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.
2022
Forfattere
Bjørn Arild Hatteland Sandra Kaasen Vestheim Jørund Johansen Silje Maria Midthjell Høydal Helene Müller Haugan Markus A. K. Sydenham Joseph Chipperfield Anders NielsenSammendrag
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Forfattere
Bjørn Arild Hatteland Sandra Kaasen Vestheim Jørund Johansen Maren Kristine Halvorsen Silje Maria Midthjell Høydal Ingrid Vaksvik Stein Joar Hegland Joseph Chipperfield Markus A. K. Sydenham Anders NielsenSammendrag
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Sammendrag
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.
Sammendrag
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.).
Sammendrag
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Sammendrag
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Sammendrag
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Sammendrag
Invasive species are leading causes of biodiversity loss and economic damage. Prevention and management of invasions requires risk assessments based on ecological knowledge for species of potential concern. Interactions between introduced species and heterospecifics in the recipient community may affect the likelihood of establishment through biotic resistance and facilitation and are therefore important predictors of invasion risk. Experimentally exposing one species to another to observe their interactions is not always safe or practical, and containment facilities offer artificial environments which may limit the number of species and the types of interactions that may be tested. To predict biotic resistance and facilitation in a more natural setting, we deployed traps with pheromone lures in the field to mimic the presence of two potentially invasive spruce bark beetles, the European Ips typographus (tested in eastern Canada), and the North American Dendroctonus rufipennis (tested in Norway). We identified and counted possible predators, competitors, and facilitators that were captured in the traps. In eastern Canada, possible predators and competitors responded strongly to I. typographus lures, suggesting the potential for considerable biotic resistance. In Norway, D. rufipennis lures prompted little response by predators or competitors, suggesting that D. rufipennis may experience reduced biotic resistance in Europe. Dendroctonus rufipennis was also attracted to I. typographus pheromone, which may encourage facilitation between these species through cooperative mass attack on trees. Our findings will inform invasive-species risk assessments for I. typographus and D. rufipennis and highlight useful methods for predicting interactions between species that rely heavily on semiochemical communication.
Forfattere
Therese With Berge Jakob Geipel Maximilian Pircher El Houssein Chouaib Harik Kristian RindalSammendrag
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Sammendrag
The estimated global production of raspberry from year 2016 to 2020 averaged 846,515 tons. The most common cultivated Rubus spp. is European red raspberry (Rubus idaeus L. subsp. idaeus). Often cultivated for its high nutritional value, the red raspberry (Rubus idaeus) is susceptible to multiple viruses that lead to yield loss. These viruses are transmitted through different mechanisms, of which one is invertebrate vectors. Aphids and nematodes are known to be vectors of specific raspberry viruses. However, there are still other potential raspberry virus vectors that are not well-studied. This review aimed to provide an overview of studies related to this topic. All the known invertebrates feeding on raspberry were summarized. Eight species of aphids and seven species of plant-parasitic nematodes were the only proven raspberry virus vectors. In addition, the eriophyid mite, Phyllocoptes gracilis, has been suggested as the natural vector of raspberry leaf blotch virus based on the current available evidence. Interactions between vector and non-vector herbivore may promote the spread of raspberry viruses. As a conclusion, there are still multiple aspects of this topic that require further studies to get a better understanding of the interactions among the viral pathogens, invertebrate vectors, and non-vectors in the raspberry agroecosystem. Eventually, this will assist in development of better pest management strategies.