Petteri Seppänen, Antti Mäkinen. (2020). Comprehensive yield model for plantation teak in Panama. Silva Fennica vol. 54 no. 5 article id 10309. https://doi.org/10.14214/sf.10309

Keywords: simulation; teak; decision support system; Tectona grandis; Panama; taper curve; volume equation; yield model

Highlights: Tree level teak stem volume models, taper model and three sets of stand level yield models were developed using large empirical datasets; Tree volume models were satisfactorily validated against independent measurement data and other published models; Tree height as input parameter improved the stem volume model marginally; Stand level yield models produced comparable harvest volumes with models published in the literature; Stand level timber product outputs were found like actual harvests with an exception that the models marginally underestimate the share of logs in very large diameter classes.

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The purpose of this study was to prepare a comprehensive, computerized teak (Tectona grandis L.f) plantation yield model system that can be used to describe the forest dynamics, predict growth and yield and support forest planning and decision-making. Extensive individual tree and permanent sample plot data were used to develop tree-level volume models, taper curve models and stand-level yield models for teak plantations in Panama. Tree volume models were satisfactorily validated against independent measurement data and other published models. Tree height as input parameter improved the stem volume model marginally. Stand level yield models produced comparable harvest volumes with models published in the literature. Stand level volume product outputs were found like actual harvests with an exception that the models marginally underestimate the share of logs in very large diameter classes. The kind of comprehensive model developed in this study and implemented in an easy to use software package provides a very powerful decision support tool. Optimal forest management regimes can be found by simulating different planting densities, thinning regimes and final harvest ages. Forest practitioners can apply growth and yield models in the appropriate stand level inventory data and perform long term harvest scheduling at property level or even at an entire timberland portfolio level. Harvest schedules can be optimized using the applicable financial parameters (silviculture costs, harvesting costs, wood prices and discount rates) and constraints (market size and operational capacity).

Seppänen, Verdas Oy, Kihlinkuja 7, FI-50600 Mikkeli, Finland E-mail: petteri@verdas.fi
Mäkinen, Simosol Oy. Hämeenkatu 10, FI-11100 Riihimäki, Finland E-mail: antti.makinen@simosol.fi

article id 55, category Research article

Antti Mäkinen, Annika Kangas, Mikko Nurmi. (2012). Using cost-plus-loss analysis to define optimal forest inventory interval and forest inventory accuracy. Silva Fennica vol. 46 no. 2 article id 55. https://doi.org/10.14214/sf.55

Keywords: value of information; prediction error; inventory error

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In recent years, optimal inventory accuracy has been analyzed with a cost-plus-loss methodology, where the total costs of inventory include both the measurement costs and the losses from the decisions based on the collected information. Losses occur, when the inaccuracies in the data lead to sub-optimal decisions. In almost all cases, it has been assumed that the accuracy of the once collected data remains the same throughout the planning period, and the period has been from 10 up to 100 years. In reality, the quality of the data deteriorates in time, due to errors in the predicted growth. In this study, we carried out a cost-plus-loss analysis accounting for the errors in (stand-level) growth predictions of basal area and dominant height. In addition, we included the inventory errors of these two variables with several different levels of accuracy, and costs of inventory with several different assumptions of cost structure. Using the methodology presented in this study, we could calculate the optimal inventory interval (life-span of data) minimizing the total costs of inventory and losses through the 30-year planning period. When the inventory costs only to a small extent depended on the accuracy, the optimal inventory period was 5 years and optimal accuracy RMSE 0%. When the costs more and more heavily depended on the accuracy, the optimal interval turned out to be either 10 or 15 years, and the optimal accuracy reduced from RMSE 0% to RMSE 20%. By increasing the accuracy of the growth models, it was possible to reduce the inventory accuracy or lengthen the interval, i.e. obtain clear savings in inventory costs.

Mäkinen, Simosol Oy, Rautatietori 4, FI-11130 Riihimäki, Finland E-mail: antti.makinen@simosol.fi
Kangas, University of Helsinki, Department of Forest Sciences, Helsinki, Finland E-mail: ak@nn.fi
Nurmi, University of Helsinki, Department of Forest Sciences, Helsinki, Finland E-mail: mn@nn.fi

article id 100, category Research article

Annika Kangas, Lauri Mehtätalo, Antti Mäkinen, Kalle Vanhatalo. (2011). Sensitivity of harvest decisions to errors in stand characteristics. Silva Fennica vol. 45 no. 4 article id 100. https://doi.org/10.14214/sf.100

Keywords: forest planning; inventory; measurement errors; decision making; logistic regression; regression tree

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In forest planning, the decision maker chooses for each stand a treatment schedule for a predefined planning period. The choice is based either on optimization calculations or on silvicultural guidelines. Schedules for individual stands are obtained using a growth simulator, where measured stand characteristics such as the basal area, mean diameter, site class and mean height are used as input variables. These characteristics include errors, however, which may lead to incorrect decisions. In this study, the aim is to study the sensitivity of harvest decisions to errors in a dataset of 157 stands. Correct schedules according to silvicultural guidelines were first determined using error-free data. Different amounts of errors were then generated to the stand-specific characteristics, and the treatment schedule was selected again using the erroneous data. The decision was defined as correct, if the type of harvest in these two schedules were similar, and if the timings deviated at maximum ±2 for thinning and ±3 years for clear-cut. The dependency of probability of correct decisions on stand characteristics and the degree of errors was then modelled. The proposed model can be used to determine the required level of measurement accuracy for each characteristics in different kinds of stands, with a given accuracy requirement for the timing of treatments. This information can further be utilized in selecting the most appropriate inventory method.

Kangas, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: lm@nn.fi
Mäkinen, Simosol Oy, Riihimäki, Finland E-mail: am@nn.fi
Vanhatalo, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: kv@nn.fi

article id 111, category Research article

Ilona Pietilä, Annika Kangas, Antti Mäkinen, Lauri Mehtätalo. (2010). Influence of growth prediction errors on the expected losses from forest decisions. Silva Fennica vol. 44 no. 5 article id 111. https://doi.org/10.14214/sf.111

Keywords: growth prediction; uncertainty; forest information; updating; inoptimality loss

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In forest planning, forest inventory information is used for predicting future development of forests under different treatments. Model predictions always include some errors, which can lead to sub-optimal decisions and economic loss. The influence of growth prediction errors on the reliability of projected forest variables and on the treatment propositions have previously been examined in a few studies, but economic losses due to growth prediction errors is an almost unexplored subject. The aim of this study was to examine how the growth prediction errors affected the expected losses caused by incorrect harvest decisions, when the inventory interval increased. The growth models applied in the analysis were stand-level growth models for basal area and dominant height. The focus was entirely on the effects of growth prediction errors, other sources of uncertainty being ignored. The results show that inoptimality losses increased with the inventory interval. Average relative inoptimality loss was 3.3% when the inventory interval was 5 years and 11.6% when it was 60 years. Average absolute inoptimality loss was 230 euro ha^–1 when the inventory interval was 5 years and 860 euro ha^–1 when it was 60 years. The average inoptimality losses varied between development classes, site classes and main tree species.

Pietilä, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ip@nn.fi
Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
Mäkinen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: am@nn.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi

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