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Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut

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An Erratum to this article was published on 14 November 2016

Abstract

Defining optimal mineral-salt concentrations for in vitro plant development is challenging, due to the many chemical interactions in growth media and genotype variability among plants. Statistical approaches that are easier to interpret are needed to make optimization processes practical. Response Surface Methodology (RSM) and the Chi-Squared Automatic Interaction Detection (CHAID) data mining algorithm were used to analyze the growth of shoots in a hazelnut tissue-culture medium optimization experiment. Driver and Kuniyuki Walnut medium (DKW) salts (NH4NO3, Ca(NO3)2·4H2O, CaCl2·2H2O, MgSO4·7H2O, KH2PO4 and K2SO4) were varied from 0.5× to 3× DKW concentrations with 42 combinations in a IV-optimal design. Shoot quality, shoot length, multiplication and callus formation were evaluated and analyzed using the two methods. Both analyses indicated that NH4NO3 was a predominant nutrient factor. RSM projected that low NH4NO3 and high KH2PO4 concentrations were significant for quality, shoot length, multiplication and callus formation in some of the hazelnut genotypes. CHAID analysis indicated that NH4NO3 at ≤1.701× DKW and KH2PO4 at >2.012× DKW were the most critical factors for shoot quality. NH4NO3 at ≤0.5× DKW and Ca(NO3)2 at ≤1.725× DKW were essential for good multiplication. RSM results were genotype dependent while CHAID included genotype as a factor in the analysis, allowing development of a common medium rather than several genotype specific media. Overall, CHAID results were more specific and easier to interpret than RSM graphs. The optimal growth medium for Corylus avellana L. cultivars should include: 0.5× NH4NO3, 3× KH2PO4, 1.5× Ca(NO3)2.

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Acknowledgments

Funding for this study was provided by the U.S. Department of Agriculture, Agricultural Research Service CRIS project 5358-21000-033D. M. Akin was supported by a Higher Education Scholarship of Turkey. This study was part of a Ph.D. Dissertation by MA in the Department of Horticulture, Oregon State University, Corvallis, OR.

Authors’ contributions

MA planned and executed the experiment, collected and evaluated the data, assisted with data analysis and drafted the manuscript. EE developed and performed the CHAID statistical analysis. BMR assisted with planning and analysis, supervised the study, and edited the manuscript.

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Authors and Affiliations

  1. Department of Horticulture, Agricultural Faculty, Igdir University, Igdir, Turkey

    Meleksen Akin

  2. Biometry Genetics Unit, Department of Animal Science, Agricultural Faculty, Igdir University, Igdir, Turkey

    Ecevit Eyduran

  3. USDA-ARS-Retired, National Clonal Germplasm Repository, 33447 Peoria Rd, Corvallis, OR, 97333, USA

    Barbara M. Reed

Authors
  1. Meleksen Akin
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  2. Ecevit Eyduran
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  3. Barbara M. Reed
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Corresponding author

Correspondence to Meleksen Akin.

Additional information

An erratum to this article is available at http://dx.doi.org/10.1007/s11240-016-1125-z.

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Akin, M., Eyduran, E. & Reed, B.M. Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. Plant Cell Tiss Organ Cult 128, 303–316 (2017). https://doi.org/10.1007/s11240-016-1110-6

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  • DOI: https://doi.org/10.1007/s11240-016-1110-6

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