Luke
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Tervetuloa käyttämään Jukuria, Luonnonvarakeskuksen (Luke) avointa julkaisuarkistoa. Jukurissa on tiedot Luken julkaisutuotannosta. Osa julkaisuista on vapaasti ladattavissa. Luken muodostaneiden tutkimuslaitosten aikaisemmasta julkaisutuotannosta osan tiedot ovat järjestelmässä jo nyt ja kattavuus paranee jatkuvasti.

Kokoelmat

Viimeksi tallennetut

  • Opportunities and computational challenges in large-scale whole-genome sequencing data analysis
    Zaabza, Hafedh Ben; Ferdosi, Mohammad H; Strandén, Ismo; Cuyabano, Beatriz C D; Neupane, Mahesh; Misztal, Ignacy; Lourenco, Daniela; Gondro, Cedric
    Journal of animal science (American Society of Animal Science, 2026)
    Genomic selection has been used in animal breeding for c. 15 yr and continues to be an important tool in predicting genetic merit in livestock populations. The dairy cattle industry was the first to adopt genomic selection, initially based on some 50K single-nucleotide polymorphism (SNP) arrays for thousands of animals. Later advances in genome-scanning technologies have enabled inexpensive genotyping and sequencing, leading to wider adoption, and constantly increasing amounts of genomic data, both as to the number of genotyped animals and variants genotyped per animal. Full sequence data are expected to supersede SNP chips in the coming years. We review the methods and computational approaches used with sequence data and the impact of the methods and model assumptions on genomic prediction accuracy. The modeling, development, and applicability of these methods to sequence data are discussed, as well as the computational resources required. Sequence data should, in principle, provide full information on genetic variability, which should lead to higher prediction accuracy. In practice, there is limited evidence of additional benefit from using sequence data over medium- or high-density SNP panels. This is particularly true for small effective population sizes (Ne) such as cattle populations, where animals within a breed have many common ancestors and thus longer chromosome segments with high linkage disequilibrium accurately trackable with a relatively small number of markers. A population with a small Ne has long haplotype blocks, from 1 to 5 Mb, making it hard to identify causal variants within blocks. However, in major cattle breeds, a medium-density SNP panel is sufficient to tag the blocks themselves, and prediction with large datasets is highly accurate. Clearly, sequence data should not be used directly for genomic prediction, but for identifying putative causal variants to improve the accuracy and stability of subsequent predictions. We show that the best strategy to deal with any large data with high SNP densities is to use only a subset of (important) markers and determine the most appropriate model for exploiting the preselected variants in the genomic evaluation. Novel prediction methods that subset trait-specific informative markers could offer the advantage of using sequence data by potentially linking individuals through underlying functional variants rather than simply through shared haplotype blocks inherited from ancestors. Further research is required to clarify this aspect.
  • Omasta vai vieraasta pellosta?
    Karikallio, Hanna-Maija
    Sitra : 308 (Suomen itsenäisyyden juhlavuoden 1967 rahasto, 2026)
  • Metsän talous ja tulevaisuus
    Lintunen, Jussi; Assmuth, Aino; Pihlainen, Sampo
    Sitra : 308 (Suomen itsenäisyyden juhlavuoden 1967 rahasto, 2026)
  • Measuring forest inventory attributes using Faro Orbis Mobile laser scanner in managed boreal forests
    Liikonen, Lauri; Yrttimaa, Tuomas; Erkkilä, Aapo; Paakkari, Johanna; Pitkänen, Timo; Kotivuori, Eetu; Vastaranta, Mikko
    Forestry : 3 (Oxford University Press, 2026)
    Mobile laser scanning (MLS) provides detailed point cloud reconstructions of forest environments and has potential for operational forest sample-plot surveying. This study evaluated the accuracy of MLS in deriving forest inventory attributes, including basal area (G), number of trees per hectare (TPH), total stem volume (V), basal area-weighted mean tree diameter (Dg) and height (Hg), and dominant height (Hdom). Experiments were conducted in managed boreal forests across 44 sample plots (370–2000 m2) using a Faro Orbis MLS system. Field measurements collected tree-by-tree (n = 4472) with callipers and clinometers during the previous summer served as reference data. We compared two alternative MLS data acquisition trajectories—closed loops (MLS-loop) and line transects (MLS-line)—and two processing workflows: (i) manually assisted tree detection followed by automatic tree measurements, and (ii) a fully automatic workflow. MLS-line provided similar or marginally improved accuracy compared with MLS-loop; however, the substantially shorter acquisition time of MLS-loop (19.0 min per plot on average) favoured its operational use over MLS-line (30.5 min). Clearer differences emerged between processing workflows. The fully automatic workflow identified and measured 74.1% of trees with diameter at breast height (DBH) > 5 cm, whereas manual assistance in tree detection increased this proportion to 97.1%. DBH accuracy was similar for both workflows (root-mean-square-error [RMSE] ≈ 2.4 cm), but tree-height estimates were substantially less accurate under automatic processing (RMSE 6.2 m) than under the assisted workflow (RMSE 2.1 m). These differences propagated to plot-level estimates. Using the automatic workflow, RMSEs were 4.2 m2/ha for G, 610 trees/ha for TPH, 29.3 m3/ha for V, 2.3 cm for Dg, 1.6 m for Hg, and 1.9 m for Hdom. The assisted workflow notably improved accuracy, yielding RMSEs of 3.5 m2/ha for G, 54.0 trees/ha for TPH, 20.2 m3/ha for V, 1.2 cm for Dg, 1.3 m for Hg, and 1.2 m for Hdom when using closed-loop trajectories. Overall, the results emphasize the importance of assisted workflows for attributes sensitive to detection completeness, particularly TPH, while showing that kinematic MLS can efficiently capture forest structure for sample plot measurements.
  • Voimaa sienimetsästä
    Tyrväinen, Liisa
    Sitra : 308 (Suomen itsenäisyyden juhlavuoden 1967 rahasto, 2026)