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Using portfolio theory to improve yield and reduce risk in black spruce family reforestation

Open Access
|Jul 2017

Abstract

Family forestry, defined as the deployment of families in mixture into plantations, is becoming an attractive option for black spruce (Picea mariana (Mill.) BSP) in New Brunswick, Canada. With many elite families of black spruce being available, there is a knowledge gap regarding how to compose a mixture of families that optimally balances the objectives of increased yield and reduced risk. This study, based on real field test data, investigates the application of a model based on the modern portfolio theory to optimally balance yield and risk when selecting a portfolio (mixture) of black spruce families to deploy in reforestation. The risk was expressed as the variance of the family portfolio, an effective indicator of yield stability. This is an innovative approach in forestry and it is compared to the currently used method, truncation-deployment, defined as the equal deployment of seed of selected families. Results show that the portfolio theory searched for the combination of yield and stability and produced family portfolios maximizing yield at a given stability or minimizing yield instability at a given yield. The portfolio theory was never inferior in maximizing yield to the truncation- deployment approach when yield stability is a concern. We recommend using portfolio theory to determine family portfolios for family forestry. While this study targets to family forestry, the results may be relevant to other deployment strategies where stability is a concern, such as clonal forestry.

DOI: https://doi.org/10.1515/sg-2013-0028 | Journal eISSN: 2509-8934 | Journal ISSN: 0037-5349
Language: English
Page range: 232 - 238
Submitted on: Jan 18, 2013
Published on: Jul 1, 2017
Published by: Johann Heinrich von Thünen Institute
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2017 Y. H. Weng, K. A. Crowe, W. H. Parker, D. Lindgren, M. S. Fullarton, K. J. Tosh, published by Johann Heinrich von Thünen Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.