Research Article, Geoinfor Geostat An Overview Vol: 4 Issue: 3
Climate Influences on Yield, Berry and Wine Quality in Monastrell Wine Grapes in a Warm Winegrowing Region (Jumilla Area, SE Spain)
| Romero P1*, Fernández-Fernández JI2, Bravo-Cantero AF3, Ayala MC4 and Botía P1 | |
| 1Grupo de Riego y Fisiologa del Estrés. Departamento de Recursos Naturales. Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA), c/ Mayor s/n, 30150, La Alberca, Murcia, Spain | |
| 2Departamento de Viticultura, Instituto Murciano de Investigación y Desarrollío Agrario y Alimentario (IMIDA), c/ Mayor s/n, 30150, La Alberca, Murcia, Spain | |
| 3Estadística Murcia (Consultoría estadística). Calle Juan de Borbón, nº 24, Torres de Cotillas, Murcia, Spain | |
| 4Servicio de Información Agraria de Murcia (SIAM). Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA), c/ Mayor s/n, 30150, La Alberca, Murcia, Spain | |
| Corresponding author : Pascual Romero Grupo de Riego y Fisiología del Estrés. Departamento de Recursos Naturales. Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA), c/ Mayor s/n, 30150, La Alberca, Murcia, Spain Tel: +34 968 366739 Fax: +34 968 366792 E-mail: pascual.romero@carm.es |
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| Received: March 01, 2016 Accepted: June 01, 2016 Published: June 08, 2016 | |
| Citation: Romero P, Fernández-Fernández JI, Bravo-Cantero AF, Ayala MC, Botía P (2016) Climate Influences on Yield, Berry and Wine Quality in Monastrell Wine Grapes in a Warm Winegrowing Region (Jumilla Area, SE Spain). Geoinfor Geostat: An Overview 4:3. doi:10.4172/2327-4581.1000145 |
Abstract
The effects of climatic factors on yield, berry, and wine quality for long-term (7 years) deficit irrigated (DI) Monastrell wine grapes under the semiarid conditions of SE Spain were analyzed. The relationships between climatic variables and the yield, berry (QIoverallberry), and wine quality (QIwine) novel indices confirmed that the most important climatic factors were rainfall, temperature, and radiation. Climate was more influential in determining yield, berry, and wine composition in some important physiological periods, such as early season (budburst-fruit set) and ripening (veraison-harvest). Greater rainfall during dormancy and early season was related with greater yield, QIoverallberry, and QIwine; in contrast, rainfall late in the season, during the veraison-harvest period, was related negatively with berry and wine quality. Similarly, solar radiation impacted positively during dormancy and early season and negatively during late season. In addition, greater Tªmax during the dormancy, early season (budburst-fruit set) and veraison-harvest periods influenced negatively the QIoverallberry and QIwine, while greater Tªmax during other periods, such as fruit set-veraison were - in general - positive for berry and wine quality. Besides greater Tªmin (high nighttime temperatures) also exerted a negative influence, reducing QIphenolicberry, although had positive effects in yield, QItechnologicalberry and QIwine. In general, climate had more influence on berry quality than on wine quality.
Keywords: Berry quality index; Climatic Factors; Phenological Periods; Regression models; Wine quality index; Yield
Keywords |
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| Berry quality index; Climatic Factors; Phenological Periods; Regression models; Wine quality index; Yield | |
Introduction |
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| Climate strongly influences the development of wine grapes [1] and is a strong modulator of berry composition [2]. The relationship between wine grape yield, quality and climatic factors have been previously analysed [2-6] and strong relationships between climatic conditions and wine quality have been highlighted, with higher quality wines obtained in the years characterized by a reduction in rainfall and warmer temperatures [3,4,6]. However the issue of how climate affect grape production and berry and wine quality is more complex, and depends on the values of the climate variables in the different phenological periods [2,4,5], showing unfavourable and beneficial effects in yield and wine quality depending on the climate factor and the values achieved in the different phenological periods during growing season. | |
| For this study we focused on the effects of inter-annual climatic variability on long-term yield response and berry and wine quality attributes in deficit irrigated Monastrell grapevines under semiarid conditions, using multivariate analysis. Specifically, we employed multiple regression procedures to relate viticulture (dependent) variables (yield, berry, and wine quality indices) to the climate (independent) variables. We determined the climatic factors that had most impact on the yield and quality of Monastrell grapes and what phenological periods were more critical. Thus, using climatic factors, significant multiple linear regression models for yield and berry and wine quality indices were established. | |
Materials and Methods |
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| Field conditions, plant materials, and irrigation treatments | |
| This research was carried out in a 1-ha vineyard at the CIFEA experimental station in Jumilla, Murcia (SE Spain, Lat: 38º 2´ N; Lon: 1º 58´ W, 395 m a.s.l.). The climate is Mediterranean semiarid, with hot, dry summers, scarce annual rainfall (less than 300 mm year-1), a mean annual atmospheric vapour pressure deficit of 1.12 kPa, and a total annual reference evapotranspiration (ETo) of around 1,200 mm for the period 2006-2012 (Table 1). | |
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Table 1: Monthly rainfall and atmospheric vapour pressure deficit (VPD) at the experimental site every year and in different representative phenological stages during experimental period (2006-2012). |
| During seven consecutive years (2006-2012), a moderate RDI strategy was applied under conventional drip irrigation (RDI-1) and under PRD (PRD-1) (Table 2). A more-severe RDI strategy was also applied under conventional drip irrigation (RDI-2) and under PRD (PRD-2). Depending on the year, controlled DI was applied during two or three phenological periods: 1) early in the season, from budburst to fruit set; 2) from fruit set to veraison and 3) after veraison, during fruit ripening until harvest (Table 2). These DI treatments were also compared with a sustained DI treatment (SDI) involving irrigation at 40-60% ETc throughout the season, a treatment which allowed us to minimise vine water stress and which served as a control (Table 2). | |
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Table 2: Irrigation strategies applied during the experimental period (2006-2012) |
| The soil and water characteristics, field conditions, plant material, Kc used, fertilizers applied, experimental design and the methodology used to calculate ETc have been described in detail previously [7-10]. | |
| Climatic factors | |
| During the experimental 7-year period (2006-2012), the daily climatic data (rainfall, number of hours of sunshine, incident global solar radiation, daily Tªmax, daily Tªmin, ETo, and vapour pressure deficit (VPD) were collected every year in a meteorological station (Campbell mod. CR 10X, Campbell Scientific, Inc. Logan, UT, USA) located at the experimental vineyard (and belonging to the Servicio de Información Agraria de Murcia (IMIDA- SIAM)). The maximum and minimum daily air temperatures were calculated each hour as the average of Tªmax and Tªmin, respectively, for a 24 hr period. The number of hours of sunshine was computed when the average of one hour was > 1200 W m-2 of solar radiation (Table S1, supplementary information). | |
| Yield response, berry and wine composition and quality indices | |
| Each year at harvest, the yield components were measured for 13 vines per plot (52 vines per treatment). The yield per vine, number of clusters per vine, cluster weight, berry number per cluster, berry weight was determined. At harvest, the fresh berry weight, total soluble solids (TSS in ºBrix), solutes per berry (g), and the juice pH, titratable acidity (TA), organic acids (malic and tartaric) and phenolic potential of the grapes were determined as described in detail previously [8,10-12]. Twenty micro-vinifications were carried out during five years (2006-2010) (4 per treatment, 1 per plot) as described in detail previously [8,10]. The wines were analyzed at the end of the alcoholic and malolactic fermentation. Absorbance measurements for color intensity, CIElab parameters, total phenol index, and total anthocyanins were made using a Shimadzu UV-1603 spectrophotometer (Shimadzu Deutschland GmbH), using glass cells of 0.2 cm path length, according to the methodology described [8,10]. | |
| We calculated different berry and wine quality indices; we included several important technological and phenolic parameters in order to have a more global, quantitative view of the quality. To establish these novel quality indices, firstly we chose some berry and wine attributes (technological and phenolic parameters which have been used traditionally in the wine industry) important for the harvest and winemaking process and based on the literature [13,14], the recommendations of local winemakers and in our own results in the study´s area, we defined ranges (max and min) for the different parameters chosen. Then, every year, we classified the grapes and wines of the different irrigation treatments in four groups according to their composition (Tables 3, 4 and 5). Each group was given a value between 0 and 3: group 1, with the lowest score (0), had the worst composition and lowest quality and group 3, with the highest score (3), had the best composition and highest quality of grapes and wines (Tables 3, 4 and 5). According to this classification, the berry and wine quality indices were calculated using the following equations: | |
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Table 3: Berry parameters and classification used to establish berry technological quality index (QITechnologicalberry) in Monastrell grapevines. |
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Table 4: Berry parameters and classification used to establish berry phenolic quality index (QIphenolicberry) in Monastrell grapevines. |
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Table 5: Wine parameters and classification used to establish wine quality index in Monastrell grapevines. |
| QITechnologicalberry= ºBrix+Total acidity+ Tartaric/Malic ratio+ pH +sugars +sugar/acidity ratio. | |
| QIphenolicberry = Anttot +Polyphextr +A520+ Berry weight+ SM | |
| Where Polyphextr are extractable polyphenols, A520 (absorbance at 520 nm), and SM (seed maturity index), that measures the contribution of seeds to the total amount of polyphenols, mainly tannins from seeds (Ribéreau-Gayon et al. 2006). | |
| QIwine = (1)TPI +(0.5)*Anttot+ (0.5) *CI | |
| In accordance to the recommendations of winemakers, in the QIwine, a coefficient value of 1 was given to the Total phenol index (TPI) due to the greater importance of this parameter in the stability and wine aging, and a lower coefficient value (0.5) was given to the total anthocyanins (Anttot) and color intensity (CI) of the wine. | |
| Overall berry quality (QIoverallberry) was calculated as: | |
| QItechnologicalberry + QIphenolic | |
| Statistical Analysis | |
| Significant differences among irrigation treatments for each variable were assessed by analysis of variance (ANOVA) and means were separated by Duncan´s Multiple Range Test (P <0.05), using Statgraphics 5.0 Plus software (Statistical Graphics Corp. USA). Multiple linear regression models for yield and berry and wine quality (dependent variables) were established, introducing into the models the following independent variables: the irrigation treatments applied (irrigation volume and placement), phenological periods, and climatic factors, in order to see to what extent these variables predicted each of the dependent variables. In addition, multiple regression procedures helped to relate viticulture variables (yield, berry and wine quality) to the climate variables, thereby revealing the climatic factors that had most impact on the yield and quality of Monastrell grapes and wines and in what phenological periods these climatic variables were more critical. | |
Results |
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| Multiple linear regression models for yield, berry and wine quality | |
| The multiple linear regression model for yield was significant [F(15, 684) = 49.11***, Table 6] and indicates that: 1) all DI treatments reduced the yield significantly, compared to SDI (with greater irrigation), 2) all phenological periods influenced significantly and positively the yield response, especially the veraison-harvest and post-harvest periods, compared to dormancy period, 3) regarding the climatic factors, the main climatic factors impacting yield were in this order (according to ß parameter, data not shown): ETo, VPD and Tªmin and 4) rainfall, Tªmin, and ETo had a positive and significant effect on yield, whereas the hours of sunshine, global solar radiation, Tª max, and VPD affected yield negatively - hence, an increase in these parameters (warmer years) decreased grape yield (Table 6). | |
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Table 6: Multiple linear regression models for yield, technological berry quality, phenolic berry quality, overall berry and wine quality. The independent variables introduced in the model were irrigation treatments, phenological periods and climatic factors. |
| The regression model for the technological quality index (QITechnologicalberry) of the berries (important for winemaking) was significant [F(15, 682) = 19.50***, Table 6] and indicates that: 1) PRI-2 and RDI-2 decreased significantly the long-term technological grape quality, compared to SDI and the other treatments, 2) all phenological periods were positively related with berry technological quality, particularly the fruit set to veraison and post-veraison periods, 3) the main climatic factor impacting technological berry quality was Tªmax 4) rainfall and Tªmin influenced significantly and positively the technological berry quality, whereas Tªmax affected it negatively (Table 6). | |
| The model for the berry polyphenolic quality (QIphenolicberry) was significant [F(15, 682) = 15.40***, Table 6] and indicates that: 1) all DI treatments increased significantly berry phenolic content compared to the SDI treatment (with greater irrigation), 2) as for technological quality, all phenological periods were related positively with berry polyphenolic quality, particularly the fruit set to veraison and post-veraison periods, 3) according to ß parameter (data not shown), the main climatic factor impacting berry polyphenolic quality index was Tªmax followed by global solar radiation and 4) rainfall, hours of sunshine, and ETo influenced positively the phenolic index (Table 6), while solar radiation and Tªmax affected it negatively; thus, higher average Tªmax and solar radiation gave grapes of lower phenolic quality. | |
| The predictive model for the overall quality of grapes (QIoverallberry) (taking into account both technological and phenolic quality) was significant [F(15, 684) = 19.363***, Table 6] and shows that 1) the moderately water stressing treatments (PRI-1 and RDI-1) improved the long-term overall berry quality index significantly, compared to the SDI treatment (greater irrigation), but this was not so with the severely water stressing treatments, PRI-2 and RDI-2, 2) all phenological periods were positively related with overall berry quality, especially the fruit set to veraison and post-veraison periods, 3) the main climatic factor impacting overall berry quality index was Tªmax and 4) rainfall and hours of sunshine were the principal climatic factors affecting positively the overall grape quality, while the global solar radiation and Tªmax in this warm wine growing region affected negatively the overall berry quality (Table 6). | |
| The predictive model for the overall quality of wine (QIwine) was significant [F(15, 684) = 19.36***, Table 6] and indicates that: 1) PRI-1 and RDI-1 improved wine quality significantly, compared to SDI, but this was not the case with PRI-2 and RDI- 2, 2) phenological periods, in particular the fruit set to veraison and post-veraison periods, were also positively related with wine quality, and 3) the main and only climatic factor impacting wine quality index was Tªmin, which was positively and significantly related with wine quality (Table 6). | |
| Influence of climate and phenological period on yield, overall berry and wine quality index | |
| According to greater ß values found in the regression models (Tables 7 and 8), the main climatic factors affecting negatively QIoverallberry were solar radiation (dormancy and veraison-harvest), hours of sunshine (budburst-fruit set), VPD (fruit-set-veraison) and rainfall (postharvest). In contrast, the main climatic factors affecting positively QIoverallberry were: ETo (dormancy), solar radiation (budburstfruit set), Tªmin (fruit set-veraison) and hours of sunshine (veraisonharvest and post-harvest). In addition, the multiple linear regression models for the overall berry quality index (QIoverallberry), calculated for each phenological stage (Table 7), also showed that: 1) rainfall during early season (budburst (B)-fruit set (F)) and pre-veraison period (fruit set-veraison) impacted positively on berry quality; however, late season (veraison-harvest) and post-harvest rainfall impacted negatively, 2) solar radiation also influenced QIoverallberry positively during early season and pre-veraison, but negatively during late season (veraison-harvest and post-harvest), 3) by contrast, Tªmax affected berry quality negatively during dormancy period and early season (B-F) and positively during fruit-set-veraison period and postharvest, 4) Tªmin also influenced QIoverallberry negatively during dormancy and early in the season (B-F) and positively during pre-veraison (fruit set-veraison) and post-veraison (veraison-harvest), 6) ETo had a positive influence during the veraison-harvest period, while VPD had a negative influence during most of the growing season, except budburst-fruit set period (Table 7) and 7) the number of hours of sunshine impacted negatively during bud-burst-fruit set interval, but positively during fruit set-harvest and postharvest periods. | |
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Table 7: Multiple linear regression models for overall berry quality index (QIoverallberry) for each phenological period. |
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Table 8: Multiple linear regression models for wine quality index (QIwine) for each phenological period. |
| Also, multiple linear regression models for the wine quality index (QIwine), calculated for each phenological period, show (Table 8): 1) similarly to the QIoverallberry, rainfall impacted positively on QIwine during early season (budburst-fruit set) and postharvest and negatively during dormancy period, 2) global solar radiation had significant, negative effects during the fruit set-veraison and dormancy periods, 3) Tªmax during the budburst-fruit set and veraison-harvest periods had significant negative effects on wine quality, while Tªmin late in the season, (fruit set-veraison, veraison-harvest and post-harvest) had a positive effect, 4) the atmospheric VPD had a significant, negative effect on wine quality in most of the growing season, except postveraison (veraison-harvest period) (Table 8). According to ß values found in the regression models, the main climatic factors affecting negatively QIwine were rainfall (dormancy), VPD (budburst-fruit set, fruit set-veraison and post-harvest) and Tªmax (veraison-harvest). In contrast, rainfall (budburst-fruit set and post-harvest), Tªmin (fruit set-veraison), VPD (veraison-harvest) were the main climatic factors affecting positively QIwine (Table 8). | |
Discussion |
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| Climate is a very strong modulator of yield and berry and wine composition and can be satisfactorily described using climatevariable- based empirical models [2]. The multiple linear regression models using climate variables reported significant effects on yield and berry and wine quality (Table 6). Thus, in general, rainfall, ETo (reference crop evapotranspiration), and Tªmin were positively related with yield; in contrast, yield was negatively affected by the hours of sunshine, global solar radiation, Tªmax, and VPD, indicating that - regardless of the irrigation - warmer and drier years have a negative impact on the yield response. This contrast with other wine-growing regions, where regression models related increased temperatures with higher yields [15], probably because they are colder climate compared to our warm study area. The models also showed a significant positive effect of ETo (on yield and QIphenolicberry, (Table 6); this is a climatic parameter that can be computed from weather data and that expresses the evaporating power of the atmosphere [16]. Greater ETo during ripening period (veraison-harvest), which also indicates greater transpiration and water use by the plant, was beneficial for final berry quality (Table 7). | |
| Rainfall was found to have a positive influence on yield and on technological and phenolic berry quality, probably as a result of a reduction in air temperature and increased vegetative growth and soil moisture availability. Nevertheless, the rainfall effect (positive or negative) depended on the phenological period (Table 7). Greater rainfall during early season (budburst-fruit set) and pre-veraison (fruit-set veraison) was related positively with greater berry and wine quality (Table 7 and 8). It appears that improved water availability, caused by rainfall early in the growing season, is beneficial as it promotes the initial events and subsequent adequate vegetative growth [17] and, hence, proper development of the berry and its composition [2]. In contrast, rainfall late in the season, during the veraison-harvest period, was related negatively with QIoverallberry. Similarly, another study found that rainfall during physiologically important periods (flowering, veraison, and maturation) tended to decrease crop production, sugar levels, and vintage quality [4]. Rainfall late in the season and associated adverse weather can affect inflorescence differentiation and berry set, produce berry dilution, and may also aggravate moisture related problems [1,4]. | |
| Of the climate variables, air temperature is considered the most important factor in the overall growth and productivity of wine grapes and has been recognized as a primary driver of berry and wine composition [1]. In our study, the regression models revealed at least four salient features related to temperature: 1) in general, Tªmax was negatively related with most yield and berry quality indices (Table 6), 2) vapour pressure deficit (a variable derived from temperature and relative humidity (RH)) was also negatively correlated with yield and global berry and wine quality indices during most of the growing season (Table 6), 3) by contrast, in general Tªmin was positively related with yield, technological berry quality, and wine quality indices, and 4) the negative or positive effect of the temperature also depended on the phenological period (Tables 7 and 8). Our results demonstrate that the climate (and especially Tª) in some important physiological periods is more influential, in determining berry composition at maturity, than in others [2,4], and can have a negative or beneficial effect depending on the phenological period. Thus, greater Tªmax during dormancy and early season (budburstfruit set) were generally negative for QIoverallberry and QIwine. Unlike our results, a greater number of days with Tª greater than 30ºC during floraison in Cabernet Sauvignon was positively related to quality, due to the influence on early-growth events and complete maturation [4]. Besides in our study greater Tªmax during ripening (veraison-harvest) was also associated with lower QIwine. However greater Tªmax during other important periods (fruit set-veraison or postharvest period) impacted positively the berry or wine quality indices (Tables 7 and 8). A significant negative effect of Tªmax and VPD during most part of the growing season on QIoverallberry and QIwine, suggest that years with higher air temperature associated with low RH can be detrimental of berry traits as previously reported in CS and Shiraz [2] and wine quality under these warm semiarid conditions. Although increased mean air temperatures (warmer years) and lower rainfall have been frequently related with greater potential berry and wine quality in terms of sugar, acidity, sugar/acidity ratio or berry weight [3,4,6,15], other studies found that berries from warmer regions had low levels of anthocyanins and titratable acidity as well as high pH, compared to berries from the cooler regions [2]. These contrasting results provide evidence for the differential influence of temperature, not only among cultivars and wine growing regions but also on different berry traits [2]. | |
| In addition, the average minimum Tªmin was also an influential variable overall in specific periods such as ripening (Tables 6-8); In general, greater Tªmin was significantly and positively related with yield, QItechnologicalberry and QIwine, but negatively related with QIphenolicberry (Table 6). It is known that night-time minimum temperatures above or below 15ºC can, respectively, reduce or increase berry anthocyanins levels [19]. Cool nights during maturation/ripening, combined with high diurnal temperatures, stimulate the synthesis of anthocyanins and other phenolic compounds, and are thus beneficial for high quality wines [20]. In this regard, a maximum night/day temperature range of 15ºC-25ºC for color and flavor has been proposed [21]. In our study, the Tªmin during ripening (August to mid-September) generally exceeded 15ºC (Table S1, supplementary information) and night/day temperature difference ranged 15ºC or below, and therefore may have also exerted a negative influence on the synthesis and accumulation of anthocyanins and other polyphenols, reducing QIphenolicberry. This suggests clearly that our study area experienced excessive climatic stress and is not suitable for the production of high phenolic quality Monastrell grapes. The expected increase in Tª and drought periods in warm regions (as SE Spain, Jumilla area) due to climate change may further aggravate this situation, reducing the climatically more suitable areas for producing high quality of grapes and wines. | |
| The data also reveal that warmer dormancy periods (warmer winters) had, in general, a negative impact on global berry quality. One possible explanation is that higher temperatures during winter shorten the season, by advancing sprouting and bringing forward ripening to the hottest period of the year, which is detrimental to the accumulation of anthocyanins and other polyphenols in berries. Besides, winter chill is an important aspect in vine growth as it promotes bud dormancy, initiating carbohydrate reserves for the following year [1]. | |
| While temperature, rainfall, and radiation are the basic components of climate that affect vine growth and development, other variables may also influence berry composition. In our study, the variables identified as determining berry and wine quality included other climatic factors such as solar radiation and the number of hours of sunshine. Solar radiation influences the common fruit maturity attributes indirectly, by driving berry temperature. The regression models revealed that the hours of sunshine affected positively the phenolic berry quality, while global solar radiation affected it negatively, although this depended on the phenological stage. Thus, the number of hours of sunshine impacted global berry quality positively for almost the entire growing season (from fruit set to post-harvest), but negatively during budburst-fruit set. The solar radiation impacted it positively during early and mid-season (from budburst to veraison) and negatively during late season (ripening and post-harvest). Similarly, more hours of sunshine and a higher mean temperature in May were shown to be important in determining higher quality wines [5], and the insolation levels during the budburst-flowering interval also had a positive effect on sugar levels and CS vintage quality, presumably by initiating high levels of photosynthetic activity early in the season [4]. The apparent negative effect of late season solar radiation on berry attributes (such as berry anthocyanins during ripening) may be a reflection of the attendant elevated temperature load effect [22], especially in low-vigour DI vines due to excessive exposure to solar radiation in a warm climate as in our study area [18,23]. In general, climate had more influence in berry quality (QIoverallberry) than in wine quality (QIwine) (Tables 7 and 8), and indicates that, other factors (regardless climate) such as wine-making processes are also important in determining final wine quality. | |
Conclusions |
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| The most important climatic factors for yield and berry and wine quality were rainfall, temperature, and radiation, but the phenological period was influential too. Greater rainfall during dormancy and early season was related with greater yield, QIoverallberry, and QIwine; in contrast, rainfall late in the season, during the fruit-set-harvest period, was related negatively with berry and wine quality. In addition, greater Tªmax influenced negatively the QIoverallberry and QIwine. Thus, greater Tªmax during periods such as dormancy, budburst-fruit set or veraison-harvest had - in general – a negative impact, while in other periods (fruit set-veraison) had a positive effect in QIoverallberry. In addition, greater Tªmin, (high night-time temperatures) exerted a negative influence, reducing QIphenolicberry, but in general greater Tªmin had a positive influence in yield, QItechnologicalberry and QIwine. Greater insolation levels (high solar radiation) during the budburst period also had a positive effect in QIoverallberry, while later in the season had a negative effect on berry quality. In general, climate had more influence on berry quality than on wine quality index. | |
Acknowledgments |
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| This work was financed by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Subprograma Nacional de Recursos y Tecnologías Agrarias, through the Projects RTA2005-00103-00-00 and RTA2008-00037-C04-04, with the collaboration of the European Social Fund. Pascual Romero gratefully acknowledges a doctoral contract in the INIA-CCAA system, supplied by INIA and co-financed by the European Social Fund from 2005-2010. We thank Manuel Caro Ayala for his support with the analysis of climate data. We thank Atanasio Molina Molina, Aniceto Turpín Bermejo, Jose Antonio Candel Quijada, Antonio Heras Moreno, David López Romero, Antonio Lucas Bermudez, and Cristobal Marín, for their work in vineyard management, Juan Jose Sánchez Ruiz, Jose María Rodriguez de Vera-Beltrí, and Francisco Martínez López, for field assistance and support in laboratory analyses, and David J. Walker, for assistance with manuscript preparation and the correction of the written English. | |
References |
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