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.
2024
Forfattere
Bjørn Egil FløSammendrag
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Sammendrag
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Forfattere
Beatrice HelgheimSammendrag
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Forfattere
Morten Rese Gijs van Erven Romy J. Veersma Gry Alfredsen Vincent Eijsink Mirjam A. Kabel Tina Rise TuvengSammendrag
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Forfattere
Morten Rese Gijs van Erven Romy J. Veersma Gry Alfredsen Vincent Eijsink Mirjam A. Kabel Tina Rise TuvengSammendrag
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Sammendrag
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.
Sammendrag
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.
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Populærvitenskapelig – Dette gir bedre jord og dyrefôr i Etiopia.
Siri Elise Dybdal, Marit Jørgensen
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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.