Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees

Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees Tissue culture data is non-linear in nature. Decision tree algorithms stand out in revealing the non-linear interactions and relationships between the predictors and responses. Classification and regression tree (CART), chi squared automatic interaction detector (CHAID) and exhaustive CHAID are the common decision tree algorithms. These three models were employed to predict and optimize the effect of minor mineral nutrients on shoot cultures of Corylus avellana L. cultivars. H3BO3, CuSO4·5H2O, MnSO4·H2O, Na2MoO4·2H2O and Zn(NO3)2·6H2O were tested in a range of 0.5 × to 4 × Driver and Kuniyuki (DKW) medium within a RSM optimal design. NiSO4·6H2O was also an input within the design with varying levels of 0 to 6 µM. Shoot quality and length were affected by genotype, B and Mo amounts. Multiplication rate depended on genotype, B, Zn and Cu levels. Callus formation was affected by genotype and B. Leaf size depended on genotype, Zn and Mn concentrations. Cu was a significant predictor of leaf color and Ni slightly improved SPAD readings (chlorophyll content). CART in general outperformed CHAID and exhaustive CHAID in terms of the predictive performance. Both CHAID and exhaustive CHAID failed to generate a tree model for a leaf size response. The optimal minor nutrients for hazelnuts based on the predictions of the CART algorithm were suggested to be: B 2.3 × DKW, Cu 0.5×, Mn 0.5×, 2 × Mo and Zn 2×. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Plant Cell, Tissue and Organ Culture Springer Journals

Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees

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Publisher
Springer Journals
Copyright
Copyright © 2017 by This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection
Subject
Life Sciences; Plant Sciences; Plant Physiology; Plant Genetics and Genomics; Plant Pathology
ISSN
0167-6857
eISSN
1573-5044
D.O.I.
10.1007/s11240-017-1353-x
Publisher site
See Article on Publisher Site

Abstract

Tissue culture data is non-linear in nature. Decision tree algorithms stand out in revealing the non-linear interactions and relationships between the predictors and responses. Classification and regression tree (CART), chi squared automatic interaction detector (CHAID) and exhaustive CHAID are the common decision tree algorithms. These three models were employed to predict and optimize the effect of minor mineral nutrients on shoot cultures of Corylus avellana L. cultivars. H3BO3, CuSO4·5H2O, MnSO4·H2O, Na2MoO4·2H2O and Zn(NO3)2·6H2O were tested in a range of 0.5 × to 4 × Driver and Kuniyuki (DKW) medium within a RSM optimal design. NiSO4·6H2O was also an input within the design with varying levels of 0 to 6 µM. Shoot quality and length were affected by genotype, B and Mo amounts. Multiplication rate depended on genotype, B, Zn and Cu levels. Callus formation was affected by genotype and B. Leaf size depended on genotype, Zn and Mn concentrations. Cu was a significant predictor of leaf color and Ni slightly improved SPAD readings (chlorophyll content). CART in general outperformed CHAID and exhaustive CHAID in terms of the predictive performance. Both CHAID and exhaustive CHAID failed to generate a tree model for a leaf size response. The optimal minor nutrients for hazelnuts based on the predictions of the CART algorithm were suggested to be: B 2.3 × DKW, Cu 0.5×, Mn 0.5×, 2 × Mo and Zn 2×.

Journal

Plant Cell, Tissue and Organ CultureSpringer Journals

Published: Nov 22, 2017

References

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