Canonical correlations among grapevine agronomic and processing characteristics

Abstract

Canonical correlation analysis allows conclusions to be drawn about the occurrence and magnitude of associations between two groups of characteristics. This study estimated the magnitude of association and interdependence between two trait groups of clones of two varieties of Vitis vinifera grapes. The study was based on the mean data of eight characteristics from two experiments to test the performance of these clones: the first experiment provided data from seven clones of Cabernet Sauvignon, which evaluated in the northern San Joaquin Valley, California, United States, and the second data set was from six clones of the Chardonnay variety, which were evaluated in the Napa Valley, California, United States. The canonical correlations among the two varieties were significant, indicating that the two groups of characteristics were related or not independent. The quality and quantity of the raw material required for fine-wine production were inversely proportional and specific to each variety. For Cabernet Sauvignon, the high number of berries per bunch and high number of bunches per plant resulted in fruits with a high °Brix level and increased pH. For Chardonnay, the increase in the bunch weight and number of berries decreased the fruit pH.

Correlação Canônica e efeito direto e indireto na seleção de acessos de batata-doce coletados no Vale do Ribeira

“…O uso da correlação canônica é amplamente difundido em trabalhos de melhoramento genético de plantas nas mais diversas culturas, tais como: batata-doce (Miranda et al, 1988), ciriguela (Giles et al, 2016), eucalipto (Protásio et al, 2012), feijão (Coimbra et al, 2000), girassol (Nobre et al, 2018), palma forrageira (Silva et al, 2020), trigo (Carvalho et al, 2015), uva (Cargnin, 2019), soja (Ferreira et al, 2020), algodão (Ramos et al, 2021).…”

Correlations between morpho-agronomic characters of soybean supplemented via leaf with micronutrients

“…The canonical correlation analysis also made it possible to efficiently draw inferences about agronomic characteristics in a study carried out by Pereira et al ( 2017), Leamy et al (2016) and Rigo et al (2018). In this context, the canonical correlation analysis demonstrates to be the most adequate technique to measure the relationships between two sets of characteristics, both between groups of characteristics of primary and secondary yield, as well as in groups of physiological and agronomic characteristics (Cargnin, 2019). .01 Characters -Group I: thousand grain mass TGM and yield YI; Group II: stand STD, plant height PH, first reproductive node height FRH, pods with one grain POG, pods with two grains PTWG, pods with three grains PTHG, pods with four grains PFG, pods per plant PPP and grains per plant GPP.…”

“…Multivariate methods, which evaluate many characteristics simultaneously, can effectively contribute to the identification of practical field situations, presenting itself as another tool for the technical development of agribusiness. The study of the correlations of phenotypic characteristics has become fundamental, as it allows the visualization of specific changes that may result in losses of others during the selection process (Cargnin, 2019).…”

Multitrait selection in seedless grape hybrids in semiarid regions of Brazil

“…Wei et al (2002) also found a strong positive correlation between the berry mass, length, and diameter and a strong negative correlation between the soluble solids content and titratable acidity, in addition to a high positive correlation between the soluble solids content and ratio. Cargnin (2019) evaluated the magnitude of association between traits of clones of two cultivars of Vitis vinifera using canonical correlation, obtaining correlations r = −0.71 and r = 0.55 between the yield and number of bunches, r = 0.98 and r = 0.90 between the bunch weight and yield, r = −0.82 and r = 0.16 between the number of bunches and bunch weight, r = −0.18 and r = −0.77 between the number of berries and berry weight and r = 0.13 and r = −0.05) between the soluble solid content and titratable acidity for "Cabernet Sauvignon" and "Chardonnay," respectively.…”