly for a single location, especially for Shangri-La, with an RMSE value of 1.16 d for the GDD_Duchêne model, and 27.1&nbspd for the GFV model (Figure 6c). The opposite result was observed in Laixi, with an RMSE value of 26.7&nbspd for the GDD_Duchêne model, and 10.1&nbspd for the GFV model. The GDD_Duchêne , GFV, and WE models with constant $t_0$ 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&nbspd.

Discussion

This is the first assessment of grape phenological models in China, which utilised limited phenological data. This study evaluated the performance of a set of models to simulate phenological stages for grapes grown in China. These models were previously validated under European conditions. There was certain variation in the accuracy of these models, with both variety-dependent and site-dependent differences.

Conclusions

This study assessed the performance of different phenological models to simulate the different grape phenological phases in five grape-growing regions in China.

For budburst, most models exhibited poor performance. The GDD₁₀ was the only model to perform well, irrespective of the cultivar and location in the soil-burying zones.

For flowering and veraison, most models provide relatively good performance, which varied between cultivars and regions. In general, non-linear 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 $F_{crit}$ parameter using these limited Chinese observations. The impact of the difference between air temperature (calculated temperature) and soil temperature (actual temperature) during the soil-burying 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 region-specific 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.

Acknowledgements

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).

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