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.
2024
Abstract
No abstract has been registered
2023
Authors
Richard Helliwell Tommy Ruud Davide Bochicchio Anne Grete Kongsted Matthias Koesling Stig Milan Thamsborg Marina Spinu Marina Stukelj Atle WibeAbstract
No abstract has been registered
Authors
Matthias KoeslingAbstract
Enabling the FARMnor LCA-model (Flow Analysis and Resource Management for Norway) to handle large number of farms to calculate farm-specific LCA's. For each farm, data are automatically imported, the LCA calculated and results stored before proceeding with the next farm. This update allows in addition to conduct sensitivity and uncertainty analyses.
Authors
Matthias KoeslingAbstract
Extending the FARMnor model (Flow Analysis and Resource Management for Norway) for sheep based on the guidelines from IPCC (2019) and Carbon Limits (2021). In the calultaions it is differentiated between lambs, sheep, ewes and rams and period in barn as well as grazing farm- and rangeland. In addition the possibility to include carbon sequestration due to grazing rangeland was added.
Authors
Matthias KoeslingAbstract
Updating the LCA-model FARMnor (Flow Analysis and Resource Management for Norway) to version 09 allowing to conduct environmental assessment for pigs. FARMnor runs on the LCA-software Umberto and allows to use information from the database ecoinvent.
Authors
Cristina Micheloni Frank Willem Oudshoorn María Isabel Blanco Penedo Sari Autio Andrea Beste Jacopo Goracci Matthias Koesling Ursula Kretzschmar Eligio Malusá Bernhard Speiser Jan van der Blom Felix WäckersAbstract
The Expert Group for Technical Advice on Organic Production (EGTOP) was requested to advise on the use of several substances with plant protection effects in organic production. The Group discussed whether the use of these substances and methods is in line with the objectives and principles of organic production, and whether they should be included in Reg. (EU) 2021/1165. With respect to Annex I to Reg. (EU) 2021/1165, the Group recommends the following: • Entry ‘Pheromones and other semiochemicals’: The present restriction ‘only in traps and dispensers’ should be complemented with the following text: ‘microcapsules shall be biodegradable and shall not be applied to edible parts of the crop’. • The introductions to Annex I and to the sub-chapters of Annex I refer to the authorization of pesticides pursuant to Regulation (EC) No 1107/2009. The Group recommends minor editorial amendments to clarify that these references apply only when pesticides are used within the EU.
Authors
Cristina Micheloni Frank Willem Oudshoorn María Isabel Blanco Penedo Sari Autio Andrea Beste Jacopo Goracci Matthias Koesling Ursula Kretzschmar Eligio Malusá Bernhard Speiser Jan van der Blom Felix WäckersAbstract
The Expert Group for Technical Advice on Organic Production (EGTOP) was requested to advise on the use of several substances with plant protection or fertilising effects in organic production, and to advise on aspects of organic production of yams. The Group discussed whether the use of these substances and methods is in line with the objectives and principles of organic production, and whether they should be included in Reg. (EU) 2021/1165. With
Abstract
Pandora neoaphidis is a common entomopathogenic fungus on Sitobion avenae, which is an important aphid pest on cereals in Europe. Pandora neoaphidis is known to cause epizootics (i.e. an unusually high prevalence of infected hosts) and the rapid collapse of aphid populations. We developed a weather-driven mechanistic model of the winter wheat-S. avenae-P. neoaphidis system to simulate the dynamics from spring to harvest. Aphid immigration was fixed at a rate that would lead to a pest outbreak, if not controlled by the fungus. We estimated the biocontrol efficacy by running pair-wise simulations, one with and one without the fungus. Uncertainty in model parameters and variation in weather was included, resulting in a range of simulation outcomes, and a global sensitivity analysis was performed. We identified two key understudied parameters that require more extensive experimental data collection to better assess the fungus biocontrol, namely the fungus transmission efficiency and the decay of cadaver, which defines the time window for possible disease transmission. The parameters with the largest influence on the improvement in yield were the weather, the lethal time of exposed aphids, the fungus transmission efficiency, and the humidity threshold for fungus development, while the fungus inoculum in the chosen range (between 10 and 70% of immigrant aphids carrying the fungus) was less influential. The model suggests that epizootics occurring early, around Zadoks growth stage (GS) 61, would lead to successful biocontrol, while later epizootics (GS 73) were a necessary but insufficient condition for success. These model predictions were based on the prevalence of cadavers only, not of exposed (i.e. infected but yet non-symptomatic) aphids, which in practice would be costly to monitor. The model suggests that practical Integrated Pest Management could thus benefit from including the cadavers prevalence in a monitoring program. We argue for further research to experimentally estimate these cadaver thresholds.
Abstract
Pandora neoaphidis is a common entomopathogenic fungus on Sitobion avenae, which is an important aphid pest on cereals in Europe. Pandora neoaphidis is known to cause epizootics (i.e. an unusually high prevalence of infected hosts) and the rapid collapse of aphid populations. We developed a weather-driven mechanistic model of the winter wheat-S. avenae-P. neoaphidis system to simulate the dynamics from spring to harvest. Aphid immigration was fixed at a rate that would lead to a pest outbreak, if not controlled by the fungus. We estimated the biocontrol efficacy by running pair-wise simulations, one with and one without the fungus. Uncertainty in model parameters and variation in weather was included, resulting in a range of simulation outcomes, and a global sensitivity analysis was performed. We identified two key understudied parameters that require more extensive experimental data collection to better assess the fungus biocontrol, namely the fungus transmission efficiency and the decay of cadaver, which defines the time window for possible disease transmission. The parameters with the largest influence on the improvement in yield were the weather, the lethal time of exposed aphids, the fungus transmission efficiency, and the humidity threshold for fungus development, while the fungus inoculum in the chosen range (between 10 and 70% of immigrant aphids carrying the fungus) was less influential. The model suggests that epizootics occurring early, around Zadoks growth stage (GS) 61, would lead to successful biocontrol, while later epizootics (GS 73) were a necessary but insufficient condition for success. These model predictions were based on the prevalence of cadavers only, not of exposed (i.e. infected but yet non-symptomatic) aphids, which in practice would be costly to monitor. The model suggests that practical Integrated Pest Management could thus benefit from including the cadavers prevalence in a monitoring program. We argue for further research to experimentally estimate these cadaver thresholds.
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.