Nitrogen ions and nitrogen ion proportions impact the growth of apricot (Prunus armeniaca) shoot cultures
- 105 Downloads
Nitrogen is a major driver of plant growth and the nitrogen source can be critical to good growth in vitro. A response surface methodology mixture-component design and a data mining algorithm were applied to nitrogen (N) nutrition for improving the micropropagation of Prunus armeniaca Lam. Data taken on shoot cultures included a subjective quality rating, shoot number, shoot length, leaf characteristics and physiological disorders. Data were analyzed using the Classification and Regression Tree data mining algorithm. The best overall shoot quality as well as leaf color were on medium with NO3− > 25 mM and NH4+/Ca+ > 0.8. Improving shoot length to15 mm required 25 < NO3− ≤ 35 mM with NH4+/Ca2+ ≤ 2.33. The most shoots (11.6) were produced with NO3− > 25 mM and NH4+/Ca2+ ≤ 0.8, but there were 5–10 shoots at other NO3− concentrations regardless of NH4+/Ca2+ proportion. Leaves increased in size with higher NO3− concentrations (> 55 mM). Physiological disorders were also influenced by the nitrogen components. Shoot tip necrosis was rarely present with NO3− > 45 mM. Callus production decreased somewhat with NH4+/Ca2+ > 2.33. Suggested concentrations for an improved medium considering all of these growth characteristics would be 25 < NO3− ≤ 35 mM and NH4+/Ca+ ≤ 0.8. Validation experiments comparing WPM and three trial media showed improvements in several shoot growth parameters on medium with optimized mesos and optimized nitrogen components.
KeywordsCART data mining Ion confounding Medium optimization Micropropagation Mixture component
This study was funded by International Science and Technology Center project grant K-1920 and U.S. Department of Agriculture, Agricultural Research Service CRIS project 5358-21000-038-00D.
IK assisted with planning the study, supervised the work and assisted in writing the manuscript; ZM, TT and GM set up and ran the experiments and collected data; MA assisted with data analysis and writing the manuscript; EE assisted with data analysis; BR assisted with planning the study, set up the experimental design, assisted with data analysis and writing the manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Research involving human and animal rights
No animals or humans were used in this research. All authors have agreed to this submission.
- Ali M, Eyduran E, Tariq MM, Tirink C, Abbas F, Bajwa MA, Baloch MH, Nizamani AH, Waheed A, Awan MA, Shah SH, Ahmad Z, Jan S (2015) Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep. Pak J Zool 47:1579–1585Google Scholar
- Design-Expert (2010) Design-expert 8. Stat-Ease, Inc., MinneapolisGoogle Scholar
- Lloyd G, McCown B (1980) Commercially feasible micropropagation of mountain laurel, (Kalmia latifolia) by use of shoot tip culture. Comb Proc Intern Plant Prop Soc 30:421–427Google Scholar
- Nisbet R, Elder J, Miner G (2009) Handbook of statistical analysis and data mining applications. Academic Press, LondonGoogle Scholar
- SPSS (2013) Statistics for windows, version 22.0. Armonk, IBM Corp, New YorkGoogle Scholar
- Wada S, Reed BM (2017) Trends in culture medium nitrogen requirements for in vitro shoot growth of diverse pear germplasm. Acta Hortic 1155:26–36Google Scholar