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Funding for planting missing species financially supports the conversion from pure even-aged to uneven-aged mixed forests and climate change mitigation

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Abstract

Mountain spruce forests in Central Europe decline under storms and bark beetle calamities driven by climate change. A stabilisation by planting rare or missing tree species is expensive and requires funding. A funding policy should mitigate climate change and support biodiversity. The goal of this study was to identify a conversion strategy of even-aged spruce-dominated forest stands to uneven-aged mixed stands with spruce (Picea abies (L.) H.Karst.), beech (Fagus sylvatica L.), and fir (Abies alba Mill.). A simultaneous nonlinear optimisation of the number of planted trees and harvested trees per species and per period schedules stand treatments aiming to maximise the long-term financial outcome. Planting modelling extends a density-dependent stand-level matrix transition model based on diameter classes with an age-class-based model for artificial regeneration. An optimal conversion strategy was applied for five funding policy schemes, each for five initial states representing different stages of age and species composition typical for spruce forest conversion in the mountain zone of the Western Carpathians. Only 50% and higher funding of planting costs for the minor/missing fir and beech species facilitates a substantial increase of their shares in stand volume. Funding decreases the volume failure due to mortality. Funding increases the standing and harvested volume, which mitigates climate change by increasing the carbon sequestration. Funding causes unintended effects on ecosystem services by lowering harvest diameters, decreasing the volume of less profitable beech, and temporarily reducing the stand density aimed at supporting plantings and their diameter increments.

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Summarised data generated and analysed during this study is included in this published article. Detailed data sources are cited.

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Acknowledgements

The study uses a database on forest management plans and forest management records created within the project Interreg SK-CZ BESKYDY (ITMS2014+: 304021D067) by National Forest Centre (NFC) Zvolen and Global Change Research Institute (GCRI) Brno.

Funding

This work was supported by the Slovak Research and Development Agency (project SilvaMod, APVV-18–0195), the Ministry of Agriculture and Rural Development of the Slovak Republic (project EPRIBLES, Item No. 08V0301), and the ERDF through Research & Development Operational Programme (project Centre of Excellence of Forest-based Industry, ITMS: 313011S735).

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Authors and Affiliations

  1. National Forest Centre, Forest Research Institute Zvolen, T. G. Masaryka 22, 960 01, Zvolen, Slovak Republic

    Joerg Roessiger, Ladislav Kulla, Vlastimil Murgaš, Maroš Sedliak, Miroslav Kovalčík & Vladimír Šebeň

  2. IFER– Institute of Forest Ecosystem Research, Cs. armady 655, 254 01, Jilove u Prahy, Czech Republic

    Emil Cienciala

  3. Global Change Research Institute of the Czech Academy of Sciences, V. V. I., Bělidla 4a, 603 00, Brno, Czech Republic

    Emil Cienciala

Authors
  1. Joerg Roessiger
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  2. Ladislav Kulla
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  3. Vlastimil Murgaš
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  4. Maroš Sedliak
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  5. Miroslav Kovalčík
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  6. Emil Cienciala
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  7. Vladimír Šebeň
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Contributions

Joerg Roessiger: Conceptualisation, Visualisation, Methodology (statistical analysis of inventory data, ecological and economic sub-models, simulation and optimisation model), Software, Investigation, Validation (simulation and optimisation model), Roles/Writing—original draft (except Sects. 2.1; 2.2; 5); Ladislav Kulla: Conceptualisation, Methodology (stratification and definition of scenarios), Roles/Writing—original draft (Sects. 2.1; 2.2; 5), Writing—review and editing, Supervision, Project administration, Funding acquisition; Vlastimil Murgaš: Data curation, Formal analysis (Tree inventory data, volume, timber price, harvest cost data); Maroš Sedliak: Data curation, Formal analysis (Salvage harvest data); Miroslav Kovalčík: Data curation, Formal analysis, Methodology (timber price data, harvest cost data, planting data and planting cost model); Emil Cienciala: Data curation, Formal analysis (Czech inventory data); Vladimír Šebeň: Data curation, Formal analysis (Slovak inventory data).

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Correspondence to Joerg Roessiger.

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Supplementary Information

Below is the link to the electronic supplementary material.

Initial diameter distribution for stand development types (SDTs) in the experimental object Polom (XLSX 35 kb)

10342_2022_1456_MOESM2_ESM.xlsx

Distribution of the optimised mean harvest volume per harvest diameter for the planting cost factor (CF) during equilibrium for the financially best cases per scenario (XLSX 30 kb)

10342_2022_1456_MOESM3_ESM.xlsx

Influence of the cost factor (CF) on the diameter distribution during equilibrium; each CF is represented by the minimum and maximum sum of the number of standing trees (before harvest) from the financially best cases for each scenario (XLSX 18 kb)

10342_2022_1456_MOESM4_ESM.xlsx

Financial optimisation results and their sensitivity against the optimisation parameter parscale, note the different y-axes (XLSX 40 kb)

10342_2022_1456_MOESM5_ESM.xlsx

Mean standing, harvested, mortal, and mean species-specific standing timber volume resulting from planting cost factor, initial, and parscale scenarios are shown; volume only during equilibrium (XLSX 391 kb)

10342_2022_1456_MOESM6_ESM.xlsx

Efficiency of funding measured by the differences in mean standing, harvested, mortal, and mean species-specific standing timber volume resulting from planting cost factor, initial, and parscale scenarios compared to the mean state of CF 100% per invested € into planting (calculated as NPV of funding of planting costs) are shown; CF 75% M1 excluded; CF 75% M2 partly excluded; volume only during equilibrium (XLSX 367 kb)

10342_2022_1456_MOESM7_ESM.xlsx

Sensitivity analyses of the equilibrium state by varying the factors: Planting cost factor (CF); Interest rate (Int); Mortality probability (Mo); Fir planted tree survival (Fps); Site (Increment dependent on mean site index for Ac acidic; Eu eutrophic; Mo mountain; Sm submountain); Base scenario: CF 50%, Int 2%, Mo mean; Fps mean; Site Ac Mo (XLSX 15 kb)

Volumes and net cash flows per tree species and diameter class (XLSX 9 kb)

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Roessiger, J., Kulla, L., Murgaš, V. et al. Funding for planting missing species financially supports the conversion from pure even-aged to uneven-aged mixed forests and climate change mitigation. Eur J Forest Res 141, 517–534 (2022). https://doi.org/10.1007/s10342-022-01456-6

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