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], indicating symmetrical rates of growth and development about γ 1 , a characteristic of the generalized logistic curve ( Gregorczyk, 1991 ). Interannual variability in fruit mass before ripening is indicated by α 1 , where 2009 > 2007. In Eq. [2], the

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zones) of large pear canopies. Secondary objectives were to determine if timing of fabric application affected fruit growth rate, final fruit size, and quality and to characterize the effect of shade on fruit growth and production. Materials and Methods

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., 2011 ). Anthesis dates at each node of the main stem were investigated two or three times per week from 42 to 83 DAT. Flowers were selected randomly from plants in each row. To plot a fruit growth curve, 33 fruit were harvested at 78 DAT, 25 fruit were

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early stages of fruit development ( Fig. 2A ). All cultivars exhibited rapid postbloom growth. The growth curves of most of the cultivars approached the horizontal asymptote (maximum volume) between 500 and 1000 tu, depending on the model ( Fig. 2A

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sub-model was composed by considering yield components, and the latter one was composed by considering fruit growth curves. Environmental data of the greenhouse (e.g., air temperature, solar radiation) and plant growth data (e.g., leaf area index

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, Logan, UT). Light sensors were placed at 2-ft aboveground and 1.5 ft below shadecloth. The daily light integral (DLI) was then calculated from the instantaneous PPF ( Korczynski et al., 2002 ). Plant growth and fruit yield. Plant height and two

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may negatively impact tomato plant growth and fruit yield during warm seasons. High air temperatures inside HTs negatively influenced bell pepper ( Capsicum annuum ) plant growth and yield because of poor HT ventilation in the Dominican Republic ( Díaz

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tree performance. Tree performance parameters included fruit yield, tree growth, yield efficiency, and leaf N concentration. Materials and Methods Study site. This study was established in Spring 2005 at the Wenatchee Valley College Auvil Teaching and

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An approach to studying fruit growth is presented for peach fruit (Prunus persica L. Batsch). It combines a functional description of growth curves, multivariate exploratory data analysis, and graphical displays. This approach is useful for comparing growth curves fitted to a parametric model, and analysis is made easier by the choice of the model whose parameters have a meaning for the biologist. Growth curves were compared using principal component analysis (PCA) adapted to the table of estimated parameters. Growth curves of 120 fruits were fitted to a model that assumes two growth phases. The first one described the pit growth and the first part of the flesh growth. The second described the second part of the flesh growth. From PCA, firstly it was seen that fruit growth varied according to cumulated growth during both growth phases and to date of maximal absolute growth. Secondly, fruit growth varied according to cumulated growth and relative growth rates during each phase. Further examples are presented where growth curves were compared for varying fruit number per shoot and leaf: fruit ratio, and for different sources of variation (tree, shoot, and fruit). Growth of individual fruit was not related to fruit number per shoot or to leaf: fruit ratio. Growth variability was especially high between fruit within shoots.

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Fruit growth (diameter) of purple passion fruit (Passiflora edulis Sims.) and maypop (P. incarnata L.) followed a sigmoidal growth curve. Passion fruit were larger than either greenhouse-grown or wild maypop fruit. Wild maypop produced larger fruit than greenhouse-grown maypop. Yellow passion fruit had the lowest percentage of pulp and the highest soluble solids concentration (SSC) and greenhouse-grown maypop had the lowest SSC among the four groups tested. Purple and yellow passion fruit had lower juice pH than maypop. Wild maypop fruit had the highest sucrose content and purple passion fruit had the lowest. Yellow and purple passion fruit juice had higher fructose and glucose contents than did maypop juice.

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