Ten Years of Provenance Trials and Application of Multivariate Random Forests Predicted the Most Preferable Seed Source for Silviculture of <i>Abies sachalinensis</i> in Hokkaido, Japan

Ten Years of Provenance Trials and Application of Multivariate Random Forests Predicted the Most Preferable Seed Source for Silviculture of <i>Abies sachalinensis</i> in Hokkaido, Japan

Research highlights: Using 10-year tree height data obtained after planting from the range-wide provenance trials of <i>Abies sachalinensis</i>, we constructed multivariate random forests (MRF), a machine learning algorithm, with climatic variables. The constructed MRF enabled prediction...

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Bibliographic Details
Main Authors: Ikutaro Tsuyama, Wataru Ishizuka, Keiko Kitamura, Haruhiko Taneda, Susumu Goto
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Forests
Subjects:
local adaptation
Sakhalin fir
silviculture
seed zone
tree improvement program
Online Access:https://www.mdpi.com/1999-4907/11/10/1058

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