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 climatic 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, was performed for each grape quality criterion (sugar content, total acidity, malic acid, tartaric acid, yeast available nitrogen, pH and bunch rot). This statistical analysis identified the main viticultural practices as well as soil and climate variables related to the grape quality at harvest. The results highlight the relationships between the vine pruning length (spur pruning = short pruning or cane pruning = long pruning) and pH and malic acid but also reveal even more significant relationships with tartaric acid, yeast available nitrogen and bunch rot. The dryness index and the plant density have a strong influence on the grape malic acid concentration and pH, respectively. Vine perennial practices are the greatest contributors to the grape the yeast available nitrogen concentration. Conclusion: The models note the most relevant viticultural practices and soil and climatic variables driving each studied grape quality criterion. Significance and impact of the study: The results provide a better understanding of the major variables influencing grape quality.