Applying a process-based model in Norway spruce management

Niinimäki, Sami; Tahvonen, Olli; Mäkelä, Annikki

doi:10.1016/j.foreco.2011.10.023

Cited by 39 publications

(27 citation statements)

References 36 publications

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“…The results are additionally similar to earlier, (more) detailed empirical results (Niinimäki et al 2012). Given a SI close to 17 m, an interest rate between 1% and 4%, and no thinning, they obtain optimal rotations between 69 and 53 years.…”

Section: Classic Faustmann Clear-cut Solutionssupporting

confidence: 86%

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Optimality of continuous cover vs. clear-cut regimes in managing forest resources

Tahvonen

Rämö

2016

Can. J. For. Res.

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Optimization models on continuous cover forestry are complicated and typically incompatible with rotation models. This dichotomy is theoretically unsatisfactory and makes the choice between clearcuts and continuous cover forestry vague. We present a theoretically sound and empirically detailed generalized setup with an optimal clear-cut regime (or even-aged management) and optimal continuous cover regime (or uneven-aged management) as special cases. It includes a size-structured growth model, variable and fixed harvesting costs, and allows for the completely flexible optimization of harvest timing in both regimes. Flexible harvest timing becomes essential when optimizing the transition from clear-cut regimes toward continuous cover forestry. The model is applied to Norway spruce (Picea abies (L.) Karst.) and solved as a dynamic mixed-integer problem. Low or moderate site productivity, an interest rate above 2%, and a high artificial regeneration cost support the optimality of continuous cover forestry. In its most general form, the optimal clear-cut regime does not exist when the continuous cover regime is globally optimal, and when it exists, the rotation period lengthens with interest rate. The optimal choice between forest management regimes may depend on the initial stand state and whether the naturally regenerated seedlings are utilized in solutions with clearcuts. Maximizing sustainable yield favors clearcuts.Key words: continuous cover forestry, uneven-aged forestry, even-aged forestry, optimal harvesting, optimal rotation, forest economics. Résumé :Les modèles d'optimisation pour la foresterie en couvert continu sont compliqués et généralement incompatibles avec les modèles de révolution. Cette dichotomie est théoriquement insatisfaisante et source de confusion dans le choix entre les coupes à blanc et la foresterie en couvert continu. Nous présentons un cadre général ayant de solides fondements théoriques tout en étant empiriquement détaillé dans lequel les solutions optimales pour la révolution forestière et le couvert continu sont traitées comme des cas particuliers. Il inclut un modèle de croissance structuré en fonction de la taille des arbres, des coûts de récolte variables et fixes, et permet une optimisation totalement flexible des périodes de récolte pour les deux régimes. Des périodes de récolte flexibles deviennent essentielles pour optimiser la transition des régimes de coupe à blanc vers la foresterie en couvert continu. Le modèle est appliqué à l'épicéa commun (Picea abies (L.) Karst.) et solutionné comme un problème dynamique partiellement en nombres entiers. L'optimalité de la foresterie en couvert continue est soutenue par une productivité de la station faible ou modérée, un taux d'intérêt supérieur à 2 % et le coût de la régénération artificielle. Dans sa forme la plus générale, le régime optimal de coupe à blanc n'existe pas lorsque le régime en couvert continu est globalement optimal et, lorsqu'il existe, la période de révolution s'allonge avec le taux d'intérêt. Le choix op...

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Section: Classic Faustmann Clear-cut Solutionssupporting

confidence: 86%

“…This takes 15, 20, or 25 years depending on site productivity. The initial states are x t 0 ϭ 1750, 0, …, 0 or x t 0 ϭ 2250, 0, …, 0, which are in line with silvicultural recommendations and optimal densities obtained in earlier studies (Niinimäki et al 2012).…”

Section: The Size-structured Optimization Problem For Varying Harvestsupporting

confidence: 82%

Optimality of continuous cover vs. clear-cut regimes in managing forest resources

Tahvonen

Rämö

2016

Can. J. For. Res.

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“…For stand-level optimization, there are various different algorithms to choose from [35,36], e.g., the derivative-free direct search method such as the Hooke and Jeeves method, differential evolution, particle swarm optimization [37], hybrid optimization strategies which combine separate algorithms (e.g., [16]) or depth-first search algorithms which apply a search tree consisting of a backtracking mechanism [37]. In this study, we applied a new algorithm which has recently been introduced to forest applications: sequential quadratic programming (SQP) [38].…”

Section: Discussionmentioning

confidence: 99%

“…Despite the few shortcomings associated with matrix population models, they have been applied to almost all the subject areas of forestry [1]. The advantages of population matrix models, compared to individual-based models (interchangeably empirical-statistical individual tree models, see [16]) are abundant, but dependent on the application; the best approach for a particular case should be the one that is the most consistent with modelling purposes while making the fewest assumptions according to the law of parsimony, i.e., Occam's razor [1]. Stated differently, when the two approaches (individual-based models and population matrix models) make predictions of similar quality, Occam's razor favours parsimonious population matrix models (see, e.g., [17]).…”

Section: Introductionmentioning

confidence: 99%

Introducing a Non-Stationary Matrix Model for Stand-Level Optimization, an Even-Aged Pine (Pinus Sylvestris L.) Stand in Finland

Pyy

Ahtikoski

Laitinen

et al. 2017

Forests

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Abstract:In general, matrix models are commonly applied to predict tree growth for size-structured tree populations, whereas empirical-statistical models are designed to predict tree growth based on a vast amount of field observations. From the theoretical point of view, matrix models can be considered to be more generic since their dependency on ad hoc growth conditions is far less prevalent than that of empirical-statistical models. On the other hand, the main pitfall of matrix models is their inability to include variation among the individuals within a size class, occasionally resulting in less accurate predictions of tree growth compared to empirical-statistical models. Thus, the relevant question is whether a matrix model can capture essential tree-growth dynamics/characteristics so that the model produces accurate stand projections which can further be applied in practical decision-making. Such a dynamic characteristic in our model is the basal area of trees, which causes nonlinearity in time. Therefore, our matrix model is a nonlinear model. The empirical data for models was based on 20 sample plots representing 8360 tree records. Further, according to the model, stand projections were produced for three Scots pine (Pinus sylvestris L.) sapling stands (age of 25 years, stand density fluctuating from 850 to 1400 stems ha −1 ). Then, (even-aged) stand management was optimized by applying sequential quadratic programming (SQP) among those growth predictions. The objective function of the optimization task was to maximize the net present value (NPV) of the ongoing rotation. The stands were located in Northern Ostrobothnia, Finland, on nutrient-poor soil type. The results indicated that initial stand density had an effect on optimal solutions-optimal stand management varied with respect to thinnings (timing and intensity) as well as to optimal rotation. Further, an increasing discount rate shortened considerably the optimal rotation period, and relaxing the minimum thinning removal to 30 m 3 ha −1 resulted in an increase both in number of thinnings and in the maximum net present value.

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“…For stand-level management problems, a genetic algorithm would maintain several different feasible solutions to the problem in the memory of the computer and use these to create new feasible solutions to a problem through re-combination and stochastic adjustment. Genetic algorithms have been developed recently [29][30][31] to optimise economic concerns at the stand level. Similar pmetaheuristics were recently demonstrated for maximisation of wood production [34] and forest structure [35] at the standlevel.…”

Section: Stand-level Optimisationmentioning

confidence: 99%

Optimisation in Forest Management

et al. 2016

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The field of forestry has employed various computer-assisted optimisation approaches since the early 1960s to address the efficient allocation of resources towards various forest management objectives. These approaches continue to evolve, and in the last 5 years, the research has expanded to demonstrate how complex, non-linear relationships can be recognised and incorporated into planning processes at the tree, stand, forest and landscape levels. In addition to an overview of the use of optimisation in forestry, we provide an examination of work published in the last 5 years from 30 international journals, worldwide, which consistently publish forestry and natural resource management research papers. Through this review, we found that landscape-level optimisation is a relatively new and expanding area of research, most often performed by one large public landowner in regions where the resulting plan of action has an effect on all landowners and resources. We also note that at the forest level, exact methods for optimising systems mainly continue to be used, and at the stand level, optimisation seems to now involve exploration of a variety of analytical methods. A large portion of the recent research in the optimisation of forest management have involved European forests, which is a function of large public ownership of land and the tradition and requirements for management planning, and roughly half of the effort has arisen from researchers located in Nordic countries (Denmark, Finland, Norway and Sweden).

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Applying a process-based model in Norway spruce management

Cited by 39 publications

References 36 publications

Optimality of continuous cover vs. clear-cut regimes in managing forest resources

Optimality of continuous cover vs. clear-cut regimes in managing forest resources

Introducing a Non-Stationary Matrix Model for Stand-Level Optimization, an Even-Aged Pine (Pinus Sylvestris L.) Stand in Finland

Optimisation in Forest Management

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