Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2021
Authors
Geir Wæhler Gustavsen Helge Berglann Elisabeth Jenssen Signe Kårstad Divina Gracia P. RodriguezAbstract
No abstract has been registered
Authors
Anders Nielsen Inger Elisabeth Måren Line Rosef Lawrence Richard Kirkendall Martin Malmstrøm Hugo de Boer Katrine Eldegard Kjetil Hindar Lars Robert Hole Johanna Järnegren Kyrre Linné Kausrud Erlend Birkeland Nilsen Eli Knispel Rueness Eva Bonsak Thorstad Gaute VelleAbstract
No abstract has been registered
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No abstract has been registered
Abstract
No abstract has been registered
Authors
Pia Heltoft ThomsenAbstract
No abstract has been registered
Authors
Puchun Niu Angela Dagmar Schwarm Helge Bonesmo Alemayehu Kidane Bente Aspeholen Åby Tonje Marie Storlien Michael Kreuzer Maria Clementina Alvarez Flores Jon Kristian Sommerseth Egil PrestløkkenAbstract
No abstract has been registered
Authors
Xinbing Wang Yuxin Miao Rui Dong Hainie Zha Tingting Xia Zhichao Chen Krzysztof Kusnierek Guohua Mi Hong Sun Minzan LiAbstract
Reliable and efficient in-season nitrogen (N) status diagnosis and recommendation methods are crucially important for the success of crop precision N management (PNM). The accuracy of these methods has been found to be influenced by soil properties, weather conditions, and crop management practices. It is important to effectively incorporate these variables to improve in-season N management. Machine learning (ML) methods are promising due to their capability of processing different types of data and modeling both linear and non-linear relationships. The objectives of this study were to (1) determine the potential improvement of in-season prediction of corn N nutrition index (NNI) and grain yield by combining soil, weather and management data with active sensor data using random forest regression (RFR) as compared with Lasso linear regression (LR) using similar data and simple regression (SR) models only using crop sensor data; and (2) to develop a new in-season side-dress N fertilizer recommendation strategy at eighth to ninth leaf stage (V8-V9) of corn developement using the RFR model. Twelve site-year experiments examining corn N rates and planting densities were conducted in Northeast China. The GreenSeeker sensor data and corn NNI were collected at V8-V9 stage, and grain yield was determined at the harvest stage (R6). The soil information was obtained at planting and the weather data was measured throughout the growing season. The results indicated that corn NNI and grain yield were better predicted by combining soil, weather and management information with GreenSeeker sensor data using RFR model (R2 = 0.86 and 0.79) and LR model (R2 = 0.85 and 0.76) as compared with only using GreenSeeker sensor data (R2 = 0.66 and 0.62–63) based on the test dataset. An innovative in-season side-dress N recommendation strategy was developed using the RFR grain yield prediction model to simulate corn grain yield responses to a series of side-dress N rates at V8-V9 stage. Based on these response curves, site-, and year-specific optimum side-dress N rates can be determined. The scenario analysis results indicated that this RFR model-based in-season N recommendation strategy could recommend side-dress N rates similar to those based on measured agronomic optimum N rate (AONR) or economic optimum N rate (EONR), with root mean square error (RMSE) of 17 kg ha−1 and relative error (RE) of 14–15 %. It is concluded that combining soil, weather and management information with crop sensor data using RFR can significantly improve both in-season corn NNI and grain yield prediction and N management, compared with the approach based only on crop sensor data. More studies are needed to further improve and evaluate this approach under diverse on-farm conditions.
Authors
Spiridoula Athanasiadou Marit Almvik Jarkko Hellström Eva Madland Nebojsa Simic Håvard SteinshamnAbstract
Copyright © 2021 Athanasiadou, Almvik, Hellström, Madland, Simic and Steinshamn. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Abstract
Understanding the quality of new raw material sources will be of great importance to ensure the development of a circular bioeconomy. Building up quality understanding of wood waste is an important step in this development. In this paper we probe two main questions, one substantial and one theoretical: What different understandings of wood waste quality exist and what significance do they have for the recycling and re-use of this waste fraction? And, what is the evolution of knowledge and sustainable practices of wood waste qualities a case of? The analysis is based on diverse perspectives and forms of methods and empirical material. Studies of policy documents, regulations, standards, etc. have been reviewed to uncover what kind of measures and concepts that have been important for governing and regulating wood waste handling. Interviews concerning wood and wood waste qualities have been conducted with key informants and people visiting recycling and waste management stations in Oslo and Akershus in Norway. By studying quality conceptions through the social birth, production, life, end-of-life and re-birth of wood products, we analyse socio-cultural conditions for sustainability. Furthermore we show how the evolution of knowledge and sustainable practices of wood waste qualities, in the meeting with standards and regulations, is a case of adaptation work in the evolution of Norwegian bioeconomy.
Authors
Habtamu AlemAbstract
No abstract has been registered