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Modeling Height Development of Loblolly Pine Genetic Varieties

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Knowledge of height development in different genetic varieties provides guidance in genetic selection for improved height growth. The effect of genetic variety on height development in loblolly pine was modeled for 86 loblolly pine clones, growing in a single-tree plot clonal screening trial, using a mixed-effects modeling approach. Genetic variety effects were modeled as random effects on parameters of the Chapman-Richards (C-R) height-age model. Akaike Information Criterion statistics and likelihood ratio tests were used to compare competing model forms. Genetic variety was found to significantly affect the asymptotic height and the shape parameters of the height-age model. A comparison of the coefficient of variation values of asymptotic height and the shape parameters of the C-R equation indicated that the asymptotic height parameter varied more with clone than did the shape parameter. Prediction of the height trajectory of a new genetic variety, using early-age height measurements of the new variety, was evaluated for a mixed-model calibration approach and a least squares regression calibration approach using 25 of the 86 clones as “new” genetic varieties. The mixed-model calibration approach resulted in more accurate predictions than the least squares approach, when more than two early-age height measurements were used in the calibration. Otherwise the two calibration approaches had similar levels of accuracy. Predictions from the mixed-model approach were biased for new genetic varieties near the lower end and those near the upper end of the range of age 15 clone average height. When using the mixed-model approach to calibrate existing height-age models to new genetic varieties, one should consider the appropriateness of available calibration information and account for the possibility of obtaining calibrated height-growth trajectories that are biased.
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Keywords: Pinus taeda; clonal forestry; empirical Bayes calibration; genetic screening trials

Document Type: Research Article

Publication date: 2013-06-24

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  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

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    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
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