Articles containing the keyword 'predictive model'

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, E-mail: tp@mm.unknown

Discoloration of the sapwood caused by blue-stain fungi on conifer logs during interim storage causes significant loss to the forest industry. The fungal infection is often associated with bark beetle attacks because the spores are transmitted by the beetles. They can also be disseminated by rain-splash and moist air. While there are methods to protect logs from sap-stain in wood yards, this is often not possible in the forest for practical and regulatory reasons. Timing of harvesting and timely transportation are often the only ways to prevent blue-stain. To estimate the urgency of transportation, knowledge of the growth of blue-stain fungi and its dependence on weather conditions is of great interest.   The proportion of discolored sapwood on Norway spruce logs was recorded along a time series, together with weather data in two field experiments conducted in spring and summer at two alpine sites in Austria. A predictive model was developed to estimate the proportion of blue-stained sapwood based on the temperature sum to which the logs were exposed. After harvest in March, there was a time lag of 82 and 97 days at the two respective sites, caused by initially low temperatures, before discoloration started. In contrast, sap-stain occurred 14 days after the harvest in June, when warm conditions prevailed from the start. The nonlinear least square regression model can help to estimate a window of opportunity to transport wood before it loses its value and serves as a sub model for lead time estimation within logistic decision support systems.

• Böhm, Department of Forest and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82, 1190 Vienna, Austria https://orcid.org/0000-0001-7803-6618 E-mail: stephan.boehm@boku.ac.at
• Baier, Department of Forest and Soil Sciences, Institute of Forest Entomology, Forest Pathology and Forest Protection, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82, 1190 Vienna, Austria https://orcid.org/0000-0002-1029-5637 E-mail: peter.baier@boku.ac.at
• Kirisits, Department of Forest and Soil Sciences, Institute of Forest Entomology, Forest Pathology and Forest Protection, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82, 1190 Vienna, Austria https://orcid.org/0000-0002-9918-3593 E-mail: thomas.kirisits@boku.ac.at
• Kanzian, Department of Forest and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences, Vienna (BOKU), Peter-Jordan-Strasse 82, 1190 Vienna, Austria https://orcid.org/0000-0002-1198-9788 E-mail: christian.kanzian@boku.ac.at

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 E-mail: liisa.kulmala@helsinki.fi
• 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 E-mail: zliobaite@gmail.com
• Nikinmaa, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: eero.nikinmaa@helsinki.fi
• Nöjd, Natural Resources Institute Finland (Luke), Bio-based business and industry, Tietotie 2, FI-02150 Espoo, Finland 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 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 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 E-mail: jaakko.hollmen@aalto.fi
• Mäkinen, Natural Resources Institute Finland (Luke), Bio-based business and industry, Tietotie 2, FI-02150 Espoo, Finland E-mail: harri.makinen@luke.fi