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Use of multiple regression analysis and artificial neural networks to model the effect of nitrogen in the organogenesis of Pinus taeda L.

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Abstract

Mineral nutrition is a very important factor in the success of in vitro plant cultures. The aim was to compare the predictive capacity of the models obtained using a parametric technique such as multiple regression analysis with a nonparametric one such as artificial neural networks. These techniques were used for modeling the effect of total nitrogen concentration and the ratio nitrate: ammonium in the regeneration rate, oxidation rate, callus proliferation rate, number of buds per explant and buds-forming capacity index. Both the concentration of total nitrogen and the relationship between the concentrations of nitrate and ammonium influenced the morphogenetic responses. Optimal buds regeneration was in the range of 10–20 mM of the total nitrogen concentration and 1–2 of the nitrate: ammonium ratio. Higher concentrations of nitrogen produced an increase in the oxidation rate while the low nitrate: ammonium ratio favored the callus proliferation rate. Artificial neural network models presented a better precision to predict the different responses to the total content of nitrogen and the nitrate: ammonium rate, with higher coefficients of determination and correlation. They also presented a lower root mean square error for all the variables studied than the multiple regression analysis.

Key message

The use of artificial neural networks allows obtaining a better model of the effect of nitrogen on the organogenesis of Pinus taeda L. than traditional statistical techniques.

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Abbreviations

ANNs:

Artificial neural networks

BFC:

Bud-forming capacity

MRA:

Multiple regression analysis

NO3/NH4:

Nitrate: ammonium rate

r:

Pearson’s correlation coefficient

R2 :

Coefficient of determination

RMSE:

Root mean squared error

TNC:

Total nitrogen concentration

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Acknowledgements

I thank the Botanical Institute of the Northeast (IBONE-UNNE-CONICET) for supporting this work and the forest company “Bosques del Plata S.A.” for supplying the seeds.

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Authors and Affiliations

  1. Instituto de Botánica del Nordeste (IBONE-CONICET-UNNE), Sargento Cabral 2131, W3402BKG, Corrientes, Argentina

    Javier Orlando Barone

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  1. Javier Orlando Barone
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BJO designed the experiment, executed it and wrote the manuscript.

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Correspondence to Javier Orlando Barone.

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The author declares that he has no conflict of interests.

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Communicated by M. Paula Watt.

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Barone, J.O. Use of multiple regression analysis and artificial neural networks to model the effect of nitrogen in the organogenesis of Pinus taeda L.. Plant Cell Tiss Organ Cult 137, 455–464 (2019). https://doi.org/10.1007/s11240-019-01581-y

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