Presenter/Author Information

Subana Shanmuganathan
Philip Sallis
Ajit Narayanan

Keywords

year-to-year climate variability, grapevine phenology and wine vintage

Start Date

1-7-2010 12:00 AM

Description

The paper briefly outlines recent conventional approaches to modelling/predicting seasonal climate effects on grapevine phenology and wine quality using weatherdata from national meteorological institutions and yield/ vintage ratings provided bysommeliers. The seasonal variability in climatic conditions can cause shifts in grapevinegrowth stages, phenological events, which in turn affect the formation and ratio of grapeberry components, such as sugar, and pro-phenols, that give the colour, aroma and flavourattributes to the vintage relating to its wine style. Although winemaker ability is consideredto be the major determinant of the quality of wine, the excellence of any vintage could stillbe enhanced considerably with grapes ripened under ideal weather conditions; this isevidenced by better vintage ratings and price hikes associated with better weatherconditions in the past. Hence, viticulturists and enologists continuously strive to furtherscientific understanding on climate effects to increase yield and wine quality. Recentstudies reveal that conventional rigorous statistical data analysis methodologies used longterm data on crop yield/ wine quality and weather conditions for studying the associationsbetween the variables and at regional scales. This data requirement impedes any suchmeaningful study on vineyards established recently, at a micro scale. The GeoinformaticsResearch Centre (GRC) approaches investigated to overcome this dilemma with datacovering only a decade and at a vineyard scale are discussed. Climate data, such as monthlymaximum, minimum and average temperature, monthly total rainfall, occurrence of frostdays and growing degree days (GDD) (base 10oC) along with yield is analysed using datamining techniques, such as clustering, then with regression and discriminant methods. Theresults show potential for predicting future yield/ wine quality under current weatherconditions that could enhance winegrower ability to improve practices for better outcomefrom the vineyard in terms of yield quality and quantity.

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Jul 1st, 12:00 AM

Modelling the seasonal climate effects on grapevine yield at different spatial and unconventional temporal scales

The paper briefly outlines recent conventional approaches to modelling/predicting seasonal climate effects on grapevine phenology and wine quality using weatherdata from national meteorological institutions and yield/ vintage ratings provided bysommeliers. The seasonal variability in climatic conditions can cause shifts in grapevinegrowth stages, phenological events, which in turn affect the formation and ratio of grapeberry components, such as sugar, and pro-phenols, that give the colour, aroma and flavourattributes to the vintage relating to its wine style. Although winemaker ability is consideredto be the major determinant of the quality of wine, the excellence of any vintage could stillbe enhanced considerably with grapes ripened under ideal weather conditions; this isevidenced by better vintage ratings and price hikes associated with better weatherconditions in the past. Hence, viticulturists and enologists continuously strive to furtherscientific understanding on climate effects to increase yield and wine quality. Recentstudies reveal that conventional rigorous statistical data analysis methodologies used longterm data on crop yield/ wine quality and weather conditions for studying the associationsbetween the variables and at regional scales. This data requirement impedes any suchmeaningful study on vineyards established recently, at a micro scale. The GeoinformaticsResearch Centre (GRC) approaches investigated to overcome this dilemma with datacovering only a decade and at a vineyard scale are discussed. Climate data, such as monthlymaximum, minimum and average temperature, monthly total rainfall, occurrence of frostdays and growing degree days (GDD) (base 10oC) along with yield is analysed using datamining techniques, such as clustering, then with regression and discriminant methods. Theresults show potential for predicting future yield/ wine quality under current weatherconditions that could enhance winegrower ability to improve practices for better outcomefrom the vineyard in terms of yield quality and quantity.