Terroir 1996 banner
IVES 9 IVES Conference Series 9 Application of the simplified quality bioclimatical index of Fregoni: suggestion of using its evolution curve

Application of the simplified quality bioclimatical index of Fregoni: suggestion of using its evolution curve

Abstract

Les indices bioclimatiques constituent un bon outil pour piloter le développement vitivinicole dans une région précise. Plusieurs indices bioclimatiques ont été proposés par la littérature mondiale (WINKLER 1970; HIDALGO, 1980; HUGLIN, 1986, TONIETO et CARBONEAU, 2000), mais pour des raisons physiologiques ces indices n’incluent pas dans leurs formules les températures journalières inférieures à 10 °C, à l’exception de l’indice de FREGONI (FREGONI et PEZZUTTO, 2000). Cet auteur établit une relation entre les variations thermiques, les températures inférieures à 10 °C et la qualité des vins, en particulier pour les 30 jours précédant les vendanges. Parmi les indices appliqués au Chili, celui de WINKLER et AMERINE (WINKLER, 1970) est probablement le plus utilisé, cependant il présente quelques liplites (Mc INTYRE et al. 1987; JACKSON et CHERRY, 1988) et des résultats incongrus ont été signalés pour le Chili. En effet, il classe dans le même groupe des zones littorales avec d’autres proches à la cordillère des Andes, présentant des températures moyennes similaires mais avec des variations thermiques sensiblement différentes (SANTIBANEZ et al. (1984).
FREGONI et PEZZUTTO (2000) affirment que le Chili présente les plus hautes variations thermiques journalières pendant le mois précédant la récolte, ce qui justifierait l’utilisation de l’indice de FREGONI pour la vitiviniculture de ce pays.
On a utilisé la formule simplifiée de l’indice de FREGONI (IFss), en multipliant l’amplitude thermique par le nombre de jours au-dessous de 10 °C pour le mois précédant la récolte, sans, prendre en compte le nombre d’heures pendant lesquelles ces températures au-dessous de 10 °C se maintiennent : IFss = Σ (T maxima – T minima)*Σ (N° jours < 10° C). L’indice de FREGONI est calculé pour le mois précédant la récolte, en l’occurrence, le mois de mars pour l’hémisphère sud.
Le calcul de l’indice de FREGONI pour différents lieux de la région du Maule au Chili permet de différencier 4 zones agroclimatiques. Ces valeurs obtenues ne correspondent pas .aux niveaux les plus élevés possibles pour ces zones, qui se produisent généralement pendant le mois d’avril.
Par ailleurs, au Chili et plus particulièrement dans les zones de la région du Maule, les vendanges s’étalent, en fonction du cépage, du mois de février à mai. Par conséquent, le calcul de l’indice uniquement pour le mois de mars se révèle inapproprié.
Afin de mieux caractériser chaque lieu, on propose donc l’utilisation de la courbe d’évolution de IFss, caractérisée par 4 périodes. Cette courbe d’évolution de l’indice peut avoir différentes applications pratiques.

Bioclimatic indices are good tools to orientate the development of viticultural areas. Several bioclimatic indices have been proposed in international literature (WINKLER 1970; HIDALGO, 1980; HUGLIN, 1986, TONIETO et CARBONEAU, 2000) but, for physiological reasons, daily temperatures under 10°C are not included, excepted in FREGONl’s index (FREGONI and PEZZUTTO, 2000). These authors establishes a relationship between daily temperature variations, temperatures under 10°C and wine quality, for the 30 days before harvest.
WINKLER and AMERINE’s index (WINKLER, 1970) is certainly the most frequently used, among different climatic indices used in Chile. However, it has some limitations (Mc INTYRE et al. 1987; JACKSON and CHERRY, 1988) and some wrong results have been reported for Chile. In fact, this index classifies in the same class coastal zones and closed to the Andes mountains areas. For these two areas, average temperatures are similar but daily variations oftemperature are quite different (SANTIBANEZ et al. 1984).
FREGONI and PEZZUTTO (2000) observed that Chile presents the highest daily variations of temperature during the month before harvest and suggested that it could justify the use of FREGONI’ s index for Chilean viticultural areas.
Simplified FREGONI’ s indice (lfss) was used by multiplying daily temperature amplitude and the number of days under 10°C, for the month before harvest, but not regarding duration of temperature under 10°C period: Ifss = S (T maxima – T minima)*S (N° days < 10° C). FREGONI’ s index is calculated for the month before harvest, March for the southern hemisphere.
FREGONI’ s index was applied to different areas of Chilean Maule region and 4 agroclimatic zones were distinguished. Results don’t correspond to the highest potential levels for these areas, generally found in April. In Chile, and more particularly in the Maule region, the harvest period spread from February to May, according to the cultivar. Consequently, FREGONl’s index application only for March is quite inexact. The lfss curve evolution, characterized by 4 periods, is proposed to characterize viticultural areas. This curve presents different practical applications.

 

 

 

DOI:

Publication date: February 15, 2022

Issue:Terroir 2002

Type: Article

Authors

Ph. PSZCZOLKOWSKJ (1), E. ALEMP ARTE (1) and M. I. CARDENAS (2)

(1) Departamento de Fruticultura y Enología
Facultad de Agronomia e Ingenieria Forestal
Pontificia Universidad Catolica de Chile
Casilla 306-22, Santiago, Chile
(2) CIREN-CORFO
Manuel Montt 1164; Santiago, Chile

Contact the author

Keywords

Chili, zonage vitivinicole, indice bioclimatique
Chile, viti-vinicultural zoning, bio-climatic index

Tags

IVES Conference Series | Terroir 2002

Citation

Related articles…

Grapevine yield estimation in a context of climate change: the GraY model

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.

Effect of one-year cover crop and arbuscular mycorrhiza inocululation in the microbial soil community of a vineyard

The microbial composition of the soil is an important factor to consider in viticulture, since its influence on the “terroir” and on the organoleptic properties of the wine have been demonstrated. Different agronomic techniques have the potential to modify the composition and functionality of the soil microbial community. Maintaining green covers is known to increase soil microbial diversity. The direct application of inoculum of beneficial microorganisms to the soil has also been used to increase their abundance. However, the environmental conditions of each site seem to have a determining weight in the result of these practices. In this study, we compared the effect on the microbial community of a cover crop with legumes in autumn and the inoculation of grapevines with commercial inoculum bases on Rhizophagus irregularis and Funeliformis mosseae in the previous spring. The study has been carried out in a vineyard in Binissalem, Mallorca, Spain. After applying the treatments, we will analyze the soil microbial communities using the data obtained from Illumina amplification of soil DNA from the 16S and ITS regions to analyze bacteria and fungi community, respectively. In addition, we will record the physicochemical characteristics of the soil at each sampling point. The result showed that agronomic management, in the short term, has less influence than soil characteristics on the composition of the soil microbiome. With these results, we can conclude that in a vineyard, agricultural techniques should focus on improving the characteristics of the soil to improve the biodiversity of the soil microbiota.

Influence of a spontaneous cover crop on the vineyard and soil erosion under Mediterranean climate

Sixty five % of the agricultural area of the Basque Country located in the DO Ca Rioja corresponds to vineyards. More than 40% of it has an average slope greater than 10%, which makes it sensitive to erosive processes. Furthermore, it is foreseeable that extreme weather events (storms, hail, extreme heat and cold, etc.) will be favored due to climate change. Cover cropping can mitigate this risk, and therefore the objective of this work is to evaluate the impact that a vegetable cover has on the agronomic behavior of the vineyard, the quality of the grape and soil erosion. For this, a trial has been carried out with a Graciano variety vineyard with a slope between 10% -20% during the years 2020 and 2021. Conventional tillage management in the area has been compared (4-6 passes per year of tillage machinery) versus spontaneous vegetation cover management in the vineyard. This implies not tilling and allowing the grass of the land to colonize the range between the lines of vines, controlling their height through 1-3 mowing passes per year, always trying to affect the surface of the land as little as possible. The vegetative growth, yield and quality of the grape and wine was measured. Furthermore, erosion has been measured using Gerlasch boxes. The yield was lower in the second year of the trial in the cover crop treatment, but erosion was significantly reduced.

Phenological characterization of a wide range of Vitis Vinifera varieties

In order to study the impact of climate change on Bordeaux grape varieties and to assess the adaptation capacities of candidates to the grape varieties of this wine region to the new climatic conditions, an experimental block design composed of 52 grape varieties was set up in 2009 at the INRAE Bordeaux Aquitaine center. Among the many parameters studied, the three main phenological stages of the vine (budburst, flowering and veraison) have been closely monitored since 2012. Observations for each year, stage and variety were carried out on four independent replicates. Precocity indices have been calculated from the data obtained over the 2012-2021 period (Barbeau et al. 1998). This work allowed to group the phenological behaviour of the grapevine varieties, not only based on the timing of the subsequent developmental stages, but also on the overall precocity of the cycle and the total length of the cycle between budburst and veraison. Results regarding the variability observed among the different grape varieties for these phenological stages are presented as heat maps.

Assessing the climate change vulnerability of European winegrowing regions by combining exposure, sensitivity and adaptive capacity indicators

Winegrowing regions recognized as protected designations of origin (PDOs) are closely tied to well defined geographic locations with a specific set of pedoclimatic attributes and strictly regulated by legal specifications. However, climate change is increasingly threatening these regions by changing local conditions and altering winegrowing processes. The vulnerability to these changes is largely heterogenous across different winegrowing regions because it is determined by individual characteristics of each region, including the capacity to adapt to new climatic conditions and the sensitivity to climate change, which depend not only on natural, but also socioeconomic and legal factors. Accurate vulnerability assessments therefore need to combine information about adaptive capacity and climate change sensitivity with projected exposure to new climatic conditions. However, most existing studies focus on specific impacts neglecting important interactions between the different factors that determine climate change vulnerability. Here, we present the first comprehensive vulnerability assessment of European wine PDOs that spatially combines multiple indicators of adaptive capacity and climate change sensitivity with high-resolution climate projections. We found that the climate change vulnerability of PDO areas largely depends on the complex interactions between physical and socioeconomic factors. Homogenous topographic conditions and a narrow varietal spectrum increase climate change vulnerability, while the skills and education of farmers, together with a good economic situation, decrease their vulnerability. Assessments of climate change consequences therefore need to consider multiple variables as well as their interrelations to provide a comprehensive understanding of the expected impacts of climate change on European PDOs. Our results provide the first vulnerability assessment for European winegrowing regions at high spatiotemporal resolution that includes multiple factors related to climate exposure, sensitivity, and adaptive capacity on the level of single winegrowing regions. They will therefore help to identify hot spots of climate change vulnerability among European PDOs and efficiently direct adaptation strategies.