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
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.
Authors
Sanjana De Zoysa Jeewanthi Sirisena Helani Perera Shalinda Fernando Miyuru Gunathilake Upaka RathnayakeAbstract
Rainfall Intensity Duration Frequency (IDF) curves can be identified as a major role in the planning of urban drainage infrastructure. Sri Lanka, which is an island surrounded by the Indian Ocean, is frequently exposed to various climatic alterations. Sri Lanka has specific region-wise IDF relationships for the entire country, however, these IDF curves were developed more than 30 years ago. Many in-situ rainfall observations in Sri Lanka have insufficient record lengths and the absence of finer time scale records (e.g. 15 min, hourly) leading to unreliable IDF curve developments. Given this importance, the present paper demonstrates the application of Satellite-based Precipitation Product (SbPP) daily rainfall in developing IDF curves for Sri Lanka. Rainfall satellite estimates derived from Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission (TRMM-3B42), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR) have been used to evaluate the ability of application of remote sensing data to develop IDF curves for Sri Lanka against rain gauge (RG) data. Our study breaks new ground by generating 50 IDF curves tailored to specific locations covering the whole county Sri Lanka, using daily rainfall data from RGs and three SbPPs during the period between 1990 and 2019. This marks a significant departure from the conventional approach, offering a more granular understanding of rainfall patterns in the country. By providing IDF curves for individual locations, our research presents a novel contribution to Sri Lanka's IDF history. At first, to evaluate the accuracy of SbPPs, statistical analysis was conducted using continuous and categorical evaluation indices. Second, IDF curves were developed and compared with the presently available IDF curves. Results showcased that IMERG outperformed all SbPPs, while PERSIANN-CDR showed dire performances. The IMERG and TRMM-3B42 products tend to overestimate light precipitation regions in high elevations and overestimate heavy rainfall in low elevations compared to rain gauge data. Rainfall intensities derived by rain gauge data depicted relative changes within ±30% for shorter durations and ±20% for longer durations while SbPPs showed beyond ±30% difference concerning the previously developed IDFs. It was apparent that these products have significant inaccuracies which cannot be neglected when utilizing them in developing IDF curves. This study will be beneficial in solving design problems associated with urban runoff control and disposal where knowing the rainfall intensities of different return periods with different durations is vital.
Authors
Nina Trandem Olle Anderbrant Glenn P. Svensson Christer Löfstedt Elisabeth Ôberg Ann-Kristin Isaksson Hanh Huynh Kimmo Rumpunen Line Beate Lersveen Sigrid Mogan Gunda Thöming Marja RantanenAbstract
No abstract has been registered
Authors
May Bente BrurbergAbstract
No abstract has been registered
Abstract
Genetic variation and performance of up to 40-year-old Norway spruce (Picea abies (L.) Karst.) families from half-diallel crosses made in natural populations where studied, and the results were compared with results from nursery test of seedlings (1-2 years from seed) and short-term farm-field tests at 6-10 years from seed. The diallel analyses revealed significant levels of additive genetic variance for growth and phenology traits. The non-additive genetic variance was generally small to moderate. Strong genetic correlations for growth performance were found between the short-term and long-term trials but not between the nursery and long-term trials. Similarly, genetic correlations for phenology traits were strong between short-term trials and long-term trials, whereas the nursery tests generally did not predict phenology at older age well. The findings indicate that early selection for growth and adaptive traits based on short-term trials is effective for improvement of long-term performance in field if the test site is not outside the normal range of deployment of the reproductive material.
Abstract
Female flowering and cone production took place in three Norway spruce progeny tests at ages 17 and 20 years, each planted with full-sib families from a half diallel. The number of cones on individual trees were scored in five classes. More than 50 % of the trees produced cones, and a considerable variation was found among families for the ability to produce cones (precocity) and for the number of clones scored in classes (fruitfulness). Both traits were strongly related to tree heights and diameters at the individual and at the family level. In general, tall trees produced the highest number of cones. However, some families produced many cones even if their average heights were low. In two of the half diallels, estimates of GCA variance components for the number of cones produced had twice the value of the SCA component, indicating additive genetic inheritance of cone production. Heritability estimates of cone scores were 0.10, 0.17 and 0.23, and the genetic correlations between cone production and tree heights were 0.40, 0.50 and 0.35 in the three half-diallels, respectively.