Differential crop load and hormonal applications for enhancing fruit quality and yield attributes of grapes var. Flame Seedless

Singh, Shailesh Kumar; Arora, Neelima; Gill, M. I. S.; Gill, K. S.

doi:10.22438/jeb/38/5/ms-227

Cited by 6 publications

(2 citation statements)

References 9 publications

(11 reference statements)

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“…However, the cation Ca 2+ encourages the firmness of the cell wall by chelating the free carboxylic groups of galacturic units and cross-linking the pectic polysaccharide chains, forming a firmer, tighter structure [ 47 ]. Similar physical trait variability was reported for Flame Seedless grapes by other authors [ 50 , 51 , 52 , 53 , 54 ]; however, a comparison of the supplied results with the literature data was not possible.…”

Section: Resultssupporting

confidence: 75%

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Al-Saif

Abdel-Sattar

Aboukarima

et al. 2022

Foods

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The grape is a very well-liked fruit that is valued for its distinct flavor and several health benefits, including antioxidants, anthocyanins, soluble sugars, minerals, phenolics, flavonoids, organic acids, and vitamins, which significantly improve the product’s overall quality. Today’s supply chain as a whole needs quick and easy methods for evaluating fruit quality. Thus, the objective of this study was to estimate the quality attributes of Flame Seedless grape berries cultivated under various agronomical management and other practices using color space coordinates (berry L*, berry a*, and berry b*) as inputs in an artificial neural network (ANN) model with the best topology of (3-20-11). Satisfactory predictions based on the R2 range, which was 0.9817 to 0.9983, were obtained for physical properties (i.e., berry weight, berry length, and berry diameter as well as berry adherence strength) and chemical properties (i.e., anthocyanin, total soluble solids (TSS), TSS/titratable acidity, total sugars, titratable acidity, reducing sugars, and non-reducing sugars). Meanwhile, we also performed a contribution analysis to analyze the relative importance of CIELab colorimeter parameters of berries L*, a*, and b* to determine the main fruit quality. In terms of relative contribution, berry b* contributed relatively largely to berry weight, berry adherence strength, TSS, TSS/titratable acidity, titratable acidity, total sugars, reducing sugars, and non-reducing sugars and a* contributed relatively largely to anthocyanin, berry length, and berry diameter. The developed ANN prediction model can aid growers in enhancing the quality of Flame Seedless grape berries by selecting suitable agronomical management and other practices to avoid potential quality issues that could affect consumers of them. This research demonstrated how color space coordinates and ANN model may well be utilized to evaluate the Flame seedless grape berries’ quality.

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Section: Resultssupporting

confidence: 75%

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Al-Saif

Abdel-Sattar

Aboukarima

et al. 2022

Foods

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“…Индийские ученые установили, что необходимо поддерживать оптимальное количество гроздей на кусте для достижения лучшего качества винограда, которое гарантирует надлежащее равновесие между энергией роста и урожайностью. Виноградные растения с 50 %ной нагрузкой (50…65 гроздей на куст) отличались лучшим окрашиванием ягод, созревали на 4 дня раньше, характеризовались повышенным содержанием антоцианов и более весомой гроздью, по сравнению с контрольным вариантом [13].…”

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Shoot Load Effect on Grapevine Productivity and Vigor

Sirotkina¹

2022

Vestnik of Kazan State Agrarian University

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The research was carried out in order to determine the effect of the number of shoots in the structure of bushes on their fruitfulness and annual growth, plant yield and grape quality. The work was carried out using generally accepted methods in viticulture in 2019-2021 in Rostov region. The scheme of the experiment involved the study of the following norms for loading bushes with shoots: 30, 35, 40, 45 pcs/bush. The experiment was carried out on own root plantations of phylloxera-tolerant grape variety Pervenets Magarach, planted in 1986. The feeding area of one plant is 4.5 m2 (planting pattern is 3 m × 1.5 m), the shape of the bushes is a two-sided horizontal cordon with a spare sleeve and a restoration knot (semi-covering). According to fruitfulness (95 and 94%), variants with a load of 30 and 35 shoots/bush, respectively, were identified; fruiting coefficient (K1) in both variants is 1.69. According to the size of the crop, a variant with 45 shoots per bush was selected - 17.0 t/ha with the highest quality of berry juice (mass concentration of sugars 17.9 g/100 cm3, titratable acids - 8.4 g/dm2. In the change in the content of sugars and titratable acids in the yield according to the variants of the experiment, no regularity was revealed, the difference in the value of this indicator is not significant. In terms of the productivity of one vegetative shoot, plants were noted in the variant with 40 shoots in the structure of the bush (178 g).The length of the shoots decreased with an increase in their number per plant from 90 4 cm in the variant with a load of 30 shoots to 82.2 cm in the variant with the maximum number of shoots in the experiment (45 shoots). No pattern was noted in the change in the diameter of the shoots, no significant difference was found between the options. The proportion of shoot ripening had an inverse relationship with their numbers on the plant.

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Bioinformatics analysis of WRKY transcription factors in grape and their potential roles prediction in sugar and abscisic acid signaling pathway

Huang

Yang

et al. 2020

J. Plant Biochem. Biotechnol.

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Differential crop load and hormonal applications for enhancing fruit quality and yield attributes of grapes var. Flame Seedless

Cited by 6 publications

References 9 publications

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model

Shoot Load Effect on Grapevine Productivity and Vigor

Bioinformatics analysis of WRKY transcription factors in grape and their potential roles prediction in sugar and abscisic acid signaling pathway

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