Even-aged or uneven-aged modelling approach? A case for Pinus brutia
- First Online:
- 163 Downloads
The past management of Pinus brutia forests in Lebanon has led to diverse stand structures that cannot be easily classified as even-aged (EA) or uneven-aged (UA). Most stands are between these stand types, and they may be called as “semi-even-aged”. This is a very common characteristic throughout the Mediterranean conifer forests and makes the choice between the EA and UA approaches problematic, in both management and modelling. However, previous research has devoted little attention to the performance of growth and yield models when applied to transitional stand structures.
The aim of this study was to find the best modelling approach and to recommend equations for simulating the dynamics of the semi-even-aged P. brutia stands of Lebanon on an individual-tree basis.
Fifty sample plots were measured in Lebanon. Individual-tree growth models were fitted to the whole dataset using either UA or EA modelling approach. Models were also fitted using two sub-samples containing the most EA and the most UA plots. The performance and accuracy of the two modelling approaches were evaluated in all three datasets.
The article provides the first complete growth model for uneven-aged P. brutia stands. The EA sub-models presented better statistical fitting. However, the UA sub-models enabled more accurate predictions of wood production and were almost as good as the EA sub-models when predicting stand dynamics of the EA plots. The EA approach provided poor predictions, and the errors were high when it was applied to UA stands.
In structurally complex stands, the UA modelling approach is to be preferred since it predicts the whole stand dynamics more accurately and enables simulations of a broader range of silvicultural treatments.
KeywordsStand dynamics Forest management Semi-even-aged stands Growth model Simulation Model evaluation
- Assaf N (2010) A growth and yield model for even-aged Pinus brutia Ten. stands in Lebanon. M.Sc.Thesis, University of Lleida, SpainGoogle Scholar
- Buongiorno J, Dahir S, Lu HC, Lin CR (2004) Tree size diversity and economic returns in uneven-aged forest stands. For Sci 40:83–103Google Scholar
- Clutter JL, Forston JC, Piennar LV, Brister GH, Bailey RL (1983) Timber management—a quantitative approach. Willey, New York, 333 pGoogle Scholar
- de-Miguel S, Pukkala T, Shater Z, Assaf N, Kraid B, Palahí M (2010) Models for simulating the development of even-aged Pinus brutia stands in Middle East. Forest Systems 19:449–457Google Scholar
- de-Miguel S, Shater Z, Kraid B, Mehtätalo L, Pukkala T (2011) Modelling the taper of Pinus brutia in Syria (in press)Google Scholar
- Fischer R, Lorenz M, Köhl M, Becher G, Granke O, Christou A (2008) The condition of forests in Europe: 2008 executive report. United Nations Economic Commission for Europe, Convention on Longrange Transboundary Air Pollution, International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). 23 p.Google Scholar
- Gezer A (1985) The sylviculture of Pinus brutia in Turkey. In: CIHEAM, le pin d’Alep et le pin brutia dans la sylviculture méditerranéenne. Options Méditerranéennes, Série Etudes, Paris, 86:55-66.Google Scholar
- González, JM (2005) Introducción a la selvicultura general. Ed. Universidad de León. León. 309 pp. ISBN: 84-9773-223-5.Google Scholar
- Jactel H, Nicoll BC, Branco M, González-Olabarria JR, Grodzki W, Gangstrom B, Moreira F, Netherer S, Orazio C, Piou D, Santos H, Schelhaas MJ, Tojic K, Vodde F (2009) The influences of forest stand management on biotic and abiotic risks of damage. Ann For Sci 66Google Scholar
- Kobayashi K, Salam MU (2000) Comparing simulated and measured values using mean squared deviation and its components. Agron J 92:345–352Google Scholar
- Oliver CD, Larson BC (1996) Forest stand dynamics. Update edition. Wiley, New York, p 540Google Scholar
- Peschel W (1938) Die mathematischen methoden zur herleitung der wachstumsgesetze von baum und bestand und die ergebnisse ihrer anwendung. Tharandter Forstliches Jahrburch 89:169–247Google Scholar
- R Development Core Team (2011) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.
- Render B, Stair RM (1992) Introduction to management science. Allyn and Bacon, Boston, 856 ppGoogle Scholar
- Smith DM, Larson BC, Kelty MJ, Ashton P (1996) The practice of silviculture: applied forest ecology, 9th ed. Wiley, New YorkGoogle Scholar
- Sterba H (2004) Equilibrium curves and growth models to deal with forests in transition to uneven-aged structure – application in two sample stands. Silva Fennica 38:413–423Google Scholar
- Stoffels A, Van Soest J (1953) The main problems in sample plots. Ned Boschb Tijdschr 25:190–199Google Scholar
- Tomé M. (1989) Modelação do crescimiento da árvore individual em povoamentos de Eucalyptus globulus Labill. (1ª Rotação). Região Centro de Portugal. PhD thesis, ISA, Lisbon.Google Scholar
- Vanclay JK (1994) Modelling forest growth and yield: applications to mixed tropical forests. CAB International, UK, 312 pGoogle Scholar
- Vanclay JK (2003) Growth modelling and yield prediction for sustainable forest management. The Malaysian Forester 66(1):58–69Google Scholar