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
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.
Abstract
No abstract has been registered
Authors
Yanli Man Marianne Stenrød Chi Wu Marit Almvik Roger Holten Jihong Liu Clarke Shankui Yuan Xiaohu Wu Jun Xu Fengshou Dong Yongquan Zheng Xingang LiuAbstract
No abstract has been registered
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.
Authors
Fabian Sauter Harald Albrecht Johannes Kollmann Marion LangAbstract
No abstract has been registered
Authors
Anastasia Georgantzopoulou Sebastian Kühr Andy Booth Julia Farkas Claire Coutris Ralf Kaegi Mark Rehkämper Ailbhe Macken Kuria Ndungu Patricia Almeida Carvalho Saer Samanipour Kevin V. Thomas Karina Petersen Tania Gomes Maria Thérése Hultman Adam David LillicrapAbstract
The production, diversity and use of engineered nanomaterials (ENMs) increases globally as the market and number of applications for ENM expands. Silver (Ag), zinc (Zn) and titanium dioxide (TiO2) ENMs are among the most widely used in industrial processes and consumer products leading to increased releases to wastewater treatment plants (WWTP) from domestic and industrial sources. Material flow analyses suggest that landfills or agricultural soils and sediments are the main receiving compartments for ENM, depending on the application and ENM type. However, knowledge on the fate and transformation of ENMs in WWTP biosolids following their use as fertilizer on agricultural land, their impacts on soil and sediment ecosystems released through run-off after land-application are only poorly understood. ENTRANS aims to improve the understanding of the behavior and physicochemical transformation processes impacting ENM in different environmental media (wastewater, biosolids, soil, sediment) and how this transformation influences ENM bioavailability, bioaccumulation and toxicity in organisms from receiving environments considered to be the final sinks for ENMs, soil and sediments. The ENTRANS project will follow and characterize the physicochemical transformation of ENMs in WWTP and environmental compartments. Using isotopically labelled Ag, Zn and TiO2 ENMs, the transformation and further impact of these particles, including bioavailability, bioaccumulation, biodistribution and toxicity, will be tracked and studied using relevant in vitro and in vivo models to provide a better understanding of the link between transformation, uptake and observed toxicity. Existing guidelines will be improved to incorporate environmentally relevant exposures and toxicity endpoints of regulatory relevance and novel bioassays will be developed focusing on immune and stress responses. The transformation processes, exposure and uptake, biodistribution and toxicity data will be carefully generated so that the obtained results can be integrated into computational fate and exposure models and a risk assessment can be performed.
Authors
Theresa WeiglAbstract
No abstract has been registered
Authors
Mårten WennerbergAbstract
No abstract has been registered
Abstract
No abstract has been registered