Forest management optimization in Eucalyptus plantations: a goal programming approach

In Galicia (Spain), many Eucalyptus plantations are managed using the area-control method. The ultimate goal is to guarantee an even flow of wood in perpetuity by reaching the normal age-class distribution of the fully regulated forest by the end of a given planning horizon. However, given that the productivity of coppice stands differs throughout the suc- cessive rotation intervals, the application of this method triggers excessive fragmentation of the forest area. We present a model with the same long-term goal that does not force plantations into any given final age-class distribution. The model permits the plantations to reach a final structure with fewer harvest units of larger average size. To illustrate this approach, we developed two models and applied them to a case study. The first model used the principle of area control to achieve the fully regulated structure in each site and rotation interval of one full plantation cycle. The second model guaranteed a con- stant yield beyond the planning horizon without imposing any specific final age distribution on the plantation area. Both models considered objectives such as a constant yield during the planning horizon and the net present value of harvests. Resume ´ : En Galice (Espagne), plusieurs plantations d'eucalyptus sont amenagees par la methode du controle de la super- ficie. Le but ultime de cette methode est de garantir un flux constant et perpetuel de bois par l'atteinte d'une distribution normale des classes d'age, correspondant a une foret pleinement regularisee, a la fin d'un horizon donnede planification. Cependant, puisque la productivitedes taillis varie d'une rotation a l'autre, l'application de cette methode entraoˆne une fragmentation excessive de la superficie forestiere. Nous presentons un modele dont l'objectif along terme est le meme, mais qui ne necessite pas que les plantations atteignent une quelconque distribution finale des classes d'age. Le modele permet aux plantations d'atteindre une structure finale caracterisee par un plus petit nombre d'unitesd e recolte, mais dont la taille moyenne est plus grande. Pour illustrer cette approche, nous avons mis au point deux modeles qui ont eteappli- quesaune etude de cas. Le premier modele utilisait le principe du controle de la superficie pour atteindre une structure pleinement regularisee dans chaque station a la fin d'une rotation correspondant au cycle complet d'une plantation. Le sec- ond modele garantissait une production constante au-delade l'horizon de planification sans imposer de distribution finale d'age specifique sur la superficie plantee. Les deux modeles tenaient compte d'objectifs tels qu'un rendement constant pendant l'horizon de planification ainsi que de la valeur actualisee nette des recoltes. (Traduit par la Redaction)

[1]  M. A. León,et al.  A forest planning problem solved by a linear fractional goal programming model , 2006 .

[2]  Abraham Charnes,et al.  Optimal Estimation of Executive Compensation by Linear Programming , 1955 .

[3]  M Bertomeu,et al.  Forest management optimisation models and habitat diversity: a goal programming approach , 2002, J. Oper. Res. Soc..

[4]  L. Apiolaza,et al.  Integrating revenues from carbon sequestration into economic breeding objectives for Eucalyptus globulus pulpwood production , 2007, Annals of Forest Science.

[5]  H. L. Scheurman,et al.  Techniques for Prescribing Optimal Timber Harvest and Investment Under Different Objectives—Discussion and Synthesis , 1977 .

[6]  Carlos Romero,et al.  Redundancy in lexicographic goal programming: An empirical approach , 1989 .

[7]  José G. Borges,et al.  A decision support system for forest ecosystem management in Portugal. , 2003 .

[8]  Carlos Romero,et al.  Making forestry decisions with multiple criteria: A review and an assessment , 2008 .

[9]  Carlos Romero,et al.  Forest management optimisation models when carbon captured is considered: a goal programming approach , 2003 .

[10]  Chris Cocklin,et al.  The use of lexicographic goal programming in economic/ecolocical conflict analysis , 1988 .

[11]  Jyrki Kangas,et al.  A decision theoretic approach applied to goal programming of forest management. , 1992 .

[12]  Carlos Romero,et al.  Multiple Criteria Analysis for Agricultural Decisions , 1989 .

[13]  Vassiliki Kazana,et al.  A decision support modelling framework for multiple use forest management: The Queen Elizabeth Forest case study in Scotland , 2003, Eur. J. Oper. Res..

[14]  L. Rodriguez,et al.  Optimal rotations on Eucalyptus plantations including carbon sequestration- : A comparison of results in Brazil and Spain , 2006 .

[15]  Matthew H. Langholtz,et al.  Effect of dendroremediation incentives on the profitability of short-rotation woody cropping of Euca , 2005 .

[16]  Fabiane de Oliveira,et al.  Goal programming in a planning problem , 2003, Appl. Math. Comput..

[17]  C. Kao,et al.  Goal programming for reconciling economic, even-flow, and regulation objectives in forest harvest scheduling , 1979 .

[18]  J. Ignizio Multiobjective mathematical programming via the Multiplex model and algorithm , 1985 .

[19]  J. Borges,et al.  Combining random and systematic search heuristic procedures for solving spatially constrained forest management scheduling models , 2002 .

[20]  David E. Tait A dynamic programming solution of financial rotation ages for coppicing tree species , 1986 .

[21]  J. B. Dent,et al.  Goal programming: Application in the management of the miombo woodland in Mozambique , 2001, Eur. J. Oper. Res..

[22]  G. Munoz Forest management in Eucalyptus stands: the Spanish case , 2004 .

[23]  Carlos Romero,et al.  Modeling Timber Harvest Scheduling Problems with Multiple Criteria: An Application in Spain , 1998 .

[24]  Marc E. McDill,et al.  Finding the Efficient Frontier of a Bi-Criteria, Spatially Explicit, Harvest Scheduling Problem , 2006, Forest Science.

[25]  Sun Joseph Chang,et al.  A generalized Faustmann model for the determination of optimal harvest age , 1998 .

[26]  Johann Graf Lambsdorff,et al.  An Empirical Approach , 2002 .

[27]  E. L. Medema,et al.  The determination of financial rotation ages for coppicing tree species , 1985 .

[28]  Floyd H. Curtis Linear Programming the Management of a Forest Property , 1962 .

[29]  L. Apiolaza,et al.  A cash flow model to compare coppice and genetically improved seedling options for Eucalyptus globulus pulpwood plantations , 2004 .

[30]  R. Wise,et al.  Carbon-Accounting Methods and Reforestation Incentives , 2003 .

[31]  F. Cubbage,et al.  Timber investment returns for selected plantations and native forests in South America and the Southern United States , 2007, New Forests.

[32]  Abu S.M. Masud,et al.  Interactive Sequential Goal Programming , 1981 .

[33]  H. Pastijn Handbook of critical issues in goal programming: Carlos Romero Pergamon Press, Oxford, 1990, xi + 124 pages, £25.00, ISBN 008 0406610 , 1992 .

[34]  H. Pereira,et al.  Heartwood and sapwood variation in Eucalyptus globulus Labill. trees at the end of rotation for pulp wood production , 2007, Annals of Forest Science.