Long observation period improves growth prediction in old Sugi (Cryptomeria japonica) forest plantations

ABSTRACT

It is important to predict the growth of Sugi forest plantations in old age. When predictions about the growth of Sugi forest plantations are made and there is a lack of growth data from older trees, it is possible that the accuracy of these predictions becomes worse. For example, it is known that the growth of Sugi does not get slower at older ages as expected from past growth predictions based on growth data from young to middle-aged trees. This study investigated the changes in extrapolated values of diameter at breast height (DBH) in old Sugi forest plantations with changes in the observation period of training data for model calibration. The study sites were long-term growth observation sites of Sugi forest plantations in the University of Tokyo Chiba Forest and Chichibu Forest. In this study, both DBH of individual trees and mean DBH of stands were analyzed by fitting Richards growth functions. The results showed that the accuracy of growth predictions in old ages was improved by including growth data from a sufficient number of older trees. From another point of view, growth prediction in old ages tended to underestimate actual growth if growth data did not include enough older trees.

KEYWORDS:

• Sugi
• forest plantation
• growth prediction
• long-term

Acknowledgments

We would like to thank current and former staff members at the University of Tokyo Chiba Forest and the University of Tokyo Chichibu Forest for their substantial ongoing efforts for experimental plot measurements. We are grateful to three anonymous reviewers and editors for their helpful and useful comments on an earlier version of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number [JP18K05742] and JSPS Core-to-Core Program, B. Asia-Africa Science Platforms.