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
Ola FlatenSammendrag
Det er ikke registrert sammendrag
Forfattere
Eva Farkas Teresa Gómez de la Bárcena Tatiana Francischinelli Rittl Trond Henriksen Peter Dörsch Sigrid Trier Kjær Randi Berland FrøsethSammendrag
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Forfattere
Abirami Ramu Ganesan Philipp Hoellrigl Hannah Mayr Demian Martini Loesch Noemi Tocci Elena Venir Lorenza ConternoSammendrag
This study aimed to evaluate the rheological properties of doughs with 50% brewers’ spent grain (BSG) derived from a rye-based (RBSG) and barley-based (BBSG) beer added, and the textural profile of the related baked products. Simple model systems using BSG flour mixed with water were studied. Two bakery products, focaccia and cookies, were made as food systems using BSG in a 1:1 ratio with wheat flour (WF). Their rheological properties and texture after baking were characterized. BSG-added dough exhibited viscoelastic properties with a solid gel-like behavior. The addition of BSG increased G′ > G″ and decreased the dough flexibility. BSG addition in baked RBSG focaccia increased the hardness, gumminess, and chewiness by 10%, 9%, and 12%, respectively. BBSG cookies had a 20% increase in fracturability. A positive correlation was found between the rheological metrics of the dough and the textural parameters of BBSG-added cookies. PCA analysis revealed that complex viscosity, G′, G″, and cohesiveness separated BBSG focaccia from RBSG focaccia and the control. Therefore, the rheological properties of BSG dough will have industrial relevance for 3D-printed customized food products with fiber. Adding RBSG and BBSG to selected foods will increase the up-cycling potential by combining techno-functional properties.
Sammendrag
This chapter describes the work performed within the Sinograin II project on implementation of new precision nitrogen management technologies in three regions of North China. Each of the analyzed regions represents a different crop and scale of a farming system: large-scale rice farming system in Heilongjiang province, medium-scale maize farming system in Jilin province, and small-scale wheat farming system in the North China Plain. A village was selected in each region to represent the agricultural practices and current nutrient and crop management strategies of the tested region. Moreover, the initial regional optimum crop management, the current agricultural extension, as well as the precision nitrogen technologies implemented in the respective regions are described. During the course of the project, a number of novel tools and strategies for precision nitrogen management were developed for the respective regions and published in scientific papers. This chapter reviews and discusses the selected findings and indicates directions of the upcoming research.
Sammendrag
Soil management is important for sustainable agriculture, playing a vital role in food production and maintaining ecological functions in the agroecosystem. Effective soil management depends on highly accurate soil property estimation. Machine learning (ML) is an effective tool for data mining, selection of key soil properties, modeling the non-linear relationship between different soil properties. Through coupling with spectral imaging, ML algorithms have been extensively used to estimate physical, chemical, and biological properties quickly and accurately for more effective soil management. Most of the soil properties are estimated by either near infrared (NIR), Vis-NIR, or mid-infrared (MIR) in combination with different ML algorithms. Spectroscopy is widely used in estimation of chemical properties of soil samples. Spectral imaging from both UAV and satellite platforms should be taken to improve the spatial resolution of different soil properties. Spectral image super-resolution should be taken to generate spectral images in high spatial, spectral, and temporal resolutions; more advanced algorithms, especially deep learning (DL) should be taken for soil properties’ estimation based on the generated ‘super’ images. Using hyperspectral modeling, soil water content, soil organic matter, total N, total K, total P, clay and sand were found to be successfully predicted. Generally, MIR produced better predictions than Vis-NIR, but Vis-NIR outperformed MIR for a number of properties. An advantage of Vis-NIR is instrument portability although a new range of MIR portable devices is becoming available. In-field predictions for water, total organic C, extractable phosphorus, and total N appear similar to laboratory methods, but there are issues regarding, for example, sample heterogeneity, moisture content, and surface roughness. More precise and detailed soil property estimation will facilitate future soil management.
Forfattere
Xiande Li Zhilu Sun Giovanna Ottaviani Aalmo Fangfang Cao Divina Gracia P. Rodriguez Chen Qian Yongxun Zhang Knut ØistadSammendrag
Agricultural extension services are integral to technology adoption where they play a key role in delivering relevant agricultural information and technologies to farmers. In China, agricultural extension services are provided through experimentation, demonstration, training, and consulting. In Norway, agricultural extension is focused on collecting, developing, and coordinating agricultural knowledge to farmers. This chapter focuses on why agricultural extension is needed, how it is developed, and what services agricultural extension provides to its clients. It discusses experiences from China and Norway where agricultural extension has led to or is necessary for boosting agricultural productivity, increasing food security and safety, and improving the well-being of farmers.
Forfattere
Martha Grøseth Linda Karlsson Håvard Steinshamn Marianne Johansen Alemayehu Kidane Egil PrestløkkenSammendrag
Increasing the protein value in grass silages for dairy cows is of interest to increase use of homegrown protein sources and reduce nitrogen (N) losses to the environment. Studies have shown that wilting of grass silage can improve the metabolizable protein (MP) value by increasing the rumen microbial protein yield (MCP) and rumen escaped feed protein. We hypothesised that feeding wilted grass silage can improve milk and milk protein production in dairy cows and reduce the need for MP, estimated as amino acids absorbed in the small intestine (AAT), in concentrate. To test this, a continuous feeding experiment with 48 early to mid-lactation Norwegian Red dairy cows, kept in a loose housing system was conducted. Treatments were first cut grass silages from round bales, harvested at early booting from a sward of timothy (Phleum pratense), perennial rye grass (Lolium perenne) and meadow fescue (Festuca pratensis), wilted to 260 and 417 g dry matter (DM)/kg fresh matter. The grass silage was fed ad libitum and supplied with 8.3 kg/d of concentrate, either low (108 g AAT/kg DM) or high (125 g AAT/kg DM) in MP concentration, in a 2×2 factorial arrangement. The experiment lasted for 11 weeks, with the 2 first weeks, where cows received same feeding, used as covariate, and the last 4 weeks were used as data collection period. Wilting reduced fermentation products, ammonia and soluble N in the grass silage, while increased residual water-soluble carbohydrates, like expected. However, there was no difference between treatments in daily silage DM intake (13.1 kg) and milk yield (30.2 kg) or milk content, but feeding high MP concentrate increased urea and uric acid in urine. No major differences were found for rumen pH, amino acids in blood plasma or purine derivatives over creatinine index, as indirect estimate for MCP. In conclusion, high silage DM and high MP in concentrate did not increase the milk production in this study.
Forfattere
Shelemia Nyamuryekung'e Glenn Duff Santiago Utsumi Richard Estell Matthew M. McIntosh Micah Funk Andrew Cox Huiping Cao Sheri Spiegal Andres Perea Andres F. CibilsSammendrag
Det er ikke registrert sammendrag
Forfattere
Arne Stensvand Nan-Yi Wang Vinh Hong Le Claudio Dias Da Silva Jr Belachew Asalf Tadesse Chloé Grieu William W. Turechek Natalia A. PeresSammendrag
Powdery mildew, caused by the ascomycete Podosphaera aphanis, is an important disease of strawberry. A slightly modified version of a method using steam thermotherapy to rid diseases and pests from strawberry transplants was tested against strawberry powdery mildew. Experiments took place in Norway and Florida, with potted strawberry plants heavily infected with the fungus. Aerated steam treatments of the plants were carried out as follows: a pre-treatment with steaming at 37 °C for 1 h was followed by 1 h at ambient temperature before plants were exposed to steaming at 40, 42, or 44 °C for 2 or 4 h in Norway and 44 °C for 4 h in Florida. Following steaming, plants from the different treatments and the untreated control were kept apart and protected from outside contamination of powdery mildew by growing them in closed containers with over-pressure. On steamed plants, hyphae of P. aphanis were dead and without any new spore formation after treatments, independent of temperature or exposure time; however, up to 99% of the area infected with powdery mildew prior to treatments contained actively sporulating lesions on non-steamed plants. None of the new leaves formed after steaming had powdery mildew, whereas more than half of the new leaves on non-treated plants were infected by P. aphanis. This investigation clearly indicates that steam thermotherapy can eradicate powdery mildew from strawberry transplants, and this can be achieved at lower temperatures and exposure times than previously reported for other pathogens.
Sammendrag
Det er ikke registrert sammendrag