Introduction

Botrytis cinerea is a polyphagous fungus that infects more than 1400 species of cultivated plants, including grapevines (Elad et al., 2016). This necrotrophic pathogen, responsible for Botrytis bunch rot disease (BBR), can infect all the organs of the plant but mainly damages ripening berries. BBR can reduce vine yield and compromise the wine quality, since a vintage contaminated by the pathogen at harvest at 5% severity will show irreversible consequences on the organoleptic features in a qualitative red wine (Ky et al., 2012). Botrytis cinerea can penetrate vine tissues either through wounds or directly via the cell wall. During springtime, airborne spores of B. cinerea can infect floral tissues and/or fruit pedicel and the pathogen remains in a latency state until veraison, the point when the susceptibility of grape berries to B. cinerea begins to increase (Pezet et al., 2003; Pezet et al., 2004; Elmer and Michailides, 2004; Deytieux-Belleau et al., 2009). Furthermore, although flowering is a key BBR epidemiological stage (Gonzalez-Domínguez et al., 2019), latent infections may also establish at later stages throughout the season (Hill et al., 2014) and direct infections may play an important epidemiological role during berry maturation, when the ontogenic resistance of berries decreases (Elmer and Michailides, 2004; Deytieux-Belleau et al., 2009; Hill et al., 2014).

The control of BBR still largely relies on the use of synthetic fungicide spraying at predetermined intervals. However, restrictions on the use of fungicides are increasing to reduce the negative effects of these pesticides in terms of fungal resistance, human health, and the environment (Fenner et al., 2013). Therefore, strategies to control B. cinerea must be optimized, notably by considering the main principles of integrated pest management (IPM), according to which the risk to disease must be assessed before direct control measures are used (IOBC, 2007). Disease risk assessment should consider factors that favor disease development, including weather conditions, crop susceptibility and key grapevine phenological stages.

The weather conditions and canopy microclimate are considered the main factors involved in B. cinerea development (Pieri and Fermaud, 2005; Latorre et al., 2015; Hill et al., 2019). Specifically, the temperature and relative humidity within the cluster zone are key conducive factors to BBR infections. These factors contribute to the presence of free water at the berry surface, which is essential for conidial germination and berry infection. Rainfall and fog periods also contribute to the presence of free water on berries that is crucial during flowering, by increasing both direct and/or latent infections, and during the ripening post-veraison period by facilitating secondary infections via asexual conidia from sporulating host tissues (Broome et al., 1995; Elmer and Michailides, 2004). Accordingly, different weather-based models have used this information to predict or explain B. cinerea infections and disease progress (Broome et al., 1995; Ciliberti et al., 2015a; Ciliberti et al., 2015b, Ciliberti et al., 2016; Hill et al., 2019).

Furthermore, grapevine susceptibility and/or resistance to BBR depends on various genetic and phenotypic traits, such as morphological, anatomical, and chemical features of the berry skin (Deytieux-Belleau et al., 2009; Latorre et al., 2015; Blanco-Ulate et al., 2016). The skin tissue is acting as a barrier by both physical mechanisms, including cuticle integrity, skin anatomy, microcracks, and biochemical mechanisms based on induced and/or pre-formed defense compounds. The cell walls are among the first plant tissue structures that B. cinerea encounters when infecting and colonizing the berry skin, and therefore can contribute to susceptibility to the pathogen (Blanco-Ulate et al., 2016). An important component of berry skin cell walls consists of pectins, polysaccharidic polymers that are abundant in the middle lamella and the cell corners and are the main cell wall targets during infection by B. cinerea (Blanco-Ulate et al., 2016). Then, the growth of B. cinerea may be favored by the sugar nutrients released from hydrolyzed pectins. In contrast to pectins, other components, including tannins, are deposited in the berry skin cell walls, which provide a protective barrier against the fungus (Lecas and Brillouet, 1994; Schlosser et al., 2008; Deytieux-Belleau et al., 2009). Tannins are produced at variable concentrations and inhibit the fungal enzymes (such as laccases) degrading the cell wall, giving partial resistance against the pathogen (Goetz et al., 1999).

Another key feature involved in BBR development is grapevine vigor favoring B. cinerea infection as the foliar density increases (Valdés-Gómez et al., 2008; Latorre et al., 2015). Dense grapevine canopies have been associated with increased periods of wetness within the cluster zone, following rainfall, dew or fog, which results in more conducive environmental conditions to the pathogen (Fermaud et al., 2001; Elmer and Michailides, 2004; Pieri and Fermaud, 2005; Hill et al., 2019). Therefore, important prophylactic cultural methods for controlling BBR include grapevine canopy management practices that reduce canopy vegetative expression, growth, and/or foliar density around the clusters, for improving air circulation and exposing fruit to light. First, vine training and pruning systems may be manipulated (Elmer and Michailides, 2004; Elad, 2016). Second, a very effective practice to reduce BBR development is leaf-removal in the fruit zone (Percival et al., 1994; Elmer and Michailides, 2004). In New Zealand, as in other grapevine-growing countries such as France, leaf-removal is implemented between late flowering and berry pea size stages, and is considered the most effective cultural management for controlling BBR (Elmer and Wood, 2016). Leaf-removal before flowering has also been shown to be effective to reduce BBR, by interfering with bunch compactness of Merlot grapes (Sivilotti et al., 2016).

In this context, it is important to quantify the vegetative expression of the vineyards and associate it with BBR development. In grapevine, the vegetative expression can be quantified by direct methods (leaf area and/or pruning mass) or indirect methods, such as the normalized difference vegetation index (NDVI). The NDVI was originally used to determine the density of green parts on a patch of land, using the difference between spectral bands (near-infrared and red light) recorded by multispectral cameras mounted on satellites (Rouse et al., 1974). The NDVI is the ratio of the difference between the reflectance in the near-infrared and the red regions of the spectrum to the sum of these two values (Rouse et al., 1974). The NDVI exploits the spectral properties of green plant tissues due to two complementary grounds. First, light reflected from leaves is enriched in the far-red region of the spectrum. Second, chlorophyll makes leaves strong absorbers of photons in the red region of the spectrum. Thus, in viticulture, when considering vertical shoot positioned vines during the growing season, the NDVI has the potential to represent, quantitatively, the leaf area index in the center of the row, taking also into account the gap fraction of the canopy (Drissi et al., 2009). Furthermore, the use of NDVI may have many applications, for example, used as a proxy for tree biophysical features (van Dijke et al., 2019) or for studying cultivated plant biomass and the nitrogen status (Cao et al., 2013; Yao et al., 2014; Zhang et al., 2017). In viticulture, it has been also used near harvest for predicting yield (Sun et al., 2017; Di Gennaro et al., 2019) and characterizing the mature grape composition (Ferrer et al., 2019). The veraison stage has been proposed as the best phenological stage to measure and correlate the NDVI value with harvest yield and/or grape composition (Sun et al., 2017; Di Gennaro et al., 2019; Ferrer et al., 2019). However, under Mediterranean conditions the spatial NDVI pattern (airborne image) acquired at 15 to 20 days before veraison showed a significant correlation with an image acquired at harvest (Kazmierski et al., 2011).

Furthermore, different studies have been published that show the relationship between grapevine vegetative growth and BBR development (Table 1). Unlike the other studies based on one cultivar in one site for –two or three seasons, only one study by Valdés-Gómez et al. (2008) proposed a vegetative growth threshold by integrating different cultivars and management conditions worldwide. Furthermore, in all the studies (Table 1), the relationship between grapevine vegetative growth and BBR development was always based on a late vigor evaluation during the season, i.e. from veraison onwards including winter. Such late-assessed management indicators are then of very limited practical use for scheduling of phytosanitary applications and/or adjustment of the management prophylactic practices that must be implemented as early as possible during the season.

Key stages in grapevine BBR control are flowering and bunch closure, and presumably stages between these phases (Zoffoli et al., 2009). Although B. cinerea develops mostly late in the growing season, early infections play a key role in disease development and fungicide applications are used during these periods to reduce the early fungal inoculum and/or infection (Ciliberti et al., 2015a; Elmer and Michailides, 2004; Hill et al., 2014; Pezet et al., 2003; Pezet et al., 2004). Despite the importance of these critical stages, only a few studies have investigated the relationships between disease development at harvest and grapevine features evaluated at early vine phenological stages (e.g., berry pea size and pre bunch closure) (Deytieux-Belleau et al., 2009; Intrieri et al., 2001; Percival et al., 1994; Valdés-Gómez et al., 2008). Consequently, no forecasting tools and/or risk assessment indices are available to evaluate early grapevine susceptibility to the fungus.

We hypothesized that some specific grapevine features evaluated at early phenological stages (berry pea size and pre bunch closure), including pectin and tannin contents in berry skin and the grapevine vegetative growth, may account for the BBR incidence and severity at harvest. Thus, the objective of this work was to develop and evaluate early BBR risk indicators related either to the potential favorable microclimatic effect of the vine vigor on B. cinerea or to the grapevine fruit susceptibility to the pathogen. A more focused and key aim was to establish and characterize the relationships between the early NDVI, considered as a crucial factor influencing microclimate in the bunch zone, and the BBR incidence and severity at harvest.

Materials and methods

Results

1. Climatic conditions

In France, all growing seasons evaluated were characterized by humid and temperate conditions, which favored B. cinerea development. The average temperature between budbreak and harvest was similar in all seasons, fluctuating between 18°C and 19°C (data not shown), i.e., during the spring and summer period (April to October). Pluviometry differed between seasons, with the lowest rainfall amount in 2011 (275 mm) and the highest value in 2013 (593 mm) (Figure 1a).

The climate conditions in Chile were characterized by dry and temperate spring and summer periods, which were not conducive to BBR development. From budbreak to harvest, the average air temperature was similar in all seasons, fluctuating between 18°C and 19°C, as in France (data not shown). However, the total rainfall during the spring and summer period (October to April) was much lower than in France (Figure 1b). Only 36 mm, 115 mm, and 96 mm were recorded in 2014–15, 2015–16 and 2016–17, respectively. Furthermore, rain periods were mostly distributed before bunch closure, leading to unfavorable conditions, during fruit maturation, for BBR development and disease symptom expression.

Figure 1. Monthly cumulated rainfall (mm) in (a) France and (b) Chile.

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Bud= Budbreak; Flo= Flowering; Ver= Veraison; Har= Harvest

2. BBR incidence and severity

In France, for all study seasons, the Merlot cultivar showed average values of disease incidence and severity of 57.1% and 8.6%, respectively. Nonetheless, the disease level varied greatly between years (Figure 2a), depending mainly on weather conditions. For example, 2013 was the most conducive year to BBR development (rainfall of 593 mm in the spring-summer period from 1 April to 31 Oct.), with mean incidence and severity values of 98.7% and 20.5%, respectively. In contrast, disease pressure in 2010 was the lowest among all years (rainfall of 295 mm in the spring-summer period), with an incidence of 28.7% and severity of 4.7% (Figure 2a). In Chile, under less conductive environmental conditions to BBR, the Merlot cultivar evaluated did not show any disease development in all studied seasons. The Sauvignon blanc vineyard, as expected, showed a mean disease incidence and severity of 16.3% and 2.7%, respectively. Thus, Sauvignon blanc in Chile always showed lower disease incidence and severity than the Merlot cultivar in France in all studied seasons (Figure 2).

Figure 2. Mean BBR incidence and severity (%) according to the season under field conditions for (a) the Merlot cultivar in France and (b) the Sauvignon blanc cultivar in Chile.

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Merlot in Chile did not show any BBR development and symptoms.

3. Relationships between berry skin components and BBR intensity at harvest

Positive relationships were observed between the pectin content in the berry skin and BBR incidence and severity at harvest when considering all study seasons (Figure 3a,c), whereas negative relationships were observed between the tannin content and disease incidence and severity at harvest (Figure 3b,d). All correlations, obtained by analyzing pooled data from France and Chile, were significant at p = 0.05, except for the relationship between the pectin and BBR incidence (Table 3). However, it is important to highlight that the R² of all these overall relationships were relatively low (<0.5) (Table 3).

Figure 3. Relationships between BBR incidence (%) with the concentration in the young berry skin of (a) pectins and (b) tannins. Relationships between BBR severity (%) with the concentration in the young berry skin of (c) pectins (d) and tannins.

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In France (•) using cv. Merlot noir; in Chile (□) using cv. Merlot noir or Sauvignon Blanc. Each point corresponds to a study season. Statistical significances are indicated in Table 3.

Table 3. Statistical significance with corresponding overall coefficients for relationships between BBR incidence (%) and severity (%), and pectin and tannin concentration in the berry skin.

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	Pectins				Tannins
	dF	R2	r	P- value	dF	R2	r	P- value
BBR incidence	12	0.27	0.52 ns	0.07	12	0.40	-0,63	*0,02
BBR severity	12	0,46	0,68	*0,01	12	0,37	-0,61	*0,03

dF = residual degrees of freedom; R2 = coefficient of determination; R = Pearson’s correlation coefficient, significant at p = 0.05 (*)

4. Relationships between vegetative growth and BBR intensity at harvest

Exponential relationships were established between the NDVI values evaluated at 300 GDD after flowering and BBR incidence and severity at harvest (Figure 4). The relationship between BBR (incidence and severity) and vegetative growth (NDVI) was plotted and modeled using a nonlinear model based on the equation BBRinc/sev = a*(NDVI) ^b, which was the best model according to the R² value. The relationships between these variables were positive, showing that vines with higher vegetative growth in an early phenological stage were more likely to be attacked by B. cinerea during the season. The pattern was similar for both disease variables, but with a steeper curve between NDVI and disease severity (Figure 4b). A trend was noticeable, showing a change in BBR incidence and severity when NDVI values reached approximately 0.5 and 0.6, respectively.

Figure 4. Relationships in France (●) and Chile (□) between (a) BBR incidence (%) with early NDVI evaluation and (b) BBR severity (%) with NDVI.

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5. BBR at harvest related to explanatory variables: berry skin components, vegetative growth and climate

Multiple linear regression analyses were performed to study the relationships between the two response variables of BBR incidence and BBR severity at harvest and the following explanatory variables: i) pectins, ii) tannins, iii) vegetative growth early in the season, and iv) rainfall late in the season using the two indices PL15 and PL35 (Tables 4 and 5). All variance inflation factor (VIF) values were low (5.6 or less), indicating that multicollinearity was unlikely to exist with the set of explanatory variables. Therefore, all the predictor variables were used for the following regression analyses. Despite the contrasting weather conditions in Chile and France, it was possible to identify the main factors explaining the BBR development. The best models, explaining both BBR incidence and severity, included the pluviometry, a few weeks before harvest, and the NDVI evaluated early in the season.

First, the best model explaining BBR incidence, showing the highest R² value, included the variables i) pluviometry cumulated 35 days before harvest (PL35) as the predominant explanatory variable (R² = 0.74), and ii) NDVI early in the season, which added 0.06 to the model’s overall R². The overall R² was high, reaching a value of 0.80, which was highly significant (p = 0.0003). Figure 5 shows the good relationship (close to the Y = X) between observed BBR incidence and modeled BBR incidence (Table 4, first incidence model) from the long-term experiments in France and Chile (2010–2017). Second, according to the R² values, the best regression model for estimating BBR severity was also based on two variables: i) vegetative growth (NDVI), which was the predominant explanatory variable (R² = 0.60), and ii) pluviometry cumulated 15 days before harvest (PL15) (Table 5). This second variable added 0.14 to the model’s overall R², which reached the high value of 0.74 and was highly significant (p = 0.0012).

Table 4. Summary of the multiple regression models for explaining BBR incidence at harvest.

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Independent variables	VIF	Regression function	Model variables	R-Square	P-value
PL35	5.55	BBRinc = -15.96 + 0.38 (PL35) +	PL35	0.74	0.0303
WSP	2.27	55.87 (NDVI)	NDVI	0.06	0.0303
TAN	2.37
NDVI	2.85		Model	0.80	0.0003
PL15	4.11	BBRinc = -30.69 + 87.69 (NDVI) +	NDVI	0.68	0.0104
WSP	3.22	0.38 (PL15)	PL15	0.10	0.0581
TAN	2.01
NDVI	2.11		Model	0.78	0.0006

Where Inc = BBR Incidence at harvest (%); PL35 = Pluviometry cumulated 35 days before harvest (mm); PL15 = Pluviometry cumulated 15 days before harvest (mm); WSP= Pectins (mg galacturonic acid g-1 NAS); TAN = Tannins (mg tannins g^-1 skin); NDVI = Normalized Difference Vegetation Index (dimensionless); VIF = Variance Inflation Factor.

Figure 5. Relationship between observed and modeled BBR incidences (Table 4, first incidence model) from the long-term experiments in France and Chile (2010–2017). The Y = X equation is also indicated.

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Table 5. Summary of the multiple regression models for explaining BBR severity at harvest.

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Independent variables	VIF	Regression function	Model variables	R-Square	P-value
PL35	5.55	BBRsev = 4.78 + 0.18 (PL35)	PL35	0.66	0.0007
WSP	2.27
TAN	2.37
NDVI	2.85		Model	0.66	0.0007
PL15	4.11	BBRsev =-7.68+0.15 (PL15) + 24.67	PL15	0.14	0.0411
WSP	3.22	NDVI	NDVI	0.60	0.0334
TAN	2.01
NDVI	2.11		Model	0.74	0.0012

Where Inc = BBR Incidence at harvest (%); PL35 = Pluviometry cumulated 35 days before harvest (mm); PL15 = Pluviometry cumulated 15 days before harvest (mm); WSP= Pectins (mg galacturonic acid g-1 NAS); TAN = Tannins (mg tannins g^-1 skin); NDVI = Normalized Difference Vegetation Index (dimensionless); VIF = Variance Inflation Factor.

6. BBR at harvest related to berry skin tannin content and vegetative growth in the controlled leaf-removal (LR) experiment in France (2019)

The three experimental controlled LR-intensity treatments were associated with NDVI levels of 0.45 following severe LR, 0.60 with intermediate LR, and 0.78 in the control treatment without LR. As shown in Figure 6, this led to significant differences in the final BBR severity (ANOVA, Pr > F = 0.028). The severity reached 9.9% of rotted berries at harvest in the control without leaf-removal, whereas the severity was minimal (2.4%) in the severe LR treatment and in between (6.6%) following the intermediate LR treatment. Similarly, although not significantly (ANOVA, Pr > F = 0.095), the same trend was shown for BBR incidence at harvest. Furthermore, very interestingly, these differences in the early NDVI at berry pea size stage and in disease severity at harvest were also associated with significant differences in the tannin content in the fruit skin (Figure 6). The LR treatments caused significant differences in the tannin content measured in herbaceous berries sampled at veraison (ANOVA, Pr > F = 0.001). There was a marked increase from 22 mg/g of fresh skin in the control treatment with the higher canopy density (no LR) to 46.8 mg/g of fresh skin following the severe LR treatment. Thus, the tannin content in the berry skin, measured at the beginning of fruit maturation, increased significantly as the leaf density in the cluster zone decreased and this was also clearly correlated with the final BBR severity.

Figure 6. Effects on skin tannin concentration and on BBR development of controlled leaf-removal (LR) treatments leading to different NDVI levels of 0.45 (severe LR), 0.60 (intermediate LR) and 0.78 (control without LR).

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Skin tannins content was measured in herbaceous berries at veraison, and both BBR incidence and severity were assessed at harvest (7 October) (cv. Merlot noir, Nouvelle-Aquitaine Region in France, 2019). NDVI measurements and leaf-removal were implemented in a combined way by controlling the LR intensity according to Greenseeker® NDVI measurements, on one day (3 July 2019), at berry pea size stage, at exactly 319 growing degree days from flowering.

Discussion

The contrasting weather and cropping conditions between Chile and France allowed us to identify robust BBR risk indicators that are potentially useful under conditions from not conducive to very conducive to the disease. The primary risk factor put forward, favoring Botrytis development, is weather conditions, i.e. rainfall near harvest associated with high RH, confirming several previous studies (Pieri and Fermaud, 2005; Valdés-Gómez et al., 2008; Molitor et al., 2016; Ferrer et al., 2017; Hill et al., 2019). BBR development was always much higher in France than in Chile, and this main difference in disease incidence and severity between the two countries was attributed to the weather conditions before harvest. Different climatic factors, including temperature, relative humidity, and rainfall after veraison, were tested for correlation with BBR (data not shown). Among them, rainfall was the most relevant factor involved in Botrytis development, in agreement with previous studies (Fermaud et al., 2001; Pieri and Fermaud 2005; Molitor et al., 2016; Ferrer et al., 2017). This may be related to the absolute need for liquid water for conidia to germinate and, then, facilitate conidial infections during the ripening period in increasingly susceptible berries (Broome et al., 1995; Steel et al., 2011; Ciliberti et al., 2015 a,b, 2016). Therefore, it was possible to analyze the relative importance of other proposed risk indicators than the climatic factors. In this context, the development and use of an early risk disease index, such as the early NDVI, was of prime importance.

Conclusions

The management indicators developed in this study significantly explained the BBR development at harvest and therefore may be used as new tools to develop specific DSS to limit botryticide treatments. Main regression models included the cumulated rainfall before harvest, corroborating the importance of this factor in BBR development, and the early grapevine vegetative growth (NDVI), which was the other major factor governing B. cinerea infection. Regarding tannin content in berry skin, evaluated at the berry pea size stage, this BBR risk indicator could be used as a trend indicator in IPM strategies. Further, new investigations should consider the cluster compactness and pathogen inoculum throughout the season, to better understand their relationships with BBR at harvest and to optimize more deeply the management and control methods in IPM strategies.

Acknowledgments

The authors are grateful to the Conseil Interprofessionnel du Vin de Bordeaux (CIVB) for its crucial and continuous financial support, for many years, of such a research topic. The research was also supported by the National CONICYT Doctoral Fellowship 2013 N° 21130505 and by the Chilean projects CONICYT-FONDECYT/Regular 1191244 and Inserción académica PUC. We are also grateful to Panguilemo Experimental Station, to the Inrae Grande Ferrade experimental unit, and Inrae UMR EGFV (1287), for the vineyard plots and technical help. We are also very grateful to UMR SAVE staff (Bordeaux)– B. Dubos, Dr. I. Chénet, Prof. L. Geny-Denis, L. Delbac, J.M. Brustis – for their great scientific and/or technical support, notably concerning the vineyard plots and early scientific B. cinerea studies involving phenolics in the berry skins. This study has been performed in the framework of the French Cluster of Excellence COTE, Bordeaux.

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