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
Milica Fotirić Akšić Dragana Dabić Zagorac Uroš Gašić Tomislav Tosti Maja Natić Mekjell MelandSammendrag
The aim of this study was to compare total phenolic content (TPC), radical-scavenging activity (RSA), total anthocyanin content (TAC), sugar and polyphenolic profiles of two apple cultivars (‘Discovery’ and ‘Red Aroma Orelind’) from organic and integrated production systems in climatic conditions of Western Norway. Sixteen sugars and four sugar alcohols and 19 polyphenols were found in the peel, but less polyphenols were detected in the pulp. The peel of both apples and in both production systems had significantly higher TPC and RSA than the pulp. The peel from integrated apples had higher TPC than the peel from organic apples, while organic apples had higher TAC than the integrated. Sucrose and glucose levels were higher in organic apples; fructose was cultivar dependent while minor sugars were higher in integrated fruits. The most abundant polyphenolic compound in the peel of the tested cultivars was quercetin 3-O-galactoside, while chlorogenic acid was most abundant in the pulp. Regarding polyphenols, phloretin, phloridzin, protocatechuic acid, baicalein and naringenin were higher in organic apple, while quercetin 3-O-galactoside, kaempferol 3-O-glucoside, chlorogenic acid and syringic acid was higher in integrated fruits. In conclusion, organic ‘Discovery’ and integrated ‘Red Aroma Orelind’ had higher bioavailability of health related compounds from the peel and the pulp.
Sammendrag
Active crop sensor-based precision nitrogen (N) management can significantly improve N use efficiency but generally does not increase crop yield. The objective of this research was to develop and evaluate an active canopy sensor-based precision rice management system in terms of grain yield and quality, N use efficiency, and lodging resistance as compared with farmer practice, regional optimum rice management system recommended by the extension service, and a chlorophyll meter-based precision rice management system. Two field experiments were conducted from 2011 to 2013 at Jiansanjiang Experiment Station of China Agricultural University in Heilongjiang, China, involving four rice management systems and two varieties (Kongyu 131 and Longjing 21). The results indicated that the canopy sensor-based precision rice management system significantly increased rice grain yield (by 9.4–13.5%) over the farmer practice while improving N use efficiency, grain quality, and lodging resistance. Compared with the already optimized regional optimum rice management system, in the cool weather year of 2011, the developed system decreased the N rate applied in Kongyu 131 by 12% and improved N use efficiency without inducing yield loss. In the warm weather year of 2013, the canopy sensor-based management system recommended an 8% higher N rate to be applied in Longjing 21 than the regional optimum rice management, which improved rice panicle number per unit area and eventually led to increased grain yield by over 10% and improved N use efficiency. More studies are needed to further test the developed active canopy sensor-based precision rice management system under more diverse on-farm conditions and further improve it using unmanned aerial vehicle or satellite remote sensing technologies for large-scale applications.
Forfattere
Ingerd Skow Hofgaard Guro Brodal Marit Almvik Morten Lillemo Aina Lundon Russenes Simon Graham Edwards Heidi Udnes AamotSammendrag
Over recent decades, the Norwegian cereal industry has had major practical and financial challenges associated with the occurrence of Fusarium head blight (FHB) pathogens and their associated mycotoxins in cereal grains. Deoxynivalenol (DON) is one of the most common Fusarium-mycotoxins in Norwegian oats, however T-2 toxin (T2) and HT-2 toxin (HT2) are also commonly detected. The aim of our study was to rank Nordic spring oat varieties and breeding lines by content of the most commonly occurring Fusarium mycotoxins (DON and HT2 + T2) as well as by the DNA content of their respective producers. We analyzed the content of mycotoxins and DNA of seven fungal species belonging to the FHB disease complex in grains of Nordic oat varieties and breeding lines harvested from oat field trials located in the main cereal cultivating district in South-East Norway in the years 2011–2020. Oat grains harvested from varieties with a high FHB resistance contained on average half the levels of mycotoxins compared with the most susceptible varieties, which implies that choice of variety may indeed impact on mycotoxin risk. The ranking of oat varieties according to HT2 + T2 levels corresponded with the ranking according to the DNA levels of Fusarium langsethiae, but differed from the ranking according to DON and Fusarium graminearum DNA. Separate tests are therefore necessary to determine the resistance towards HT2 + T2 and DON producers in oats. This creates practical challenges for the screening of FHB resistance in oats as today’s screening focuses on resistance to F. graminearum and DON. We identified oat varieties with generally low levels of both mycotoxins and FHB pathogens which should be preferred to mitigate mycotoxin risk in Norwegian oats.
Sammendrag
The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods in the prediction of crop status from hyperspectral data. The present study evaluates GPR model accuracy in the context of spring wheat dry matter, nitrogen content, and nitrogen uptake estimation. Models with the squared exponential covariance function were trained on images from two hyperspectral cameras (a frenchFabry–Pérot interferometer camera and a push-broom scanner). The most accurate predictions were obtained for nitrogen uptake (R2=0.75–0.85, RPDP=2.0–2.6). Modifications of the basic workflow were then evaluated: the removal of soil pixels from the images prior to the training, data fusion with apparent soil electrical conductivity measurements, and replacing the Euclidean distance in the GPR covariance function with the spectral angle distance. Of these, the data fusion improved the performance while predicting nitrogen uptake and nitrogen content. The estimation accuracy of the latter parameter varied considerably across the two hyperspectral cameras. Satisfactory nitrogen content predictions (R2>0.8, RPDP>2.4) were obtained only in the data-fusion scenario, and only with a high spectral resolution push-broom device capable of capturing longer wavelengths, up to 1000 nm, while the full-frame camera spectral limit was 790 nm. The prediction performance and uncertainty metrics indicated the suitability of the models for precision agriculture applications. Moreover, the spatial patterns that emerged in the generated crop parameter maps accurately reflected the fertilization levels applied across the experimental area as well as the background variation of the abiotic growth conditions, further corroborating this conclusion.
Forfattere
Saheba Bhatnagar Stefano Puliti Bruce Talbot Joachim Bernd Heppelmann Johannes Breidenbach Rasmus AstrupSammendrag
Wheel ruts, i.e. soil deformations caused by harvesting machines, are considered a negative environmental impact of forest operations and should be avoided or ameliorated. However, the mapping of wheel ruts that would be required to monitor harvesting operations and to plan amelioration measures is a tedious and time-consuming task. Here, we examined whether a combination of drone imagery and algorithms from the field of artificial intelligence can automate the mapping of wheel ruts. We used a deep-learning image-segmentation method (ResNet50 + UNet architecture) that was trained on drone imagery acquired shortly after harvests in Norway, where more than 160 km of wheel ruts were manually digitized. The cross-validation of the model based on 20 harvested sites resulted in F1 scores of 0.69–0.84 with an average of 0.77, and in total, 79 per cent of wheel ruts were correctly detected. The highest accuracy was obtained for severe wheel ruts (average user’s accuracy (UA) = 76 per cent), and the lowest accuracy was obtained for light wheel ruts (average UA = 67 per cent). Considering the nowadays ubiquitous availability of drones, the approach presented in our study has the potential to greatly increase the ability to effectively map and monitor the environmental impact of final felling operations with respect to wheel ruts. The automated mapping of wheel ruts may serve as an important input to soil impact analyses and thereby support measures to restore soil damages.
Sammendrag
Zoogenic faecal contamination of the environment is one of the indices included in the evaluation of ecological threats, health hazards and adverse impacts on various ecosystems. The risks and environmental concerns are associated with the fact that faeces of wild and domesticated animals constitute the largest source of environmental loading of enteropathogens associated with transmission of zoonotic diseases (enteric zoonoses). Although sick animals are more likely to transmit pathogens, healthy ones can also be the carriers and defecate them into the environment. This is of particular importance given the close human-animal interactions and health effects resulting from human and ecological exposures to faecal hazards from companion and farm animals. We have therefore set out to investigate whether healthy equines can carry and defecate human infectious pathogens. For this purpose, we set up a pilot study to examine the faecal DNA of horses using culture-independent molecular diagnostics – fluorescent probe-based quantitative real-time PCR. Our results revealed that among a total of 23 horses, 6 were found to carry Campylobacter jejuni (C. jejuni), and 5 had Salmonella enterica serovar Typhimurium (S. Typhimurium). Moreover, Enterococcus faecalis (E. faecalis) was found in 14 horses, while 19 were positive for Clostridium perfringens (C. perfringens). Furthermore, the frequently reported protozoan parasites in livestock, Cryptosporidium parvum (C. parvum) and Giardia lamblia (G. lamblia), were discovered in 8 and 7 samples, respectively. This pilot study shed new light on the phenomenon of healthy horses carrying C. jejuni and other human-health-related enteropathogens.
Forfattere
Ilaria Piccoli Till Seehusen Jenny Bussell Olga Vizitu Irina Calciu Antonio Berti Gunnar Börjesson Holger Kirchmann Thomas Kätterer Felice Sartori Chris Stoate Felicity Crotty Ioanna S. Panagea Abdallah Alaoui Martin A. BolinderSammendrag
Soil compaction (SC) is a major threat for agriculture in Europe that affects many ecosystem functions, such as water and air circulation in soils, root growth, and crop production. Our objective was to present the results from five short-term (<5 years) case studies located along the north–south and east–west gradients and conducted within the SoilCare project using soil-improving cropping systems (SICSs) for mitigating topsoil and subsoil SC. Two study sites (SSs) focused on natural subsoil (˃25 cm) compaction using subsoiling tillage treatments to depths of 35 cm (Sweden) and 60 cm (Romania). The other SSs addressed both topsoil and subsoil SC (˃25 cm, Norway and United Kingdom; ˃30 cm, Italy) using deep-rooted bio-drilling crops and different tillage types or a combination of both. Each SS evaluated the effectiveness of the SICSs by measuring the soil physical properties, and we calculated SC indices. The SICSs showed promising results—for example, alfalfa in Norway showed good potential for alleviating SC (the subsoil density decreased from 1.69 to 1.45 g cm−1) and subsoiling at the Swedish SS improved root penetration into the subsoil by about 10 cm—but the effects of SICSs on yields were generally small. These case studies also reflected difficulties in implementing SICSs, some of which are under development, and we discuss methodological issues for measuring their effectiveness. There is a need for refining these SICSs and for evaluating their longer-term effect under a wider range of pedoclimatic conditions.
Forfattere
Marzieh Hasanzadeh Saray Aziza Baubekova Alireza Gohari Seyed Saeid Eslamian Bjørn Kløve Ali Torabi HaghighiSammendrag
Water-Energy-Food (WEF) Nexus and CO2 emissions for a farm in northwest Iran were analyzed to provide data support for decision-makers formulating national strategies in response to climate change. In the analysis, input–output energy in the production of seven crop species (alfalfa, barley, silage corn, potato, rapeseed, sugar beet, and wheat) was determined using six indicators, water, and energy consumption, mass productivity, and economic productivity. WEF Nexus index (WEFNI), calculated based on these indicators, showed the highest (best) value for silage corn and the lowest for potato. Nitrogen fertilizer and diesel fuel with an average of 36.8% and 30.6% of total input energy were the greatest contributors to energy demand. Because of the direct relationship between energy consumption and CO2 emissions, potato cropping, with the highest energy consumption, had the highest CO2 emissions with a value of 5166 kg CO2eq ha−1. A comparison of energy inputs and CO2 emissions revealed a direct relationship between input energy and global warming potential. A 1 MJ increase in input energy increased CO2 emissions by 0.047, 0.049, 0.047, 0.054, 0.046, 0.046, and 0.047 kg ha−1 for alfalfa, barley, silage corn, potato, rapeseed, sugar beet, and wheat, respectively. Optimization assessments to identify the optimal cultivation pattern, with emphasis on maximized WEFNI and minimized CO2 emissions, showed that barley, rapeseed, silage corn, and wheat performed best under the conditions studied.
Forfattere
Therese With BergeSammendrag
Glyfosat er det aktive stoffet i flere ugrasmidler (f.eks. Roundup Eco) og er det mest brukte plantevernmidlet i Norge og globalt. I kornproduksjonen brukes det til å bekjempe ugras i stubbåker, men også i modent bygg mot bl.a. det flerårige grasugraset kveke (Elymus repens). Fordi ugras ofte opptrer flekkvis på åkeren, bør man sprøyte glyfosat flekkvis for å spare miljø og ressurser. Hensikten med dette forprosjektet var å teste om flekksprøyting kan automatiseres ved bruk av kommersielt tilgjengelig sensor-teknologi (WeedSeeker, Trimble Navigation Ltd.). Enkelt forklart er WeedSeeker en integrert enhet bestående av sensor, lyskilde, ventil og dyse. WeedSeeker ble opprinnelig utviklet for automatisk flekksprøyting av ugras på harde flater som grusganger, asfalt o.l. Sensoren kan ikke skille mellom plantearter, ei heller ugras og nytteplanter (inkl. prydplanter). Unntaket er hvis det er vesentlig forskjell mellom nytteplantenes og ugrasets evne til å reflektere rødt og nær-infrarødt lys. Det er åpenbart en relativt stor forskjell i denne evnen mellom korn og mange ugras når det nærmer seg tresketid, men også i stubbåker. Vi undersøkte derfor om automatisk flekksprøyting (WeedSeeker) med glyfosat til å bekjempe ugras fungerer i modent bygg og i stubbåker (om høsten). Resultatene gjelder to feltforsøk i modent bygg og to feltforsøk i stubbåker utført over to år. Bruk av WeedSeeker ga 17- 80 % ugraskontroll i modent bygg. I stubb ble det bedre effekt, 67 – 98 %. Til sammenligning ga ordinær breisprøyting 95-100 prosent effekt. I moden bygg ga WeedSeeker 57-63 % reduksjon i glyfosat-forbruket. I stubbåker ble besparelsen større, 79-82 %. Kommersielt tilgjengelig sensor-teknologi for automatisk flekksprøyting medførte en vesentlig reduksjon i forbruk av glyfosat. Men virkningen, spesielt på kveke, var til dels betydelig dårligere enn ordinær breisprøyting. Effekten på ugraset ville sannsynligvis blitt bedre og mindre variabel med mer erfaring med teknologien og et mer praksisnært oppsett enn det var mulig å bruke i dette forprosjektet.
Forfattere
Kamilla Skaalsveen John Ingar Øverland Marianne Bechmann Marie Uhlen Maurset Valborg Kvakkestad Julie Wiik Ingvild Evju Hanna Næss HolmSammendrag
Formålet med prosjektet var å øke kunnskapen om gårdbrukeres motivasjon, samt barrierer og muligheter for å gjennomføre vannmiljøtiltak i ulike regioner av landet. Det ble i den forbindelse gjennomført fokusgruppemøter i Innlandet, Trøndelag, Vestfold og Telemark, Nordland og Viken med bønder som hadde kunnskap om vannmiljøtiltak i jordbruket. Målet med fokusgruppene var å diskutere og belyse ulike barrierer, både praktiske, økonomiske, kunnskaps- og motivasjonsbaserte, samt løsninger, tiltak og virkemidler som kan bidra til å fjerne eller redusere disse. Det ble i tillegg gjennomført et områdestudie i Vestfold og Telemark med evaluering av tiltaksgjennomføring i nedbørfeltet til Akersvannet. Dette på bakgrunn av en tidligere utarbeiding av tiltakspakker for 26 grunneiere i nedbørfeltet utarbeidet av NLR Viken. Grunneierne ble intervjuet for å undersøke hvilke tiltak som ble gjennomført, hvilke som ikke ble gjennomført, samt årsaker til dette. I denne rapporten presenteres en utredning av hvilke faktorer som avgjør i hvilken grad gårdbrukere velger å gjennomføre vannmiljøtiltak, i tillegg til hvilke motivasjonsfaktorer som spiller inn i deres avgjørelse. Viktige fellestrekk mellom resultater fra fokusgruppene i Innlandet, Nordland, Trøndelag, Vestfold og Telemark og Viken, og områdestudien fra Akersvannet var at gårdbrukerne motiveres av synlige eller målbare resultater av tiltak og av å bevare jordressursene ved å motvirke erosjon. God informasjon og kunnskap om tiltak (og tilskuddsordninger) er også avgjørende, men er delvis mangelfulle. I tillegg er økonomiske insentiver for å veie opp for økonomiske ulemper ved tiltak viktige. Det ble også etterlyst bedre samhandling mellom ulike fylker, kommuner, Statsforvalteren og rådgivingen, samt større forutsigbarhet i ordningene over tid.