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
Andres Perea Lara Macon Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung'e Sara Campa Madrid Huiping Cao Andres F. Cibils Richard Estell Glenn Duff Santiago A. UtsumiSammendrag
Monitoring cattle on rangelands is a daunting task that can be improved by using wearable sensors that are capable of transmitting motion and position data in real time and at low cost. This study tested the performance of machine learning (ML) classifiers to discriminate among foraging activities of cows based on triaxial accelerometer data collected in real-time by LoRa WAN networks. Trials were conducted at the New Mexico State University Chihuahuan Desert Rangeland Research Center and the USDA Jornada Experimental Range in Doña Ana County, NM. A total of 24 Brangus, Brahman, Raramuri Criollo and Angus x Hereford mature cows fitted with LoRa WAN tracking collars housing GPS and triaxial accelerometers were monitored across four periods during the 2022 summer and fall seasons on desert rangeland pastures. Trackers integrated and transmitted activity count (Ac) data from accelerometers at one-minute intervals. Video recording of focal cows (n = 24) was undertaken during daylight hours (0630 to 2000 h) from a distance of ~30 m to minimize interference with natural behaviors. A total of 168 hours of video were recorded and inspected by an experienced observer to label video files according to a classification tree of four main activities: grazing (GR), walking (WA), resting (RE) and ruminating (RU), and two states: active (AC) or static (ST). Individualized activities and states were considered when cows performed the same predefined activity or state for more than 30 secs. Retrieved sensor data from collar trackers were labeled by state and activity according to labels collected from video records. This classification resulted in a dataset containing 9,222 events, including 3,928 for GR, 2,286 for WA, 2,032 for RE, and 976 for RU, as well as 6,214 labels for AC and 3,008 labels for ST. Deep learning through Multilayer Perceptron Classifiers (MLPC) were coded and implemented using a split configuration of 70% of the data for training and 30% for testing, respectively. In preliminary runs, models had reduced ability to properly discriminate among RE (F1 = 0.42) and RU (F1 = 0.43) Thus, RE and RU were merged on subsequent tests, resulting in 3,928 labels for GR, 2,286 labels for WA, and 3,008 labels for merged RE. Deep learning models successfully classified between AC vs. ST behavior with an overall F1 performance score of 0.96. Further use of the same deep learning models successfully classified among GR, WA, and RE activities with an overall F1 performance score of 0.91, suggesting satisfactory application of the trained models to monitor cattle grazing activities on desert rangeland.
Forfattere
Paul Eric Aspholm Simo Maduna Juho Matti Vuolteenaho Cornelya Klutsch Hallvard Jensen Ida Marie Luna Fløystad Ingrid Søvik Ane-Sofie Bednarczyk Hansen Josefine Bergs Snorre HagenSammendrag
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Forfattere
Alice Budai Daniel Rasse Teresa Gómez de la Bárcena Hugh Riley Vegard Martinsen Ievina Sturite Adam Thomas O'Toole Samson Øpstad Thomas CottisSammendrag
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Sammendrag
Wood modification using polyesterification of sorbitol and citric acid is a novel environmentally friendly strategy for wood protection improving its dimensional stability and acts against fungal deterioration. Inelastic Raman scattering is sensitive to the molecules of high polarizability and both lignocellulose and aliphatic esters formed during the treatment are polar. Therefore, in the present study, the quality control of the treatment using a handheld Raman spectrometer equipped with 830 nm laser is suggested as a rapid and reliable approach. Raman spectra from six wood modification levels (resulting in different weight percent gain, WPG) of three different wood species (Silver birch, Scots pine and Norway spruce) as well as three sample preparation strategies (intact, sanded and milled wood samples) were collected, and further analyzed using a chemometric method. Best performing models based on Powered Partial Least Squares Regression predicted the WPG level at R2 = 0.85, 0.95 and 0.98 for birch, pine and spruce, respectively. In addition, a clear separation between hard and soft wood species was also captured. Especially for softwood species, the sample preparation method affected the model accuracy, revealing the best performance in milled material. It is concluded that by using handheld Raman spectrometer it is possible to perform accurate quality control of wood modified by polyesterification of citric acid and sorbitol.
Sammendrag
The aim of this study was to contribute to development of organic fertiliser products based on fish sludge (i.e. feed residues and faeces) from farmed smolt. Four dried fish sludge products, one liquid digestate after anaerobic digestion and one dried digestate were collected at Norwegian smolt hatcheries in 2019 and 2020. Their quality as fertilisers was studied by chemical analyses, two 2-year field experiments with spring cereals and soil incubation combined with a first-order kinetics N release model. Cadmium (Cd) and zinc (Zn) concentrations were below European Union maximum limits for organic fertilisers in all products except one (liquid digestate). Relevant organic pollutants (PCB7, PBDE7, PCDD/F + DL-PCB) were analysed for the first time and detected in all fish sludge products. Nutrient composition was unbalanced, with low nitrogen/phosphorus (N/P) ratio and low potassium (K) content relative to crop requirements. Nitrogen concentration in the dried fish sludge products varied (27–70 g N kg-1 dry matter), even when treated by the same technology but sampled at different locations and/or times. In the dried fish sludge products, N was mainly present as recalcitrant organic N, resulting in lower grain yield than with mineral N fertiliser. Digestate showed equally good N fertilisation effect as mineral N fertiliser, but drying reduced N quality. Soil incubation in combination with modelling is a relatively cheap tool that can give a good indication of N quality in fish sludge products with unknown fertilisation effects. Carbon/N ratio in dried fish sludge can also be used as an indicator of N quality.
Forfattere
Trond Mæhlum Bente Føreid Maria Dietrich Melesse Eshetu_Moges Trine Hvoslef-Eide Ola Lødøen Vethe Petter D. JenssenSammendrag
This report (D2.5) presents a qualitative and quantitative assessment for nutrients and energy regarding circular fertilizers and biogas production from waste resources. A transformation towards sustainable food production for the growing urban population requires improved circular urban nutrient management. Urban agriculture (UA), like any agricultural system, needs input of resources in terms of growth media, nutrients, and water. Resources that are often imported into cities, especially in the form of food, generate urban waste. Current environmental, social, and economic challenges of cities are seen as opportunities that can be derived locally, as this project demonstrates. The domestic organic waste and wastewater contains energy (thermal and chemical) and nutrients that could play a role in the urban circular economy if proper technology and management are applied. Urban organic waste contains relevant nutrients including nitrogen (N) and phosphorus (P), as well as organic matter, yet less than 5% of the global urban resources are presently recycled. One recycling approach is the composting of urban organic wastes, recovery of nutrients from source-separated urine and anaerobic digestate of blackwater, and biogas and biochar produced as sources of energy. At the NMBU showcase different technologies were assessed to demonstrate how to achieve sustainable and circular urban farming systems. Qualitative and quantitative information about organic fertilizers, making budgets for the nutrient contents of waste resources and organic fertilizer and comparing this with the nutrient needs of the plants in the relevant cultivation area, as shown in this report, can provide better fertilization and less loss to the environment. We need more information on the fertilizer value of waste resources and how these nutrients can be best utilised. Due to the increased interest, more information about health and environmental challenges by implementing circular UA should be obtained
Forfattere
Gabriela WagnerSammendrag
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Forfattere
Ruchiru D. Herath Uttam Pawar Dushyantha M. Aththanayake Kushan D. Siriwardhana Dimantha I. Jayaneththi Chatura Palliyaguru Miyuru Gunathilake Upaka RathnayakeSammendrag
Climate change, urbanization, and many anthropogenic activities have intensified the floods in today’s world. However, poor attention was given to mitigation strategies for floods in the developing world due to funding and technical limitations. Developing flood inundation maps from historical flood records would be an important task in mitigating any future flood damages. Therefore, this study presents the predictive capability of the Rainfall-Runoff-Inundation (RRI) model, a 2D coupled hydrology-inundation model, and to build flood inundation maps utilizing available ground observation and satellite remote sensing data for Kalu River, Sri Lanka. Despite the lack of studies in predicting flood levels, Kalu River is an annually flooded river basin in Sri Lanka. The comparative results between ground-based rainfall (GBR) measurement and satellite rainfall products (SRPs) from the IMERG satellite have shown that SRPs underestimate peak discharges compared to GBR data. The accuracy and the reliability of the model were assessed using ground-measured discharges with a high coefficient of determination (R2 = 0.89) and Nash–Sutcliffe model efficiency coefficient (NSE = 0.86). Therefore, the developed RRI model can successfully be used to simulate the inundation of flood events in the KRB. The findings can directly be applied to the stakeholders.