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Publikasjoner

NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2022

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The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods in the prediction of crop status from hyperspectral data. The present study evaluates GPR model accuracy in the context of spring wheat dry matter, nitrogen content, and nitrogen uptake estimation. Models with the squared exponential covariance function were trained on images from two hyperspectral cameras (a frenchFabry–Pérot interferometer camera and a push-broom scanner). The most accurate predictions were obtained for nitrogen uptake (R2=0.75–0.85, RPDP=2.0–2.6). Modifications of the basic workflow were then evaluated: the removal of soil pixels from the images prior to the training, data fusion with apparent soil electrical conductivity measurements, and replacing the Euclidean distance in the GPR covariance function with the spectral angle distance. Of these, the data fusion improved the performance while predicting nitrogen uptake and nitrogen content. The estimation accuracy of the latter parameter varied considerably across the two hyperspectral cameras. Satisfactory nitrogen content predictions (R2>0.8, RPDP>2.4) were obtained only in the data-fusion scenario, and only with a high spectral resolution push-broom device capable of capturing longer wavelengths, up to 1000 nm, while the full-frame camera spectral limit was 790 nm. The prediction performance and uncertainty metrics indicated the suitability of the models for precision agriculture applications. Moreover, the spatial patterns that emerged in the generated crop parameter maps accurately reflected the fertilization levels applied across the experimental area as well as the background variation of the abiotic growth conditions, further corroborating this conclusion.

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Ongoing efforts focus on quantifying plastic pollution and describing and estimating the related magnitude of exposure and impacts on human and environmental health. Data gathered during such work usually follows a receptor perspective. However, Life Cycle Assessment (LCA) represents an emitter perspective. This study examines existing data gathering and reporting approaches for field and laboratory studies on micro- and nanoplastics (MNPs) exposure and effects relevant to LCA data inputs. The outcomes indicate that receptor perspective approaches do not typically provide suitable or sufficiently harmonised data. Improved design is needed in the sampling, testing and recording of results using harmonised, validated and comparable methods, with more comprehensive reporting of relevant data. We propose a three-level set of requirements for data recording and reporting to increase the potential for LCA studies and models to utilise data gathered in receptor-oriented studies. We show for which purpose such data can be used as inputs to LCA, particularly in life cycle impact assessment (LCIA) methods. Implementing these requirements will facilitate proper integration of the potential environmental impacts of plastic losses from human activity (e.g. litter) into LCA. Then, the impacts of plastic emissions can eventually be connected and compared with other environmental issues related to anthropogenic activities.

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Ongoing efforts focus on quantifying plastic pollution and describing and estimating the related magnitude of exposure and impacts on human and environmental health. Data gathered during such work usually follows a receptor perspective. However, Life Cycle Assessment (LCA) represents an emitter perspective. This study examines existing data gathering and reporting approaches for field and laboratory studies on micro- and nanoplastics (MNPs) exposure and effects relevant to LCA data inputs. The outcomes indicate that receptor perspective approaches do not typically provide suitable or sufficiently harmonised data. Improved design is needed in the sampling, testing and recording of results using harmonised, validated and comparable methods, with more comprehensive reporting of relevant data. We propose a three-level set of requirements for data recording and reporting to increase the potential for LCA studies and models to utilise data gathered in receptor-oriented studies. We show for which purpose such data can be used as inputs to LCA, particularly in life cycle impact assessment (LCIA) methods. Implementing these requirements will facilitate proper integration of the potential environmental impacts of plastic losses from human activity (e.g. litter) into LCA. Then, the impacts of plastic emissions can eventually be connected and compared with other environmental issues related to anthropogenic activities.

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Ongoing efforts focus on quantifying plastic pollution and describing and estimating the related magnitude of exposure and impacts on human and environmental health. Data gathered during such work usually follows a receptor perspective. However, Life Cycle Assessment (LCA) represents an emitter perspective. This study examines existing data gathering and reporting approaches for field and laboratory studies on micro- and nanoplastics (MNPs) exposure and effects relevant to LCA data inputs. The outcomes indicate that receptor perspective approaches do not typically provide suitable or sufficiently harmonised data. Improved design is needed in the sampling, testing and recording of results using harmonised, validated and comparable methods, with more comprehensive reporting of relevant data. We propose a three-level set of requirements for data recording and reporting to increase the potential for LCA studies and models to utilise data gathered in receptor-oriented studies. We show for which purpose such data can be used as inputs to LCA, particularly in life cycle impact assessment (LCIA) methods. Implementing these requirements will facilitate proper integration of the potential environmental impacts of plastic losses from human activity (e.g. litter) into LCA. Then, the impacts of plastic emissions can eventually be connected and compared with other environmental issues related to anthropogenic activities.

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In total, 154 wild raspberry samples were collected from 26 localities representing a large area in Norway (21 localities) and a narrowly defined region of the Giant Mountains in the northern parts of the Czech Republic (5 localities). The samples were characterized for genetic diversity and population differentiation as well as for their potential use in crop breeding. Choice of plant material was based on the biogeographical similarity between the Giant Mountains and relevant areas in Norway, where plant communities may have evolved in parallel since the ice ages. The overall level of genetic diversity ĥ = 0.786, I = 2.153 was high. Numerous rare alleles were found for raspberries originating especially from the East Giant Mountains populations Jeleni louky and Krakonosuv lom. The overall degree of population subdivision measured by Wright’s fixation index (FST) was of a moderate level of 0.28. The highest level 0.33 was found between populations in Northern Norway and 0.31 between populations in the Giant Mountains. The genetic structure was evaluated using Bayesian analyses as implemented using STRUCTURE software. According to the ΔK value, eight clusters (K8) were identified among all the analysed samples. The results of the analysis of molecular variance (AMOVA) indicated that 79.7% of the total variation could be attributed to differences among individuals within populations, 15.3% was credited to differences among populations within regions, and only 5.0% was attributed to differences among regions. We concluded based on the results that Czech and Norwegian raspberry (R. idaeus) populations growing in natural high altitude and northern ecosystems are important genetic resources and represent a valuable source of genes and unique allele compositions for in situ and ex situ conservation and future raspberry breeding programmes.

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In order to best conserve, as well as utilize, traditional apple germplasm in Norway, an apple heritage cultivar collection was established in Ullensvang, western Norway, which aims to become the National Clonal Germplasm Repository. The establishment of the apple heritage cultivar collection was preceded by a molecular study that aimed to genotype a large number of apple accessions maintained in various ex situ sites in western and south-eastern Norway, using a rather small set of eight SSR markers. However limited, the marker set managed to identify synonyms, homonyms, and duplicates within and among the investigated collections. In this study, 171 apple accessions from the Ullensvang apple heritage cultivar collection were genotyped using a set of 20 different SSR markers. Approximately half of the accessions have been previously genotyped using eight SSR markers, enabling an assessment of whether the use of a larger marker set would yield a more accurate characterization. Based on the obtained molecular data, the apple heritage cultivar collection was determined to hold a key part of the overall genetic diversity of the Norwegian apple germplasm. Furthermore, the twelve additional SSR markers were able to differentiate several accessions groups originally thought to be synonyms, as well as to provide a more detailed insight into the genetic structure of this germplasm.

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Continued anthropogenic environmental change is wreaking havoc on natural populations, with the stresses and pulses of induced ecological processes affecting a species' local habitat, resulting in inadvertent distribution shifts, hybridization events, and eventual biodiversity loss. It is more critical than ever to monitor the unintended consequences of human activity on not only natural populations, but also community structures and ecosystems. DNA-based (genetic and genomic) monitoring is a critical component of biodiversity monitoring because it allows for the tracking and quantification of temporal changes in population genetic metrics or other population data. Genetic/genomic monitoring enables the estimation of a variety of biological parameters, including demographic parameters (abundance, occupancy, hybridization, and disease status), population genetic parameters (genetic diversity, structure, and effective population size), and responses to anthropogenic selective pressures (exploitation, biological invasions, and climate change). This keynote address will highlight the practical implications of integrating genetic data into management, conservation objectives, and policymaking, as well as capacity building through international partnerships, using case studies from the Norwegian Barents Region.

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The fall armyworm, Spodoptera frugiperda (J.E. Smith) is native to the Americas and a major pest of corn and several other crops of economic importance. The species has characteristics that make it of particular concern as an invasive pest, including broad host range, long-distance migration behavior, and a propensity for field-evolved pesticide resistance. The discovery of fall armyworm in western Africa in 2016 was followed by what was apparently a remarkably rapid spread throughout sub-Saharan Africa by 2018, causing economic damage estimated in the tens of billions USD and threatening the food security of the continent. Understanding the history of the fall armyworm invasion of Africa and the genetic composition of the African populations is critical to assessing the risk posed to different crop types, the development of effective mitigation strategies, and to make Africa less vulnerable to future invasions of migratory moth pests. This paper tested and expanded on previous studies by combining data from 22 sub-Saharan nations during the period from 2016 to 2019. The results support initial descriptions of the fall armyworm invasion, including the near absence of the strain that prefers rice, millet, and pasture grasses, while providing additional evidence that the magnitude and extent of FAW natural migration on the continent is more limited than expected. The results also show that a second entry of fall armyworm likely occurred in western Africa from a source different than that of the original introduction. These findings indicate that western Africa continues to be at high risk of future introductions of FAW, which could complicate mitigation efforts.