Optimizing the management of a <i>Picea abies</i> stand under risk of butt rot

Möykkynen, Timo; Miina, Jari; Pukkala, Timo

doi:10.1046/j.1439-0329.2000.00187.x

Cited by 31 publications

(29 citation statements)

References 22 publications

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“…Valsta 1992a, 1992b, Möykkynen et al 2000, Pukkala and Miina 2005. In the other fields a new type of methods, called as population-based methods or evolutionary computation methods, have also been used to solve complicated optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Population-based methods in the optimization of stand management

Pukkala¹

2009

Silva Fenn.

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In Finland, the growth and yield models for tree stands are simulation programs that consist of several sub-models. These models are often non-smooth and non-differentiable. Direct search methods such as the Hooke-Jeeves algorithm (HJ) are suitable tools for optimizing stand management with this kind of complicated models. This study tested a new class of direct search methods, namely population-based methods, in the optimization of stand management. The tested methods were differential evolution, particle swarm optimization, evolution strategy, and the Nelder-Mead method. All these methods operate with a population of solution vectors, which are recombined and mutated to obtain new candidate solutions. The management schedule of 719 stands was optimized with all population-based methods and with the HJ method. The population-based methods were competitive with the HJ method, producing 0.57% to 1.74% higher mean objective function values than HJ. On the average, differential evolution was the best method, followed by particle swarm optimization, evolution strategy, and Nelder-Mead method. However, differences between the methods were small, and each method was the best in several stands. HJ was alone the best method in 7% of stands, and a population based method in 3% (Nelder-Mead) to 29% (differential evolution) of stands. All five methods found the same solution in 18% of stands.

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Section: Introductionmentioning

confidence: 99%

Population-based methods in the optimization of stand management

Pukkala¹

2009

Silva Fenn.

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“…The method has also been employed to solve the optimisation problem when risk is involved (Thorsen and Helles 1998, Möykkynen et al 2000, in spatial thinning problems (Pukkala and Miina 1998) and to optimise the management of agroforestry systems (Muchiri et al 2002). These examples showed that Hooke and Jeeves´algorithm is an interesting and reliable technique to be applied in optimization at the stand level.…”

Section: Methods Of Hooke and Jeevesmentioning

confidence: 99%

“…Rotation length has been the only decision variable and the risk of fire has been assumed to be age-dependent (Martell 1980), constant over time (Routledge 1980) or a time-independent Poisson process (Reed 1984). Some years ago, thinnings have been integrated in optimisations problems that include risks (Thorsen and Helles 1998, Möykkynen et al 2000, Amacher et al 2005a, González-Olabarría et al 2008.…”

Section: Stand Level Optimisationmentioning

confidence: 99%

Optimising forest stand management in Galicia, north-western Spain

Pasalodos-Tato¹

2010

Diss. For.

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The present thesis aims at developing instructions for the optimal management of pine stands in Galicia in order to maximise the economic profitability of these stands. Galicia is a Spanish region characterised by the high productivity of its forests, Pinus pinaster, Pinus radiata and Pinus sylvestris being Galicia's most important conifers. Analytical methods have not been effectively applied in Galician forest management. The main objective of the present thesis was to develop management tools that ease the management in Galician pine stands, thus enhancing the profitability of forestry. Employing stand growth and yield models in combination with an optimisation algorithm, models for the optimal management of pine stands were developed. Since optimal management is sensitive to market conditions, timber price and discounting rate were used as predictors in the models (studies I-III) in addition to other predictors such as site index and planting density. The main challenge for the development of Galician pine stands is the constant presence of forest fires that complicate both management and economic analyses. High risk of fire makes it necessary to include fire risk considerations in the analyses when developing instructions for the optimal management. Fire risk was characterised and analysed as an exogenous (study II) or endogenous (studies III-IV) factor, observing that the manner in which fire risk is characterised affects the conclusions about optimal forest management. Another novelty of this thesis was the analysis of silvopastoral systems as an alternative to mere timberoriented schedules under conditions of high fire risk (study IV). The results (studies I-IV) showed that optimal management was highly sensitive to market and fire risk conditions, an increase in both discounting rate and fire risk shortening rotation optimal lengths. The models for optimal management developed in the thesis allow great flexibility for adapting the instructions to every possible economic and risk situation. Study IV showed that grazing in the forest improved profitability.

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“…This technique is very popular because it is easy to use and can perform well with discreteness of growth models on the expected concavity of the response surface. The Hooke and Jeeves method is described as a Bdirect search method^, which involves a sequential examination of the changes that occur when a problem is solved and the results are compared to the Bbest^solution among the derived [25][26][27][28][29][30][31][32][33]. Generally, the Hooke-Jeeves algorithm consists of two major phases: an Bexploratory search^around the base point and a Bpattern search^in a direction selected for optimization (minimization or maximization) [16].…”

Section: Non-linear Programming (Nlp) Approachmentioning

confidence: 99%

“…Several researchers including Miina [82], Vettenranta and Miina [83], Miina and Pukkala [30], Möykkynen et al [29], Wikström and Eriksson [84], Wikström [46], Hyytiäinen and Tahvonen [85], and Ribeiro et al [74] tackled the thinning optimization problems with distance-dependent control variables for the growth simulator. Only Ribeiro et al [74] applied DP by MSPATH.…”

Section: General Formulation Of Forest Stand-level Planning Problemmentioning

confidence: 99%

Stand-Level Forest Management Planning Approaches

2016

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Seeking an optimal operational regime under different management environments has been one of the main concerns of forest managers. Traditionally, the main operational regime includes planting density or regeneration scheme, thinning time/intensity, and optimal time to harvest over the given time horizon. Deterministic approaches to tackle this type of optimization problem with different controls have dominated the solution techniques in forestry literature. We present in this paper an overview of the methodologies used in stand-level optimization, in which we show the strengths and weaknesses of these methodologies as well as provide comments on the effectiveness of the methodology. We then propose a new dynamic programing approach for generalizing solution specification and techniques.

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Optimizing the management of a Picea abies stand under risk of butt rot

Cited by 31 publications

References 22 publications

Population-based methods in the optimization of stand management

Population-based methods in the optimization of stand management

Optimising forest stand management in Galicia, north-western Spain

Stand-Level Forest Management Planning Approaches

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