Improved walnut mass micropropagation through the combined use of phloroglucinol and FeEDDHA

Despite the socioeconomic importance of walnut trees, poor rooting and recalcitrance to in vitro culture have hampered the establishment of high-yield clonal plantations. To improve walnut micropropagation, we introduced several modifications to current methods and evaluated the effects on microshoot performance and acclimatization. Nine selected genotypes (13-year-old trees) of the commercial hybrid Juglans major 209 x J. regia were cultured in vitro on DKW-C medium supplemented with 4.4 µM BA and 50 µM IBA. A protocol was developed that relies on the use of 0.40 mM phloroglucinol during shoot multiplication, 0.20 mM previous root induction, and 6.81 mg/L Fe3+ (FeEDDHA). Moreover, the addition of 83.2 µM glucose during the root expression phase significantly improved plant survival during acclimatization. Phloroglucinol promoted microshoot elongation but inhibited rooting, especially at concentrations above 0.40 mM. Replacing FeEDTA by FeEDDHA diminished chlorotic symptoms and improved rooting, with up to 90% microshoots developing viable roots. Likewise, glucose was more efficient than sucrose or fructose in promoting plant survival. At the proposed working concentrations, neither glucose nor FeEDDHA caused any noticeable deleterious effect on walnut micropropagation. Microscopic analysis revealed the physical continuity between adventitious roots and stem pericycles. Analysis of leaf genomic DNA with eight polymorphic microsatellite markers was supportive of the clonal fidelity and genetic stability of the micropropagated material. Successful clonal plantations (over 5,800 ramets) have been established by applying this protocol.

Determination of preventive maintenance lead time using hybrid analysis

The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.