Annals of Forest Science

, 75:73 | Cite as

SEGMOD—a techno-economic model for evaluating the impact of segregation based on internal wood properties

  • Glen E. Murphy
  • John R. Moore
Research Paper


Key message

Segregating stands and logs based on internal wood properties is likely to lead to improvements in value for forest and mill owners, but some situations were found where no segregation was the best alternative. Where segregation was the best alternative, segregating logs at the landing, or stands based on pre-harvest inventory assessments, led to the greatest value improvements.


The benefits of segregating stands, stems and logs based on wood properties are not clear due to the high variability of wood properties, poor market signals for wood with superior properties and poor understanding of the costs across the value chain.


The aim of this study was to determine if the benefits of segregating stands and logs outweighed the additional costs.


A techno-economic model (SEGMOD) was constructed that allowed comparisons of segregation at different approaches in the supply chain. The model was populated with Pinus radiata (D.Don) stand, cost and price data from companies operating in four forestry regions of New Zealand. A total of 255 segregation scenarios were modelled, which included variations in segregation approach, stand type, stand location, terrain type, market focus and market horizon.


Segregating logs based on internal wood properties led to improvements in stumpage and mill door value for most of the scenario sets evaluated. The No Segregation option was found (infrequently) to be best in unpruned stands. Segregating logs based on pre-harvest inventory assessments or at the landing would appear to be the best approach.


The economic benefits of segregating stands and logs for forest and mill owners outweighed the additional costs in most of the scenarios evaluated.


Acoustic velocity Resin Structural mill Appearance mill Radiata pine 



We would like to thank those New Zealand forestry companies and wood processors who gave permission for their data to be used in the four case studies reported here. Mark Kimberley and Joel Gordon (Scion) helped to recreate SAWMOD, and Solid Wood Innovation Ltd. gave permission for their sawing study data to be used.


This work was supported by the New Zealand Ministry for Business, Innovation and Employment (C04X1306) and the Forest Growers’ Levy Trust as part of the Growing Confidence in Forestry’s Future research programme.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Amishev D, Murphy GE (2009) Estimating breakeven prices for Douglas-fir veneer quality logs from stiffness graded stands using acoustic tools. For Prod J 59(4):45–52Google Scholar
  2. Bilek EM (2009) LSY: documentation for a spreadsheet tool to evaluate log-sort yard economics. General Technical Report FPL-GTR-184. USDA Forest Service, Forest Products Laboratory, Madison, WIGoogle Scholar
  3. Briggs D (1989) Tree value system – description and assumptions. General Technical Report PNW-GTR-239. UDSA Forest Service, Pacific Northwest Research Station, Portland, ORCrossRefGoogle Scholar
  4. Cass RD, Baker SA, Greene WD (2009) Cost and productivity impacts of product sorting on conventional ground-based timber harvesting operations. For Prod J 59(11):108–114. CrossRefGoogle Scholar
  5. Cown DJ (1978) Comparison of the Pilodyn and Torsiometer methods for the rapid assessment of wood density in living trees. N Z J For Sci 8:384–391Google Scholar
  6. Cown DJ, Kimberley MO, Whiteside ID (1987) Conversion and grade recoveries from radiata pine logs. In: Kininmonth JA (ed) Proceedings of the conversion planning conference. FRI Bulletin, vol 128. Ministry of Forestry, Forest Research Institute, Wellington, pp 147–161Google Scholar
  7. Deadman MW, Goulding CJ (1978) A method for assessment of recoverable volume by log types. N Z J For Sci 9(2):225–239Google Scholar
  8. Forest Owners Association (2017) New Zealand plantation forest industry 2016/17 facts & figures. New Zealand Forest Owners Association Inc., Wellington, New ZealandGoogle Scholar
  9. Fox TR, Jokela EJ, Allen HL (2007) The development of pine plantation silviculture in the southern United States. J For 105(7):337–347Google Scholar
  10. Gardiner B, Moore J (2014) Creating the wood supply of the future. In: Fenning TM (ed) Challenges and opportunities for the world’s forests in the 21st century. Springer Science+Business Media, Dordrecht, pp 677–704. CrossRefGoogle Scholar
  11. Kimberley MO, Cown DJ, McKinley RB, Moore JR, Dowling LJ (2015) Modelling variation in wood density within and among trees in stands of New Zealand-grown radiata pine. N Z J For Sci 45(1).
  12. Manley B (2002) Fitness for purpose, wood quality and the value chain. N Z J For 47(3):2Google Scholar
  13. McConchie DL (2003) Field guide to assist recognition and classification of resinous defects on the bark of radiata pine. WQI Report No APP 12. Wood Quality Initiative Ltd, Rotorua, New ZealandGoogle Scholar
  14. Ministry for Primary Industries (2018) Historic indicative New Zealand radiata pine log prices. Accessed 08/03/2018
  15. Moore JR, Cown DJ (2015) Processing of wood for wood composites. In: Ansell MP (ed) Wood composites. Woodhead Publishing, Cambridge, pp 27–45. CrossRefGoogle Scholar
  16. Moore JR, Cown DJ (2017) Corewood (juvenile wood) and its impact on wood utilisation. Current Forestry Reports 3(2):107–118. CrossRefGoogle Scholar
  17. Murphy G, Cown D (2015) Stand, stem and log segregation based on wood properties: a review. Scand J For Res 30:1–47. CrossRefGoogle Scholar
  18. Murphy G, Lyons J, O’Shea M, Mullooly G, Keane E, Devlin G (2010) Management tools for optimal allocation of wood fibre to conventional log and bio-energy markets in Ireland: a case study. Eur J For Res 129(6):1057–1067. CrossRefGoogle Scholar
  19. Nurminen T, Heinonen J (2007) Characteristics and time consumption of timber trucking in Finland. Silva Fennica 41(3):471–487CrossRefGoogle Scholar
  20. Nurminen T, Korpunen H, Uusitalo J (2009) Applying the activity-based costing to cut-to-length timber harvesting and trucking. Silva Fennica 43(5):847–870CrossRefGoogle Scholar
  21. Palmer DJ, Kimberley MO, Cown DJ, McKinley RB (2013) Assessing prediction accuracy in a regression kriging surface of Pinus radiata outerwood density across New Zealand. For Ecol Manag 308:9–16. CrossRefGoogle Scholar
  22. Park J (1989) Pruned log index. N Z J For Sci 19(1):44–53Google Scholar
  23. Perstorper M, Pellicane PJ, Kliger IR, Johansson G (1995) Quality of timber products from Norway spruce. Part 1. Optimization, key variables, and experimental study. Wood Sci Technol 29(3):157–170. CrossRefGoogle Scholar
  24. Pnevmaticos SM, Mann SH (1972) Dynamic programming in tree bucking. For Prod J 22(2):26–30Google Scholar
  25. Sessions J, Boston K, Hill R, Stewart R (2005) Log sorting location decisions under uncertainty. For Prod J 55(12):53–57Google Scholar
  26. Standards New Zealand (1993) Timber Structures Amendment 4 NZS3603:1993. Standards New Zealand, Wellington, New ZealandGoogle Scholar
  27. Tolan A, Visser R (2015) The effect of the number of log sorts on mechanized log processing productivity and value recovery. Int J For Eng 26(1):36–47. CrossRefGoogle Scholar
  28. Wang X, Carter P, Ross RJ, Brashaw BK (2007) Acoustic assessment of wood quality of raw forest materials—a path to increased profitability. For Prod J 57(5):6–14Google Scholar
  29. Woodweek (2014) Call for less log sorts. Accessed 07/03/2018
  30. Zobel BJ, Sprague JR (1998) Juvenile wood in forest trees. Springer, BerlinCrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  1. 1.GE Murphy & Associates LtdRotoruaNew Zealand
  2. 2.ScionRotoruaNew Zealand

Personalised recommendations