Mathematical models to estimate leaf area of grapevine varieties Chardonnay, Marselan and Pinot Noir

Sautchuk, Sabrina;

Brighenti, Alberto Fontanella;

Tomazetti, Tiago Camponogara;

Votre, Thainá Carolina Graciano;

Cardoso, Jackson Felipe;

Silva, Aparecido Lima Da;

Abstract: This study aimed to develop mathematical models for estimating leaf area in three grapevine cultivars (‘Chardonnay’, ‘Marselan’, and ‘Pinot Noir’) grown with plastic overhead cover in Santa Catarina, Brazil. Linear measurements of midvein length, left and right secondary veins, and the sum of secondary veins were taken from 200 leaves per variety during the 2020/21 and 2021/22 cycles. Linear regression models were developed using leaf area and midvein length, midvein length squared, sum of secondary veins, and sum of secondary veins squared as predictors. Model performance was evaluated based on Pearson correlation coefficient, root mean square error, bias, and efficiency. The developed models accurately estimate leaf area for the tested grapevine cultivars under plastic cover conditions, with the best-performing model using the sum of secondary veins squared as a predictor variable. These models can be useful for researchers and grapevine growers for estimating leaf area and improving plant growth management.Sautchuk, Sabrina;

Brighenti, Alberto Fontanella;

Tomazetti, Tiago Camponogara;

Votre, Thainá Carolina Graciano;

Cardoso, Jackson Felipe;

Silva, Aparecido Lima Da;

Abstract: This study aimed to develop mathematical models for estimating leaf area in three grapevine cultivars (‘Chardonnay’, ‘Marselan’, and ‘Pinot Noir’) grown with plastic overhead cover in Santa Catarina, Brazil. Linear measurements of midvein length, left and right secondary veins, and the sum of secondary veins were taken from 200 leaves per variety during the 2020/21 and 2021/22 cycles. Linear regression models were developed using leaf area and midvein length, midvein length squared, sum of secondary veins, and sum of secondary veins squared as predictors. Model performance was evaluated based on Pearson correlation coefficient, root mean square error, bias, and efficiency. The developed models accurately estimate leaf area for the tested grapevine cultivars under plastic cover conditions, with the best-performing model using the sum of secondary veins squared as a predictor variable. These models can be useful for researchers and grapevine growers for estimating leaf area and improving plant growth management. Read More