Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2024
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Floral initiation in biennial-fruiting red raspberry is controlled by the interaction of temperature and photoperiod. To determine the threshold temperatures for short day (SD) floral initiation in early- and late-flowering cultivars, we exposed plants of ‘Glen Ample’, ‘Glen Mor’ and ‘Duo’ to 12°, 16° and 20°C in a daylight phytotron under naturally decreasing autumn daylength at Ås, Norway (59°40’N). While none of the cultivars ceased growing or initiated floral primordia at 20°C, ‘Glen Ample’ and ‘Glen Mor’ initiated buds at 12° and 16°C, whereas ‘Duo’ formed flower buds at 12°C only. Surprisingly, however, all plants flowered abundantly in spring after winter chilling in the dark at −1.5 ± 0.5°C for 7 months. We discuss two possible explanations for this unusual and novel flowering response. Fractional induction is well known in raspberry, and we visualise that in SD at 20°C, the SD requirement is fulfilled, while floral induction is still blocked by inappropriate temperature. A vernalisation-like response is alternatively suggested as this can take place at near-freezing temperatures in the dark. A combination of the two mechanisms is also possible and likely. We conclude, however, that the two floral induction processes are fundamentally different and controlled by different physiological mechanisms.
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Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories. We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees. Unlike object reconstruction methods, our approach is based on simple metrics computed on vertical slices that summarize information on point distances, angles, and geometric attributes of the space between and around the points. Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans. We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios. Our approach provides a simple, clear, and scalable solution that can be adapted to different situations both for research and more operational mapping.
Authors
Kalkidan Mulatu Ayele Shimelis Gizachew Raji Bimrew Asmare Niklas Wickander Solomon Mwendia Peter Dörsch W. A. Worku Caroline Brophy Karl Thunes Marit JørgensenAbstract
EthiopiaGrass Fact sheet. 1 p.
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Jorunn BørveAbstract
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