Stand-level Optimization Research Articles

Opportunity Costs of In Situ Carbon Storage Derived by Multiple-Objective Stand-Level Optimization—Results from Case Studies in Portugal and Germany

Considering in situ carbon storage in forest management has gained momentum under increasing pressure to decarbonize our economies. Here, we present results from case studies in Portugal and Germany showing the opportunity costs of in situ carbon storage derived by multiple-objective optimization. We used a stand-level model to optimize land expectation value under uncertainty as a reference, then derived opportunity costs by including the enhancement of the average carbon storage in aboveground biomass as a second objective. Using the optimal (compromise) solution when considering both objectives simultaneously, we show opportunity costs of EUR 119 (Portugal) and EUR 68 (Germany) per Mg CO2eq. These opportunity costs are higher than conservative, but lower than alternative cost estimates for future damages caused by current CO2 emissions. An important result was that suggested reference solutions in both countries (though only for low discount rates in Portugal) were mixed forests without clearfelling. In Germany, this implicitly elevated carbon storage. Such “closer-to-nature-forest-management” systems were also mostly suggested by the optimization tool when carbon storage was an objective.

Profitability of the First Commercial Thinning, a Simulation Study in Northern Finland

Despite positive signals from increasing growing stock volumes and improved roundwood trade, first commercial thinnings (FCTs) tend to be a bottleneck in Finnish forest management and forestry. The reasons are many, but probably the most crucial would be the lack of simultaneous economic incentives for participating agents, i.e., private forest owners and forest machine contractors. This is due to poor stand characteristics in most FCT cases: low cutting removal with small average stem size. There are five predetermined management options: (1) Industrial wood thinning with only two timber assortments, pulpwood and saw logs, (2) Integrated procurement of industrial and energy wood, (3) Energy wood thinning solely consisting of delimbed stems, (4) Whole-tree energy wood thinning with an energy price of 3 € m−3 and (5) Whole-tree energy wood thinning with energy price of 8 € m−3, that were applied for six separate forest stands located in Northern Finland, and derived from a database representing stands with an urgent need for FCT. Then, a two-phase financial analysis consisting of stand-level optimization (private forest owners) and profitability assessment (contractor) was conducted in order to find out whether there would be simultaneous economic incentives for both participants of FCT. The stand-level optimization revealed the financially best management options for a private forest owner, and then, for a contractor, the profitability assessment exposed the profit (or loss) associated with the particular management option. In brief, our results demonstrated that conducting either an industrial wood thinning (1) or an integrated procurement (2) resulted in a positive economic incentive for both the private forest owner and the contractor in all six cases (stands). Further, applying energy wood thinning with delimbed stems (3) would even generate a financial loss for the contractor, given the roadside prices applied in this study.

Economic Performance of Genetically Improved Reforestation Material in Joint Production of Timber and Carbon Sequestration: A Case Study from Finland

Genetically improved material has been proven to financially outperform unimproved material in timber production according to various studies. Genetic improvements in carbon sequestration are also promising, implying a possibility for further financial gains. Research Highlights: Including carbon pricing (i.e., timber production combined with carbon sequestration) with genetically improved material improves financial performance compared to pure timber production. Furthermore, the proportion of carbon benefit (%) of absolute bare land value fluctuates between 27% and 42%, indicating a substantial role of carbon sequestration to financial performance. Background and Objectives: Until now, economic analyses of the impact of tree improvement have mainly dealt with growth performance: volume yield or height growth. Yet planted forests can have a significant contribution to carbon sequestration, which will play a major role in carbon markets. This study focuses on comparing the financial performance between genetically improved and unimproved reforestation material when stand management is optimized according to timber production or to joint production (timber and carbon sequestration together). Another goal is to reveal possible differences in financial performance related to climatic conditions along the south–north gradient. Materials and Methods: The stand projections are based on simulations with and without genetic gains for joint production (timber + carbon) and merely timber production in eight locations in Finland. Stand-level optimization is applied for financial analyses. Results: Genetically improved reforestation material considerably enhanced financial performance when the joint production of timber and carbon was applied, regardless of the climatic region. Conclusions: If carbon pricing became a reality, there would be a distinctive shift in bare land values, which is further boosted by a genetic gain.

Financial assessment of alternative breeding goals using stand-level optimization and data envelopment analysis

ABSTRACT Choosing traits for breeding requires financial and genetic information. This study introduces a theoretically sound and empirically detailed assessment setup for analyzing two traits from the financial perspective: decay resistance and tree growth. The analysis is conducted in two stages: first (1), a stand-level optimization analysis is applied producing maximum bare land value with the presence of enhanced tree growth or decay resistance. Realized genetic improvement effects in growth were incorporated in the existing growth models by means of genetic multipliers. For decay resistance the genetic gain was based on an assumption of full resistance to decay. Then (2), applying the optimal solutions as outputs, and investments for enhanced tree growth and decay resistance as inputs in a data envelopment analysis, DEA efficient frontiers for three climatic regions, Southern, Central and Northern Finland were revealed. The results indicated that focusing on tree growth in Southern Finland would be more beneficial than in Central and Northern Finland. Further, the DEA showed that efficient frontiers are not identical among the climatic regions, suggesting that separate breeding goals would be applied, too.

Demonstrating the Effect of Height Variation on Stand-Level Optimization with Diameter-Structured Matrix Model

The weakness of the population matrix models is that they do not take into account the variation inside the class. In this study, we introduce an approach to add height variation of the trees to the diameter-structured matrix models. In this approach, a new sub-model that describes the height growth of the trees is included in the diameter-structured model. We used this height- and diameter-structured matrix model to maximize the net present value (NPV) for the remaining part of the ongoing rotation for Scots pine (Pinus sylvestris L.) stand and studied how the height variation affects to the results obtained through stand-level optimization. In the optimization, the height variation was taken into account by setting the lower saw-log price for the short trees. The results show that including the height variation into the optimization reduced the financial outcome by 16–18% and considerably changed the structure of optimal management (e.g., timings for thinnings, rotation period and intensity of thinnings). We introduced an approach that can be applied to include not only height variation but also variation of other tree properties (such as branchiness or the amount of heartwood and sapwood) into the matrix models.

Effect of Stand Structure and Number of Sample Trees on Optimal Management for Scots Pine: A Model-Based Study

This study presents an attempt to discover the effect of sample size on the financial outcome derived by stand-level optimization with individual tree modeling. The initial stand structure was altered to reflect sparse, average, and dense Scots pine (Pinus sylvestris L.) stands. The stands had varying numbers of stems but identical weighted median diameters and stand basal areas. The hypothetical Weibull diameter distributions were solved according to the parameter recovery method. The trees were systematically sampled with respect to the tree basal area corresponding to sample sizes of 10, 20, or 40 trees. We optimized the stand management with varying numbers of sample trees and with varying stand structures and compared the optimal solutions with respect to the objective function value (maximum net present value) and underlying management schedule. The results for the pine stands in southern and central Finland indicated that the variations in the objective function value relating to sample size were minor (<2.6%) in the sparse and average stand densities but exceeded 3% in the dense stands. Generally, the stand density is not always known, and thus, we may need to generalize the average density for all cases in question. This assumption, however, resulted in overestimations with respect to the optimal rotation period and financial performance in this study. The overestimations in the net present value decreased along with the increasing sample size, from 22% to 14% in the sample sizes of 10 and 40 trees, respectively.

Instructions for optimal any-aged forestry

In this study, any-aged forestry (AAF) refers to forest management in which no explicit choice is made between even- and uneven-aged management, or between rotation forest management and continuous cover forestry. Optimal AAF is more profitable than optimal even- or uneven-aged management because AAF has fewer constraints. This study developed management instructions for optimal AAF. The instructions consist of four models, the first indicating the probability that an immediate cutting in the stand is the optimal decision. In case of cutting, the second model gives the probability that partial cutting (thinning) is optimal. If thinning is selected, the remaining two models indicate how many trees should be removed from different diameter classes. The models for optimal management were based on optimized cutting schedules of 2095 stands, located in different parts of Finland. The use of the model requires that discount rate is specified, and site fertility and temperature sum of the stand are known. The required growing stock characteristics are stand basal area, mean tree diameter and the basal area of pulpwood-sized trees (dbh 8–18 cm). High stand basal area and large mean tree size increase the probability that cutting is the optimal decision. High basal area of pulpwood-sized trees increases the probability that partial cutting is optimal. Thinning from above is the optimal type of cutting in most cases. The models were tested by comparing the model-driven stand management schedules with stand-level optimizations. Schedules based on the models resulted in equally good net present values as schedules based on optimizations. When the discount rate was 3 per cent or more, the models led to similar profitability as stand-level optimization.

Genetically improved reforestation stock provides simultaneous benefits for growers and a sawmill, a case study in Finland

ABSTRACTRecent research has demonstrated benefits of genetically improved seed material (from seed orchards) for growers. In this study, we analyzed whether the profits of genetically faster growing material are also realized as simultaneous economic benefits for a sawmill. We compared three types of improved Scots pine (Pinus sylvestris L.) seedlots to unimproved seedlots on three climatically different sites using stand-simulation software MOTTI. Genetic gains in early-rotation growth traits were incorporated in the growth models as genetic multipliers. Bare land values of the simulated stands were maximized using stand-level optimization. Consequently, removals of each thinning and final harvest were used to estimate the total value of sawing yield. This was subsequently maximized by optimizing the sawing procedure of the logs assuming identical timber quality between improved and unimproved seedlots. The results indicate that simultaneous financial incentives from tree breeding are achievable for both agents (growers and a sawmill). In general, the financial potential increases with the growth potential of the regeneration material, although the form of the relationship is not linear in all cases.

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

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.

Stand-Level Forest Management Planning Approaches

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.

Optimizing stand management involving the effect of genetic gain: preliminary results for Scots pine in Finland

A solid starting point for assessing tree-improvement programs would be to determine the effect of genetic gain in economic terms at stand level. This paper presents a stand-level optimization analysis of the use of improved seed material in reforestation from the perspective of forest owners. We used a genetic algorithm to study the effects of optimized stand management on the bare land value (BLV) for both genetically improved and unimproved reforestation material, with increase in BLV (ΔBLV > 0) representing the deployment benefit over the standard tree-improvement program. The stand-level optimization analysis was applied to a case representative of economic and climatic circumstances in Finland. The results show that the absolute increase in the BLV is distinctly higher in southern Finland than in central Finland, let alone northern Finland, regardless of the interest rate (3% or 4%) or genetic gain (3% or 15%). Sensitivity analyses revealed that market-related risks need to be taken into account carefully. Our tentative results provide new insight on the financial incentives for using genetically improved seed material in Scots pine (Pinus sylvestris L.) stand establishment under varying climatic conditions, but the subject merits further investigation — with greater detail and a more systematic data structure.

Using lumber grade and by-products’ yield predictions for standing scots pine trees in stand level optimization

Using lumber grade and by-products’ yield predictions for standing scots pine trees in stand level optimization

Using stand-level optimization to reduce crown fire hazard

This study evaluated the ability to generate prescriptions for a wide variety of stands when the goal is to reduce crown fire potential. Forest managers charged with reducing crown fire potential while providing for commodity and ecological production have been hampered by the complexity of possible management options. A program called Stand-Level Optimization with Multiple Objectives (SLOMO) was developed that uses a dynamic programming based algorithm to find near-optimal solutions for a number of problem formulations. Sixty-four stand types from eastern Oregon were evaluated with SLOMO with the goal of reducing crown fire potential. Two attributes of stand canopies were manipulated, canopy base height (CBH) and canopy bulk density (CBD). Additionally, a baseline case of “grow only” was evaluated for each stand to help understand the potential effects of active management. Results illustrate the potential for optimization methods to provide useful management strategies to reduce crown fires. Relative to the grow only goal, treatments are derived that achieve and maintain target levels for CBH and CBD over a 100-year projection. Simulations show a substantive decrease in crown fires, especially active crown fires, with treatments derived from optimization procedures.

A density-dependent stand-level optimization approach for deriving management prescriptions for interior northwest (USA) landscapes

Stand-level forest management prescriptions for federal forests in the interior northwest (USA) have changed emphasis in the past decade from being influenced by economic criteria to being influenced by ecological criteria. The design of forest management prescriptions that maintain stand density levels within a target range is now preferred over the design of prescriptions that maximize net present value. We describe a stand-level optimization process for developing efficient management regimes that uses dynamic programming and a region-limited search strategy. The process develops management regimes by penalizing deviations from a preferred range of stand density. Operational considerations, such as minimum harvest levels, can be important, and are considered as constraints in this modeling process. The timing of harvests is such that entries are only permitted when a minimum harvest can be obtained, when a minimum residual basal area can be maintained, and when harvests are limited to trees with a certain diameter range. Lists of residual, harvested, and dead tree records are available for each decade of the optimal regime. These records of information can facilitate further forest product or habitat suitability analysis associated with forest policy analyses.

A New Heuristic To Solve Spatially Constrained Long-Term Harvest Scheduling Problems

Abstract Because of capability limitations of the integer programming solution technique, a new heuristic algorithm was developed to solve spatially constrained long-term harvest scheduling problems. The proposed algorithm can handle multiple harvesting for each harvest unit over a long time horizon. The heuristic utilizes random ordering heuristic optimization and the PATH algorithm adapted from stand level optimization. Employing the proposed algorithm, a harvest scheduling system was constructed. The performance of the proposed algorithm is presented compared to the branch-and-bound algorithm in terms of the computational time as well as the objective value. Using two example forests, solutions by the proposed algorithm are stable in terms of the objective value and have harvest flow fluctuation much less than 3%. For short-term problems, solutions by the proposed algorithm tend to be optimal. For those problems, for which an optimal solution is found by the branch-and-bound algorithm, the solution can produce an objective value with deviation less than 2% from the optimum. The proposed algorithm yields better solutions for long-term problems than the branch-and-bound algorithm with the 1,000,000 limited number of iterations, and the lower bound derived by the proposed algorithm. Computational results reveal that as the time horizon increases, the proposed algorithm significantly and increasingly outperforms the "limited" branch-and-bound algorithm in terms of required computational time. The advantage of the proposed algorithm results from partitioning the problem into subproblems period by period using the PATH algorithm, and defining the objective function of the subproblem by minimizing absolute infeasibility on harvest flow constraints at each period under a two-period sequential feasibility condition. For. Sci. 40(3):365-396.

Efficient Specification and Solution of the Even-aged Rotation and Thinning Problem

Abstract Dynamic programming is frequently used for stand level optimization. In recent years, solution methods have been made available for a number of growth models. However, increasingly complex growth models require network specifications with high dimensionality and its associated high computational cost. This paper presents a more efficient solution algorithm that overcomes the computational burdens of the traditional dynamic programming approach. The algorithm is described using LaGrange multipliers and network theory. An illustrative example is presented, and the computational efficiency of the new method is demonstrated by comparing its performance with an existing dynamic programming algorithm. The scope of applicability of the proposed method is discussed as well as aspects requiring further analysis and research. For. Sci. 33(1):14-29.