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
Karin Juul Hesselsøe Anne Friederike Borchert Anne Falk Øgaard Tore Krogstad Yajun Chen Wolfgang Prämaßing Trygve S. AamlidSammendrag
Sustainable phosphorus use is essential in golf course management to prevent eutrophication and overconsumption. The study aimed to investigate if phosphorus fertilization can be reduced without negative effects on turf quality. We compared two P fertilization recommendations based on soil analyses, one based on the annual nitrogen rate, and a zero-P control. The recommendations were the “minimum level of sustainable nutrition” (MLSN), which aims to keep treatment soil levels above 18 mg P kg–1 dry soil (Mehlich-3); the “sufficiency level of available nutrition” (SLAN), in which the threshold for excluding P fertilization is >54 mg P kg–1 dry soil (Mehlich-3); and “Scandinavian precision fertilization” (SPF), which recommends applying P at 12% of the annual N rate. The treatments were compared via monthly assessments of turf quality and the coverage of sown species and annual bluegrass (Poa annua L.) from 2017 to 2020 on five golf courses from Germany, Sweden, China, Norway, and the Netherlands. MLSN and SPF significantly reduced soil P at all sites compared with SLAN recommendations. Turf quality showed no significant differences. The results from the mixed creeping bentgrass (Agrostis stolonifera L.)–annual bluegrass green showed a 2 to 4% increase in annual bluegrass coverage with P fertilization over the zero-P treatments. The MLSN guideline is recommended for sustainable P fertilization on established greens with low P sorption capacity under diverse climatic and management conditions. The SPF may result in application of excess P to soils with high Mehlich-3 values, as soil analyses are not considered.
Sammendrag
Since 2020, the Norwegian Institute of Bioeconomy (NIBIO) Turfgrass Research Group has been studying agronomic, environmental, and economic consequences of switching to light-weight robotic mowers on golf course fairways and semiroughs. Preliminary results from field trials in 2020 and 2021 at the NIBIO Turfgrass Research Center Landvik, Norway, and demonstration trials on one golf course in each of the five Nordic countries, showed that turfgrass quality with robotic mowing was similar to manual mowing. At Landvik, robotic mowing resulted in less disease in both fairway and semirough but more infestation of white clover than manual mowing in the semirough. A survey of players’ attitudes to robotic mowers conducted on the five golf courses showed that about 90% of the players were positive or neutral to the new technology. However, respondents asked for better adaptation of the local rules on the golf course and even the international rules of golf to robotic mowing.
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Forfattere
Elena Zubiaurre Bolette Bele Veronique Karine Simon Guillermo S. Reher Ana Delia Rodríguez Rodrigo Alonso Benedetta CastiglioniSammendrag
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Forfattere
Misganu Debella-GiloSammendrag
Parts of the limited agricultural land area in Norway are taken up by buildings, roads, and other permanent changes every year. A method that detects such changes immediately after they have taken place is required in order to monitor the agricultural areas closely. To that end, Sentinel-2 satellite image time series (SITS) acquired during the summer of 2019 were used to detect the agricultural areas taken up by permanent changes such as buildings and roads. A deep-learning algorithm using 1D convolutional neural network (CNN), with the convolution in the temporal dimension, was applied to the SITS data. The training data was collected from the building footprints dataset filtered using a mono-temporal image aided with the areal resource map (AR5). The deep-learning model was trained and evaluated before being used for prediction in two regions of Norway. Procedures to reduce overfitting of the model to the training data were also implemented. The trained model showed a high level of accuracy and robustness when evaluated based on a test dataset kept out of the training process. The trained model was then used to predict new built-up areas in agricultural fields in two Sentinel-2 tiles. The prediction was able to detect areas taken by new buildings, roads, parking areas and other similar changes. The prediction was then evaluated with respect to the existing building footprints after a few post-processing procedures. A high percentage of the buildings were detected by the method, except for small buildings. The details of the methods and the results obtained, together with brief discussion, are presented in this paper.
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