Date of Award


Level of Access

Campus-Only Thesis

Degree Name

Master of Science (MS)


Forest Resources


Aaron R. Weiskittel

Second Committee Member

Laura S. Kenefic

Third Committee Member

Anthony D'Amato


Long-term silvicultural experiments are used to study growth and yield at multiple temporal and spatial scales in forestry. Differences in forest type, stand characteristics, site conditions, and silvicultural system implemented affect outcomes across studies. Due to their varied implementation and methodologies, it is difficult to compare results across experiments at different locations, i.e. on different experimental forests. This study investigates the required effort and potential conclusions that can be garnered using previously collected data from independent long-term U.S. Forest Service silvicultural studies across a subset of Northern Forest types. Results from long-term studies are utilized for site-specific conclusions, with results pertinent to similar forested areas. While site-specific conclusions have furthered understanding of growth and yield, cross-site comparisons would provide new perspectives on regional variation in growth response. Large-scale comparisons across long-term silvicultural experiments could provide multiple comparison metrics to further understanding of growth and yield within and between stand types. This project presents a start-to-finish description of how to utilize historical forest growth records to quantify regional variation in growth responses attributed to factors at multiple spatial scales. The first chapter provides a rationale and methodology for data standardization necessary for synthesis of silvicultural experiment results. Database construction was focused on maximizing flexibility for additional synthesis. Data used were collected across the northern United States from 1927 to 2010. Multiple gradients of Northern Forest complexity are realized in these data, i.e. forest type, stand structure, and silvicultural system. Long-term trends across silvicultural treatments are observed in the standardized raw tree records, which could facilitate a variety of novel comparisons. The second chapter presents a non-parametric technique, Boosted Regression Tree (BRT) analysis, utilized cross-site comparisons of long-term forest growth records. The relative influence of climate, stand attributes, soil, and silvicultural variables were identified at a regional and site- specific level. Influential factors, or the relationship between independent variables and the dependent variable of growth models provide large-scale trends influential factors across multiple stand and landscape scales. Within-site influential factors varied, with different factors driving growth response (PAI). While rank and relative importance varied, climatic factors, in addition to density- and diameter-related variables, were the most common influential factors on site-level PAI. Periodic annual increment (0.48±0.25 m2 ha'1 yr'1) was relatively similar across all sites, from Minnesota south to Missouri and north-east to Maine. This initial effort to understand growth and influential factors across multiple experiments demonstrated the potential for analysis once initial data preparation is complete. To increase the inherent value and utility of standardized forest growth databases, site-specific data management support is required. Initial efforts should focus on taking stock of available data, with efforts to increase quality and robustness of records. The compilation of metadata and standardized raw record formats would facilitate necessary data archival, and increase potential future uses of these data.

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