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.
2024
Abstract
Background The Norwegian sheep farming system relies on forages, such as grass silage during winter and grazing cultivated leys and rangeland pastures during summer. Sheep and other ruminants produce enteric methane (CH4), a greenhouse gas of interest, and there is a need for reliable data on gas emissions from sheep capturing both the indoor feeding period and the grazing season. This study utilized an in vitro gas technique (with standard cow rumen fluid) and modeling approach to estimate CH4 production and fermentation patterns based on two different qualities of Italian ryegrass (Lolium multiflorum) pasture under sheep grazing. Results Herbage quality was examined for two 10-day periods, in July and August. Differences in chemical composition of the herbage during these periods had an impact on herbage digestibility and CH4 production. Total gas production and CH4 levels were significantly higher for lower quality herbage grazed in July than for higher quality herbage grazed in August (p < 0.005). Production of volatile fatty acids in the rumen remained constant between the two periods, but the higher acetate to propionate (A/P) ratio correlated with the higher CH4 production. Conclusion These findings suggest that pasture quality is an important factor to consider when implementing grazing strategies to reduce enteric CH4 production in sheep.
Authors
Bert van der Veen Robert Brian O'Hara Francis K. C. Hui Knut Anders HovstadAbstract
The ecological niche is a fundamental concept in ecology that can be used in order better understand species relationships. The overlap in species niches provides a measure of the likelihood for species to co-occur. Most approaches that quantify niche overlap have been based on distance and similarity indices, for pairwise combinations of species. In this paper, we suggest that niche overlap can be calculated from the predictions of a model. Using a statistical model to predict niche overlap provides various benefits, includes the possibility to adjust the model to properties of the data. We demonstrate this using an example dataset of an ecological community of Foraminifera species, to which we fit a generalized linear latent variable model (GLLVM). GLLVMs are a flexible class of models that allow to estimate the distribution of species using both measured environmental predictors and residual covariation between species. We demonstrate how to calculate niche overlap from GLLVMs for any combination of species, and separately for different environments. Predicting niche overlap from a model further expands the toolset available to ecologists for the exploration of species co-occurrence patterns.
Abstract
Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.
Authors
Ståle Haaland Josef Hejzlar Bjørnar Eikebrokk Geir Orderud Ma. Cristina Paule‐Mercado Petr Porcal Jiří Sláma Rolf David VogtAbstract
Over the past four decades, an increase in Dissolved Natural Organic Matter (DNOM) and colour, commonly referred to as browning, has been noted in numerous watercourses in the northern hemisphere. Understanding the fluctuations in DNOM quality is a prerequisite for gaining insights into the biogeochemical processes governing DNOM fluxes. Such knowledge is also pivotal for water treatment plants to effectively tailor their strategies for removing DNOM from raw water. The specific ultraviolet absorbance (sUVa) index has been a widely applied measurement for assessing DNOM quality. The sUVa index is the UV absorbance (OD254) of water normalized for DNOM concentration. We have used a long-term dataset spanning from 2007 to 2022, taken from the Malše River in South Bohemia, to model DNOM and the sUVa index. We have applied regression models with a process-oriented perspective and have also considered the influence of climate change. Both DNOM and the sUVa index is positively related to temperature, runoff and pH, and negatively related to ionic strength over the studied period. Two distinct model approaches were employed, both explaining about 40% of the variation in sUVa over the studied period. Based on a moderate IPCC monthly climate scenario, simulations indicate that both DNOM and the sUVa index averages remain fairly stable, with a slight increase in winter season minima projected towards the year 2099. A slight decline in summer season maxima is simulated for DNOM, while the sUVa summer maximum remain stable. These findings suggest a robust resilience in both DNOM and the sUVa index against anticipated changes in temperature and runoff for the Malše River in South Bohemia.
Authors
Jaime Candelas Bielza Lennart Noordermeer Erik Næsset Terje Gobakken Johannes Breidenbach Hans Ole ØrkaAbstract
Tree species composition is essential information for forest management and remotely sensed (RS) data have proven to be useful for its prediction. In forest management inventories, tree species are commonly interpreted manually from aerial images for each stand, which is time and resource consuming and entails substantial uncertainty. The objective of this study was to evaluate a range of RS data sources comprising airborne laser scanning (ALS) and airborne and satellite-borne multispectral data for model-based prediction of tree species composition. Total volume was predicted using non-linear regression and volume proportions of species were predicted using parametric Dirichlet models. Predicted dominant species was defined as the species with the greatest predicted volume proportion and predicted species-specific volumes were calculated as the product of predicted total volume multiplied by predicted volume proportions. Ground reference data obtained from 1184 sample plots of 250 m2 in eight districts in Norway were used. Combinations of ALS and two multispectral data sources, i.e. aerial images and Sentinel-2 satellite images from different seasons, were compared. The most accurate predictions of tree species composition were obtained by combining ALS and multi-season Sentinel-2 imagery, specifically from summer and fall. Independent validation of predicted species proportions yielded average root mean square differences (RMSD) of 0.15, 0.15 and 0.07 (relative RMSD of 30%, 68% and 128%) and squared Pearson's correlation coefficient (r2) of 0.74, 0.79 and 0.51 for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and deciduous species, respectively. The dominant species was predicted with median values of overall accuracy, quantity disagreement and allocation disagreement of 0.90, 0.07 and 0.00, respectively. Predicted species-specific volumes yielded average values of RMSD of 63, 48 and 23 m3/ha (relative RMSD of 39%, 94% and 158%) and r2 of 0.84, 0.60 and 0.53 for spruce, pine and deciduous species, respectively. In one of the districts with independent validation plots of mean size 3700 m2, predictions of the dominant species were compared to results obtained through manual photo-interpretation. The model predictions gave greater accuracy than manual photo-interpretation. This study highlights the utility of RS data for prediction of tree species composition in operational forest inventories, particularly indicating the utility of ALS and multi-season Sentinel-2 imagery.
Authors
Åsgeir R. Almås Susanne Eich-Greatorex Trine Aulstad Sogn Tomasgaard Jan Mulder Manoj Kumar Pandey Vincent Dauby David Powlson Roberta Farina Jeroen Watte Daniel RasseAbstract
The main objective of this scoping document, is to deliver an overall roadmap for the EU commission, targeting the soil mission objective “conserving and increasing soil organic carbon stocks”. The objective addresses the importance of maintaining, or in many situations increasing the soil organic carbon stocks. The soil organic carbon stock is often declining, and vulnerable to further losses due to intensive management and climate change. The soil mission objective aims at identifying actions that can limit the current carbon losses from cultivated soils and preferably reverse it to a rate of 0,1 - 0,4% increase per year (European Commission n.d.). The mission's objectives are relevant not only for supporting the aim to improve soil health by 2030, but also for the member states to become carbon neutral by 2050 (European Commission, n.d.). This think tank addresses the importance of maintaining, or in many situations increasing the soil organic carbon stocks by addressing the impacts of: • Management - Forestry management - Agronomic and land use managements - Climate change and adaptation technologies - Biodiversity and soil health • Societal - Urbanization and circular economy - Education and awareness raising - EU-footprints on SOC-stocks outside EU • Technical - Soil carbon measuring and monitoring In general, changes in soil carbon stocks are slow and management effects will vary depending on climate zones and soil types. Dialog and interaction is essential with all relevant stakeholder including those who own or manage land, agronomic advisors (both governmental and commercial), agricultural supply companies, policy makers, those involved in the food supply chain, and others, for the successful implementation of soil carbon management technologies. Practitioners holds essential knowledge and experience about their own land, and mutual knowledge and practice exchange, will facilitate and stimulate the necessary engagement for innovative technology implementation within the various aspects of soil carbon stocks and improvement of soil health in general.
Authors
Nicola Bozzolan Frits Mohren Giacomo Grassi Mart-Jan Schelhaas Igor Staritsky Tobias Stern Mikko Peltoniemi Vladimír Šebeň Mariana Hassegawa Pieter Johannes Verkerk Marco Patacca Aris Jansons Martin Jankovský Petra Palátová Hanna Blauth Daniel McInerney Jan Oldenburger Eirik Ogner Jåstad Jaroslav Kubista Clara Antón Fernández Gert-jan NabuursAbstract
As the overall demand for wood-based products continues to grow, questions arise on how local wood resources and industry characteristics can effectively meet this growing demand. In the European Union (EU) 550 million m3 of wood is harvested annually, and is to a large extent processed by the wood industry. Little is known about the interplay between industrial capacity and the regional availability of timber resources. We compared the capacities from the European Forest Industry Facilities Database (EUFID) with the estimated wood supply from the procurement areas around processing industries, calculated using a spatially explicit resource model (EFISCEN-Space). We found that the estimated total capacity for the available European countries is 427 M m3 roundwood equivalent (rw. Eq.) for pulp and paper (including both virgin and recycled fibres), 102 M m3 for bioenergy (only bioenergy plants), and 153 M m3 for sawmills. We then conducted an in-depth analysis of three case studies: Norway, the Czech Republic, and Germany. Given the current probability of trees being harvested (excluding disturbances) and the hypothetical optimal grading of the logs, the volume for each assortment type is closely aligned with the current capacity of each industry branch, indicating no overcapacity. We found undersupply of softwood of 3.4 M m3 for the Czech Republic, 1.5 M m3 for Norway, and 3.8 M m3 for Germany. At the same time, in Germany, we found an oversupply of hardwood of 3.0 M m3. Additionally, a substantial amount of biomass graded as bioenergy was found for Germany and the Czech Republic, potentially serving as fuelwood in households. Concerning wood procurement areas, we concluded that a fixed radius of 100 km from the facility limited the availability of raw material procurement, particularly for bioenergy and pulp and paper mills, suggesting that these two product chains use a broader procurement basin than sawlogs. This study provides a high-resolution, spatially explicit modelling methodology for assessing the interaction between potential wood harvest and industrial processing capacity, which can support projections of sustainable development of the forest industry.
Authors
Anne Friederike BorchertAbstract
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