Temperatures in France have significantly increased during the 20th century (Moisselin et al., 2002). Climate projections suggest that they could rise by 3 to 5°C during the 21st century (IPCC, 2007). For Burgundy, recent projections (using the Weather Research and Forecasting (WRF) regional climate model) of the SRES-A2 scenario have shown an increase up to 3°C for 2030-2050 and 5°C by the end of the century (Xu et al., 2012). Several studies suggest that increased temperatures due to climate change will highly impact the growth and yield of crops (Olesen et al., 2011). Much of the decline in yield is due to shorter crop durations at these warmer temperatures (Wheeler et al., 2000). Some studies, based on observations (Duchêne and Schneider, 2005; Jones and Davis, 2000) or simulations (Brisson and Levrault, 2010; Hannah et al., 2013; Webb et al., 2007) of grapevine growth stages, have shown significant advances in phenology and some have indicated a delay in budburst due to insufficient chilling requirements (Webb et al., 2007). Various models have been used to simulate grapevine development rate and phenology (Bindi et al., 1997a; Bindi et al., 1997b; Caffarra and Eccel, 2010; Chuine et al., 2004; García de Cortázar-Atauri et al., 2009; Parker et al., 2011; Riou, 1994). Other studies have assessed the responses of grapevine phenology in a climate change context (Duchêne et al., 2010; Webb et al., 2007).
The response of grapevine to temperature is often described by linear or non-linear heat summations (Gladstones, 1992): linear models use a sum of temperatures above a base temperature from a fixed day of the year or previous phenological stage to the appearance of the next phenological stage (Hall and Jones, 2010); non-linear models include a temperature threshold (optimal temperature) above and below which plant development is limited or at its maximum or minimum rate (García de Cortázar-Atauri et al., 2010). For non-linear models, Beta models are generally used to describe in a more mechanistic way the non-linear relationship between temperature and crop development rate (i.e., physiological process efficiency) within the thermal kinetic window (Yin et al., 1995). Such linear and non-linear models are often calibrated using phenological observations from a single site (Duchêne et al., 2010) or from a collection of sites (García de Cortázar-Atauri et al., 2009; Parker et al., 2011) but may not cover the full range of climate conditions encountered by a given grapevine variety worldwide. Consequently, models might be used beyond their calibration limits, especially when they are applied to future warmer climate conditions. Hence, the date of a phenological stage, estimated with the climate projections for the 21st century, may differ according to the type of linear and non-linear phenological model that is applied. This question is critical to adapt viticulture to climate change, and to our knowledge it has not been yet fully addressed in previous studies. Various approaches exist for the simulation of possible future global warming. One approach is dynamic or statistical downscaling using Regional Climate Models (RCM) (White et al., 2006; Xu et al., 2012), empirical functions (Jones and Alves, 2012) or climate generators (Webb et al., 2007). All of these methods require additional uncertainty analysis to produce relevant patterns of temperature. Their performances have already been discussed elsewhere (Huth et al., 2003; Teutschbein et al., 2011).
The present work evaluates the performance of two types of phenological models, Grapevine Flowering Véraison (GFV – a simple linear temperature summation model (Parker et al., 2011; Parker et al., 2013) – Figure 1A) and Wang and Engel (WE – a non-linear model first proposed for wheat crop by Wang and Engel (1998) – Figure 1B), parameterized for Vitis vinifera L cv. Pinot noir under a range of different temperature conditions. The aims were 1) to assess whether the models were able to accurately simulate the observed véraison dates for Pinot noir in Burgundy from the original historical records of 1973-2005, 2) to assess whether the performance of the models under warmer conditions was significantly different in Burgundy and 3) to compare the results from increased temperature scenarios in Burgundy with three other sites of different temperature profiles under warmer temperature conditions.
Figure 1. Daily response of plant development to temperature between 0 and 40°C for the linear Grapevine Flowering Véraison (A) and curvilinear Wang and Engel (B) models.
Phenological observations were collected by the Service Régional de l’Alimentation (SRAL) in Savigny-lès-Beaune (France, 47°3’N – 4°49’E, 267 m above sea level (asl)). The database contained the date when 50% véraison (Stage 85 of the BBCH scale, 2001) was observed (referred to as "véraison" herein) for Pinot noir for each year of the 1973-2005 period (Figure 2A), excluding the following five years for which no values were measured: 1982, 1987, 1995, 2000, and 2001. The observation site, also located in Savigny-lès-Beaune, was close to the meteorological site (~100 m).
Figure 2. Box and whisker plot (gray line: median and black asterisk: mean) of (A) annual observed véraison dates for the whole period (1973-2005) and sub-periods (1973-1987 and 1988-2005) for Savigny-lès-Beaune weather station and (B) average mean temperature from March to September. Open circles represent the year 2003.
Daily minimum and maximum temperature measured in Savigny-lès-Beaune between January 1 1973 and December 31 2005 (hereafter referred to as “original temperature dataset”) was collected from the French national (Météo France) meteorological station network (Cuccia, 2013). The daily mean temperature was calculated as the arithmetic mean of the minimum and maximum daily temperature (Figure 2B).
To investigate warmer climate conditions, we increased the temperature by adding a constant temperature shift on a daily time step to existing temperature datasets (Asseng et al., 2013). Three temperature scenarios were considered: +3°C and +5°C to daily mean temperatures, which represent realistic increases for the second half of the 21st century (IPCC, 2007; Xu et al., 2012); +10°C, which represents extreme conditions (unlikely over the 21st century – Terray and Boé, 2013).
Furthermore, the impact of these three scenarios on both phenological models was tested for three different sites in Europe (ECA temperature series (Klein Tank et al., 2002)): Carcassonne (France – 43°12’N – 02°18’E, 126 m asl), Cagliari (Italy – 39°14’N – 9°03’E, 21 m asl) and Seville (Spain – 37°25’N – 5°52’W, 34 m asl). These three sites offer a wide range of annual mean temperatures to compare and contrast with Savigny-lès-Beaune: 11.3°C (Savigny-lès-Beaune), 13.9°C (Carcassonne), 16.9°C (Cagliari) and 18.8°C (Seville).
The GFV and WE models were tested against the original temperature dataset and the three temperature-shifted datasets using parameters from their previous calibrations (García de Cortázar-Atauri et al., 2010; Parker et al., 2011).
The GFV model computes the linear response of Pinot noir to temperature by the two equations described below (see also Figure 1):
where Sf is the state of forcing, t0 is the 60th Day of Year (DOY), ts is the véraison date, Rf is the rate of forcing, GDD is the Growing Degree Day, xt is the average of the minimum and maximum (mean) temperature for day t, F* is equal to 2511°C, which is the value calculated for Pinot noir in Parker et al. (2013), and Tb is the base temperature (0°C). This simple model only uses average temperature data as input with no action threshold on grapevine development at high temperatures. Hence, it is implicitly assumed that grapevine development is optimal even at temperatures above 40°C.
The WE model adapted for grapevine and parameterized for Pinot noir by García de Cortázar-Atauri et al. (2010) takes into account three cardinal temperatures: a temperature optimum bounded by two threshold temperature values corresponding to minimum and maximum temperature below and above which no action on the plant is considered. The model computes the rate of phenological development of the grapevine in response to temperature by weighted forcing units (Chuine et al., 2013) where the rate of thermal summation C(Tt) falls in the range from 0 to 1 and follows a Beta curve (equation 3, i.e., bell-shaped – Figure 1):
with the A parameter of equation (3) given by:
where C(Tt) corresponds to the temperature action on grapevine development by day, ts is the véraison date, Tt is the temperature for the day t and t0 is the day when temperature action accumulation starts. Véraison occurs when the forcing units accumulation is equal or greater than the threshold F*. The following parameter values were fixed according to those obtained by García de Cortázar-Atauri et al. (2010): F* = 89.2 is the number of forcing units calculated for Pinot noir, t0 = the 74th DOY (March 15), Tmin = 0°C (minimum temperature), Tmax = 40°C (maximum temperature) and Topt = 27.4°C (optimal temperature).
4. Assessment of models’ performance
Model performance was assessed by the Root Mean Square Error (RMSE):
where xa = observed day of véraison, xb = simulated day of véraison and N = number of observations. The determination coefficient (R²) was also used to identify the percentage of common variability between observations and simulations as well as between the two models.
Both models were applied on the 26-years (1973-2005 period) of observed climate data. The 26 simulated véraison dates were compared to the 26 véraison dates recorded in Savigny-lès-Beaune. A robust Bayesian estimation was used to test the significance of the average difference in mean véraison dates between the two models. The Bayesian estimation was based on Bayesian posterior probability distribution, which evaluates whether the probability of a difference may be too small to matter. This was assessed using the 95% Highest Density Interval (HDI - Kruschke, 2013), which is a useful summary of the probability that the true value lies within the HDI. The values of the 95% HDI bounds are used to define the 95% confidence interval.
The performance of the simulation models using the original temperature records (1973-2005) are summarized in Table 1. For both models, 62 to 65% of differences between observed and simulated véraison dates fell within the range of -5 and +5 days (Figure 3). There was no difference (p<0.05) between the models for the dataset: the simulated mean DOY using the original temperature data was 228.1 for the GFV model and 227.9 for the WE model (Figure 4); the RMSE was 5.7 days for the GFV model and 5.8 days for the WE model; and the R² was 0.81 for the GFV model and 0.84 for the WE model. For both models, estimated véraison dates were later than originally observed for almost 60% of the dataset. This bias was particularly strong in years with late véraison (i.e., colder years). The performance of both models was significantly reduced after DOY 240 (August 28 – vertical dashed line in Figure 3); pre-DOY 240 RMSE values were 4.6 and 5.2 days and post-DOY 240 RMSE values were 8.9 and 8.4 days for the GFV and WE models, respectively.
Table 1. Statistical analysis of the performance of the Grapevine Flowering Véraison (GFV) and Wang and Engel (WE) models using the original 1973-2005 dataset.
|GFV model||WE model|
|Underestimated values (%)||57.7||61.5|
|Overestimated values (%)||34.6||27|
|Simulated values = Observed values (%)||7.7||11.5|
|Over RMSE (%)||27||31|
|RMSE before DOY 240 observations||4.6||5.2|
|RMSE after DOY 240 observations||8.9||8.4|
R2, determination coefficient; RMSE, root mean square error; DOY, day of year.
Figure 3. Estimated versus observed véraison Day of Year (DOY) for the Grapevine Flowering Véraison (cross) and Wang and Engel (open circle) models. Upper and lower dashed lines indicate delays of +/- 5 days between observed and simulated véraison date, respectively. The vertical dashed line corresponds to DOY 240.
The simulated DOY for véraison were similar (p>0.05) for both models in response to warmer temperature scenarios of +3°C (average DOY = 206.9 and 206.6 for GFV and WE, respectively) and +5°C (average DOY = 195 for both models) (Figure 4).
Figure 4. Boxplot (gray line: median and black asterisk: mean) of simulated véraison dates by the Grapevine Flowering Véraison (GFV) and Wang and Engel (WE) models for measured temperature data (Obs.) and +3°C, +5°C and +10°C temperature scenarios. Each boxplot represents 26 values (26 years; 1973-2005); véraison day corresponds to the Day of Year (DOY).
For the +10°C scenario, there was a significant difference of 4.7 days (p<0.05) between the two models (average DOY = 172.7 for the GFV model and 177.5 for the WE model). However, the difference between model simulations (4.9 days) was marginal compared to the advance in véraison caused by this +10°C temperature shift (55.4 days earlier).
Inter-annual variability of the véraison dates was reduced as temperature increased. For simulations with the original temperature dataset (1973-2005), the maximum difference between the earliest and latest dates of véraison ranged from 40 days for the GFV model to 42 days for the WE model (Figure 4). For warmer scenarios, these ranges were reduced to 30 and 28 days (+3°C), 28 and 22 days (+5°C) and finally 18 and 15 days (+10°C), respectively for the GFV and WE models (Figure 4). The variances for both models were very similar when they were applied either to original or artificially increased temperature data up to +5°C (data not shown). With a 10°C positive shift, the variance was more reduced for the WE model than for the GFV model.
Figure 5 presents the day to day spread (boxplot of the 26-year historic dataset) of temperature action for the WE model with original data and the +10°C scenario data. The simulations for the original temperature data (light gray curve) showed a sigmoid shape in response to forcing temperatures; however, even during the warmest part of the summer (DOY 200 to 230, late July to mid-August), the temperature actions of WE barely reached 1 with the original temperature data. With the +10°C scenario (10°C added to the daily observed temperature), the temperature actions often reached the optimum value (= 1) from mid-spring (around DOY = 125, early May) to late June (DOY = 175) when véraison occurred. The temperature actions started to decline during summer (daily average temperature often exceeded 27.4°C, the optimum temperature), when véraison had already occurred (Figure 5).
Figure 5. Boxplot of the daily action of temperature for the Wang and Engel model for the 1973-2005 period. Light gray boxes represent daily temperature action using the original historical temperature dataset. Dark gray boxes represent the daily temperature action for the +10°C scenario. Vertical solid/dashed lines (light gray for the historical temperature dataset, dark gray for the +10°C scenario) indicate the mean véraison date and the date above which temperature action is >0.8 (i.e., approaching optimal). Vertical solid lines (light gray for the historical temperature dataset, dark gray for the +10°C scenario) indicate the mean véraison date for the 1973-2005 period.
When the difference in mean véraison dates between model simulations was calculated and compared between different grape growing regions along a latitudinal gradient from Dijon (47.3°N) - Carcassonne (43.2°N), Cagliari (39.2°N) down to Seville (37.3°N), results were similar for three of the four geographical places (Seville was the exception) up to the +5°C scenario. Seville had a greater difference between simulations for all temperature scenarios (+3, +5 and +10°C). For the +10°C scenario, the differences between models were significant irrespective of the location (Figure 6). Moreover, we systematically observed an advance of the véraison date independent of the model and the site (data not shown).
Figure 6. Evolution of the average difference (WE - GFV) in mean véraison date predicted by the two models as a function of the warming scenarios and for four different geographical places following a latitudinal gradient from 47.3°N (Dijon) to 37.3°N (Seville). The difference in means and the 95% Highest Density Interval (HDI – vertical error bars) are derived from a robust Bayesian estimation following the method described in Kruschke (2013). 95% HDI is a useful summary of where the bulk of the most credible values falls. Note that the values with a black cross (+) indicate that the difference is significant, i.e., the 95% HDI interval does not include zero (no difference between models).
The aim of this work was to assess the performance of two different phenological model types (GFV model – linear and WE model – non-linear) for predicting véraison under current and future climate conditions in Burgundy, using the method of increasing mean surface temperatures by a constant temperature increase (+3, +5 and +10°C). When using the original temperature dataset up until 2005, both models accurately simulated véraison for Pinot noir in Burgundy. Although the RMSE value of approximately 6 days that was obtained for these simulations might be considered as large, it is smaller (1.5 to 2 days less) than those calculated during the calibration and validation phases of both models (García de Cortázar-Atauri et al., 2010; Parker et al., 2011).
The inter-annual variations of temperature actions were reduced during spring for the WE model (with values close to 1), which is not the case using uncapped degree day models (the warmer the temperature, the higher the temperature actions). Bell-shaped models like the WE model as well as bilinear capped models like the Biologically Efficient Degree Days (as proposed by Gladstones, 1992) reduce the inter-annual variability of véraison dates in a stronger way than uncapped degree day models do when temperatures increase. The models’ performance may also change when integrating chilling effects on the starting date of heat summation such as proposed by several authors (Caffarra and Eccel, 2010; García de Cortázar-Atauri et al., 2009).
Model parameters of the WE and GFV models were not calibrated to our data in this study, because we consider that the original parameterization of these models, based upon large datasets within which a great diversity of genetic and climate features are found, offers more robustness, especially when testing their responses to huge variations of temperature. It does not exclude the possibility of clonal variation resulting in slightly different F* values. However, given that the models performed well compared with the observed data, the model simulations were considered satisfactory. Predictions were less accurate for dates of véraison later than DOY 240. This may represent an upper limitation for Pinot noir when using fixed start dates for modelling phenology, which may not correspond entirely to development phases, rather than using the date of a prior phenological event (i.e., in cooler years, thermal accumulation starts before the variety has actually reached certain development stages it would have reached in ‘normal’ years).
For the end of the 21st century, simulations indicate a maximum increase in temperature of about 5°C (IPCC, 2007). The GFV model satisfactorily simulated véraison under our +5°C temperature scenario, indicating that this simple linear temperature summation would be adequate in these future climate conditions for Pinot noir in Burgundy. Therefore, although the WE model may be more plausible mechanistically, this result indicates that climate change studies addressing warming impacts on grapevine phenology based upon uncapped degree day summations (e.g., Duchêne et al., 2010; Nemani et al., 2001) are comparable to those based upon more mechanistic models (Caffarra and Eccel, 2011; García de Cortázar-Atauri et al., 2010) considering a null or negative effect of elevated temperature increases on phenological development.
The choice to add a constant temperature shift on a daily time step to increase temperatures enabled a simple method to emulate warmer temperature conditions and then compare the two models’ performance. The limitation of this approach are: 1) it keeps the same temperature variability and may therefore not capture subtle differences in maximum and minimum temperature profiles that may occur with climate change and 2) it may create upper limits on plausible future climate scenarios. This is an area of interest for future research, which would require the accurate combination of high resolution numerical climate models with phenological model predictions. However, for this study, as indicated above, the models performed satisfactorily at +3 and +5°C scenarios.
The significant difference between both models with an unrealistic very warm climate (+10°C) suggests that with the WE model, the optimum temperature is exceeded, reducing the development rate of grapevine. This difference may reflect the inhibitory effect of high temperatures on plant development incorporated in the equations of the WE model. As the parameters used for the WE model could be considered close to physiologically relevant temperatures (Buttrose, 1969; Zufferey et al., 2000), having an optimum temperature value close to 30°C, it is likely that similar results would have been reached with other early grapevine cultivars, for similar climatic conditions. Further studies need to be carried out to support this hypothesis as well as to assess the response of both temperature-based models used in this study with later parameters (i.e., later cultivars).
Model responses may differ for Pinot noir in other geographical regions. For latitudes above 40°N, the GFV and WE models produced similar predictions of véraison dates even for a warming up to +5°C. For geographical places below 40°N latitude (Seville and Cagliari), significant differences associated with a more pronounced shift appeared between the GFV and WE model predictions. The GFV model was developed with the objective to create a model that can predict both flowering and véraison and to use this model to classify many varieties. Therefore, the limitations of this approach are: 1) the use of a single set of parameters for all the varieties, that is not optimized for each phenological stage of each variety and 2) a common start date must be used to compare varieties and needs to be a calendar day of the year rather than a prior development stage; therefore, the start date DOY = 60 may not correspond well in warmer climates (e.g., Seville) or under warmer climate conditions (+5° or +10° scenarios) where development may be too advanced relative to the model start date.
The +10°C scenario at sites below 40°N indicated that results may also differ with other cultivars especially under extremely high temperature conditions or for late ripening cultivars or geographical regions. In future research, inter-annual temperature variability of the future climate may also need to be assessed. Finally, other factors could affect grapevine growth in warmer climate conditions, particularly the dynamics of water and nitrogen stress (Celette and Gary, 2013; Pellegrino et al., 2006). In the future, crop models need to consider not only direct effects like advanced phenology but also indirect effects of climate change on grapevine growth and development (Dai et al., 2009; Valdés-Gómez et al., 2008).
Considering the range of projected warming for the 21st century, the results show that the phenological model choice should have no effect on véraison date simulations for Pinot noir in Burgundy. The same conclusion can be made for Pinot noir in warmer places (i.e., southern Europe) above 40°N. The linear temperature summation used in the GFV model diverged substantially from the WE model for very high temperatures (over 40°C and the +5°C and +10°C scenarios) which may be encountered in the near future for more southern locations (i.e., below latitude 40°N), while inter-annual variability decreased for both models in warmer conditions. The GFV model is a suitable phenological model choice to address warming impact on early grapevine cultivar such as Pinot noir grown at northern latitudes above 40°N such as Burgundy.
Acknowledgments: The authors would like to thank the Bureau Interprofessionnel des Vins de Bourgogne for providing phenological data (especially Christine Monamy), Météo France (Côte d’Or center) for providing meteorological data series, and Claude Magnien, from the SRAL, who kindly provided the phenological series of Pinot noir véraison in Savigny-lès-Beaune. This study was funded by the Conseil Régional de Bourgogne and the Bureau Interprofessionnel des Vins de Bourgogne. The authors would also like to thank the anonymous reviewers for their valuable comments and suggestions.