Publikasjoner
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.
2023
Sammendrag
Carrot is the main field vegetable in Norway and is stored at 0-1°C for up to 8 months. In long-stored carrots, postharvest diseases are problematic. Better knowledge of the causal agents of postharvest diseases can help producers apply appropriate control measures to reduce carrot loss and waste. The objectives of this study were to determine the extent of the tip rot problem and the prevalence of other storage diseases of carrots in different regions in Norway. The study was conducted from 2019 to 2021 by collecting carrots from 16 commercial cold storages, representing four regions. From each storage, representative carrots were randomly sampled, washed, and sorted into different disease categories and healthy carrots. Representative samples from each category were further analyzed in the NIBIO laboratory to identify the causal agent(s) and confirm the disease. Causal agent(s) were identified by symptom and sign description, microscopy and when necessary, DNA sequencing. One or more pathogens were identified from each symptomatic carrot. The incidence of postharvest diseases significantly varied among regions. Taking all years and regions together, only 42% of the carrots were healthy. In other words, 58% of the carrots were with one or more diseases, representing a waste due to diseases alone. Tip rot alone contributed to about 30% of the postharvest loss. Our results indicate that the burden of postharvest diseases of carrots is increasing. This includes diseases like tip rot disease complex, Cylindrocarpon root rot and cavity spot. The results are useful to direct research investigation of the most problematic diseases. Most of the post-harvest diseases of carrots are the result of latent infections that occur in the field, and stress during handling, storage, and processing operations. Hence, postharvest disease control measures should consider the pre-and postharvest predisposing factors of carrots for storage rot.
2022
Sammendrag
Flere økobønder kan dyrke høstraps. Nye sorter som egner seg for norsk klima gjør det mulig å utvide dyrkingsområdet. Etterspørselen er stor. Rapsdyrker Thorbjørn Lund forteller om gode erfaringer med å dyrke den etterspurte oljeveksten.
Sammendrag
Miljømessig og økonomisk bærekraft ble analysert og evaluert på 20 melkeproduserende gårder med økologisk og konvensjonell drift i Møre og Romsdal. Overskuddet av nitrogen, fosfor og kalium, energiforbruket og klimagassutslippet per daa var høyest ved konvensjonell drift og steg ved økende intensitet.
Sammendrag
I flere tusen år har sau og ull gitt oss mat og varme klær. Sauen har gitt oss kulturlandskap og biologisk mangfold, kultur og tradisjoner, bosetting og aktivitet, verdiskaping og bygdeutvikling. Så ble sauen plutselig en trussel mot klima og bærekraft. Er dette ryktet som fortjent.
Sammendrag
Fagansvar for tema skogbiologi (>100 artikler) som gir informasjon om skog, skogtyper, skogbruk og skogens biologi til skoleelever, studenter og befolkningen generelt.
Forfattere
Lars Sandved DalenSammendrag
Fagansvar for tema skogbiologi (>100 artikler) som gir informasjon om skog, skogtyper, skogbruk og skogens biologi til skoleelever, studenter og befolkningen generelt.
Sammendrag
Fagansvar for tema skogbiologi (>100 artikler) som gir informasjon om skog, skogtyper, skogbruk og skogens biologi til skoleelever, studenter og befolkningen generelt.
Sammendrag
Etter tørkesommeren 2020 står det mye tørrgran igjen i skogen. Hva om dette kunne vært benyttet som fyringsved? Vedekspert Simen Gjølsjø har undersøkt om det er god fyringsøkonomi å ta tørrgran direkte fra skogen og inn i vedovnen.
Forfattere
Brigitta Szabó János Mészáros Piroska Kassai Péter Braun Attila Nemes Csilla Farkas Natalja Cerkasova Federica Monaco Enrico Antonio Chiaradia Felix WitingSammendrag
An important aim of the OPTAIN project is to derive missing information on necessary model input variables in a harmonized way to allow for a sound cross-case study assessment of NSWRM effectiveness. Therefore, in this report we provide approaches applicable for all OPTAIN case studies (CS) to fill data gaps. The specific objective of OPTAINs task 3.3 was to provide methods to cover missing input data that is required for the environmental modelling and socio-economic analysis. The deliverable includes guidelines with detailed explanations about the derivation of missing data for the CS leaders. Based on the information provided by CS leaders in the OPTAIN milestone “MS7 Data inventory of input data for integrated modelling collected from all case studies”, the following information had to be covered by approaches provided by WP3 to fulfil the input requirements of the models and analysis: 1) soil phosphorus content, 2) effective bulk density, 3) moist soil albedo of the top layer, 4) USLE soil erodibility (K) factor, 5) available water capacity, 6) saturated hydraulic conductivity, 7) time series crop data. The mapping of soil phosphorus content is based on the LUCAS topsoil dataset. During the mapping the geometric mean phosphorus content by land use types – characteristic for the region of the CS – is applied. Further required data are the LUCAS Land Use / Cover Area Frame Survey, European agro-climate zone map and the land use or land cover map of the CS – a local one, if available. For the calculation of soil physical and hydraulic properties we apply methods available from the literature. The derivation of crop maps is based on remote sensing data. A crop classification model was trained on the cropland data of the LUCAS Land Use / Cover Area Frame Survey of the years 2015 and 2018, merged with the Sentinel-1A and -1B satellite radar images. The pixel based crop classification was carried out with a random forest algorithm on the Google Earth Engine platform. The method can be applied for 2015 and all following years. By adding a map of field boundaries, the pixel based crop prediction can be aggregated to field level using the majority of the predicted crop. Regarding the socio-economic data, missing information is planned to be covered from official statistics. The EU database does not account properly for the Norwegian and Swiss sites, therefore required data will be retrieved ex novo from local sources or literature.
Forfattere
Čerkasova, Natalja Nemes, Attila Szabó, Brigitta Idzelytė, Rasa Cüceloğlu, Gökhan Mészáros, János Kassai, Piroska Moritz Shore Csilla Farkas Czelnai, LeventeSammendrag
Deliverable report D3.3 of the EU Horizon 2020 Project OPTAIN (Grant agreement No. 862756) Description of the pre-processing scripts and routines for the harmonisation of the data to be used as input, adapted to the needs of the modelling approaches. Summary The OPTAIN project aims to identify efficient measures for the retention and reuse of water and nutrients (NSWRM - Natural/Small Water Retention Measures) in small agricultural catchments based on empirical data and scale-adapted integrated modelling approaches. The project involves international partners with case study sites in 14 small agricultural catchments (including one cross-border), all having different data availability, measurement protocols, data handling policies and formats. Based on the agreed data harmonisation procedures within the OPTAIN project, this deliverable D3.3 provides data pre-processors for input data restructuring to overcome the aforementioned differences among the partners. The projects’ case study leaders collected the input data necessary for the modelling tasks structured according to the derived guidelines. Available input information from different sources (both national and global or European scale) and formats had to be harmonised and standardised where relevant and reasonable. Pre-processing tools have been developed, which were used for data compilation and reformatting of the input data in line with the needs of basin-scale (SWAT+) and the field scale (SWAP) modelling approaches. Freely available and distributable software, programming languages, and technologies (Python, R, JavaScript) were used for these tasks.