Abstract

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 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.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call