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.
2022
Authors
Carrie Brady Jo Ann Asselin Steven Beer May Bente Brurberg Bridget Crampton Stephanus Venter Dawn Arnold Sandra DenmanAbstract
Bacteria isolated from onion bulbs suffering from bacterial decay in the United States and Norway were previously shown to belong to the genus Rahnella based on partial housekeeping gene sequences and/or fatty acid analysis. However, many strains could not be assigned to any existing Rahnella species. Additionally, strains isolated from creek water and oak as well as a strain with bioremediation properties were assigned to Rahnella based on partial housekeeping gene sequences. The taxonomic status of these 21 strains was investigated using multilocus sequence analysis, whole genome analyses, phenotypic assays and fatty acid analysis. Phylogenetic and phylogenomic analyses separated the strains into five clusters, one of which corresponded to Rahnella aceris . The remaining four clusters could be differentiated both genotypically and phenotypically from each other and existing Rahnella species. Based on these results, we propose the description of four novel species: Rahnella perminowiae sp. nov. (type strain SL6T=LMG 32257T=DSM 112609T), Rahnella bonaserana sp. nov. (H11bT=LMG 32256T=DSM 112610T), Rahnella rivi sp. nov. (FC061912-KT=LMG 32259T=DSM 112611T) and Rahnella ecdela sp. nov. (FRB 231T=LMG 32255T=DSM 112612T).
Authors
Bjørn Arild Hatteland Sandra Kaasen Vestheim Jørund Johansen Silje Maria Midthjell Høydal Stein Joar Hegland Joseph Chipperfield Markus A. K. Sydenham Anders NielsenAbstract
No abstract has been registered
Authors
Haftamu Gebretsadik Gebrehiwot Jens Bernt Aune Ole Martin Eklo Torfinn Torp Lars Olav BrandsæterAbstract
Field experiments were conducted in 2015 and 2016 to study the effect of tillage frequency, seed rate, and glyphosate on teff and weeds. The experiments were arranged in a split plot design with three replications consisting of tillage frequency (conventional, minimum, and zero tillage) as the main plot and the combination of seed rate (5, 15, and 25 kg ha−1) and glyphosate (with and without) as subplots. Results showed that zero tillage reduced teff biomass yield by 15% compared to minimum tillage and by 26% compared to conventional tillage. Zero tillage and minimum tillage also diminished grain yield by 21% and 13%, respectively, compared to conventional tillage. Lowering the seed rate to 5 kg ha−1 reduced biomass yield by 22% and 26% compared to 15 and 25 kg ha−1, respectively. It also reduced the grain yield by around 21% compared to 15 and 25 kg ha−1 seed rates. Conventional tillage significantly diminished weed density, dry weight, and cover by 19%, 29%, and 33%, respectively, compared to zero tillage. The highest seed rate significantly reduced total weed density, dry weight, and cover by 18%, 19%, and 15%, respectively, compared to the lowest seed rate. Glyphosate did not affect weed density but reduced weed dry weight by 14% and cover by 15%. Generally, sowing teff using minimum tillage combined with glyphosate application and seed rate of 15 kg ha−1 enhanced its productivity and minimized weed effects.
Abstract
Weeds may reduce crop yields significantly if managed improperly. However, excessive herbicide use increases risk of unwanted effects on ecosystems, humans and herbicide resistance development. Weed harrowing is a traditional method to manage weeds mechanically in organic cereals but could also be used in conventional production. The weed control efficacy of weed harrowing can be adjusted by e.g. the angle of the tines. Due to its broadcast nature (both crop and weed plants are disturbed), weed harrowing may have relatively poor selectivity (i.e. small ratio between weed control and crop injury). To improve selectivity, a sensor-based model which takes into account the intra-field variation in weediness and “soil density” in the upper soil layer (draft force of tines), is proposed. The suggested model is a non-linear regression model with three parameters and was based on five field trials in spring barley in SE Norway. The model predicts the optimal weed harrowing intensity (in terms of the tine angle) from the estimated total weed cover and SD per sub-field management unit, as well as a pre-set biological weed threshold (defined as the acceptable total weed cover left untreated). Weed cover and SD were estimated with RGB images (analysed with custom-made machine vision) and an electronic load cell, respectively. With current parameter values, the model should be valid for precision weed harrowing in spring barley in SE Norway. The next step is to test the model, and if successful, adjust it to more cereal species. Weeds may reduce crop yields significantly if managed improperly. However, excessive herbicide use increases risk of unwanted effects on ecosystems, humans and herbicide resistance development. Weed harrowing is a traditional method to manage weeds mechanically in organic cereals but could also be used in conventional production. The weed control efficacy of weed harrowing can be adjusted by e.g. the angle of the tines. Due to its broadcast nature (both crop and weed plants are disturbed), weed harrowing may have relatively poor selectivity (i.e. small ratio between weed control and crop injury). To improve selectivity, a sensor-based model which takes into account the intra-field variation in weediness and “soil density” in the upper soil layer (draft force of tines), is proposed. The suggested model is a non-linear regression model with three parameters and was based on five field trials in spring barley in SE Norway. The model predicts the optimal weed harrowing intensity (in terms of the tine angle) from the estimated total weed cover and SD per sub-field management unit, as well as a pre-set biological weed threshold (defined as the acceptable total weed cover left untreated). Weed cover and SD were estimated with RGB images (analysed with custom-made machine vision) and an electronic load cell, respectively. With current parameter values, the model should be valid for precision weed harrowing in spring barley in SE Norway. The next step is to test the model, and if successful, adjust it to more cereal species.
Abstract
Precision weeding or site-specific weed management (SSWM) take into account the spatial distribution of weeds within fields to avoid unnecessary herbicide use or intensive soil disturbance (and hence energy consumption). The objective of this study was to evaluate a novel machine vision algorithm, called the ‘AI algorithm’ (referring to Artificial Intelligence), intended for post-emergence SSWM in cereals. Our conclusion is that the AI algorithm should be suitable for patch spraying with selective herbicides in small-grain cereals at early growth stages (about two leaves to early tillering). If the intended use is precision weed harrowing, in which also post-harrow images can be used to control the weed harrow intensity, the AI algorithm should be improved by including such images in the training data. Another future goal is to make the algorithm able to distinguish weed species of special interest, for example cleavers (Galium aparine L.).
Abstract
Strawberry powdery mildew, caused by Podosphaera aphanis, can be particularly destructive in glasshouse and plastic tunnel production systems, which generally are constructed of materials that block ultraviolet (UV) solar radiation (about 280 to 400 nm). We compared epidemic progress in replicated plots in open fields and under tunnels constructed of polyethylene, which blocks nearly all solar UV-B, and two formulations of ethylene tetrafluoroethylene (ETFE), one of which contained a UV blocker and another that transmitted nearly 90% of solar UV-B. Disease severity under all plastics was higher than in open-field plots, indicating a generally more favorable environment in containment structures. However, the foliar severity of powdery mildew within the tunnels was inversely related to their UV transmissibility. Among the tunnels tested, incidence of fruit infection was highest under polyethylene and lowest under UV-transmitting ETFE. These effects probably transcend crop, and the blocking of solar UV transmission by glass and certain plastics probably contributes to the widely observed favorability of greenhouse and high-tunnel growing systems for powdery mildew.
Authors
Kyrre Linné Kausrud Vigdis Vandvik Daniel Flø Sonya Rita Geange Stein Joar Hegland Jo Skeie Hermansen Lars Robert Hole Rolf Anker Ims Håvard Kauserud Lawrence Richard Kirkendall Jenni Nordén Line Nybakken Mikael Ohlson Olav Skarpaas Per Hans Micael Wendell Hugo de Boer Katrine Eldegard Kjetil Hindar Paal Krokene Johanna Järnegren Inger Elisabeth Måren Anders Nielsen Erlend Birkeland Nilsen Eli Knispel Rueness Eva Bonsak Thorstad Gaute VelleAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Anne-Grete Roer Hjelkrem Heidi Udnes Aamot Morten Lillemo Espen Sannes Sørensen Guro Brodal Aina Lundon Russenes Simon G. Edwards Ingerd Skow HofgaardAbstract
Fusarium graminearum is regarded as the main deoxynivalenol (DON) producer in Norwegian oats, and high levels of DON are occasionally recorded in oat grains. Weather conditions in the period around flowering are reported to have a high impact on the development of Fusarium head blight (FHB) and DON in cereal grains. Thus, it would be advantageous if the risk of DON contamination of oat grains could be predicted based on weather data. We conducted a functional data analysis of weather-based time series data linked to DON content in order to identify weather patterns associated with increased DON levels. Since flowering date was not recorded in our dataset, a mathematical model was developed to predict phenological growth stages in Norwegian spring oats. Through functional data analysis, weather patterns associated with DON content in the harvested grain were revealed mainly from about three weeks pre-flowering onwards. Oat fields with elevated DON levels generally had warmer weather around sowing, and lower temperatures and higher relative humidity or rain prior to flowering onwards, compared to fields with low DON levels. Our results are in line with results from similar studies presented for FHB epidemics in wheat. Functional data analysis was found to be a useful tool to reveal weather patterns of importance for DON development in oats.
Abstract
No abstract has been registered