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.
2023
Forfattere
Ievina SturiteSammendrag
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Forfattere
Paul Eric Aspholm Benedicte Beddari Ingrid Søvik Ida Marie Bardalen Fløistad Monika Strasser Englund Ellen Katrin Enge Marit Vadset Eldbjørg Sofie Heimstad Snorre HagenSammendrag
Under høstjakta på elg (Alces alces) i 2020 ble det tatt vevsprøver til analyser av tungmetaller og PCB. Tungmetallprøver ble tatt av 24 individer; 4 hunnkalver, 4 hannkalver, 3 hanner av åringer og 13 okser (voksne hanner). PCB analyser ble gjort av vevsprøver som ble tatt av 2 hunnkalver, 3 hannkalver, 2 hann-åringer og 9 okser (totalt 16 dyr). De felte dyrene har god geografisk spredning fra sør til nord og nord-øst i kommunen. Tungmetallene som ble analysert var krom (Cr), nikkel (Ni), kobber (Cu), sink (Zn), arsen (As), sølv (Ag), kadmium (Cd), tinn (Sn), bly (Pb) og kvikksølv (Hg). PCB ble analysert for 34 kongenere pluss sumPCB6 og sumPCB7. De fleste konsentrasjonene av tungmetallene var svært lave og flere var under deteksjonsgrensene. Ellers var det bare sporadiske lave forekomster av de 32 PCBene som ble funnet i noen av de undersøkte elgene. Det var bare heksaklorbensen som ble detektert i alle prøvene fra elgene).
Forfattere
Nina TrandemSammendrag
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Forfattere
Jørgen A.B. MølmannSammendrag
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Forfattere
Jørgen A.B. MølmannSammendrag
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Sammendrag
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Sammendrag
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Sammendrag
A process-based model was developed to predict dry matter yields and amounts of harvested nitrogen in conventionally cropped grassland fields, accounting for within-field variation by a node network design and utilizing remotely sensed information from a drone-borne system for increased accuracy. The model, named NORNE, was kept as simple as possible regarding required input variables, but with sufficient complexity to handle central processes and minimize prediction errors. The inputs comprised weather data, soil information, management data related to fertilization, and a visual estimate of clover proportion in the aboveground biomass. A sensitivity analysis was included to apportioning variation in dry matter yield outputs to variation in model parameter settings. Using default parameter values from the literature, the model was evaluated on data from a two-year study (2016–2017, 264 research plots in total each year) conducted at two locations in Norway (i.e. in South-East and in Central Norway) with contrasting climatic conditions and with internal variation in soil characteristics. The results showed that the model could estimate dry matter yields with a relatively high accuracy without any corrections based on remote sensing, compared with published results from comparable model studies. To further improve the results, the model was calibrated shortly before harvest, using predictions of above ground dry matter biomass obtained from a drone-borne remote sensing system. The only parameters which were hereby adjusted in the NORNE model were the starting values of nitrogen content in soil (first cut) and the plant available water capacity (second cut). The calibration based on the remotely sensed information improved the predictive performance of the model significantly. At first cut, the root mean square error (RMSE) of dry matter yield prediction was reduced by 20% to a mean value of 58 g m−2, corresponding to a relative value (rRMSE) of 0.12. For the second cut, the RMSE decreased by 13% to 66 g m−2 (rRMSE: 0.18). The model was also evaluated in terms of the predictions of amounts of nitrogen in the harvested crop. Here, the calibration reduced the RMSE of the first cut by 38%, obtaining a mean RMSE value of 2.1 g N m−2 (rRMSE: 0.28). For the second cut, the RMSE reduction for simulated harvested N was 16%, corresponding to a mean RMSE value of 2.3 g N m−2 (rRMSE: 0.33). The large improvements in model accuracy for simulated dry matter and nitrogen yields obtained through calibration by utilizing remotely sensed information, indicate the importance of considering spatial variability when applying models under Nordic conditions, both for yield predictions and for decision support for nitrogen application.
Sammendrag
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Sammendrag
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