Identification and Regionalization of Cold Resistance of Wine Grape Germplasms (V. vinifera)

Abstract

With the extreme changes of the global climate, winter freezing injury has become an important limiting factor for the development of the global grape industry. Therefore, there is a significant need for the screening of cold-resistant wine grape germplasms and cold regionalization for cold-resistant breeding and the development of grapevine cultivation in cold regions. In this study, the low-temperature half-lethal temperature (LT₅₀) values were determined for the annual dormant branches of 124 wine grape germplasms (V. vinifera) to evaluate their cold resistance. The LT₅₀ values of the 124 tested germplasms ranged from −22.01 °C to −13.18 °C, with six cold-resistant germplasms below −20 °C. Based on the LT₅₀ values, the 124 germplasms were clustered into four types, with cold resistance from strong to weak in the order of type Ⅱ > type Ⅰ > type Ⅳ > type Ⅲ, corresponding to the four cold hardiness zones. Zones 1, 2, 3, and 4 included 6, 22, 68, and 28 germplasms, respectively, with decreasing cold resistance. The number of germplasms in different hardiness zones followed a normal distribution, with the most in zone 3. In Type Ⅱ, the fruit skin color of germplasms was positively correlated with cold hardiness, while the temperature of origin was negatively correlated with cold hardiness. The average LT₅₀ of germplasms in different origin regions ranged from −17.44 °C to −16.26 °C, with differences among some regions. The cold regionalization analysis resulted in the distribution of 124 germplasms in four temperature regions in China with six germplasms in region A (−22 °C ≤ LT₅₀ ≤ −20 °C), 30 germplasms in region B (−20°C ≤ LT₅₀ ≤ −18°C), 71 germplasms in region C (−18 °C ≤ LT₅₀ ≤ −15 °C), and 17 germplasms in region D (−15 °C ≤ LT₅₀ ≤ −13 °C). Strong cold-resistant wine grape germplasms (V. vinifera) were identified, and these could be used as parental material for cold-resistant breeding. In some areas in China, soil-burial over-wintering strategies are used, but our results suggest that some wine grapes could be cultivated without requiring winter burial during overwintering. The results of this study should provide guidance for the selection of promising strains for cold-resistant breeding for expanded cultivation of improved varieties for wine grape production in China.

1. Introduction

Mean climate change and increased extreme events (i.e., days with Temperature max > 30–35 °C, or days with Temperature min < 0 °C) results in colder winters, especially in Europe, Russia, and China [1,2]. Most wine grape varieties cultivated in these regions are V. vinifera, with the highest economic value globally [3,4,5,6,7,8]. Rapid temperature drops in late fall, freezing temperatures in mid-winter, and early spring frost can seriously damage grapevines, resulting in fruit production losses [9,10,11]. For this reason, low temperature stress is one of the main challenges restricting the development of viticulture globally, especially given the recent increased frequency of frost impacts in many different regions (i.e., France, [12]; England, [13]; Romania, [14]; and China, [15,16]). In northern China and the far eastern region of Russia, soil-burial over-wintering has become the main cultivation mode of V. vinifera. However, soil-burial practices can limit production, with problems including the acceleration of plant senescence, reduction of grape quality, reduced ability to utilize mechanization, and substantial additional expenses for labor, facilities, and operating costs [11,17,18,19,20,21,22]. Therefore, it is critical to develop new, high-quality cultivars with increased cold tolerance. However, to achieve this goal, it is necessary to breed new burial-free varieties with high quality and cold resistance. Cold-resistant varieties can be generated by seedling selection and hybrid breeding, with improved varieties chosen for specific local climate conditions.

Conventional cold-resistant breeding of grapevines is mainly done by the cross-breeding of wild species or V. labrusca with V. vinifera, so that new varieties obtain major cold-resistance genes from wild species or V. labrusca but maintain the high-quality genes of V. vinifera [17,20,23,24,25,26,27,28,29,30]. However, we still lack cultivars with high quality and high cold resistance, many interspecific hybrids have been found to have poor fruit and wine quality [5,7,17,24,30,31,32,33,34,35,36]. Recent studies have shown that intraspecific recurrent selection in V. vinifera is an effective method for breeding of high quality, disease-, cold-, and drought-resistance grapes [37,38,39,40,41,42,43,44,45]. In other parts of the world, a strategy of intraspecific hybridization was successfully applied to select hybrid progenies of V. vinifera with cold-resistance and high quality [33,34,35,46,47]. To breed more high-quality cold-resistant wine grape varieties, we need to characterize the cold resistance of V. vinifera and screen parent materials suitable for intraspecific recurrent selection in V. vinifera. In most previous studies, cold-resistant parent materials were selected from wild species or V. labrusca, and characterization of the cold resistance of V. vinifera varieties has also been limited to a few groups [17,48,49,50].

Grape cold resistance breeding is key for the further development of the grape industry in cold regions [51]. In China, many commonly cultivated varieties of wine grapes have been introduced from abroad, including Cabernet Sauvignon, Merlot, Syrah, Marselan, and Chardonnay [52,53,54]. To increase the selection process of fine varieties, it is important to perform regionalization studies. Wang et al. determined the regionalization for climate and different wine varieties in China using the accumulated temperature of growing activities as the first index and frost-free period and dryness as the second index [55,56]. In other studies, regionalization of wine grapes (V. vinifera) in China was studied based on the dominant climatic factors affecting the planting distribution of V. vinifera [57,58]. Yue et al. constructed the spring frost risk regionalization of major grape-producing areas in China based on the daily minimum temperature; applied indexes for spring frost, including the occurrence days, frequency, and station number ratio; and the disaster risk of a spring frost disaster in areas of grape producing [59,60]. However, there have only been a few studies conducted on the regionalization of overwintering freezing injury of grapes, and detailed and comprehensive cold resistance regionalization remains lacking [61]. Overall, cold regionalization can provide important guidance for the introduction of varieties with improved cold resistance and the optimal cultivation distribution for varieties currently in use.

In this study, the cold resistance was determined for 124 wine grape germplasms (V. vinifera) originating from eight countries. This was done using a conductivity method, followed by cluster analysis of cold resistance and analysis of the factors affecting cold resistance. Based on the climate regionalization of wine grapes in China, cold regionalization was performed. Building on previous studies, this analysis of cold resistance and cold regionalization provides a reference basis for the evaluation of cold resistance of wine grapes and the optimal selection of parent strains for the breeding of cold resistance. This work should provide important reference value for the future selection of varieties for cultivation in Chinese wine grape production areas.

2. Materials and Methods

2.1. Sampling

The annual branches of 124 perennial wine grape germplasms (V. vinifera) in the Zhengzhou National Grape Germplasm Repository in China (38°48′ N, 113°42′ E; 114 m above sea level) were used as the test materials (

Table 1. Grape varieties or strains used in this study.

Number	Sink Color	Cultivars or Species	Number	Sink Color	Cultivars or Species
USSR1	yellow-green	Artin	GER 8	yellow-green	Riesling 237
USSR2	yellow-green	Asageni	GER 9	yellow-green	Risi
USSR3	yellow-green	Aybat ♀	GER 10	yellow-green	Muller Thurgau
USSR4	black	Epac Hehu	GER 11	yellow-green	Morio Muscat
USSR5	yellow-green	Bakhtiori	GER 12	yellow-green	Neuburgske
USSR6	yellow-green	Kokour Blanc	GER 13	black	Dunkelfelder
USSR7	yellow-green	Bahian Chirei	CHN1	yellow-green	8802
USSR8	yellow-green	Biaz Abat	CHN2	yellow-green	8803
USSR9	yellow-green	Belakay	CHN3	yellow-green	Ecolly
USSR10	yellow-green	Plavaie	CHN4	crimson	Meili
USSR11	crimson	Polechskik ♀	CHN5	black	Yan73
USSR12	Black	Bouaki Tach	FRA1	yellow-green	Pinot Blanc
USSR13	light-red	Bugisuri	FRA2	yellow-green	Gamay Blanc
USSR14	yellow-green	Duma ♀	FRA3	yellow-green	Clairette Blanc
USSR15	light-red	Pink yurapi	FRA4	yellow-green	Ugni Blanc
USSR16	yellow-green	Garandmak	FRA5	black	North Saigon
USSR17	yellow-green	Gordan	FRA6	black	Portan Grenache
USSR18	yellow-green	Hamilak	FRA7	black	Cabernet Sauvignon
USSR19	black	Assyl Kara	FRA8	black	Gros Noir
USSR20	crimson	Stary Goru	FRA9	black	Blauer Burgunder 2
USSR21	black	Tzimlansky Noir	FRA10	crimson	Gamay Freo
USSR22	black	Chabasse Noir ♀	FRA11	black	Morastel
USSR23	black	Hevanisko	FRA12	black	Ganson
USSR24	crimson	Red Aiki ♀	FRA13	yellow-green	Colombard
USSR25	black	Hongjin Giri	FRA14	yellow-green	Ribola Grun
USSR26	light-red	Red Pawan	FRA15	black	Heigland
USSR27	light-red	Calomat	FRA16	black	Hermandok
USSR28	light-red	Kamatt	FRA17	black	Hesselin
USSR29	black	Kamrasia	FRA18	black	Red Sauce Camry
USSR30	yellow-green	Condavas	FRA19	black	Pinot Grigio
USSR31	yellow-green	Kukhaba	FRA20	yellow-green	Kara
USSR32	yellow-green	Cock Pundas	FRA21	black	Court
USSR33	yellow-green	Kosorodov	FRA22	black	Marcelin
USSR34	light-red	Krasjan	FRA23	black	Merlot
USSR35	yellow-green	Crimean Cornish ♀	FRA24	black	Cabernet Franc
USSR36	yellow-green	Kuchazki	FRA25	crimson	Cabernet Franc 214
USSR37	yellow-green	Rasmi	FRA26	black	Cabernet Gernischet
USSR38	black	Magaratche 217	FRA27	yellow-green	Perlant Muscat
USSR39	black	Magaratche 56	FRA28	black	Sauvignon Blanc
USSR40	yellow-green	Mahbo	FRA29	black	Tenat
USSR41	black	Malachi	FRA30	yellow-green	Viognier
USSR42	yellow-green	Mathis	FRA31	black	Petit Verdot
USSR43	yellow-green	Nayili	FRA32	black	Syrah
USSR44	black	Sateni	FRA33	yellow-green	Chardonnay
USSR45	yellow-green	Sary Pandass	FRA34	yellow-green	Little Munson
USSR46	yellow-green	Spitak	FRA35	crimson	Xin Nong Hong
USSR47	yellow-green	Tarnau	FRA36	black	Cinsault
USSR48	yellow-green	Tavris	FRA37	black	Drunk poetry
USSR49	black	Taushi	ESP1	yellow-green	Petroximegne
USSR50	yellow-green	Tokyh	ESP2	black	Black Camry
USSR51	light-red	Vagesberg	ESP3	black	Carignane
USSR52	yellow-green	Sibirkovy	ESP4	yellow-green	Marivasia
USSR53	yellow-green	Shavrani	ESP5	black	Tempranillo
USSR54	yellow-green	Champagne Koji	ITA1	yellow-green	Gewurztraminer
USSR55	black	Ida	ITA2	black	Aleakico
GER 1	yellow-green	Ehrenfels	ITA3	black	Barbera
GER 2	yellow-green	Bacchus	ITA4	yellow-green	Forastier Muscat
GER 3	black	Domina	ITA5	yellow-green	Italian Riesling
GER 4	black	Pinot Noir	ITA6	yellow-green	Raisin de Calabre
GER 5	yellow-green	Kemed	ITA7	black	Sangiovese
GER 6	yellow-green	Rhein Riesling	SUI1	crimson	Granoir
GER 7	yellow-green	Riesling	ROU1	yellow-green	Alb Rominesc

USSR (the Soviet Union), GER (Germany), CHN (China), FRA (France), ESP (Spain), ITA (Italy), SUI (Switzerland), and ROU (Romania) indicate germplasm origin regions. ♀ refers to female flowers, not common bisexual flowers.

). Grapevines were planted 1.2 m apart within rows with 2.5 m distance between rows planted with a north–south orientation. Grapevines were trained to bilateral cordons under similar soil, irrigation, pruning, and disease control management [62]. Sampling was conducted on 10 January 2020, and three strains of each variety with good growth condition were randomly selected as three biological replicates. An amount of 6 dormant branches of the current year were taken from each strain, for a total of 18 branches collected from each variety. All branches were wax-sealed at both ends, sealed in plastic sealing bags, and stored in a refrigerator at 4 °C prior to testing.

2.2. Low Temperature Treatment and LT₅₀ Determination

The collected branches were rinsed with tap water and then deionized water, three times for each, and then the water was adsorbed using filter paper. Each sample was divided into six equal parts and placed in a heat test box with controlled temperature and humidity (Model: YSGJS-408, Shanghai lanhao instrument & equipment Co., Ltd., Shanghai, China) for low temperature treatment. The groups were as follows: 4 °C (control group, no low temperature treatment), −10 °C, −14 °C, −18 °C, −22 °C, and −26 °C, for a total of six temperature gradient treatments. For each condition, the temperature was decreased to the treatment temperature at a rate of 4 °C/h for 12 h, and then heated to 4 °C at a rate of 4 °C/h. At the conclusion of the treatment, the samples were removed and placed at room temperature for 4 h prior to testing.

Since the buds are greatly affected by the full state and the position of the internodes, we choose the more stable internodes of the branches as the identification material for cold resistance. Remove the epidermis of the processed branches, avoiding the bud eyes, select the stems and cut them into 1~2 mm slices and mix evenly. Samples weighing 2 g were transferred into a 25 mL test tube with a stopper, and then 20 mL deionized water was added and shaken well. This was done three times for each treatment. After shaking for 12 h in a shaker, a conductivity meter (DDS-11C, Shanghai optical instrument factory, Shanghai, China) was used to determine the initial conductivity value. Each test tube was then boiled for 40 min, allowed to stand for 2 h, and then the final conductivity value was determined. The relative conductivity was calculated as follows.

$$\mathrm{Relative}\mathrm{conductivity}(\%)=\left(\mathrm{initial}\mathrm{conductivity}\mathrm{value}/\mathrm{final}\mathrm{conductivity}\mathrm{value}\right)\times 100$$

(1)

The semi-lethal temperature (LT₅₀) value was calculated using the logistic regression equation:

Y = K/(1 + ae^−bx)

Y is the relative conductivity, x is the processing temperature, and K is the maximum leakage (K = 100).

In practical application:

Y′ = ln[(K − Y)/Y]

Y′ = lna − bx, that is, the relative conductivity Y is converted into Y′, and the relationship between it and the processing temperature is expressed by linearity. The parameter a and b of the equation were obtained by linear regression. The inflection point temperature is the LT₅₀.

2.3. Clustering of Cold Resistance of Wine Grape Germplasms (V. vinifera)

According to the measured LT₅₀ values, the cold resistance of the 124 germplasms was subjected to cluster analysis. The climate temperature of the areas where the germplasms originated were then correlated with the LT₅₀ values of germplasms in different types. Correlation of LT₅₀ values with the fruit skin color of different germplasms (yellow-green, light-red, crimson, and black) was also performed.

2.4. Zoning of Cold Resistance of Wine Grape Germplasms (V. vinifera)

According to the results of cluster analysis, germplasms were divided into cold hardiness zones. The LT₅₀ values were organized from low to high, and the temperature range and the distribution of different germplasms were determined for each zone. Average LT₅₀ values were calculated for germplasms from different origins.

2.5. Cold Regionalization of Wine Grape Germplasms (V. vinifera) in China

The daily minimum temperatures of surface meteorological elements for 30 consecutive years (1982–2011) provided by the National Climate Center at 2294 weather stations across the country (Figure 1) were used in this study. The temperature range of each cold hardiness zone was rounded to the nearest tenth degree to determine the standards for the cold regionalization of wine grape germplasms (V. vinifera) in China. Based on the climatic regionalization, cold regionalization of wine grape was next carried out using the daily minimum temperature of surface meteorological elements in each region and the critical low temperature occurring at least three times in 30 years as the applied index. The specific distribution of 124 germplasms in cold regionalization were determined according to LT₅₀.

2.6. Data Analysis

Origin 9.0 (OriginLab, Northampton, MA, USA) software was used to fit the logistic equation, and LT₅₀ values were obtained. Descriptive statistics were analyzed via SPSS 22.0 (Statistical Product and Service Solutions, Inc., Chicago, IL, USA). Values were presented as the mean ± standard deviation using triplicate measurements. Cluster analysis and graphing were carried out using R language software and complete-linkage clustering. ArcGIS 10.2 (Environmental Systems Research Institute, Inc., California, CA, USA) software spatial analysis module was used to carry out spatial interpolation processing and to construct the cold regionalization map.

3. Results

3.1. LT₅₀ Values

LT₅₀ values were obtained by fitting the relative electrical conductivity of grapevine branches under different temperature stresses with the corresponding stress temperatures. As shown in

Table 2. LT₅₀ values of 124 germplasms subjected to different cryogenic treatments.

Number	Regression Equation	R2	LT50	Number	Regression Equation	R2	LT50
USSR1	Y = 0.08x + 1.62	0.69	−19.91	GER 8	Y = 0.08x + 1.62	0.69	−19.95
USSR2	Y = 0.19x + 2.75	0.90	−14.65	GER 9	Y = 0.22x + 3.06	0.74	−14.05
USSR3	Y = 0.18x + 2.75	0.79	−15.28	GER 10	Y = 0.20x + 3.11	0.66	−15.35
USSR4	Y = 0.19x + 2.82	0.64	−14.67	GER 11	Y = 0.15x + 2.64	0.92	−17.03
USSR5	Y = 0.15x + 2.64	0.95	−17.07	GER 12	Y = 0.19x + 3.59	0.94	−18.01
USSR6	Y = 0.05x + 0.90	0.95	−19.09	GER 13	Y = 0.26x + 4.61	0.97	−17.78
USSR7	Y = 0.19x + 2.52	0.94	−13.58	CHN1	Y = 0.20x + 2.79	0.72	−14.15
USSR8	Y = 0.20x + 2.90	0.89	−14.37	CHN2	Y = 0.23x + 3.64	0.98	−16.68
USSR9	Y = 0.16x + 2.84	0.93	−17.76	CHN3	Y = 0.15x + 3.14	0.93	−20.92
USSR10	Y = 0.19x + 2.98	0.88	−15.23	CHN4	Y = 0.17x + 3.31	0.89	−19.48
USSR11	Y = 0.13x + 2.04	0.99	−15.73	CHN5	Y = 0.25x + 3.93	0.99	−15.95
USSR12	Y = 0.19x + 2.53	0.90	−13.18	FRA1	Y = 0.13x + 1.88	0.90	−14.35
USSR13	Y = 0.18x + 3.54	0.82	−19.20	FRA2	Y = 0.17x + 2.53	0.90	−14.45
USSR14	Y = 0.17x + 3.22	0.82	−18.74	FRA3	Y = 0.10x + 1.75	0.98	−17.61
USSR15	Y = 0.13x + 2.40	0.74	−18.09	FRA4	Y = 0.14x + 2.20	0.99	−15.72
USSR16	Y = 0.20x + 3.59	0.94	−18.18	FRA5	Y = 0.15x + 2.25	0.84	−15.09
USSR17	Y = 0.16x + 3.66	0.87	−22.01	FRA6	Y = 0.14x + 2.20	0.99	−15.53
USSR18	Y = 0.13x + 2.04	0.99	−15.69	FRA7	Y = 0.07x + 1.39	0.65	−18.48
USSR19	Y = 0.12x + 1.84	0.92	−15.89	FRA8	Y = 0.31x + 4.38	0.95	−14.01
USSR20	Y = 0.10x + 2.04	0.85	−19.51	FRA9	Y = 0.23x + 3.25	0.98	−16.72
USSR21	y = 0.30x + 4.70	0.99	−15.71	FRA10	Y = 0.12x + 1.89	0.61	−15.99
USSR22	Y = 0.20x + 3.38	0.96	−18.95	FRA11	Y = 0.14x + 2.20	0.99	−16.09
USSR23	Y = 0.19x + 3.76	0.83	−19.36	FRA12	Y = 0.07x + 1.51	0.62	−19.64
USSR24	Y = 0.15x + 2.50	0.99	−16.22	FRA13	Y = 0.30x + 4.60	0.98	−15.19
USSR25	Y = 0.18x + 2.79	0.63	−15.68	FRA14	Y = 0.24x + 3.84	0.99	−15.90
USSR26	Y = 0.17x + 2.96	0.66	−17.10	FRA15	Y = 0.10x + 1.55	0.59	−15.79
USSR27	Y = 0.27x + 4.85	0.99	−17.94	FRA16	Y = 0.19x + 3.22	0.89	−16.97
USSR28	Y = 0.25x + 4.60	0.85	−18.17	FRA17	Y = 0.07x + 1.39	0.65	−18.49
USSR29	Y = 0.16x + 2.78	0.98	−17.86	FRA18	Y = 0.21x + 3.09	0.99	−16.99
USSR30	Y = 0.27x + 3.70	0.95	−13.67	FRA19	Y = 0.21x + 3.36	0.99	−15.95
USSR31	Y = 0.11x + 1.85	0.51	−16.24	FRA20	Y = 0.26x + 3.98	0.92	−15.21
USSR32	Y = 0.14x + 2.54	0.86	−18.29	FRA21	Y = 0.23x + 3.81	0.84	−16.66
USSR33	Y = 0.12x + 2.14	0.57	−17.29	FRA22	Y = 0.26x + 4.10	0.99	−15.98
USSR34	Y = 0.23x + 3.48	0.66	−15.41	FRA23	Y = 0.12x + 1.89	0.93	−15.22
USSR35	Y = 0.05x + 1.06	0.93	−20.88	FRA24	Y = 0.11x + 1.74	0.85	−15.51
USSR36	Y = 0.30x + 4.80	0.95	−16.09	FRA25	Y = 0.25 + 3.93	0.99	−15.89
USSR37	Y = 0.16x + 2.84	0.94	−17.62	FRA26	Y = 0.18x + 3.32	0.98	−18.46
USSR38	Y = 0.21x + 3.09	0.99	−16.99	FRA27	Y = 0.14x + 2.17	0.65	−15.51
USSR39	Y = 0.17x + 2.96	0.66	−17.28	FRA28	Y = 0.18x + 2.86	0.97	−16.26
USSR40	Y = 0.22x + 3.05	0.95	−14.11	FRA29	Y = 0.25x + 3.89	0.96	−15.36
USSR41	Y = 0.13x + 2.03	0.93	−16.06	FRA30	Y = 0.22x + 3.06	0.65	−15.87
USSR42	Y = 0.15x + 2.62	0.99	−16.95	FRA31	Y = 0.27x + 4.47	0.80	−16.83
USSR43	Y = 0.29x + 5.21	0.97	−17.63	FRA32	Y = 0.12x + 1.84	0.92	−15.90
USSR44	Y = 0.14x + 3.05	0.99	−21.95	FRA33	Y = 0.15x + 2.62	0.99	−16.90
USSR45	Y = 0.08x + 1.62	0.69	−19.75	FRA34	Y = 0.26x + 4.62	0.99	−17.77
USSR46	Y = 0.03x + 0.67	0.96	−20.68	FRA35	Y = 0.16x + 2.57	0.99	−16.21
USSR47	Y = 0.15x + 2.60	0.91	−17.29	FRA36	Y = 0.07x + 1.13	0.81	−16.58
USSR48	Y = 0.75x + 1.39	0.65	−18.49	FRA37	Y = 0.14x + 2.29	0.78	−16.41
USSR49	Y = 0.13x + 2.03	0.93	−16.19	ESP1	Y = o.11x + 2.34	0.65	−20.46
USSR50	Y = 0.17x + 3.37	0.98	−19.91	ESP2	Y = 0.16x + 3.14	0.86	−19.64
USSR51	Y = 0.08x + 1.23	0.82	−15.82	ESP3	Y = 0.13x + 1.92	0.79	−14.48
USSR52	Y = 0.20x + 3.22	0.95	−16.42	ESP4	Y = 0.14x + 2.38	0.76	−16.97
USSR53	Y = 0.18x + 3.01	0.77	−16.38	ESP5	Y = 0.18x + 2.67	0.75	−14.58
USSR54	Y = 0.18x + 3.55	0.82	−19.35	ITA1	Y = 0.13x + 2.04	0.99	−15.60
USSR55	Y = 0.14x + 2.72	0.87	−19.43	ITA2	Y = 0.25x + 3.54	0.99	−14.07
GER 1	y = 0.35x + 6.00	0.87	−17.40	ITA3	Y = 0.14x + 2.61	0.98	−19.35
GER 2	y = 0.16x + 2.21	0.93	−13.61	ITA4	Y = 0.17x + 3.00	0.97	−17.49
GER 3	y = 0.25x + 4.69	0.85	−18.09	ITA5	Y = 0.18x + 3.48	0.95	−18.92
GER 4	Y = 0.17x + 2.90	0.90	−17.03	ITA6	Y = 0.31x + 5.20	0.99	−16.77
GER 5	Y = 0.25x + 4.60	0.85	−18.27	ITA7	Y = 0.25x + 3.54	0.99	−13.85
GER 6	Y = 0.10x + 2.04	0.85	−19.59	SUI1	Y = 0.14x + 2.33	0.79	−16.66
GER 7	Y = 0.17x + 3.01	0.59	−18.08	ROU1	Y = 0.16x + 2.71	0.88	−16.94

USSR (the Soviet Union), GER (Germany), CHN (China), FRA (France), ESP (Spain), ITA (Italy), SUI (Switzerland), and ROU (Romania) indicate germplasm origin regions.

, the LT₅₀ values of the 124 tested germplasms ranged from −22.01 °C to −13.18 °C. There were six germplasms with values below −20 °C: Gordan (−22.01 °C), Sateni (−21.95 °C), Crimean Cornish ♀ (−20.88 °C) and Spitak (−20.68 °C) from the Soviet Union; Petroximegne (−20.46 °C) from Spain; and Ecolly (−20.92 °C) from China, indicating strong cold resistance. Five germplasms had values above −14 °C: Bacchus (−13.61 °C) from Germany; Bahian Chirei (−13.58 °C), Bouaki Tach (−13.18 °C), and Condavas (−13.67 °C) from the Soviet Union; and Sangiovese (13.85 °C) from Italy, indicating poor cold resistance. The LT₅₀ values of most germplasms were distributed between −14 °C and −20 °C, corresponding to moderate cold resistance.

3.2. ClusterAanalysis of LT₅₀ Values

Cluster analysis was conducted on the determined LT₅₀ values, and the results are shown in Figure 2. The germplasms were divided into four types. Type-I included 22 germplasms, with the lowest LT₅₀ −19.95 °C and the highest LT₅₀ −18.46 °C. Of these germplasms, 9 germplasms had grapes with black skin, 10 germplasms had grapes with yellow-green skin, 1 germplasm had grapes with light-red skin, and 2 germplasms had grapes with crimson skin. Among these germplasms, 2 germplasms originated from Germany, 12 germplasms originated from the Soviet Union, 4 germplasms originated from France, 1 germplasm originated from Spain, 1 germplasm originated from China, and 3 germplasms originated from Italy. Type Ⅱ included six germplasms, ranging in LT₅₀ value from −22.01 °C to −20.46 °C. Among these germplasms, 5 germplasms had grapes with yellow-green skin and 1 germplasm had grapes with black skin. A total of four originated from the Soviet Union, one germplasm originated from Spain, and one germplasm originated from China. Type Ⅲ included 28 germplasms, ranging in LT₅₀ value from −15.51 °C to −13.18 °C. Of these, 11 germplasms had grapes with black skin, 16 germplasms had grapes with yellow-green skin, and 1 germplasm had light-red skin color. A total of 3 of these germplasms originated from Germany, 10 germplasms originated from the Soviet Union, 10 germplasms originated from France, 2 germplasms originated from Spain, 1 germplasm originated from China, and 2 germplasms originated from Italy. Type Ⅳ included 68 germplasms, with LT₅₀ values ranging from −18.29 °C to −15.53 °C. Among these germplasms, 26 germplasms had grapes with black skin, 31 germplasms had grapes with yellow-green skin, five germplasms had grapes with light red skin, and six germplasms had grapes with crimson skin. For these germplasms, 8 germplasms originated from Germany, 29 germplasms originated from the Soviet Union, 23 germplasms originated from France, 1 germplasm originated from Spain, 2 germplasms originated from China, 3 germplasms originated from Italy, 1 germplasm originated from Romania, and 1 germplasm originated from Switzerland. The cold resistance based on LT₅₀ value was from strong to weak as follows: type II > type I > type IV > type III, with temperature ranges of type II: −22.01–−20.46 °C; type I: −19.95–−18.46 °C; type IV: −18.29–−15.53°C; and type III: −15.51–−13.18 °C.

The correlation between LT₅₀ and temperature of germplasm origin and fruit skin color was further explored, and the results are shown in

Table 3. Correlation between LT₅₀ and temperature of germplasm origin and fruit skin color.

Item	LT50
	Type II	Type I	Type IV	Type Ⅲ
Temperature of germplasms origin	0.580	0.224	0.280 *	0.343
Fruit skin color of germplasms	−0.591	0.249	0.336 **	−0.099

Data in Table 3 were tested by Student’s test, * p < 0.05 and ** p < 0.01 represent significant differences between treatments.

. The temperature of germplasm origin was positively correlated with LT₅₀ for the different types, with correlation coefficients of 0.580, 0.224, 0.280, and 0.343, respectively, for Type I, II, III, and Type IV; this correlation was significant for Type IV. The fruit skin color was positively correlated with LT₅₀ value in Type I and Type IV, with correlation coefficients of 0.249 and 0.336, respectively, reaching an extremely significant level for Type IV. The fruit skin color was negatively correlated with LT₅₀ in Type II and Type Ⅲ, with correlation coefficients of −0.591 and −0.099, respectively.

3.3. Hardiness Zones of Wine Grape Germplasms for Different Origin Areas

Based on the cluster analysis combined with the cold resistance, the 124 germplasms were divided into four hardiness zones. From zone 1 to zone 4, the cold resistance decreased successively: hardiness zone 1: −22.01–20.46 °C; hardiness zone 2: −19.95–18.46 °C; hardiness zone 3: −18.29–15.53 °C; and hardiness zone 4: −15.51–13.18 °C. These correspond to type Ⅱ, type Ⅰ, type Ⅳ, and type Ⅲ in the cluster analysis.

Table 4. Cold hardiness in V. vinifera germplasms from different origin areas.

Origin Area	Average LT50 (°C)	Total Number	Hardiness Zone	Number of Germplasms	Frequency Distribution Histogram
Germany	17.25 ± 1.20 b	13	Zone 2	3
			Zone 3	8
			Zone 4	2
France	16.26 ± 1.21 a	37	Zone 2	4
			Zone 3	23
			Zone 4	10
Soviet Union	17.29 ± 0.95 b	55	Zone 1	4
			Zone 2	12
			Zone 3	29
			Zone 4	10
Spain	17.22 ± 1.61 b	5	Zone 1	1	/
			Zone 2	1
			Zone 3	1
			Zone 4	2
Italy	16.58 ± 1.49 a	7	Zone 2	2
			Zone 3	3
			Zone 4	2
China	17.44 ± 1.79 b	5	Zone 1	1	/
			Zone 2	1
			Zone 3	2
			Zone 4	1
Romania	/	1	Zone 3	1	/
Switzerland	/	1	Zone 3	1	/

The data based on three replicates are represented as mean ± standard deviation. Data were tested by one-way ANOVA, and means followed by the same letter do not differ. Significance analysis and frequency histogram were not performed for quantities ≤5.

shows the distribution of 124 germplasms in the four hardiness zones. The mean LT₅₀ values of germplasms from different origin regions ranged from −17.44 °C to −16.26 °C, with no difference between the mean LT₅₀ values of germplasms from France and Italy or those from Germany, the Soviet Union, Spain, and China. Histograms of the distribution of germplasms from different regions in different hardiness zones were generated, as shown in Table 4. The results showed that the cold-tolerance distributions of the tested germplasms originating in Germany, France, the Soviet Union, and Italy all showed a normal distribution, with most germplasms from different regions clustered in zone 3, and discretely distributed in zones 1, 2, and zone 4. With fewer germplasm samples from Spain, China, Romania, and Switzerland, frequency histograms were not drawn.

3.4. Cold Regionalization of Wine Grape Germplasms (V. vinifera) in China

Based on the climatic regionalization of wine grape in China and the clustering results, the critical low temperatures for cold regionalization of V. vinifera were selected as −13 °C, −15 °C, −18 °C, −20 °C, and −22 °C. Using the daily minimum temperature measured at 2294 meteorological stations for 30 consecutive years (1982–2011) in China, the critical low temperature occurring at least three times in 30 years was used as the dividing line to regionalize the cold resistance of V. vinifera, as shown in Figure 3. The line of −13 °C is mainly south of the Ancient Yellow River. The line of −15 °C spans the Loess Plateau and is mainly north of the Ancient Yellow River. The line of −18 °C spans the Loess Plateau and Shandong, mainly south of Jin-Jin-Ji. The line of −20 °C spans the Loess Plateau and Jin-Jin-Ji. The line of −22 °C spans the Hexi Corridor and Jin-Jin-Ji, mostly north of the Loess Plateau and south of Helan Mountain East.

Finally, based on the LT₅₀ values of the 124 germplasms, the distribution of each variety in the region was determined. The results are shown in

Table 5. Wine grape hardiness regionalization in China.

Division	LT50 (°C)	Germplasms
A	−22 ≤ LT50 ≤ −20	USSR17, USSR44, CHN3, USSR35, USSR46, ESP1
B	−20 ≤ LT50 ≤ −18	GER8, USSR1, USSR50, USSR45, ESP2, FRA12, GER6, USSR20, CHN4, USSR55, USSR23, ITA3, USSR54, USSR13, USSR6, USSR22, ITA5, USSR14, USSR48, FRA17, FRA7, FRA26, USSR32, GER5, USSR16, USSR28, GER3, USSR15, GER7, GER12
C	−18 ≤ LT50 ≤ −15	USSR27, USSR29, GER13, FRA34, USSR9, USSR43, USSR37, FRA3, ITA4, GER1, USSR47, USSR33, USSR39, USSR26, USSR5, GER11, GER4, USSR38, FRA18, FRA16, ESP4, USSR42, ROU1, FRA33, FRA31, ITA6, FRA9, CHN2, SUI1, FRA21, FRA36, USSR52, FRA37, USSR53, FRA28, USSR31, USSR24, FRA35, USSR49, FRA11, USSR36, USSR41, FRA10, FRA22, CHN5, FRA19, FRA32, FRA14, USSR19, FRA25, FRA30, USSR51, FRA15, USSR11, FRA4, USSR21, USSR18, USSR25, ITA1, FRA6, FRA27, FRA24, USSR34, FRA29, GER10, USSR3, USSR10, FRA23, FRA20, FRA13, FRA5
D	−15 ≤ LT50 ≤ −13	USSR4, USSR2, ESP5, ESP3, FRA2, USSR8, FRA1, CHN1, USSR40, ITA2, GER9, FRA8, ITA7, USSR30, GER2, USSR7, USSR12

USSR (the Soviet Union), GER (Germany), CHN (China), FRA (France), ESP (Spain), ITA (Italy), SUI (Switzerland), and ROU (Romania) indicate germplasm origin regions.

. More than half (71) of the tested germplasms were distributed in the C region (−18°C ≤ LT₅₀ ≤ −15°C), with 6, 30, and 17 germplasms in A (−22 °C ≤ LT₅₀ ≤ −20 °C), B (−20 °C ≤ LT₅₀ ≤ −18 °C), and D (−15 °C ≤ LT₅₀ ≤ −13 °C), respectively.

4. Discussion

4.1. Evaluation of Cold Resistance of Wine Grape Germplasms (V. vinifera)

The LT₅₀ value is used as an indicator of plant stress injury, and has been widely applied in grape cold resistance identification [63,64]. The lower the LT₅₀ value, the stronger the cold resistance [65]. With the most economically valuable cultivars in the world, V. vinifera varieties exhibit good drought resistance, but poor resistance to disease and cold [6,11]. Although there is variation in cold resistance among different germplasms of V. vinifera, reported differences are not considered significant [17]. However, there are some varieties with strong cold resistance [47,66]. For example, Ecolly, a V. vinifera variety, allows burial-free cultivation overwintering in some areas in China where soil-burial practices are required for over-wintering of grapevines [47]. In this work, 124 wine grape germplasms (V. vinifera) were tested for cold resistance by conductivity method. The LT₅₀ values of Gordan, Sateni, Crimean Cornish ♀, Ecolly, Spitak, and Petroximegne were all lower than −20 °C, indicating that these germplasms exhibit strong cold resistance and could be used as parent materials for cold resistance breeding.

Cold resistance is an important characteristic of fruit trees and is acquired by genetic variation and natural selection during long-term adaptation of different plants to low temperature and cold environment [67]. The cold resistance of grapevine is controlled by the genetic characteristics of the variety [68], grapevine conditions such as age, growth, development stage, and nutrient accumulation [69]; the morphological structure, thickness, and maturity of branches [70]; and environmental conditions such as external light and temperature [66,71]. In this study, the fruit skin color of five of the six germplasms in type II (hardiness zone 1) were yellow-green, while in the other three types, black, yellow-green, light-red and crimson showed discrete distributions. Therefore, there may be a relationship between fruit skin color and cold resistance. Correlation analysis of fruit skin color and LT₅₀ of germplasms showed that type II had the highest correlation coefficient between LT₅₀ and fruit skin color and this correlation was negative, where the lighter the fruit skin color, the stronger the cold resistance.

In addition to fruit skin color, grapevine cold resistance is closely related to its origin and geographical distribution area [72]. Previous studies have shown that the cold resistance of germplasm (V. amurensis) is related to the climate temperature in the original distribution area [70]. The lower the temperature in the original area, the stronger the cold resistance of germplasm in this area. Our results showed that the average LT₅₀ values of germplasms from different origin areas ranged from −17.44 °C to −16.26 °C, though greater variation in LT₅₀ values might be observed if a large number of germplasms were screened. The analysis revealed that the cold resistance of germplasms from different origins was normally distributed, mainly concentrated in hardiness zone 3 and distributed discretely in zones 1, 2, and 4. Correlation analysis showed that geographical origin exhibited a positive correlation with LT₅₀ for different types. In Type II, LT₅₀ and origin of germplasm were most highly correlated, indicating that in germplasms with high cold resistance, the lower the temperature of origin, the stronger the cold resistance might be. Therefore, it may be a feasible strategy to select wine grape germplasms that originated in colder areas for cultivation in cooler parts of China.

4.2. Cold Regionalization of Wine Grape Germplasms (V. vinifera) in China

Starting with the previous climatic regionalization of wine grape in China [73] and utilizing the clustering results from this work, the daily minimum temperature data were applied to delineate four regions of cold regionalization for more accurate regionalization. There has been significant work on grape regionalization for wine making [57,58,73]. Wang et al. used the low temperatures of −15 °C and −18 °C that occurred more than three times in a certain area within 30 consecutive years to determine the soil-burial over-wintering line for V. vinifera and V. labrusca grapevines [55]. The critical low temperature line for that cold regionalization strategy is quite consistent with the regionalization strategy used here. Overall, the regionalization results are consistent with the currently used overwintering and cold protection regions of grapevines in China, suggesting this is an effective strategy for the regional mapping of cold resistance.

In some marginal areas in China, soil-burial over-wintering is required, as well as other modifications of cultivation conditions [47,66,74]. In this study, six germplasms correlating to region A (−22 °C ≤ LT₅₀ ≤ −20 °C) showed strong cold resistance, which should enable these strains to be able to adapt to burial-free cultivation in the Loess Plateau, Jin-Jin-Ji, and Shandong regions of China. The 30 germplasms for region B (−20 °C ≤ LT₅₀ ≤−18 °C) showed medium cold resistance, which should enable their adaptation to burial-free cultivation in the Loess Plateau and Shandong. Overall, the cold regionalization information can be used to provide reference and cultivation guidance for areas of China that currently utilize soil-burial over-wintering of grape vines.

Annual extreme minimum temperature is the most important determinant of the safe overwintering of grapevines, but other factors can affect grapevine survival. For example, dryness in winter and low temperature frost in late spring can significantly affect the safe overwintering of grapevines. The cold regionalization strategy applied here is solely based on the extreme low temperature, and there are limitations of this approach. In future work, other factors affecting the safe overwintering of grapevines can be considered, and a more comprehensive approach to overwintering regionalization can further guide the optimal selection of new varieties for cultivation in different regions.

5. Conclusions

In this study, 124 V. vinifera germplasms were screened for cold resistance, allowing their classification into four hardiness zones by cluster analysis. Of particular interest, Gordan, Sateni, Crimean Cornish ♀, and Spitak from Soviet Union, Ecolly from China, and Petroximegne from Spain have high cold resistance are distributed in hardiness zone 1, suggesting these varieties are promising parent materials for cold-resistant breeding. The skin color of grapes and the climate temperature of the areas where the germplasms originated were also related to cold resistance. Based on the cold regionalization of V. vinifera in China, the wine grape germplasms were classified into four regions. This distribution of different germplasms provides reference value for cultivation and introduction of varieties into each region. Importantly, germplasms were identified that may be suitable for burial-free cultivation in colder regions that typically require the burial of grapevines for safe overwintering.