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
Forfattere
Arne Stensvand Nan-Yi Wang Vinh Hong Le Claudio Dias Da Silva Jr Belachew Asalf Tadesse Chloé Grieu William W. Turechek Natalia A. PeresSammendrag
Powdery mildew, caused by the ascomycete Podosphaera aphanis, is an important disease of strawberry. A slightly modified version of a method using steam thermotherapy to rid diseases and pests from strawberry transplants was tested against strawberry powdery mildew. Experiments took place in Norway and Florida, with potted strawberry plants heavily infected with the fungus. Aerated steam treatments of the plants were carried out as follows: a pre-treatment with steaming at 37 °C for 1 h was followed by 1 h at ambient temperature before plants were exposed to steaming at 40, 42, or 44 °C for 2 or 4 h in Norway and 44 °C for 4 h in Florida. Following steaming, plants from the different treatments and the untreated control were kept apart and protected from outside contamination of powdery mildew by growing them in closed containers with over-pressure. On steamed plants, hyphae of P. aphanis were dead and without any new spore formation after treatments, independent of temperature or exposure time; however, up to 99% of the area infected with powdery mildew prior to treatments contained actively sporulating lesions on non-steamed plants. None of the new leaves formed after steaming had powdery mildew, whereas more than half of the new leaves on non-treated plants were infected by P. aphanis. This investigation clearly indicates that steam thermotherapy can eradicate powdery mildew from strawberry transplants, and this can be achieved at lower temperatures and exposure times than previously reported for other pathogens.
Sammendrag
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Sammendrag
Active canopy sensors (ACSs) are great tools for diagnosing crop nitrogen (N) status and grain yield prediction to support precision N management strategies. Different commercial ACSs are available and their performances in crop N status diagnosis and recommendation may vary. The objective of this study was to determine the potential to minimize the differences of two commonly used ACSs (GreenSeeker and Crop Circle ACS-430) in maize (Zea mays L.) N status diagnosis and recommendation with multi-source data fusion and machine learning. The regression model was based on simple regression or machine learning regression including ancillary information of soil properties, weather conditions, and crop management information. Results of simple regression models indicated that Crop Circle ACS-430 with red-edge based vegetation indices performed better than GreenSeeker in estimating N nutrition index (NNI) (R2 = 0.63 vs. 0.50–0.51) and predicting grain yield (R2 = 0.56–0.57 vs. 0.49). The random forest regression (RFR) models using vegetation indices and ancillary data greatly improved the prediction of NNI (R2 = 0.81–0.82) and grain yield (R2 = 0.87–0.89), regardless of the sensor type or the vegetation index used. Using RFR models, moderate degree of accuracy in N status diagnosis was achieved based on either GreenSeeker or Crop Circle ACS-430. In comparison, using simple regression models based on spectral data only, the accuracy was significantly lower. When these two ACSs were used independently, they performed similarly in N fertilizer recommendation (R2 = 0.57–0.60). Hybrid RFR models were established using vegetation indices from both ACSs and ancillary data, which could be used to diagnose maize N status (moderate accuracy) and make side-dress N recommendations (R2 = 0.62–0.67) using any of the two ACSs. It is concluded that the use of multi-source data fusion with machine learning model could improve the accuracy of ACS-based N status diagnosis and recommendation and minimize the performance differences of different active sensors. The results of this research indicated the potential to develop machine learning models using multi-sensor and multi-source data fusion for more universal applications.
Sammendrag
Mange bønder har byrja å bestemme slåttetidspunktet ved aktiv bruk av varmesum. Det sikraste er nok likevel å observere utviklingstrinnet på deu artane du har i enga.
Sammendrag
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Sammendrag
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Forfattere
Gabriella do Vale Pereira Luis E. C. Conceição Filipe Soares Jessica Petereit Bela H. Buck Johan Johansen Jorge Dias Filippo FaccendaSammendrag
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Forfattere
Caroline Chylinski Kristin Fløgstad Degnes Inga Marie Aasen Sokratis Ptochos Berit Marie Blomstrand Karl-christian Mahnert Heidi L Enemark Stig Milan Thamsborg Håvard Steinshamn Spiridoula AthanasiadouSammendrag
The antiparasitic potential of plants could offer a vital solution to alleviating the costs of gastrointestinal nematode (GIN) infections in ruminant production globally. Leveraging known bioactive molecules, however, is complex, where plant species, extraction processes and seasonality impact bioavailability and efficacy. This study assessed the impact of a comprehensive set of factors on the antiparasitic activity of Norwegian conifers to identify bark compounds specific against GIN. Antiparasitic activity was determined using in vitro assays targeting morphologically distinct life stages of ovine GIN: the egg hatch assay and larval motility assay. In depth characterisation of the chemical composition of the bark extracts was carried out using chromatographic separation, UV-absorbance, and molecular mass profiles to identify compounds implicated in the activity. Three key findings emerged: (1) the activity of bark extracts varied markedly from 0 to 100% antiparasitic efficacy, owing to tree species, extraction solvent and seasonality; (2) the GIN exhibited species-and stage-specific susceptibility to the bark extracts; (3) the presence of condensed tannins, amongst other compounds, was associated with anthelmintic activity. These findings add new insights into urgently needed alternative parasite control strategies in livestock.
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