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
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Forfattere
Jana Fránová Jaroslava Pribylová Rostislav Zemek Jiunn Luh Tan Zhibo Hamborg Dag-Ragnar Blystad Ondrej Lenz Igor KoloniukSammendrag
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Albert Kjartan Dagbjartarson Imsland Patrick Reynolds Lauri Teemu Kapari Simo Maduna Snorre Hagen Anna Hanssen Ólöf Dóra Bartels JónsdóttirSammendrag
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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
Emmanuel Oladeji Alamu Njoloma Joyce Akello Juliet Ngumayo Joel Ray Chazangwe Mehreteab Tesfai Chikoye David Nyoka Isaac Dale Lewis Sekhar Udaya NagothuSammendrag
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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.
Sammendrag
Up-to-date and reliable information on land cover and land use status is important in many aspects of human activities. Knowledge about the reference dataset, its coverage, nomenclature, thematic and geometric accuracy, spatial resolution is crucial for appropriate selection of reference samples used in the classification process. In this study, we examined the impact of the selection and pre-processing of reference samples for the classification accuracy. The classification based on Random Forest algorithm was performed using firstly the automatically selected reference samples derived directly from the national databases, and secondly using the pre-processed and verified reference samples. The verification procedures involved the iterative analysis of histogram of spectral features derived from the Sentinel-2 data for individual land cover classes. The verification of the reference samples improved the accuracy of delineation of all land cover classes. The highest improvement was achieved for the woodland broadleaved and non- and sparce vegetation classes, with the overall accuracy increasing from 51% to 73%, and from 33% to 74%, respectively. The second objective of this study was to derive the best possible land cover classification over the mountain area in Norway, therefore we examined whether the use of the Digital Elevation Model (DEM) can improve the classification results. Classifications were carried out based on Sentinel-2 data and a combination of Sentinel-2 and DEM. Using the DEM the accuracy for nine out of ten land cover classes was improved. The highest improvement was achieved for classes located at higher altitudes: low vegetation and non- and sparse vegetation.