2018
DOI: 10.2503/hortj.okd-051
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Growth Analysis of Potted Seedlings of Satsuma Mandarin (<i>Citrus unshiu</i> Marc.) under Different Light Conditions and Air Temperatures

Abstract: To establish cultural practice based on a consecutive growth model for potted 1-year-old seedlings of Satsuma mandarin (Citrus unshiu Marc.), growth analysis by classical and functional approaches was conducted under different light conditions and air temperatures over 2.5 years, and the active growth of potted seedlings in the greenhouse was investigated. Under the classical approach, the general change patterns of relative growth rate (RGR) and net assimilation rate (NAR) were hard to determine because of ir… Show more

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Cited by 6 publications
(12 citation statements)
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“…In addition, NAR is difficult to measure because it requires the dry mass of the tree, including the roots. In the latter relationship, ΣR S was already excluded by the stepwise AIC, and SLA has been shown to be a suitable and precise parameter representing light conditions (Yano et al, 2018). Therefore, three of the short models [Model 2, consisting of Model 1 without NAR; Model 3, consisting of Model 1 without NAR and SLA; and Model 4, consisting of Model 1 without NAR and LMR] were not the best models, but would be useful for predicting growth because the models that included SLA and/or LMR as explanatory variables reduced the AIC as compared with Model 5 (Table 3).…”
Section: Multiple Regression Models To Predict Plant Massmentioning
confidence: 99%
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“…In addition, NAR is difficult to measure because it requires the dry mass of the tree, including the roots. In the latter relationship, ΣR S was already excluded by the stepwise AIC, and SLA has been shown to be a suitable and precise parameter representing light conditions (Yano et al, 2018). Therefore, three of the short models [Model 2, consisting of Model 1 without NAR; Model 3, consisting of Model 1 without NAR and SLA; and Model 4, consisting of Model 1 without NAR and LMR] were not the best models, but would be useful for predicting growth because the models that included SLA and/or LMR as explanatory variables reduced the AIC as compared with Model 5 (Table 3).…”
Section: Multiple Regression Models To Predict Plant Massmentioning
confidence: 99%
“…Under equal pot-size conditions, a previous study indicated that the naturallog-transformed plant mass of Satsuma mandarin seedlings (m P ) fitted a 4-parameter logistic (4L) model on a tt basis (Yano et al, 2018). However, even under equal tt conditions, different environmental conditions (e.g.…”
Section: Introductionmentioning
confidence: 99%
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