Current issue: 53(3)

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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles containing the keyword 'predictive modelling'.

Category: Research article

article id 1680, category Research article
Liisa Kulmala, Indre Žliobaitė, Eero Nikinmaa, Pekka Nöjd, Pasi Kolari, Kourosh Kabiri Koupaei, Jaakko Hollmén, Harri Mäkinen. (2016). Environmental control of growth variation in a boreal Scots pine stand – a data-driven approach. Silva Fennica vol. 50 no. 5 article id 1680. https://doi.org/10.14214/sf.1680
Highlights: High water potential and carbon gain during bud forming favoured height growth; High water potential during the elongation period favoured height growth; A spring with high carbon gain favoured diameter growth; The obtained regression models had generally low generalization performance.

Despite the numerous studies on year-to-year variation of tree growth, the physiological mechanisms controlling annual variation in growth are still not understood in detail. We studied the applicability of data-driven approach i.e. different regression models in analysing high-dimensional data set including continuous and comprehensive measurements over meteorology, ecosystem-scale water and carbon fluxes and the annual variation in the growth of app. 50-year-old Scots pine stand in southern Finland. Even though our dataset covered only 16 years, it is the most extensive collection of interactions between a Scots pine ecosystem and atmosphere. The analysis revealed that height growth was favoured by high water potential of the tree and carbon gain during the bud forming period and high water potential during the elongation period. Diameter growth seemed to be favoured by a winter with high precipitation and deep snow cover and a spring with high carbon gain. The obtained models had low generalization performance and they would require more evaluation and iterative validation to achieve credibility perhaps as a mixture of data-driven and first principle modeling approaches.

  • Kulmala, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: liisa.kulmala@helsinki.fi (email)
  • Žliobaitė, Aalto University, Department of Computer Science and Helsinki Institute for Information Technology, P.O. Box 11000, FI-00076 Aalto, Finland; University of Helsinki, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: zliobaite@gmail.com
  • Nikinmaa, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: eero.nikinmaa@helsinki.fi
  • Nöjd, Natural Resources Institute Finland (Luke), Bio-based business and industry, Tietotie 2, FI-02150 Espoo, Finland ORCID ID:E-mail: pekka.nojd@luke.fi
  • Kolari, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland; University of Helsinki, Department of Physics, P.O. Box 64, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: pasi.kolari@helsinki.fi
  • Kabiri Koupaei, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: kourosh.kabiri@helsinki.fi
  • Hollmén, Aalto University, Department of Computer Science and Helsinki Institute for Information Technology, P.O. Box 11000, FI-00076 Aalto, Finland; University of Helsinki, Department of Geosciences and Geography, P.O. Box 64, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: jaakko.hollmen@aalto.fi
  • Mäkinen, Natural Resources Institute Finland (Luke), Bio-based business and industry, Tietotie 2, FI-02150 Espoo, Finland ORCID ID:E-mail: harri.makinen@luke.fi

Category: Article

article id 5311, category Article
Timo Pukkala. (1987). Kuusen ja männyn siemensadon ennustemalli. Silva Fennica vol. 21 no. 2 article id 5311. https://doi.org/10.14214/sf.a15468
English title: Model for predicting the seed crop of Picea abies and Pinus sylvestris.

The seed crop of Norway spruce (Picea abies (L.) H. Karst.) and Scots pine (Pinus sylvestris L.) is predicted with the help of mean monthly temperatures during May–August one and two years before the flowering year. The prediction models were made separately for Lapland and for the rest of Finland. The models are based on 10-year periods of seed crop measurements and climatic data. The total number of time series was 59.

In Lapland, Norway spruce flowered abundantly and produced an abundant seed crop after warm July–August and two years after cool July–August. In other parts of Finland, warm June and July produced a good flowering year, especially if these months were cool two years before the flowering year.

In Lapland, Scots pine flowered abundantly if the whole previous growing season was warm. Elsewhere in Finland, a cool June preceded prolific flowering in the coming year if the rest of the growing season was considerably warmer than the average.

The prediction models explained 37–49 % of the variation in the size of the seed crop. The occurrence of good and poor seed years was usually predicted correctly. Using the presented models, the prediction of the seed crop is obtainable 1.5 year for Norway spruce and 2.5 year for Scots pine before the year of seed fall.

The PDF includes an abstract in English.

  • Pukkala, ORCID ID:E-mail:

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