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<p>In a&#xa0;scenario of climate change, including climate information in agronomic models is essential for identifying the variables that most strongly influence grape yield variance as well as must and wine variables. This study aimed to (a)&#xa0;propose and validate agronomic models to predict grape yield as well as must and wine composition variables; (b)&#xa0;quantify the relevance of predictor variables in explaining the variance of response variables in vineyards cultivated with cultivars ‘Pinot Noir’ and ‘Chardonnay’ in sandy soil, under a subtropical climate. A&#xa0;database comprising 180 ‘Chardonnay’ and ‘Pinot Noir’ grapevine samples subjected to the application of 0, 10, 20, 40, 60 and 80 kg&#xa0;ha<sup>−1</sup> year<sup>−1</sup> of nitrogen assessed over six growing seasons (2018/2019 to 2023/2024), was used to develop the agronomic models. The model was developed through multiple linear regression. The grape yield prediction model achieved a&#xa0;coefficient of determination of 0.72 for ‘Chardonnay’ and 0.70 for ‘Pinot Noir’, indicating strong predictive accuracy for both cultivars. Climatic variables often exhibit the greatest relative importance in explaining variances, mainly in grape must and wine variables. Climatic variables stood out for their importance (&gt; 80%) in agronomic models used to predict grape must and wine variables. The inclusion of climatic variables in agronomic models can help increase nutrient uptake in grapevines. Their use also has a&#xa0;positive influence on grape yield and berry quality.</p>

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Grape Yield Prediction and Grape Must and Wine Composition Based on an Agronomic Model

  • 期刊论文

  • 发表日期：

    2026年6月24日

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  • Adriele Tassinari,
  • Jean Michel Moura-Bueno,
  • Gustavo Nogara-Siqueira,
  • Guilherme Zanon Peripolli,
  • Bianca Goularte Dias,
  • Wellynthon Machado da Cunha,
  • Luana Paula Garlet,
  • Rafael Lizandro Schumacher,
  • Gustavo Brunetto

摘要

In a scenario of climate change, including climate information in agronomic models is essential for identifying the variables that most strongly influence grape yield variance as well as must and wine variables. This study aimed to (a) propose and validate agronomic models to predict grape yield as well as must and wine composition variables; (b) quantify the relevance of predictor variables in explaining the variance of response variables in vineyards cultivated with cultivars ‘Pinot Noir’ and ‘Chardonnay’ in sandy soil, under a subtropical climate. A database comprising 180 ‘Chardonnay’ and ‘Pinot Noir’ grapevine samples subjected to the application of 0, 10, 20, 40, 60 and 80 kg ha−1 year−1 of nitrogen assessed over six growing seasons (2018/2019 to 2023/2024), was used to develop the agronomic models. The model was developed through multiple linear regression. The grape yield prediction model achieved a coefficient of determination of 0.72 for ‘Chardonnay’ and 0.70 for ‘Pinot Noir’, indicating strong predictive accuracy for both cultivars. Climatic variables often exhibit the greatest relative importance in explaining variances, mainly in grape must and wine variables. Climatic variables stood out for their importance (> 80%) in agronomic models used to predict grape must and wine variables. The inclusion of climatic variables in agronomic models can help increase nutrient uptake in grapevines. Their use also has a positive influence on grape yield and berry quality.