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A computer program to predict the quality of longleaf pine seed cropsAuthor(s): Daniel J. Leduc; Shi-Jean S. Sung
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: General Technical Report (GTR)
Station: Southern Research Station
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DescriptionLongleaf 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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CitationLeduc, 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.
Keywordslongleaf pine, Pinus palustris, seed crop, weather, modelling
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