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.
2025
Authors
Darius KviklysAbstract
No abstract has been registered
Authors
Darius KviklysAbstract
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Abstract
This study presents information about the variability between and within populations of Norway spruce in lammas shoot formation. Assessments of lammas shoots were conducted in two short-term trials involving full sib families of Norway spruce from two complete diallel crosses, each originating from a natural population. These assessments were made over two growing seasons when the trees were six and seven years from seed, during which early summer temperatures varied significantly. The trees were grown on former agricultural land with large variation in soil fertility across the field. The proportion of trees with lammas shoots varied among blocks, ranging from 1 to 14 %, with the highest values in the blocks with the most fertile soil conditions. A substantial variation was also found among families from each population regarding the percentage of trees with lammas shoots, varying among half-sib families from 2 to 20 % and 1 to 19 % in the two populations, respectively. The largest part of this genetic variation was additive, with high values for the general combining ability (GCA) variance components and low values of the specific combining ability (SCA), maternal and reciprocal components. Estimates of narrow sense heritability were 0.40 for transformed lammas shoot scores in both diallels. Generally, families with an early start and early cessation of shoot elongation had the highest frequency of lammas shoots. In one of the diallels, families with a high lammas shoot percentage also had the highest number of ramicorn branches in a field trial at age 12 and 26 years from seed.
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Abstract
Observations of two apparent phenotypic expressions (morphodemes) of the composite thallus of the lichen-forming ascomycete species Ricasolia amplissima (Scop.) De Not. (formerly Lobaria amplissima (Scop.) Forss.) inspired us to investigate the morphological and genetic variation of the species in Norway. The morphodemes differ in thallus shape and occurrence of apothecia and/or cephalodia, each dominating in climatically different parts of southern Norway. We investigated the morphology of herbarium collections as well as fresh samples from various areas, including localities where the two morphodemes occur together. The nrITS barcode marker was sequenced to investigate the genetic variation along the climatic gradient of the Hardangerfjord area. We also included barcode sequences of specimens from other parts of the world in order to establish if the Norwegian pattern of variation has a wider geographical significance. Results suggest that the genetic variation found in Ricasolia amplissima corresponds to morphology independently of geography/climate. The two haplotype groups cluster in two distinct sister clades, however, the within-species variation is too small to justify taxonomic recognition. Specimens with the cephalodiate morphodeme and its haplotypes are mainly found in the oceanic west, whereas specimens with apotheciate morphodemes and its haplotypes occur mainly in the drier eastern parts. The results can be interpreted as 1) immigration to Norway from different gene pools in separate glacial refugia, or, 2) natural selection for water efficient cephalodiate morphodemes in the oceanic west and apotheciate (sexually reproducing) in drier suboceanic east parts of Norway. We argue that within-species genetic variation should always be considered before conservation actions such as transplants of lichen thalli are taken.
Authors
Tatsiana EspevigAbstract
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Abstract
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Authors
Kristian Hansen Håvard Steinshamn Sissel Hansen Matthias Koesling Tommy Dalgaard Bjørn Gunnar HansenAbstract
To evaluate the environmental impact across multiple dairy farms cost-effectively, the methodological frame- work for environmental assessments may be redefined. This article aims to assess the ability of various statistical tools to predict impact assessment made from a Life Cyle Assessment (LCA). The different models predicted estimates of Greenhouse Gas (GHG) emissions, Energy (E) and Nitrogen (N) intensity. The functional unit in the study was defined as 2.78 MJMM human-edible energy from milk and meat. This amount is equivalent to the edible energy in one kg of energy-corrected milk but includes energy from milk and meat. The GHG emissions (GWP100) were calculated as kg CO2-eq per number of FU delivered, E intensity as fossil and renewable energy used divided by number of FU delivered, and N intensity as kg N imported and produced divided by kg N delivered in milk or meat (kg N/kg N). These predictions were based on 24 independent variables describing farm characteristics, management, use of external inputs, and dairy herd characteristics. All models were able to moderately estimate the results from the LCA calculations. However, their precision was low. Artificial Neural Network (ANN) was best for predicting GHG emissions on the test dataset, (RMSE = 0.50, R2 = 0.86), followed by Multiple Linear Regression (MLR) (RMSE = 0.68, R2 = 0.74). For E intensity, the Supported Vector Machine (SVM) model was performing best, (RMSE = 0.68, R2 = 0.73), followed by ANN (RMSE = 0.55, R2 = 0.71,) and Gradient Boosting Machine (GBM) (RMSE = 0.55, R2 = 0.71). For N intensity predictions the Multiple Linear Regression (MLR) (RMSE = 0.36, R2 = 0.89) and Lasso regression (RMSE = 0.36, R2 = 0.88), followed by the ANN (RMSE = 0.41, R2 = 0.86,). In this study, machine learning provided some benefits in prediction of GHG emission, over simpler models like Multiple Linear Regressions with backward selection. This benefit was limited for N and E intensity. The precision of predictions improved most when including the variables “fertiliser import nitrogen” (kg N/ha) and “proportion of milking cows” (number of dairy cows/number of all cattle) for predicting GHG emission across the different models. The inclusion of “fertiliser import nitrogen” was also important across the different models and prediction of E and N intensity.