Evaluation of grape and wine quality according to harvest date, in a tropical region in Northeast Brazil

The definition of the term “quality” in sensory evaluation of food products does not seem to be consensual. Descriptive or liking methods are generally used to differentiate between wines (Lawless et al., 1997). Nevertheless, quality evaluation of a product such as wine can also relate to emotional aspects. As exposed by Costell (2002), product quality is defined as an integrated impression, like acceptability, pleasure, or emotional experiences during tasting. According to the ‘modality appropriateness’ hypothesis which predicts that wine tasters weigh the most suitable sensory inputs for a specific assess- ment (Freides, 1974; Welch & Warren, 1980), the nature of the quality definitions may modulate sensory influences.

Contribution Foam is the characteristic that differentiates sparkling wines from still wines, being the first sensory attribute that tasters and consumers perceive and that determines the final quality of sparkling wines [1]. The foaming properties mainly depend on the chemical composition of wines [2-3], and different factors involved in wine composition will have an effect on foam quality. In Spain, the sparkling wine market focuses on the production of white and rosé sparkling wine, with very low production of red sparkling wines. However, this type of wines is elaborated in countries like Australia, South-Africa, Argentina, Italy or Portugal, with a great acceptance by consumers. No studies on the foaming characteristics of red sparkling wines have been found.

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.

According to the last IPCC report, the scale of recent climate changes are unprecedented over many centuries. Each of the last four decades has been successively warmer than any decade since 1850. Projections for the future foresee that temperature could reach +3.3°C to +5.7°C under the most pessimistic scenario. It is also projected that every region will face more concurrent and multiple changes in climatic impact-drivers. The frequency of extreme climate events is also likely to increase, as well as the occurrence of indirect constraints. These evolving climatic conditions are alrealdy affecting and will continue to affect the suitability of traditional wine grape production areas, but also create opportunities in new locations.

For several years, the development of computer resources, and in particular of Geographic Information Systems, have allowed the emergence of a new approach to the analysis and characterization of wine-growing areas (Morlat, 1989; Laville, 1990). These methods, which make it possible to identify homogeneous areas or units of terroir, are based on crossing, statistical analysis (in particular Principal Component Analysis: PCA) and the integration of parameters describing the natural environment in which develop the vine.