NSERC Industrial Research Chair in Applied Forest Genetics and Biotechnology

在过去的十年里，国际上对高产林业的兴趣和实施激增，利用以种植园为基础的集约化管理的森林来最大限度地增加产量和价值。高产林业通过降低风险和轮换时间，提供更高和更快的投资回报，为森林业提供财政利益。在较小的土地基础上生产更多的木材，对于满足预计的纤维需求的急剧增长和减少林业的生态足迹至关重要。不列颠哥伦比亚省林场研究和树木改良部门、加拿大林务局以及包括种子和苗木生产者在内的大小林业行业成员正在积极开发、改良和保护几种具有重要商业价值的BC树的新开发的高收益和遗传多样性种质。该教席研究计划的重点是在种质开发商和操作用户之间建立联系，通过纳入包括适当技术在内的新创新来提高当前树木改良系统的效率。这项建议有三个主要目标：(1)通过创建有效、无缝和集成的树木改良交付系统来最大化不列颠哥伦比亚省树木改良计划的效率；(2)开发有效的现场工具来调查木材质量属性，以便通过树木改良和营林将木材质量属性整合到最终产品价值中；以及(3)将人工神经网络(ANN)应用于种子园管理和木材质量评估。该计划将培养森工及相关生物科学所需的高素质人才。

During the past decade, there has been an international surge in interest and implementation of high-yield forestry, using plantation-based intensively-managed forests to maximize the volume and value. High-yield forestry provides fiscal benefits to the forest industry by lowering risks and rotation times, providing higher and more quickly realized investment returns. Producing more wood on a smaller landbase is critical to meet the dramatic increase projected for fibre demand and to reduce the ecological footprint of forestry. The BC Ministry of Forests and Ranges Research and Tree Improvement Branches, Canadian Forest Service, and large and small forest industry members including seed and seedling producers, are actively developing, improving, and protecting the newly developed high gain and genetically diverse germplasm for several commercially important BC trees. The focus of this Chairs research program is to create linkages between germplasm developers and operational users to enhance the efficiency of the current tree improvement system by incorporating new innovations including appropriate technologies. There are three primary aims of this proposal: (1) maximizing the efficiency of British Columbias tree improvement programs through the creation of effective, seamless and integrated tree improvement delivery system, (2) developing effective in situ tools for investigating wood quality attributes for their integration into end-product value via tree improvement and silviculture, and (3) application of Artificial Neural Networks (ANNs) to seed orchards management and wood quality assessment. This program will train highly qualified personnel needed in the forest industry and the related biological sciences.