Development of a molecular test to determine the vitality status of Norway spruce (Picea abies) seedlings during frozen storage

In boreal forest regions, a great portion of forest tree seedlings are stored indoors in late autumn to prevent seedlings from outdoor winter damage. For seedlings to be able to survive in storage it is crucial that they store well and can cope with the dark and cold storage environment. The aim of this study was to search for genes that can determine the vitality status of Norway spruce (Picea abies (L.) Karst.) seedlings during frozen storage. Furthermore, the sensitivity of the ColdNSure (TM) test, a gene activity test that predicts storability was assessed. The storability of seedlings was tested biweekly by evaluating damage with the gene activity test and the electrolyte leakage test after freezing seedlings to -25 A degrees C (the SELdiff-25 method). In parallel, seedlings were frozen stored at -3 A degrees C. According to both methods, seedlings were considered storable from week 41. This also corresponded to the post storage results determined at the end of the storage period. In order to identify vitality indicators, Next Generation Sequencing (NGS) was performed on bud samples collected during storage. Comparing physiological post storage data to gene analysis data revealed numerous vitality related genes. To validate the results, a second trial was performed. In this trial, gene activity was better in predicting seedling storability than the conventional freezing test; this indicates a high sensitivity level of this molecular assay. For multiple indicators a clear switch between damaged and vital seedlings was observed. A collection of indicators will be used in the future development of a commercial vitality test.

Simulation model using meta heuristic algorithms for achieving optimal arrangement of storage bins in a sawmill yard

Bin planning (arrangements) is a key factor in the timber industry. Improper planning of the storage bins may lead to inefficient transportation of resources, which threaten the overall efficiency and thereby limit the profit margins of sawmills. To address this challenge, a simulation model has been developed. However, as numerous alternatives are available for arranging bins, simulating all possibilities will take an enormous amount of time and it is computationally infeasible. A discrete-event simulation model incorporating meta-heuristic algorithms has therefore been investigated in this study. Preliminary investigations indicate that the results achieved by GA based simulation model are promising and better than the other meta-heuristic algorithm. Further, a sensitivity analysis has been done on the GA based optimal arrangement which contributes to gaining insights and knowledge about the real system that ultimately leads to improved and enhanced efficiency in sawmill yards. It is expected that the results achieved in the work will support timber industries in making optimal decisions with respect to arrangement of storage bins in a sawmill yard.