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Articles by Rasmus Astrup

Category: Research article

article id 10732, category Research article
Ana Aza, A. Maarit I. Kallio, Timo Pukkala, Ari Hietala, Terje Gobakken, Rasmus Astrup. (2022). Species selection in areas subjected to risk of root and butt rot: applying Precision forestry in Norway. Silva Fennica vol. 56 no. 3 article id 10732. https://doi.org/10.14214/sf.10732
Keywords: Norway spruce; Scots pine; growth modelling; precision forestry; root and butt rot severity; tree species selection
Abstract | Full text in HTML | Full text in PDF | Author Info

Norway’s most common tree species, Picea abies (L.) Karst. (Norway spruce), is often infected with Heterobasidion parviporum Niemelä & Korhonen and Heterobasidion annosum (Fr.) Bref.. Because Pinus sylvestris L. (Scots pine) is less susceptible to rot, it is worth considering if converting rot-infested spruce stands to pine improves economic performance. We examined the economically optimal choice between planting Norway spruce and Scots pine for previously spruce-dominated clear-cut sites of different site indexes with initial rot levels varying from 0% to 100% of stumps on the site. While it is optimal to continue to plant Norway spruce in regions with low rot levels, shifting to Scots pine pays off when rot levels get higher. The threshold rot level for changing from Norway spruce to Scots pine increases with the site index. We present a case study demonstrating a practical method (“Precision forestry”) for determining the tree species in a stand at the pixel level when the stand is heterogeneous both in site indexes and rot levels. This method is consistent with the concept of Precision forestry, which aims to plan and execute site-specific forest management activities to improve the quality of wood products while minimising waste, increasing profits, and maintaining environmental quality. The material for the study includes data on rot levels and site indexes in 71 clear-cut stands. Compared to planting the entire stand with a single species, pixel-level optimised species selection increases the net present value in almost every stand, with average increase of approximately 6%.

  • Aza, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, PO Box 5003, NO-1432, Ås, Norway ORCID https://orcid.org/0000-0002-6416-6697 E-mail: anfe@nmbu.no (email)
  • Kallio, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, PO Box 5003, NO-1432, Ås, Norway E-mail: maarit.kallio@nmbu.no
  • Pukkala, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: timo.pukkala@uef.fi
  • Hietala, Norwegian Institute of Bioeconomy Research, PO Box 115, NO-1431 Ås, Norway E-mail: ari.hietala@nibio.no
  • Gobakken, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, PO Box 5003, NO-1432, Ås, Norway E-mail: terje.gobakken@nmbu.no
  • Astrup, Norwegian Institute of Bioeconomy Research, PO Box 115, NO-1431 Ås, Norway E-mail: rasmus.astrup@nibio.no
article id 10627, category Research article
Christian Kuehne, J. Paul McLean, Kobra Maleki, Clara Antón-Fernández, Rasmus Astrup. (2022). A stand-level growth and yield model for thinned and unthinned even-aged Scots pine forests in Norway. Silva Fennica vol. 56 no. 1 article id 10627. https://doi.org/10.14214/sf.10627
Keywords: Pinus sylvestris; mortality; volume growth; seemingly unrelated regression; production forestry; system of equations
Highlights: The presented growth and yield model consists of component equations for dominant height, stem density, total basal area, and total stem volume; The component equations were fitted simultaneously using seemingly unrelated regression; The model is capable to forecast and compare outcomes of varying thinning regimes; The new component equations better represent the improved growing conditions for Scots pine in Norway.
Abstract | Full text in HTML | Full text in PDF | Author Info

Management of Scots pine (Pinus sylvestris L.) in Norway requires a forest growth and yield model suitable for describing stand dynamics of even-aged forests under contemporary climatic conditions with and without the effects of silvicultural thinning. A system of equations forming such a stand-level growth and yield model fitted to long-term experimental data is presented here. The growth and yield model consists of component equations for (i) dominant height, (ii) stem density (number of stems per hectare), (iii) total basal area, (iv) and total stem volume fitted simultaneously using seemingly unrelated regression. The component equations for stem density, basal area, and volume include a thinning modifier to forecast stand dynamics in thinned stands. It was shown that thinning significantly increased basal area and volume growth while reducing competition related mortality. No significant effect of thinning was found on dominant height. Model examination by means of various fit statistics indicated no obvious bias and improvement in prediction accuracy in comparison to existing models in general. An application of the developed stand-level model comparing different management scenarios exhibited plausible long-term behavior and we propose this is therefore suitable for national deployment.

  • Kuehne, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: christian.kuehne@nibio.no (email)
  • McLean, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: paul.mclean@nibio.no
  • Maleki, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: kobra.maleki@nibio.no
  • Antón-Fernández, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: clara.anton.fernandez@nibio.no
  • Astrup, Norwegian Institute of Bioeconomy Research, Division of Forestry and Forest Resources, P.O. Box 115, NO-1431 Ås, Norway E-mail: rasmus.astrup@nibio.no

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