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
Heat treatment increases the decay resistance of wood by decreasing its hygroscopicity, but the wood material remains degradable by fungi. This study investigated the degradation of heat-treated wood by brown rot fungi, with the aim of identifying fungal-induced hygroscopicity changes that facilitate degradation. Scots pine sapwood samples were modified under superheated steam at 200 and 230 °C and then exposed to Coniophora puteana and Rhodonia placenta in a stacked-sample decay test to produce samples in different stages of decay. Sorption isotherms were measured starting in desorption from the undried, decaying state to investigate their hygroscopic properties. Although there were substantial differences in degradative ability between the two fungi, the results revealed that decay by both species increased the hygroscopicity of wood in the decaying state, particularly at high relative humidity. The effect was stronger in the heat-treated samples, which showed a steep increase in moisture content at low decay mass losses. The reference samples showed decreased hygroscopicity in absorption from the dry state, while the heat-treated samples still showed an increase at low mass losses. Near infrared spectroscopy showed that the early stages of decay were characterised by the degradation of hemicellulose and chemical changes to cellulose and lignin, which may explain the increase in hygroscopicity. The results provide a new perspective on brown rot decay and offer insight into the degradation of heat-treated wood.
Abstract
No abstract has been registered
Authors
Morten Rese Gijs van Erven Romy J. Veersma Gry Alfredsen Vincent Eijsink Mirjam A. Kabel Tina Rise TuvengAbstract
No abstract has been registered
Authors
Morten Rese Gijs van Erven Romy J. Veersma Gry Alfredsen Vincent Eijsink Mirjam A. Kabel Tina Rise TuvengAbstract
No abstract has been registered
Abstract
Sweet potato (Ipomoea batatas L. Lam.) is a major source of food in many parts of Ethiopia. In recent years, viral diseases have become the main threat to sweet potato production in Ethiopia. Previous virus survey studies carried out from 1986 to 2020 reported eight viruses infecting sweet potato in Ethiopia. Consequently, obtaining and multiplying virus-free planting materials have been difficult for farmers and commercial multipliers. This study was conducted to detect viruses infecting the five sweet potato varieties used as source plants and compare the virus elimination efficiency between meristem cultures from untreated and heat-treated mother plants and production of virus-free sweet-potato-planting materials. Seven common viruses were tested for, using grafting to Ipomoea setosa, enzyme-linked immunosorbent assay (ELISA) and reverse-transcription polymerase chain reaction (RT–PCR) before and after elimination procedures as screening and confirmatory methods. The sweet potato feathery mottle virus (SPFMV) elimination efficiencies of meristem cultures from untreated (grown at 25 ± 1 °C) and heat-treated (grown at 39 ± 1 °C) potted plants of sweet potato varieties were evaluated and compared. Sweet potato feathery mottle virus (SPFMV) was detected in 12 of the 15 source plants tested. Triple infections of SPFMV, sweet potato chlorotic stunt virus (SPCSV), and sweet potato virus C (SPVC) were detected in one of the fifteen plants. This study reports the detection of SPVC for the first time in sweet potato plants from Ethiopia. The cutting of meristems from heat-treated plants further increased the percentage of virus-free plantlets by ca 10% to ca 16%, depending on the plant variety. Elimination efficiency also seemed to vary among varieties: the greatest difference was observed for ‘Tola’, and the least difference was observed for ‘Guntute’. The present study provided protocols for detecting viruses and generating virus-free sweet-potato-planting materials in Ethiopia.
Abstract
The total phenolic content and antiradical activity in vitro varied significantly among the fruit mesocarps samples extracts of seven plum cultivars. It shows the influence of the cultivar factor on the quantitative composition of phenolic compounds and antiradical activity in vitro of P. domestica fruit mesocarps samples extracts. The highest total phenolic content and the strongest antiradical activ ity in vitro was determined in the fruit mesocarps samples extracts of the cultivar 'Čačanska Najbolja' (bred in Serbia). The fruit mesocarps from this cultivar could be valuable for the future researches – determination of the qualitative and quantitative composition of the individual phenolic compounds.
Abstract
Context Traditional critical nitrogen (N) dilution curve (CNDC) construction for N nutrition index (NNI) determination has limitations for in-season crop N diagnosis and recommendation under diverse on-farm conditions. Objectives This study was conducted to (i) develop a new rice (Oryza sativa L.) critical N concentration (Nc) determination approach using vegetation index-based CNDCs; and (ii) develop an N recommendation strategy with this new Nc determination approach and evaluate its reliability and practicality. Methods Five years of plot and on-farm experiments involving three japonica rice varieties were conducted at fourteen sites in Qixing Farm, Northeast China. Two machine learning (ML) methods, random forest (RF) and extended gradient boosting (XGBoost) regression, were used to fuse multi-source data including genotype, environment, management, growth stage, normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) from portable active canopy sensor RapidSCAN. The CNDC was established using NDVI and NDRE instead of aboveground biomass (AGB) measured by destructive sampling. A new in-season N diagnosis and recommendation strategy was further developed using direct and indirect NNI prediction using multi-source data fusion and ML models. Results The new CNDC based on NDVI or NDRE explained 94−96 % of Nc variability in the evaluation dataset when it was coupled with environmental and agronomic factors using ML models. The ML-based PNC and NNI prediction models explained 85 % and 21–36 % more variability over simple regression models using NDVI or NDRE in the evaluation dataset, respectively. The new in-season N diagnosis strategy using the NDVI and NDRE-based CNDCs and plant N concentration (PNC) predicted with RF model and multi-source data fusion performed slightly better than direct NNI prediction, explaining 7 % more of NNI variability and achieving 89 % of the areal agreement for N diagnosis across all evaluation experiments. Integrating this new N management strategy into the precision rice management system (as ML_PRM) increased yield, N use efficiency (NUE) and economic benefits over farmer’s practice (FP) by 7–15 %, 11–71 % and 4–16 % (161–596 $ ha−1), respectively, and increased NUE by 11–26 % and economic benefits by 8–97 $ ha−1 than regional optimum rice management (RORM) under rice N surplus status under on-farm conditions. Conclusions In-season rice N status diagnosis can be improved using NDVI- and NDRE-based CNDC and PNC predicted by ML modeling with multi-source data fusion. Implications The active canopy sensor- and ML-based in-season N diagnosis and management strategy is more practical for applications under diverse on-farm conditions and has the potential to improve rice yield and ecological and economic benefits.
Abstract
The decision support indicators (DSIs) are specifically designed to inform local and regional stakeholders on the characteristics of a predicted event to facilitate decision-making. They can be classified as conventional, impact-based and event-based DSIs. This study aims to develop methodologies for calculating event-based DSIs and to evaluate the usefulness of different classes of DSIs for climate impact assessment and climate actions by learning about users' perceptions. The DSIs are calculated based on an ensemble of hydrological projections in western Norway under two representative concentration pathway (RCP) scenarios. The definitions, methodologies and results of the indicators are summarized in questionnaires and evaluated by key stakeholders in terms of understandability, importance, plausibility and applicability. Based on the feedback, we conclude that the conventional DSIs are still preferred by stakeholders and an appropriate selection of conventional DSIs may overcome the understanding problems between the scientists and stakeholders. The DSIs based on well-known historical events are easy to understand and can be a useful tool to convey climate information to the public. However, they are not readily implemented by stakeholders in the decision-making process. The impact-based DSI is generally easy to understand and important but it can be restricted to specific impact sectors.
Abstract
The study investigated the production process and properties of a new wood-based material called Bioblocks. This sustainable composite is made from medium-density-fibreboard (MDF) residues, citric acid and either sorbitol or hexanediol. The process involves mixing in-water diluted chemicals with the MDF residues and curing the mixture in a laboratory oven to esterify the sorbitol and wood components with citric acid. A design of experiment was used to determine the influence and optima of the different process factors, and an optimised trial further investigated the material properties. The density distribution, compression strength, and TS after 24 h immersion in water according to EN 317 of the Bioblocks were tested. The first trial showed that mainly the amount of water added impacts the product’s properties. The optimised material achieved a sufficient density distribution with an average density of about 420 kg/m3, a compression strength of up to 3.5 N/mm2, and a TS of about 2%. Therefore, Bioblocks are a promising natural material to use waste MDF and substitute fossil, unsustainable materials.
Authors
Charles D. Minsavage-Davis G. Matt Davies Siri Vatsø Haugum Pål Thorvaldsen Liv Guri Velle Vigdis VandvikAbstract
Northern European heathlands and moorlands dominated by Calluna vulgaris are internationally recognized for their conservation importance while also supporting traditional, low-intensity agriculture and game hunting. Managed burning plays an important role in maintaining these ecosystems but climate and land-use changes, including planned or unplanned transitions to forest and woodland, are now resulting in concerns about increasing wildfire frequency, intensity and severity. In combination with rapidly-changing regulations surrounding managed burning, this has highlighted the need to understand current and potential future fuel structures to effectively model fire behaviour and develop evidence-based regulations surrounding managed burning. We developed standardized heathland fuel descriptions and modeled associated fire behaviour for heathlands in the UK (England, Scotland) and Norway. Utilizing existing fuel and biomass data, we used cluster analysis to identify five distinct fuel models and assessed how they were represented across C. vulgaris life-stages, geographic locations and EUNIS habitat-types. We validated their independence by examining predicted fire rates of spread based across three representative fire weather scenarios. Fire rates of spread differed between C. vulgaris life stages, regardless of EUNIS community or country. Mature stage and taller building stage fuels produced the highest fire rates of spread and early, shorter building and pioneer stage fuels produced the lowest. Moss and litter fuel loads proved to be important determinants of fire rate of spread in a high-risk fire weather scenario. An understanding of links between fuel types and potential fire behaviour can be used to inform management and policy decisions. To aid in this, we used classification tree analysis to link fuel types to easily-observable characteristics. This will facilitate pairing the fuel models with fire behaviour prediction software to make evidence-based assessments of management fire safety and wildfire risk.