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.
2023
Abstract
A process-based model was developed to predict dry matter yields and amounts of harvested nitrogen in conventionally cropped grassland fields, accounting for within-field variation by a node network design and utilizing remotely sensed information from a drone-borne system for increased accuracy. The model, named NORNE, was kept as simple as possible regarding required input variables, but with sufficient complexity to handle central processes and minimize prediction errors. The inputs comprised weather data, soil information, management data related to fertilization, and a visual estimate of clover proportion in the aboveground biomass. A sensitivity analysis was included to apportioning variation in dry matter yield outputs to variation in model parameter settings. Using default parameter values from the literature, the model was evaluated on data from a two-year study (2016–2017, 264 research plots in total each year) conducted at two locations in Norway (i.e. in South-East and in Central Norway) with contrasting climatic conditions and with internal variation in soil characteristics. The results showed that the model could estimate dry matter yields with a relatively high accuracy without any corrections based on remote sensing, compared with published results from comparable model studies. To further improve the results, the model was calibrated shortly before harvest, using predictions of above ground dry matter biomass obtained from a drone-borne remote sensing system. The only parameters which were hereby adjusted in the NORNE model were the starting values of nitrogen content in soil (first cut) and the plant available water capacity (second cut). The calibration based on the remotely sensed information improved the predictive performance of the model significantly. At first cut, the root mean square error (RMSE) of dry matter yield prediction was reduced by 20% to a mean value of 58 g m−2, corresponding to a relative value (rRMSE) of 0.12. For the second cut, the RMSE decreased by 13% to 66 g m−2 (rRMSE: 0.18). The model was also evaluated in terms of the predictions of amounts of nitrogen in the harvested crop. Here, the calibration reduced the RMSE of the first cut by 38%, obtaining a mean RMSE value of 2.1 g N m−2 (rRMSE: 0.28). For the second cut, the RMSE reduction for simulated harvested N was 16%, corresponding to a mean RMSE value of 2.3 g N m−2 (rRMSE: 0.33). The large improvements in model accuracy for simulated dry matter and nitrogen yields obtained through calibration by utilizing remotely sensed information, indicate the importance of considering spatial variability when applying models under Nordic conditions, both for yield predictions and for decision support for nitrogen application.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Mekjell MelandAbstract
No abstract has been registered
Authors
Mekjell MelandAbstract
No abstract has been registered
Abstract
An expert workshop on C and N interactions was held online 6 – 7/11 2023, within project Kvävebegränsningar för kolbindning i skandinaviska skogar/Nitrogen constraints to carbon sequestration in Scandinavian forests, financed by the Nordic Working Group for Climate and Air (NKL). The workshop was organized in two half-day sessions. Sweden, Finland, Norway and Denmark were represented by experts involved in national reporting to the UNECE Air Convention (CLRTAP) and to the UN Climate convention (UNFCCC). This workshop report was prepared by the workshop organizers at IVL Swedish Environmental Research Institute, with contributions from all workshop participants.
Abstract
No abstract has been registered
Authors
Inger Martinussen Mathias Amundsen Aksel Granhus Antje Gonera Marius Hauglin Anne Linn Hykkerud Laura Jaakola Mikko Kurttila Jari Miina Rainer Peltola Gesine Schmidt Josefine Skaret Baoru Yang Kjersti AabyAbstract
Almost 95% of the area in Norway is wilderness and 38% of the land area is covered by woods. These areas are abundant in valuable renewable resources, including wild berries. In our neighbouring countries, Sweden and Finland, wild berries are already a big industry. At the same time, on the market the Norwegian wild berries are almost non-existent and berries are left unexploited. Lingonberry (Vaccinium vitis-idaea) is one of the most abundant and economically important wild berries in the Nordic countries. Nevertheless, lingonberry has a large untapped potential due to its unique health effects and potential for increased value creation. It is estimated that 111,500 t of lingonberry are produced in the Norwegian woods. Norway is a long and diverse country with a range of climatic conditions. Adaptations to different conditions can give differences in both yield and quality of wild berries. Yields vary enormously from year to year and among different locations. A steady supply, predictable volumes and high quality are vital for successful commercialization of wild berries. To increase the utilization of berries, there is a need for increased knowledge regarding availability and quality variation of the berries. In addition, the Norwegian market suffers from high labour costs and cannot compete in product price. Innovative solutions and new knowledge on quality aspects can open possibilities for value creation. Toward achieving this goal, we have created a project called “WildBerries”, the main objective of which is to produce research-based knowledge that will create the basis for increased commercial utilization of Norwegian wild berries.
Authors
Yi Zhang Yun Zhao Yijing Feng Yating Yu Yeqing Li Jian Li Zhonghao Ren Shuo Chen Lu Feng Junting Pan Hongjun Zhou Yongming HanAbstract
Industrial-scale garage dry fermentation systems are extremely nonlinear, and traditional machine learning algorithms have low prediction accuracy. Therefore, this study presents a novel intelligent system that employs two automated machine learning (AutoML) algorithms (AutoGluon and H2O) for biogas performance prediction and Shapley additive explanation (SHAP) for interpretable analysis, along with multiobjective particle swarm optimization (MOPSO) for early warning guidance of industrial-scale garage dry fermentation. The stacked ensemble models generated by AutoGluon have the highest prediction accuracy for digester and percolate tank biogas performances. Based on the interpretable analysis, the optimal parameter combinations for the digester and percolate tank were determined in order to maximize biogas production and CH4 content. The optimal conditions for the digester involve maintaining a temperature range of 35–38 °C, implementing a daily spray time of approximately 10 min and a pressure of 1000 Pa, and utilizing a feedstock with high total solids content. Additionally, the percolate tank should be maintained at a temperature range of 35–38 °C, with a liquid level of 1500 mm, a pH range of 8.0–8.1, and a total inorganic carbon concentration greater than 13.8 g/L. The software developed based on the intelligent system was successfully validated in production for prediction and early warning, and MOPSO-recommended guidance was provided. In conclusion, the novel intelligent system described in this study could accurately predict biogas performance in industrial-scale garage dry fermentation and guide operating condition optimization, paving the way for the next generation of intelligent industrial systems.