Modeling grape quality by multivariate analysis of viticulture practices, soil and climate

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.

Keywords

• Explanatory model
• Partial least squares regression (PLS)
• Vineyard management
• Chemical maturity of the grapes
• Grape health
• Acid balance