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.
2021
Forfattere
Kemeng Xiao Karin Juul Hesselsøe Christer Magnusson Tatsiana Espevig Trond Olav Pettersen Trygve S. AamlidSammendrag
In 2018–2019, establishment problems were encountered, after reseeding creeping bentgrass (Agrostis stolonifera) on a sand-based putting green after ice encasement at the NIBIO Turfgrass Research Center, Norway. Seeds germinated, but the seedlings attained a purple color and died in large patches. Replacement of the top 3 cm layer with new sand amended with Sphagnum peat or garden compost did not solve the problem. To explain this phenomenon, we (1) analyzed the original substrate for nematodes in patches with and without reestablishment failure; and (2) conducted a factorial pot trial with creeping bentgrass and Chewings fescue (Festuca rubra ssp. commutata) seeded on different substrates, some of them in layers, and with and without phosphorus (P) fertilization. The nematode counts showed six times more stubby-root nematodes and two times more spiral nematodes and needle nematodes in the patches with dead seedlings than in the patches with healthy seedings. In the pot trial, the fastest and slowest reestablishment was observed with new sand amended with garden compost and in the two treatments that included the original substrate, respectively. Replacement of the top 3 cm of the old substrate with new garden compost resulted in stagnation of bentgrass seedlings from four weeks after seeding, while fescue seedlings were unaffected. We conclude that the failure to reestablish creeping bentgrass was primarily due to nematodes, which are likely to be more critical for seedlings than for established turf. The green was later reestablished successfully with a 100 % red fescue seed blend.
Sammendrag
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Forfattere
Tor Myking Jade Phillips Enrico Sturaro Kjersti Bakkebø Fjellstad Nina Svartedal Hojka Kraigher Marjana Westergren Nigel Maxted Magda Bou Dagher Kharrat Silvia Perez-EsponaSammendrag
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Forfattere
Annette Folkedal SchjøllSammendrag
https://vips.vitenparken.no/ https://www.vips-landbruk.no/
Forfattere
Torbjørn Høitomt Hans Blom Magni Olsen Kyrkjeeide Kristian Hassel John Gunnar BrynjulvsrudSammendrag
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Forfattere
Peter Horvath Hui Tang Rune Halvorsen Frode Stordal Lena M. Tallaksen Terje Koren Berntsen Anders BrynSammendrag
Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.