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
Since the world’s population is increasing, alternative food sources must be tapped. Although algae have a high potential to become a part of our diets due to their favorable nutritional properties, there is a little information on the willingness of consumers in Norway to try algae-made foods. In this paper we used a Norwegian survey to address this question. We constructed an order logistic regression model and predicted conditional probabilities to try algae food. The results show that among the most important aspect for willingness to try food with algae is age, health conscientiousness, and environmental attitudes.
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Sammendrag
Naturtypen artsrik slåttemark er sterkt truet ifølge «norsk rødliste for naturtyper», og var i 2011 utvalgt naturtype (UN) med et visst vern gjennom naturmangfoldloven. I 2022 fikk NIBIO ved Ellen Svalheim forespørsel fra Statsforvalteren i Oslo og Viken om utarbeiding av skjøtselsplan for seterområdet Søndre Haugplass på Raje. Feltarbeid ble gjennomført i oktober 2022. Denne skjøtselsplanen gir restaurerings- og skjøtselsplanråd for ivaretakelse av de kulturavhengige naturtypene. Skjøtselsplanen er utarbeidet i samarbeid med Arnfinn Tveita som driver skjøtsel for to av eiendommene i tillegg til sin egen naboeiendom og Kirsten Myhr som er grunneier og driver skjøtsel på en av eiendommene innenfor slåttemarka.
Sammendrag
Naturtypen artsrik slåttemark er sterkt truga ifølge «norsk rødliste for naturtyper», og var i 2011 utvald naturtype (UN) med eit visst vern gjennom naturmangfaldlova. I 2022 fekk NIBIO ved Ellen Svalheim førespurnad frå Farsund kommune om revidering av deler av skjøtselsplanen frå 2007 for eit område på 45 daa i Marka i Farsund. Slåttemarka ligg innanfor tidlegare Marka skyte og øvingsfelt og består av dei to eigedomane gbnr 29/29 og 29/25. Feltarbeid vart gjennomført i august 2022. Tidlegare er det gjort fleire registreringar av vegetasjonen innanfor skjøtselsplanområdet der slåttemarka er gitt verdi B-viktig (ID BN00037471). Ein liten flik av ei større kystlynghei i nord finst og innanfor området med tidlegare samla verdi A-svært viktig (ID 00070187). Denne skjøtselsplanen gir restaurerings- og skjøtselsplanråd for ivaretaking av dei kulturavhengige naturtypane. Skjøtselsplanen er utarbeid i samarbeid med grunneigarane Jan Helge Samuelsen og Terje Ersland som driv skjøtsel av slåttemarka.
Sammendrag
Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.
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Forfattere
Aurelie Wilfart Vincent Baillet Lorraine Balaine Xabier Díaz de Otálora Federico Dragoni Dominika Joanna Krol Joanna Frątczak-Müller Anna Rychła Divina Gracia P. Rodriguez James Breen Vasileios Anestis Cathal Buckley Habtamu Alem Wilfried Winiwarter Nouraya Akkal-Corfini Barbara AmonSammendrag
Growing awareness of global challenges and increasing pressures on the farming sector, including the urgent requirement to rapidly cut greenhouse gases (GHG) emissions, emphasize the need for sustainable production, which is particularly relevant for dairy production systems. Comparing dairy production systems across the three sustainability dimensions is a considerable challenge, notably due to the heterogeneity of production conditions in Europe. To overcome this, we developed an ex-post multicriteria assessment tool that adopts a holistic approach across the three sustainability dimensions. This tool is based on the DEXi framework, which associates a hierarchical decision model with an expert perspective and follows a tree-shaped structure; thus, we called it the DEXi-Dairy tool. For each dimension of sustainability, qualitative attributes were defined and organized in themes, sub-themes, and indicators. Their choice was guided by three objectives: (i) better describe the main challenges faced by European dairy production systems, (ii) point out synergies and trade-offs across sustainability dimensions, and (iii) contribute to the identification of GHG mitigation strategies at the farm level. Qualitative scales for each theme, sub-theme, and indicator were defined together with weighting factors used to aggregate each level of the tree. Based on selected indicators, a list of farm data requirements was developed to populate the sustainability tree. The model was then tested on seven case study farms distributed across Europe. DEXi-Dairy presents a qualitative method that allows for the comparison of different inputs and the evaluation of the three sustainability dimensions in an integrated manner. By assessing synergies and trade-offs across sustainability dimensions, DEXi-Dairy is able to reflect the heterogeneity of dairy production systems. Results indicate that, while trade-offs occasionally exist among respective selected sub-themes, certain farming systems tend to achieve a higher sustainability score than others and hence could serve as benchmarks for further analyses.
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