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
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Forfattere
Karin Juul Hesselsøe Olle Markusson Maria Viking Anne Friederike Borchert Trygve S. Aamlid Atle Revheim Hansen Lars Henrik Nielsen Markus Rehnström Janne Lehto Bjarni HannessonSammendrag
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On European golf courses, small lightweight robotic mowers have recently been introduced for fairway and rough mowing. In this study, turfgrass quality and the coverage of broadleaf weeds in three cool-season grasses were compared in response to robotic and traditional fairway mowing. Experiments with pure swards of red fescue (Festuca rubra L.), colonial bentgrass (Agrostis capillaris L.), and Kentucky bluegrass (Poa pratensis L.) were carried out at NIBIO Landvik, Norway, to evaluate differences between lightweight robotic mowing and reel mowing. In a mixture of the three species turfgrass quality and the coverage of broadleaf weeds were compared in response to robotic and reel mowing at yearly fertilizer levels from 0 to 120 kg N ha−1. The results showed that both robotic and reel mowing were found to provide high turfgrass quality, while lower coverage of broadleaf weeds (predominantly white clover [Trifolium repens L.]) was found with robotic mowing independent of grass species. In the mixed stand, higher turfgrass quality was found with robotic mowing regardless of N rate, but N rates above 60 kg ha−1 year−1 were necessary to keep the coverage of white clover in fall on an acceptable low level. Our results suggest that robotic mowing can decrease the spread of white clover at a fairway mowing height of 15 mm, but more research is needed to clarify at which mowing heights, mowing frequencies, and fertilizer levels we can get the best competitiveness against broadleaf weeds on fairways with robotic mowing.
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“rswap” is an R package under development for SWAP 4.2 with the goal of simplifying, automating, and improving user interaction with the model. The package functions by detecting and translating SWAP input files into R-compatible dataframes, allowing for easy and automated modifications to parameters. Modified model inputs can then be re-written to files and run in SWAP from the R console using "rswap". SWAP model output can be automatically imported into the R environment and visualized using a variety of (interactive) graphing functions. If observational data is provided by the user, then the package can adjust output settings to match (variables and depth). Modelled and observed data can then be graphically compared in-line and “goodness-of-fit” statistics can be generated and plotted. Additionally, model runs can be saved and interactively compared with each other, functions are thoroughly documented with runnable examples, and a baseline runnable model setup can be automatically initialized. Further planned developments to the package include support for parallel running of model runs, enabling rapid automated sensitivity analysis, scenario analysis, as well as automated “hard calibration” routines and parameter estimation. Through this functionality, “rswap” can connect the SWAP model to an integrated development environment (IDE), such as “RStudio”, allowing users to efficiently perform all their work (setup, calibration, execution, analysis) in a single environment. Importantly, the packages allows for direct use of SWAP with the vast array of research software on the R platform. “rswap” is an open-source project originally developed for use in OPTAIN (optain.eu) and has been applied in multiple case studies and thesis projects.