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 Stein Joar Hegland Joseph Chipperfield Markus A. K. Sydenham Anders NielsenSammendrag
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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
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.
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