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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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Semi- and nonparametric models are popular in the area-based approach (ABA) using airborne laser scanning. It is unclear, however, how many predictors and training plots are needed to provide accurate predictions without overfitting. This work aims to explore these limits for various approaches: ordinary least squares regression (OLS), generalized additive models (GAM), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and Gaussian process regression (GPR). We modeled timber volume (m3·ha–1) for four boreal sites using ABA with 2–39 predictors and 20–500 training plots. OLS, GAM, LASSO, and SVM overfitted as the number of predictors approached the number of training plots. They required ≥15 plots per predictor to provide accurate predictions (RMSE ≤30%). GAM required ≥250 plots regardless of the number of predictors. The number of predictors only mildly affected RF and GPR, but they required ≥200 and ≥250 training plots, respectively. RF did not overfit in any circumstances, whereas GPR overfit even with 500 training plots. Overall, using up to 39 predictors did not generally result in overfit, and for most model types, it resulted in better accuracy for sufficiently large datasets (≥250 plots).

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Arealbrukssektoren (engelsk: Land Use, Land-Use Change and Forestry, LULUCF) omfatter arealbruk og arealbruksendringer, med tilhørende utslipp og opptak av CO2, CH4 og N2O, og er en del av det nasjonale klimagassregnskapet under FNs klimakonvensjon. Framskrivningene presentert her er basert på data og metodikk fra Norges siste rapportering til FNs klimakonvensjon (IPCC), Norges National Inventory Report (NIR), innsendt 8. april 2022 (Miljødirektoratet mfl. 2022). Perioden 2006 – 2020 har vært lagt til grunn som referanseperiode, og framskrivning av arealutvikling og utslipp er i all hovedsak basert på rapporterte data for denne tidsperioden. Utviklingen i gjenværende skog er framskrevet ved hjelp av simuleringsverktøyet SiTree og Yasso07. Klimaendringer under klimascenariet i RCP 4.5 er lagt til grunn. Framskrivingen er framstilt på to ulike formater: Både i henhold til FNs klimakonvensjon sitt regelverk (alle arealbrukskategorier og kilder) og basert på EUs regelverk under LULUCF-forordningen (2018/841) (European Union 2018).

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Climate change is a serious and complex crisis that impacts humankind in different ways. It affects the availability of water resources, especially in the tropical regions of South Asia to a greater extent. However, the impact of climate change on water resources in Sri Lanka has been the least explored. Noteworthy, this is the first study in Sri Lanka that attempts to evaluate the impact of climate change in streamflow in a watershed located in the southern coastal belt of the island. The objective of this paper is to evaluate the climate change impact on streamflow of the Upper Nilwala River Basin (UNRB), Sri Lanka. In this study, the bias-corrected rainfall data from three Regional Climate Models (RCMs) under two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5 were fed into the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model to obtain future streamflow. Bias correction of future rainfall data in the Nilwala River Basin (NRB) was conducted using the Linear Scaling Method (LSM). Future precipitation was projected under three timelines: 2020s (2021–2047), 2050s (2048–2073), and 2080s (2074–2099) and was compared against the baseline period from 1980 to 2020. The ensemble mean annual precipitation in the NRB is expected to rise by 3.63%, 16.49%, and 12.82% under the RCP 4.5 emission scenario during the 2020s, 2050s, and 2080s, and 4.26%, 8.94%, and 18.04% under RCP 8.5 emission scenario during 2020s, 2050s and 2080s, respectively. The future annual streamflow of the UNRB is projected to increase by 59.30% and 65.79% under the ensemble RCP4.5 and RCP8.5 climate scenarios, respectively, when compared to the baseline scenario. In addition, the seasonal flows are also expected to increase for both RCPs for all seasons with an exception during the southwest monsoon season in the 2015–2042 period under the RCP4.5 emission scenario. In general, the results of the present study demonstrate that climate and streamflow of the NRB are expected to experience changes when compared to current climatic conditions. The results of the present study will be of major importance for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset the negative impacts of future changes in climate.

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In the present study, the streamflow simulation capacities between the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) were compared for the Huai Bang Sai (HBS) watershed in northeastern Thailand. During calibration (2007–2010) and validation (2011–2014), the SWAT model demonstrated a Coefficient of Determination (R2) and a Nash Sutcliffe Efficiency (NSE) of 0.83 and 0.82, and 0.78 and 0.77, respectively. During the same periods, the HEC-HMS model demonstrated values of 0.80 and 0.79, and 0.84 and 0.82. The exceedance probabilities at 10%, 40%, and 90% were 144.5, 14.5, and 0.9 mm in the flow duration curves (FDCs) obtained for observed flow. From the HEC-HMS and SWAT models, these indices yielded 109.0, 15.0, and 0.02 mm, and 123.5, 16.95, and 0.02 mm. These results inferred those high flows were captured well by the SWAT model, while medium flows were captured well by the HEC-HMS model. It is noteworthy that the low flows were accurately simulated by both models. Furthermore, dry and wet seasonal flows were simulated reasonably well by the SWAT model with slight under-predictions of 2.12% and 13.52% compared to the observed values. The HEC-HMS model under-predicted the dry and wet seasonal flows by 10.76% and 18.54% compared to observed flows. The results of the present study will provide valuable recommendations for the stakeholders of the HBS watershed to improve water usage policies. In addition, the present study will be helpful to select the most appropriate hydrologic model for humid tropical watersheds in Thailand and elsewhere in the world.

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Norwegian-grown peas and faba beans are a healthier alternative to meat and dairy products, which are over-consumed in Norway, hence these legumes represent an interesting alternative as food protein source in Norway. However, the environmental impact of these legumes compared to other protein sources has not been studied, in detail. Hence this study, where the environmental impact of this plant protein was analysed and compared to other main protein sources in the Norwegian diet, covers a research gap. The method used was Life Cycle Assessment (LCA) and a large range of impacts was covered. The climate impact for dried grain legumes were 0.55–0.57 kg CO2-eq/kg, The climate impact for dried grain legumes were 0.55–0.57 kg CO2-eq/kg, which is much lower than ruminant meat (19–38 kg CO2-eq/kg), other meat (3.6–4.2 kg CO2-eq/kg), seafood (0.8–22 kg CO2-eq/kg), dairy products (1.2–22 kg CO2-eq/kg products) and cereals (0.66–0.72 kg CO2-eq/kg product). The same trend was found for all impact categories studied. The same pattern was found when comparing the environmental impacts of grain legumes in intermediate and finished products. An evaluation of the nutrient content showed that there is no trade-off between health and environment but the effect of lower protein digestibility and anti-nutritional compounds in legumes remains to be investigated quantitatively. The study indicates that legumes are a more sustainable source of dietary protein than animal protein sources. It is recommended that more research should be done on social and economic sustainability should be done to get at more complete picture of the sustainability of these grain legumes.

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Giant panda could have bamboo as their exclusive diet for about 2 million years because of the contribution of numerous enzymes produced by their gut bacteria, for instance laccases. Laccases are blue multi-copper oxidases that catalyze the oxidation of a broad spectrum of phenolic and aromatic compounds with water as the only byproduct. As a “green enzyme,” laccases have potential in industrial applications, for example, when dealing with degradation of recalcitrant biopolymers, such as lignin. In the current study, a bacterial laccase, Lac51, originating from Pseudomonas putida and identified in the gut microbiome of the giant panda’s gut was transiently expressed in the non-food plant Nicotiana benthamiana and characterized. Our results show that recombinant Lac51 exhibits bacterial laccase properties, with optimal pH and temperature at 7–8 and 40°C, respectively, when using syringaldazine as substrate. Moreover, we demonstrate the functional capability of the plant expressed Lac51 to oxidize lignin using selected lignin monomers that serve as substrates of Lac51. In summary, our study demonstrates the potential of green and non-food plants as a viable enzyme production platform for bacterial laccases. This result enriches our understanding of plant-made enzymes, as, to our knowledge, Lac51 is the first functional recombinant laccase produced in plants.

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Motorsport is known for its high tire wear due to speed, cornering, and high acceleration/deceleration activities. However, studies on the generation of microplastics from racetracks are rare. This study aimed at quantifying microplastics concentrations in topsoil (0–5 cm) along a racetrack. The results showed that rubber materials (RM) and tire reinforcement microplastics (TRMP) were deposited in the soil along the racetrack. Concentrations of the two microplastics were affected by the distance from the edge of the racetrack (highest concentrations within 20 cm from the track) and track alignment (highest concentrations at the start/finish area). In addition, a weak correlation was observed between the concentrations of the two microplastics, suggesting the effect of track alignment on the type of microplastics abraded. The results also showed that coarser microplastics (1000–5000 μm) dominate the size distribution of microplastics along a racetrack. The findings of this study may provide racetrack managers with basic information for designing microplastic-controlling solutions. While additional studies are required to map environmental effects and policy measures, our initial results suggest that motorsport is of concern in terms of microplastics release to the environment.

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Population densities of several cervid species have increased in recent decades in North America and Europe, and cervids frequently eat and damage agricultural crops. Competition and depletion of natural food resources are the main mechanisms for the density-dependent decline in vital rates of large herbivores. The extent to which access to agricultural crops can buffer density effects in cervid populations, however, is unknown. Agricultural grasslands cover more than a third of the European agricultural area, and red deer (Cervus elaphus) use these grasslands in many European countries. Over the past few decades, such grasslands have been subject to management intensification (with renewal and fertilization) in some areas and abandonment (no longer being harvested) in other areas. We used generalized linear mixed-effects models to examine the development of body masses of red deer in Norway during a period of population density increase in 16 local management units with different availability of cultivated grasslands (0.87–6.44%) in a region with active management of grasslands (Tingvoll, n = 5,780, 2000–2019) and a region with ongoing abandonment (Hitra, n = 10,598, 2007–2020). There was a consistent decline in the body mass of red deer linked to increased population density in both regions. A higher proportion of agricultural grassland was linked to higher body mass and lower density effects in both sexes and across all age classes. There is a link between body mass, survival, and reproduction. Therefore, the buffering of density effects of access to agricultural crops will fuel cervid population growth and lead to less natural regulation of abundance, making it more difficult to control dense cervid populations by harvesting.

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The assessment of forest abiotic damages such as snow breakage is important to ensure compensation to forest owners. Currently, information on the extent of snow breakage is gathered through time-consuming and potentially biased field surveys. In such situations where field surveys are still common practice, unmanned aerial vehicles (UAVs) are increasingly being used to provide a more cost-efficient and objective methods to answer forest information needs. Further, the advent of sophisticated computer vision techniques such as convolutional neural networks (CNNs) offers new ways to analyze image data more efficiently and accurately. We proposed an object detection method to automatically identify trees and classify them according to the damage by snow based on a YOLO CNN architecture. UAV imagery collected across 89 study areas and over the course of the entire year were manually annotated into a total of >55 K single trees classified as healthy, damaged, or dead. The annotated trees, along with the corresponding UAV imagery were used to train a YOLOv5 object detection model. Furthermore, we tested the effect of seasonality, and varying atmospheric and lighting conditions on the model’s performance. Based on an independent test set of data we found that the general model including all of the data (i.e. any seasons, atmospheric conditions, and time of the day) outperformed all other tested scenarios (i.e. precision = 62 %; recall = 61 %). Furthermore, we found that despite the fact that the snow damaged trees represented a minority class (i.e. 16 % of the annotated trees), they were detected with the largest precision (76 %) and recall (78 %). Finally, the general model transferred well across the variation in seasons, atmospheric and illumination conditions, making it suitable for usage for any new UAV image acquisition.

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Management of Scots pine (Pinus sylvestris L.) in Norway requires a forest growth and yield model suitable for describing stand dynamics of even-aged forests under contemporary climatic conditions with and without the effects of silvicultural thinning. A system of equations forming such a stand-level growth and yield model fitted to long-term experimental data is presented here. The growth and yield model consists of component equations for (i) dominant height, (ii) stem density (number of stems per hectare), (iii) total basal area, (iv) and total stem volume fitted simultaneously using seemingly unrelated regression. The component equations for stem density, basal area, and volume include a thinning modifier to forecast stand dynamics in thinned stands. It was shown that thinning significantly increased basal area and volume growth while reducing competition related mortality. No significant effect of thinning was found on dominant height. Model examination by means of various fit statistics indicated no obvious bias and improvement in prediction accuracy in comparison to existing models in general. An application of the developed stand-level model comparing different management scenarios exhibited plausible long-term behavior and we propose this is therefore suitable for national deployment.