Abstract
Aims. The present study aims to model grape quality criteria by combining a large number of viticultural practices and soil and climate variables related to the main determinants.Methods and results. A database has been developed using the Chenin Blanc grape variety in a Protected Designation of Origin. A statistical model, namely a Partial Least Squares (PLS) regression, has been determined for each grape quality criterion (sugar content, total acidity, malic acid, tartaric acid, available nitrogen, pH and bunch rot). This statistical analysis identifies the main viticultural practices and soil and climate variables related to the grape quality at harvest. The results highlight relationships between the length of vine pruning and pH and malic acid but even more significant relationships with tartaric acid, available nitrogen and bunch rot.Conclusion. The models point out the most relevant viticultural practices and soil and climate variables for the explanation of each grape quality criterion studied.Significance and impact of the study. The results provide a better understanding of the major variables that influence grape quality.
Highlights
Today’s wine market is international, focusing on the quality of wine and led by wines carrying signs of quality, such as protected designation of origin (PDO) or protected geographical indication (PGI) in Europe
Grape quality is a complex reality; it is mainly evaluated through several physicochemical criteria (Jackson Lombard, 1993) that depend on both viticultural practices and natural conditions
This study presents explanatory models linking grape quality at harvest to viticultural practices, soil and climatic variables
Summary
Today’s wine market is international, focusing on the quality of wine and led by wines carrying signs of quality, such as protected designation of origin (PDO) or protected geographical indication (PGI) in Europe. Grape quality is a complex reality; it is mainly evaluated through several physicochemical criteria (Jackson Lombard, 1993) that depend on both viticultural practices and natural conditions (soil and climate). Baudrit et al (2015) and Perrot et al (2015) proposed a predictive model of grape quality (sugar and acidity concentration) from climate (air temperature, rainfall, sunshine hours, etc.) and grape maturity criteria (berry size, grape color, phenolic compounds, etc.). This model is based on a mathematical approach that integrates fuzzy logic inside a dynamic Bayesian network. Several authors have studied the influence of many anthropogenic and natural variables together in Studied years
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