A Generalized Linear Model to Predict the Growth of Potted Seedlings of Satsuma Mandarin (Citrus unshiu Marcow.) under Different Initial Plant Conditions, Environmental Conditions, and Pot Size

To predict the seasonal vegetative growth of potted seedlings of Satsuma mandarin, the effects of initial plant conditions (age, shoot pruning), environment (greenhouse, greenhouse + shading, open-field culture), and pot size on growth were researched for 1.5–2.5 years. The growth pattern was evalua...

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Bibliographic Details
Published in:Horticulture journal 2018, Vol.87(4), pp.490-498
Main Authors: Yano, Taku, Morisaki, Akiyoshi, Ito, Shun-ichiro, Kitano, Masaharu
Format: Article
Language:English
Subjects:
AIC
Biomass
Curve fitting
Environmental conditions
Generalized linear models
Greenhouse effect
Greenhouses
Growth rate
Leaf area
light condition
Mandarins
multiple regression
Parameters
Plant biomass
Plant growth
Pruning
Seedlings
Shading
Size effects
Statistical models
transplanting
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Summary:To predict the seasonal vegetative growth of potted seedlings of Satsuma mandarin, the effects of initial plant conditions (age, shoot pruning), environment (greenhouse, greenhouse + shading, open-field culture), and pot size on growth were researched for 1.5–2.5 years. The growth pattern was evaluated using a curve fitting with the 4-parameter logistic (4L) model, biomass allocation, and classical (interval) growth analysis, and a generalized linear model analysis based on thermal time (tt). Growth delays from shoot pruning were confirmed by growth trajectories with the 4L model based on tt. Plant growth was positively affected by enlarging the pot size (from 20-L to 45-L), while shading significantly suppressed the growth of 45-L potted Satsuma mandarin seedlings in the greenhouse. In the growth analysis, the relative growth rate was not always determined by the net assimilation rate (NAR), which included both shoot-pruning and pot size effects. To predict the plant mass of Satsuma mandarin seedlings (MP), we proposed several generalized linear models using a log link function assuming that MP followed a Gamma-distribution. The best model to predict MP was selected based on Akaike information criterion (AIC) values, and contained explanatory variables for initial plant biomass, NAR, specific leaf area (SLA), leaf mass ratio (LMR), pot size, and tt. Some simpler models excluding NAR, but including SLA and/or LMR as explanatory variables, were more useful than a model lacking growth analysis parameters (NAR, SLA, and LMR).
ISSN:2189-0102
2189-0110
DOI:10.2503/hortj.OKD-112