Luke
Silmäile
Hakuohjeet

Jukuri

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

  • Suuntana ilmastotehokas maatalous Suomessa
    Lehtonen, Heikki; Haario, Peppi; Jansik, Csaba; Kaartinen, Niina; Kettunen, Marita; Leino, Marianne; Mattila, Tuomas; Maukonen, Mirkka; Männistö, Satu; Saarinen, Merja; Seppälä, Jyri; Wejberg, Henrik
    Suomen ilmastopaneelin julkaisuja : 4 (Suomen ilmastopaneeli, 2026)
  • Image-based phenotyping of faba bean genetic resources for water deficit responses under controlled conditions
    Poque, Sylvain; Carlson-Nilsson, Ulrika; Omer, Muhammad; Palmé, Anna; Vågen, Ingunn M.; Poulsen, Gert; Leino, Matti W.; Himanen, Kristiina; Khazaei, Hamid
    Genetic resources and crop evolution : 5 (Springer Nature, 2026)
    Faba bean (Vicia faba L.) has great potential to contribute to sustainable agriculture and protein security globally but is known to be very sensitive to drought stress. Uncovering drought-adapted germplasm is critical for developing resilient cultivars and advancing our understanding of the mechanisms underlying stress adaptation. However, high-throughput plant phenotyping under stress conditions remain a major bottleneck in crop genetics and breeding programs. In this study, a multi-sensor indoor phenotyping platform was used to assess 44 faba bean genotypes under water deficit conditions. Standardized, monitored stress conditions were achieved by watering-by-weighing for drought onset, duration, and intensities allowing genotype-level comparisons. The genotypes showed a range of stress responses in growth and physiology, including traits such as plant height, biomass, water use efficiency (WUE), and chlorophyll fluorescence parameters. Digital biomass, derived from combined top- and side-view plant imaging, was strongly correlated with biological biomass at the experimental endpoint, validating its use as a non-destructive proxy for growth assessment in faba bean. Time-resolved generalized additive modelling further revealed genotype-specific differences in the timing and magnitude of water deficit response. Genotypes that maintained growth and WUE under water deficit conditions may serve as valuable pre-breeding materials for development of drought-adapted faba bean.
  • Stand-level incremental growth model incorporating thinning effects for Pinus densiflora, Pinus koraiensis, and Larix kaempferi plantations in Korea
    Jin, Heejun; Lee, Daesung; Choi, Jungkee
    Forest ecology and management (Elsevier, 2026)
  • Integrating Bio-Hubs for Resilience Enhancement of Wood Pellet Supply Chains: A Market Analysis using System Dynamics
    Mohagheghi, Omid; Mafakheri, Fereshteh; Nasiri, Fuzhan; Gagnon, Bruno; Prinz, Robert; Bergström, Dan; Brown, Mark
    Renewable energy (Elsevier, 2026)
  • 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.