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    Author(s): Daniel J. LeducShi-Jean S. Sung
    Date: 2018
    Source: In: Kirschman, Julia E., comp. Proceedings of the 19th biennial southern silvicultural research conference; 2017 March 14-16; Blacksburg, VA. e-Gen. Tech. Rep. SRS-234. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station
    Publication Series: Proceedings - Paper (PR-P)
    Station: Southern Research Station
    PDF: View PDF  (690.0 KB)

    Description

    Longleaf pine (Pinus palustris Mill.) has good seed years at irregular intervals. Although previous researchers found significant relationships between weather variables and size of the cone crop for a given year, they have stopped short of developing a predictive model. In this study, seed crops were classified as bumper, good to fair, and poor to failed. A canonical discriminant analysis based on weather data was performed to develop a classification function. We then developed a computer program that implemented the results of this canonical discriminant analysis to predict the class of cone crop for a given year. This prediction can be made as early as 18 months prior to seed maturity. This model should greatly help in planning site preparation or seed harvesting activities.

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    Citation

    Leduc, Daniel J.; Sung, Shi-Jean S. 2018. A computer program to predict the quality of longleaf pine seed crops. In: Kirschman, Julia E., comp. Proceedings of the 19th biennial southern silvicultural research conference; 2017 March 14-16; Blacksburg, VA. e-Gen. Tech. Rep. SRS-234. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station: 430-435.

    Keywords

    longleaf pine, Pinus palustris, seed crop, weather, modelling

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https://www.fs.usda.gov/treesearch/pubs/56974