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Determination of climate predictor variables effecting on annual cone harvest and seed yield of Korean pine (Pinus koraiensis Siebold and Zucc.) seed orchards | Abstract
Forest Research: Open Access

Forest Research: Open Access
Open Access

ISSN: 2168-9776

+32-466-90-04-51

Abstract

Determination of climate predictor variables effecting on annual cone harvest and seed yield of Korean pine (Pinus koraiensis Siebold & Zucc.) seed orchards

Kim YY*, Ku JJ, Kim JH, Lim HI and Han J

Background: Korean pine (Pinus koraiensis Siebold & Zucc.) is a native pine species of Korea. The relationship between climate conditions and seed production of Korean pine has not been clearly revealed yet compared to other pines. This study was conducted to identify climate variables that are significantly related to cone and seed production in three seed orchards of Korean pine.

Methods: Regression model for cone harvest and seed yield was built using climate elements such as monthly mean temperature, monthly total precipitation, and monthly hours of sunshine as predictor variables.

Results and Discussion: The seed yield model had higher predictive precision than the cone harvest. For the seed yield model, seven climate variables were associated with seven major phenological periods in the three-year reproductive cycle of the pines: long-shoot bud (LSB) bursting, LSB development, pollen and cone bud dormancy (in two years before the seeding year), flowering, pollination, seed cone dormancy (in one year before the seeding year) and cone and seed maturation (in the seeding year). Particularly, precipitation during LSB bursting two years before the seeding year was a major climate variable limiting seed yield the most. The remaining variables associated with other phenological periods were the minor ones significantly affecting the seed yield.

Conclusion: The model appears to be meaningful clearly showing which climate variables are associated with the seed production of Korean pine and also to what extent they affect it. Further studies on a more advanced predictive model are needed, based on this study.

Published Date: 2020-10-12; Received Date: 2020-09-17

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