Abstract
The time of flowering is key to understanding the development of grapevines. Flowering coincides with inflorescence initiation and fruit set, important determinants of yield. This research aimed to determine between and within-vine variability in 4-cane-pruned Sauvignon blanc inflorescence number per shoot, number of flowers per inflorescence and flowering progression using an objective method of assessing flowering via image capture and statistical analysis using a Bayesian modelling framework. The inflorescence number and number of flowers per inflorescence were measured by taking images over the flowering period. Flowering progression was assessed by counting open and closed flowers for each image over two seasons. An ordinal multinomial generalised linear mixed-effects model (GLMM) was fitted for inflorescence number, a Poisson GLMM for flower counts and a binomial GLMM for flowering progression. All the models were fitted and interpreted within a Bayesian modelling framework. Shoots arising from cane node one had lower numbers of inflorescences compared to those at nodes 3, 5 and 7, which were similar. The number of flowers per inflorescence was greater for basal inflorescences on a shoot than apical ones. Flowering was earlier, by two weeks, and faster in 2017/18 when compared to 2018/19 reflecting seasonal temperature differences. The time and duration of flowering varied at each inflorescence position along the cane. While basal inflorescences flowered later and apical earlier at lower insertion points on the shoot, the variability in flowering at each position on the vine dominated the date and duration of flowering.This is the first study using a Bayesian modelling framework to assess variability inflorescence presence and flower number, as well as flowering progression via objective quantification of open and closed flower counts rather than the more subjective method of visual estimation in the field or via cuttings. Although flower number differed for apical and basal bunches, little difference in timing and progression of flowering by these categories was observed. The node insertion point along a shoot was more important. Overall, the results indicate individual inflorescence variation and season are the key factors driving flowering variability and are most likely to impact fruit set and yield.
Highlights
Flowering is a key phenological stage monitored for understanding the development of grapevines during the season and for yield component formation
While flower number has been successfully counted manually (May, 2000; Poni et al, 2006), or using image processing techniques and/or automated counting systems (Diago et al, 2014; Aquino et al, 2015a; Aquino et al, 2015b; Millan et al, 2017; Liu et al, 2018; Tello et al, 2020), no studies to date have counted open and closed flowers on inflorescences to objectively score the percentage flowering as a method of flowering assessment to evaluate flowering variability, which is the method employed in this current study
Prior studies including these two, indicate that the presence or absence of an inflorescence primordia is determined the season prior to its expression, and this is determined by temperature, carbohydrate availability, light and associated plant growth regulators, a lack of carbohydrate availability at bud burst may result in inflorescence abortion (Eltom et al, 2013)
Summary
Flowering is a key phenological stage monitored for understanding the development of grapevines during the season and for yield component formation. Monitoring flowering in the vineyard can be subjective and several factors may influence the outcome for determining 50 % flowering: the number of vines observed, the position of vines within a vineyard and the frequency, accuracy and consistency of observer(s), as well as the scale used for determining flowering (for example scoring on a numeric scale such as 1–10, designating an absolute percentage value, or assigning percentage values in ‘bins’ such as each 10 %) These factors may lead to differences in determining the time of flowering, our ability to predict flowering or utilise the information from the flowering period in models. While flower number has been successfully counted manually (May, 2000; Poni et al, 2006), or using image processing techniques and/or automated counting systems (Diago et al, 2014; Aquino et al, 2015a; Aquino et al, 2015b; Millan et al, 2017; Liu et al, 2018; Tello et al, 2020), no studies to date have counted open and closed flowers on inflorescences to objectively score the percentage flowering as a method of flowering assessment to evaluate flowering variability, which is the method employed in this current study
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