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Plant Cell, Tissue and Organ Culture (PCTOC)

, Volume 140, Issue 3, pp 661–670 | Cite as

Analysis of macro nutrient related growth responses using multivariate adaptive regression splines

  • Meleksen AkinEmail author
  • Sadiye Peral Eyduran
  • Ecevit Eyduran
  • Barbara M. Reed
Original Article
  • 61 Downloads

Abstract

Strawberry micropropagation is generally based on Murashige and Skoog mineral salts, and many cultivars grow well on this medium. However, the diverse species found in germplasm collections often do not thrive, which indicates a need to optimize the mineral nutrients. In this study, Multivariate Adaptive Regression Splines (MARS), was employed to predict shoot quality, multiplication, and leaf color responses of three strawberry species in response to the major tissue culture nutrients by generating functional associations. MARS is a non-parametric approach that can be used to deal with continuous and categorical data without requiring the strict distributional assumptions of the basic linear models. The MARS algorithm is capable of capturing non-linear patterns between the input and target variables. NH4NO3, CaCl2·2H2O, MgSO4·7H2O, KNO3 and KH2PO4 were tested in a range of 0.5 × to 3 × MS medium, within a computer-generated optimal design that consisted of 32 treatment combinations. The plant responses were affected by all of the major salts tested and the genotype factor. Multivariate Adaptive Regression Splines captured the significant factors and their interactions to predict optimal major salts suitable for all three strawberry species: 3300 mg L−1 NH4NO3, 862.4 mg L−1 CaCl2, 1110 mg L−1 MgSO4, 3439 mg L−1 KNO3, and 329.8 mg L−1 KH2PO4. This study identified the major nutrient needs of the three strawberry species and provides an alternative statistical technique for tissue culture data analyses.

Key Message

The MARS statistical approach was used to predict macro nutrient related growth responses of three strawberry species. The objective of the study was to make a gentle introduction to the MARS algorithm and show its potential application to tissue culture research.

Keywords

Fragaria In vitro culture MARS Mineral nutrition Statistical analysis 

Notes

Author contributions

MA planned and executed the experiment, collected the data, drafted the manuscript and assisted with analysis of the data. SPE helped with data analysis interpretation and writing the manuscript. EE performed the statistical analysis. BMR assisted with planning and analysis, supervised the study, and edited the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that there are no potential conflicts of interest regarding the research, authorship and publication of this manuscript.

Supplementary material

11240_2019_1763_MOESM1_ESM.docx (17 kb)
Electronic supplementary material 1 (DOCX 17 kb)

References

  1. Akin M, Eyduran E, Reed BM (2016) Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. PCTOC 128(2):303–316CrossRefGoogle Scholar
  2. Akin M, Hand C, Eyduran E, Reed BM (2017) Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees. PCTOC 132(3):545–559CrossRefGoogle Scholar
  3. Darrow GM (1966) The strawberry: history, breeding and physiology. Holt, Rinehart and Winston, New YorkGoogle Scholar
  4. Design-Expert (2010) Stat-Ease, Inc., Minneapolis, MNGoogle Scholar
  5. Dettori JR, Norvell DC (2018) The anatomy of data. Global Spine J 8(3):311–313CrossRefGoogle Scholar
  6. Driver JA, Kuniyuki AH (1984) In vitro propagation of Paradox walnut rootstock. HortScience 19:507–509Google Scholar
  7. Emamgolizadeh S, Bateni SM, Shahsavani D, Ashrafi T, Ghorbani H (2015) Estimation of soil cation exchange capacity using genetic expression programming (GEP) and multivariate adaptive regression splines (MARS). J Hydrol 529(3):1590–1600CrossRefGoogle Scholar
  8. Everingham YL, Sexton J (2011) An introduction to multivariate adaptive regression splines for the cane industry. Proceedings of the 2011 Conference of the Australian Society of Sugar Cane TechnologistsGoogle Scholar
  9. Eyduran E, Akin M, Eyduran SP (2019) Application of multivariate adaptive regression splines through R Software. Nobel Academic Publishing, AnkaraGoogle Scholar
  10. Friedman J (1991) Multivariate adaptive regression splines. Ann Stat 19(1):1–67CrossRefGoogle Scholar
  11. Kovalchuk IY, Mukhitdinova Z, Turdiyev T, Madiyeva G, Akin M, Eyduran E, Reed BM (2017) Modeling some mineral nutrient requirements for micropropagated wild apricot shoot cultures. PCTOC 129(2):325–335CrossRefGoogle Scholar
  12. Kovalchuk IY, Mukhitdinova Z, Turdiyev T, Madiyeva G, Akin M, Eyduran E, Reed BM (2018) Nitrogen ions and nitrogen ion proportions impact the growth of apricot (Prunus armeniaca) shoot cultures. PCTOC 133(2):263–273CrossRefGoogle Scholar
  13. Linsmaier EM, Skoog F (1965) Organic growth factor requirements of tobacco tissue cultures. Physiol Plant 18:100–127CrossRefGoogle Scholar
  14. Lloyd G, McCown B (1980) Commercially feasible micropropagation of mountain laurel, Kalmia latifolia, by use of shoot-tip culture. Comb Proc Int Plant Prop Soc 30:421–427Google Scholar
  15. Mertler C, Vannatta R (2002) Advanced and multivariate statistical methods: practical application and interpretation, 2nd edn. Pyrczak Publishing, Los AngelesGoogle Scholar
  16. Murashige T, Skoog F (1962) A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol Plant 15:473–497CrossRefGoogle Scholar
  17. Nezami-Alanagh E, Garoosi GA, Landin M, Gallego PP (2018) Combining DOE With neurofuzzy logic for healthy mineral nutrition of pistachio rootstocks in vitro culture. Front Plant Sci 9:1474.  https://doi.org/10.3389/fpls.2018.01474 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Niedz RP, Evens TJ (2016) Design of experiments (DOE)—history, concepts, and relevance to in vitro culture. In Vitro Cell Dev Biol Plant 52(6):547–562CrossRefGoogle Scholar
  19. Niedz RP, Hyndman SE, Evens TJ (2007) Using a gestalt to measure the quality of in vitro responses. Sci Hort 112(3):349–359CrossRefGoogle Scholar
  20. Olden JD, Lawler JJ, Poff NL (2008) Machine learning methods without tears: a primer for ecologists. Q Rev Biol 83:171–193CrossRefGoogle Scholar
  21. Poothong S, Reed BM (2014) Modeling the effects of mineral nutrition for improving growth and development of micropropagated red raspberries. Sci Hort 165(0):132–141CrossRefGoogle Scholar
  22. Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  23. Quiroz KA, Berríos M, Carrasco B, Retamales JB, Caligari PDS, García-Gonzáles R (2017) Meristem culture and subsequent micropropagation of Chilean strawberry (Fragaria chiloensis (L.) Duch. Biol Res 50(1):20CrossRefGoogle Scholar
  24. R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
  25. Reed BM, Hummer KE (1995) Conservation of germplasm of strawberry (Fragaria Species). In: Bajaj YPS (ed) Cryopreservation of Plant Germplasm I, vol 32, Springer, BerlinCrossRefGoogle Scholar
  26. Reed BM, Wada S, DeNoma J, Niedz RP (2013) Improving in vitro mineral nutrition for diverse pear germplasm. In Vitro Cell Dev Biol Plant 49(3):343–355CrossRefGoogle Scholar
  27. Simpson DW, Bell JA (1989) The response of different genotypes of Fragaria × ananassa and their seedling progenies to in vitro micropropagation and the effects of varying the concentration of 6-benzylaminopurine in the proliferation medium. PCTOC 17(2–3):225–234CrossRefGoogle Scholar
  28. StatSoft (2005) STATISTICA, Inc., (Data Analysis Software System), Version 7.1. http://www.statsoft.com
  29. Zakeri IF, Adolph AL, Puyau MR, Vohra FA, Butte NF (2010) Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents. J Appl Physiol 108:128–136CrossRefGoogle Scholar
  30. Zhang W, Goh ATC (2014) Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 7:45–52CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2020

Authors and Affiliations

  1. 1.Department of Landscape Architecture, Agricultural FacultyIgdir UniversityIgdirTurkey
  2. 2.Department of Horticulture, Agricultural FacultyIgdir UniversityIgdirTurkey
  3. 3.Department of Business Administration, Economics and Administrative Sciences FacultyIgdir UniversityIgdirTurkey
  4. 4.USDA-ARS-Retired, National Clonal Germplasm RepositoryCorvallisUSA

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