Prise en compte de la notion de terroir dans les AOC en France : Aspects Culturels

The aim of this work was to analyze the phenology variability of Tempranillo and Chardonnay cultivars, related to the climatic characteristics in La Mancha Designation of Origin, and their potential changes under climate change scenarios. Phenological dates referred to budbreak, flowering, veraison and harvest were analyzed for the period 2000-2019. The weather conditions at daily time scale, recorded during the same period, were also evaluated. The thermal requirements to reach each of these phenological stages were calculated and expressed as the GDD accumulated from DOY=60. Changes in phenology were projected by 2050 and 2070 taking into account those values and the projected temperatures and precipitation, simulated under two Representative Concentration Pathway (RCP) scenarios –RCP4.5 and RCP8.5– using an ensemble of models. The average phenological dates during the period under study were, April 16th ± 6.6 days and April 5th ± 6.0 days for budbreak, May 31st ± 6.0 days and May 27th ± 5.3 days for flowering, July 26th ± 5.6 days and July 25th ± 5.8 days for veraison, and Ago 23rd ± 10.8 days and Ago 17th ± 9.0 days for harvest, respectively, for Tempranillo and Chardonnay. The projected changes in temperature imply an average change in the maximum growing season (April-August) temperatures of 1.2 and 1.9°C by 2050, and 1.6 and 2.6°C by 2070, under the RCP4.5 and RCP8.5 scenarios, respectively. A reduction in precipitation is predicted, which vary between 15% for 2050 under RCP4.5 scenario and up to 30% by 2070 under RCP8.5. The advance of the phenological dates for 2050, could be of 6, 7, 7, and 8 days for Tempranillo and 4, 6, 6 and 9 days for Chardonnay, respectively for budbreak, flowering, veraison and harvest under the RCP4.5 scenario. Under the RCP8.5 emission scenario, the advance could be up to 30% higher.

In viticulture, the challenges of local climate modelling are multiple: taking into account the local environment, fine temporal and spatial scales, reliable time series of climate data, ease of implementation and reproducibility of the method. At the local scale, recent studies have demonstrated the contribution of spatialization methods for ground-based climate observation data considering topographic factors such as altitude, slope, aspect, and geographic coordinates (Le Roux et al, 2017; De Rességuier et al, 2020). However, these studies have shown questions in terms of the reproducibility and sustainability of this type of climate study. In this context, we evaluated the potential of MODIS thermal satellite images validated with ground-based climate data (Morin et al, 2020). Previous studies have been encouraging, but questions remain to be explored at the regional scale, particularly in the dynamics of the massive use of bioclimatic indices to classify the climate of wine regions. The results at the local scale were encouraging, but this approach was tested in the current study at the regional scale. Several objectives were set: 1) to evaluate the downscaling method for land surface temperature time series, 2) to identify regional thermal structure variations. We used weekly minimum and maximum surface temperature time series acquired by MODIS satellites at a spatial resolution of 1000 m and downscaled at 500 m using topographical variables. Two types of analyses were performed:

Choosing the rootstock, the scion variety and the training system best suited to the local soil and climate are the key elements for an economically sustainable production of wine. The choice of the rootstock/scion variety best adapted to the characteristics of the soil is essential but, by changing climatic conditions, ongoing climate change disrupts the fine-tuned local equilibrium. Higher temperatures induce shifts in developmental stages, with on the one hand increasing fears of spring frost damages and, on the other hand, ripening during the warmest periods in summer. Expected higher water demand and longer and more frequent drought events are also major concerns. The genetic control of the phenotypes, by genomic information but also by the epigenetic control of gene expression, offers a lot of opportunities for adapting the plant material to the future. For complex traits, genomic selection is also a promising method for predicting phenotypes. However, ecophysiological modelling is necessary to better anticipate the phenotypes in unexplored climatic conditions Genetic approaches applied on parameters of ecophysiological models rather than raw observed data are more than ever the basis for finding, or building, the ideal varieties of the future.

Air temperature is the key driver of grapevine phenology and a significant environmental factor impacting yield and quality for a winegrape growing region. In this study the optimal downscaled CMIP5 ensemble for computing thegrowing season average temperature (GST) viticulture climate classification index was determined to spatially compute on a decadal basis predictions of the GST climate index and the grapevine sugar ripeness (GSR) model for Pinot Noir throughout the Willamette Valley (WV) American Viticultural Area (AVA). Forecasts for average temperature and a 220 g/L target sugar concentration level were computed using daily Localized Constructed Analogs (LOCA) downscaled CMIP5 historic and Representative Concentration Pathways (RCP) future climate projections of minimum and maximum daily temperature. We explore spatiotemporal trends of the GST climate classification index and Pinot Noir specific applications of the GSR phenology model for the WV AVA. Spatiotemporal computations of the GST climate index and Pinot Noir specific applications of the GSR model enable the opportunity to explore relationships between their computed values with one intent being to provide updated GST ranges that better align with current temperature-based modeling understanding of Pinot Noir grapevine phenology and the viticultural application of LOCA CMIP5 climate projections for the WV AVA. The Pinot Noir specific applications of the GSR model or the GST index with updated bounds indicate that the percent of the WV AVA area suitable for Pinot Noir production is currently at or near its peak value in the upper 80s to lower 90s of this century.

Making high quality but affordable Pinot noir (PN) wine is challenging in most terroirs and New Zealand’s (NZ) situation is no exception. To increase the probability of making highly typical PN wines producers choose to grow grapes in cool climates on lower fertility soils while adopting labour intensive practices. Stringent yield targets and higher input costs necessarily mean that PN wine cost is high, and profitability lower, in line-priced varietal wine ranges. To understand the reasons why higher yielding vines are perceived to produce wines of lower quality we have undertaken an extensive study of PN in NZ. Since 2018, we established a network of twelve trial sites in three NZ regions to find individual vines that produced acceptable commercial yields (above 2.5kg per vine) and wines of composition comparable to “Icon” labels. Approximately 20% of 660 grape lots (N = 135) were selected from within a narrow juice Total Soluble Solids (TSS) range and made into single vine wines under controlled conditions. Principal Component Analysis of the vine, berry, juice and wine parameters from three vintages found grape berry mass to be most effective clustering variable. As berry mass category decreased there was a systematic increase in the probability of higher berry red colour and total phenolics with a parallel increase in wine phenolics, changed aroma fraction and decreased juice amino acids. The influence of berry size on wine composition would appear stronger than the individual effects of vintage, region, vineyard or vine yield. Our observations support the hypothesis that it is possible to produce PN wines that fall within an “Icon” benchmark composition range at yields above 2.5kg per vine provided that the Leaf Area:Fruit Weight ratio is above 12cm2 per g, mean berry mass is below 1.2g and juice TSS is above 22°Brix.