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.
2023
Authors
Milica Fotirić Akšić Živoslav Tešić Milica Kalaba Ivanka Ćirić Lato Pezo Biljana Lončar Uroš Gašić Biljana Dojčinović Tomislav Tosti Mekjell MelandAbstract
The aim of this study was to find the chemical parameters for the differentiation of plum cultivars grown along the fjord areas of Western Norway and Eastern Norway, having specific agroclimatic conditions. Chemical analysis of the fruits confirmed the contents of 13 quantified elements, 22 sugar compounds, 11 organic acids, 19 phenolic compounds, and antioxidant activity in 68 plum cultivars. Dominated contents were noted for nitrogen (with the maximum mean value of 3.11%), potassium (8055.80 mg/kg), and phosphorous (7878.88 mg/kg). Averagely, the highest level of sugars was determined for glucose (244.46 g/kg), fructose (197.92 g/kg), sucrose (208.25 g/kg), and sorbitol (98.02 g/kg), organic acids for malic acid (24.06 g/kg), and for polyphenol compounds were 5-O-caffeoylquinic acid (66.31 mg/kg), and rutin (58.06 mg/kg). Applied principal component analysis has been useful for distinguishing the plum cultivars from three areas in Norway where copper, iron, potassium, magnesium, manganese, and sodium; sucrose, ribose, maltose, and raffinose; p-hydroxybenzoic acid, rutin, ferulic acid, kaempferol 7-O-glucoside, p-coumaric acid, and 5-Ocaffeoylquinic acid were the most influential. In regard to human health and future breeding work that will have the aim to produce functional food with high health-related compounds, the plum cultivar ‘Mallard’ should be underlined due to the high level of elements, ‘Valor’ due to high sugar content, ‘Helgøyplomme’ due to content of organic acids, and ‘Diamond’ due to the content of phenolic compounds.
Abstract
Levels of dissolved natural organic matter (DNOM) are increasing in our boreal watercourses. This is manifested by an apparent increase in its yellow to brown colour of the water, i.e., browning. Sound predictions of future changes in colour of our freshwaters is a prerequisite for predicting effects on aquatic fauna and a sustainable operation of drinking water facilities using surface waters as raw water sources. A model for the effect of climate on colour (mg Pt L-1) has been developed for two surface raw water sources in Scotland, i.e., at Bracadale and Port Charlotte. Both sites are situated far out on the Scottish west coast, without major impact of acid rain, with limited amounts of frost, and with limited recent land-use changes. The model was fitted to 15 years long data-series on colour measurements, provided by Scottish Water, at the two sites. Meteorological data were provided by UK Met. The models perform well for both sites in simulating the variation in monthly measured colour, explaining 89 and 90% of the variation at Bracadale and Port Charlotte, respectively. These well fitted models were used to predict future changes in colour due to changes in temperature and precipitation based on median climate data from a high emission climate RCP8.5 scenario from the HadCM3 climate model (UKCP18). The model predicted an increase in monthly average colour during growing season at both sites from about 150 mg Pt L-1 to about 200 mg Pt L-1 in 2050–2079. Temperature is found to be the most important positively driver for colour development at both sites.
Abstract
Accurate estimation of site productivity is essential for forest projections and scenario modelling. We present and evaluate models to predict site index (SI) and whether a site is productive (potential total stem volume production ≥ 1 m3·ha−1·year−1) in a wall-to-wall high-resolution (16 m × 16 m) SI map for Norway. We investigate whether remotely sensed data improve predictions. We also study the advantages and disadvantages of using boosted regression trees (BRT), a machine-learning algorithm, to create high-accuracy SI maps. We use climatic and topographical data, soil parent material, a land resource map, and depth to water, together with Sentinel-2 satellite images and airborne laser scanning metrics, as predictor variables. We use the SI observed at more than 10 000 National Forest Inventory (NFI) sample plots throughout Norway to fit BRT models and validate the models using 5822 independent temporary plots from the NFI. We benchmark our results against SI estimates from forest monitoring inventories. We find that the SI from BRT has root mean squared error (RMSE) ranging from 2.3 m (hardwoods) to 3.6 m (spruce) when tested against independent validation data from the NFI temporary plots. These RMSEs are similar or marginally better than an evaluation of SI estimates from operational forest management plans where SI normally stems from manual photo interpretation.
Authors
Zoé Cord'homme Nils Maximilian Dittrich Kristina Bringedal Gedde Romain Guillaume Billy Daniel Beat MuellerAbstract
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Authors
Shelemia Nyamuryekung'e Glenn Duff Santiago A. Utsumi Richard Estell Matthew M. McIntosh Micah Funk Andrew Cox Huiping Cao Huiying Chen Sheri Spiegal Andres Perea Sajidur Rahman Andres F. CibilsAbstract
LoRa-WAN sensors were used to compare methods for determining walking distances by grazing cattle in near real-time. The accuracy of relying on a global positioning system (GPS) alone or in combination with motion data derived from triaxial accelerometers was compared using stationary control trackers (Control) placed in fixed field locations (n=6) or vs. trackers (Animal) mounted on cows (n=6) grazing on pasture at the New Mexico State University’s Clayton Livestock Research Center. Trackers communicated motion data at 1-minute intervals and GPS positions at 15-minute intervals for seven days. Daily distance walked was determined using: 1) raw GPS data (RawDist), 2) data with erroneous GPS locations removed (CorrectedDist), or 3) data with erroneous GPS locations removed and with GPS data associated with the static state excluded (CorrectedDist_Act). Distances were analyzed via one-way ANOVA to compare Control vs. Animal deployment effects. No difference (P=0.43) in walking distance was detected between Control vs. Animal for RawDist. However, distances calculated for CorrectedDist differed (P<0.01) between the two tracker deployments. Due to the random error of GPS measurements, CorrectedDist for stationary devices differed (P=0.01) from zero. The walking distance calculated by CorrectedDist_Act differed (P<0.01) between Control vs. Animal trackers, with distances for Control trackers not differing (P=0.44) from zero. The fusion of GPS and accelerometer data was a more suitable method for calculating walking distance by grazing cattle. This result may highlight the value of combining more than one source of independent sensor data in Precision Livestock Farming applications.
Abstract
The fungus Neonectria ditissima causes Fruit Tree Canker on apple and pear. In the past years the disease has become a threat for Swedish and Northern European apple production since devastating outbreaks destroy large numbers of trees. To date, no complete genetic resistance to N. ditissima is known in apple but genotypes (scion cultivars and rootstocks) differ greatly in their level of partial resistance. Furthermore, the degree of susceptibility of a scion cultivar may be influenced by the rootstock it is grafted to. Thus, we aimed to improve our understanding of genetically determined differences in resistance among rootstocks and clarify cultivar/rootstock interactions with regards to canker resistance. For that, we evaluated differences in resistance to fruit tree canker in 24 rootstocks (including two M9 clones). We also evaluated differences in resistance of four most widely grown in Sweden scion cultivars grafted to four common rootstocks differing in vigour. The new knowledge will be useful for growers and breeders to minimize canker damages, prevent loss of the fruit-bearing surface in the orchards, save time and money for the growers.
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