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.
2022
Abstract
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Authors
Trond MæhlumAbstract
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Authors
Sophie Mentzel Merete Grung Roger Holten Knut-Erik Tollefsen Marianne Stenrød S. Jannicke MoeAbstract
The use of Bayesian networks (BN) for environmental risk assessment has increased in recent years as they offer a more transparent way to characterize risk and evaluate uncertainty than the traditional risk assessment paradigms. In this study, a novel probabilistic approach applying a BN for risk calculation was further developed and explored by linking the calculation a risk quotient to alternative future scenarios. This extended version of the BN model uses predictions from a process-based pesticide exposure model (World Integrated System for Pesticide Exposure - WISPE) in the exposure characterization and toxicity test data in the effect characterization. The probability distributions for exposure and effect are combined into a risk characterization (i.e. the probability distribution of a risk quotient), a common measure of the exceedance of an environmentally safe exposure threshold. The BN model was used to account for variabilities of the predicted pesticide exposure in agricultural streams, and inter-species variability in sensitivity to the pesticide among freshwater species. In Northern Europe, future climate scenarios typically predict increased temperature and precipitation, which can be expected to cause an increase in weed infestations, plant disease and insect pests. Such climate-related changes in pest pressure in turn can give rise to altered agricultural practices, such as increased pesticide application rates, as an adaptation to climate change. The WISPE model was used to link a set of scenarios consisting of two climate models, three pesticide application scenarios and three periods (year ranges), for a case study in South-East Norway. The model was set up for the case study by specifying environmental factors such as soil properties and field slope together with chemical properties of pesticides to predict the pesticide exposure in streams adjacent to the agricultural fields. The model was parameterized and evaluated for five selected pesticides: the three herbicides clopyralid, fluroxypyr-meptyl, and 2-(4-chloro-2-methylphenoxy) acetic acid (MCPA), and the two fungicides prothiocanzole and trifloxystrobin. This approach enabled the calculation and visualization of probability distribution of the risk quotients for the future time horizons 2050 and 2085. The risk posed by the pesticides were in general low for this case study, with highest probability of the risk quotient exceeding 1 for the two herbicides fluroxypyr-meptyl and MCPA. The future climate projections used here resulted in only minor changes in predicted exposure concentrations and thereby future risk. However, a stronger increase in risk was predicted for the scenarios with increased pesticide application, which can represent an adaptation to a future climate with higher pest pressures. In the current study, the specific BN model predictions were constrained by an existing set of climate projections which represented only one IPCC scenario (A1B) and two climate models. Further advancement of the BN modelling demonstrated herein, including more recent climate scenarios and a larger set of climate models, is anticipated to result in more relevant risk characterization also for future climate conditions. This probabilistic approach will have the potential to aid targeted management of ecological risks in support of future research, industry and regulatory needs.
Abstract
Enhancing carbon storage in managed soils through increased use of cover and catch crops in cereal cropping is at the heart of a carbon-negative agriculture. However, increased C storage by additional biomass production has a nitrogen cost, both in form of increased N fertilizer use and by potentially increasing nitrous oxide (N2O) emissions when cover crops decay. Frost-sensitive, N-rich aboveground biomass may be a particular problem during wintertime, as it may fuel off season N2O emissions during freezing-thawing cycles, which have been shown to dominate the annual N2O budget of many temperate and boreal sites. Here we report growing season and winter N2O emissions in a plot experiment in SE Norway, testing a barley production system with seven different catch and cover crops (perennial and Italian ryegrass, oilseed radish, summer and winter vetch, phacelia and an herb mixture) against a control without cover crops. Cover crops where either undersown in spring or established after harvesting barley. While ryegrass undersown to barley marginally reduced N2O emissions during the growing season, freeze-thaw cycles in winter resulted in significantly larger N2O emissions in treatments with N-rich cover crops (oilseed reddish, vetch) and Italian ryegrass. N2O budgets will be presented relative to aboveground yield and quality of cover crops and compared to potential souil organic carbon gains.
Authors
Atle Wehn HegnesAbstract
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Maja Turnšek Siv Skar Marit Piirman Ragnheiður I. Thorarinsdottir Martina Bavec Ranka JungeAbstract
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Milica Fotirić Akšić Milica Nešović Ivanka Ćirić Živoslav Tešić Lato Pezo Tomislav Tosti Uroš Gašić Biljana Dojčinović Biljana Lončar Mekjell MelandAbstract
No abstract has been registered
Abstract
Tree diameter increment (ΔDBH) and total tree height increment (ΔHT) are key components of a forest growth and yield model. A problem in complex, multi-species forests is that individual tree attributes such as ΔDBH and ΔHT need to be characterized for a large number of distinct woody species of highly varying levels of occurrence. Based on more than 2.5 million ΔDBH observations and over 1 million ΔHT records from up to 60 tree species and genera, respectively, this study aimed to improve existing ΔDBH and ΔHT equations of the Acadian Variant of the Forest Vegetation Simulator (FVS-ACD) using a revised method that utilize tree species as a random effect. Our study clearly highlighted the efficiency and flexibility of this method for predicting ΔDBH and ΔHT. However, results also highlighted shortcomings of this approach, e.g., reversal of plausible parameter signs as a result of combining fixed and random effects parameter estimates after extending the random effect structure by incorporating North American ecoregions. Despite these potential shortcomings, the newly developed ΔDBH and ΔHT equations outperformed the ones currently used in FVS-ACD by reducing prediction bias quantified as mean absolute bias and root mean square error by at least 11% for an independent dataset and up to 41% for the model development dataset. Using the revised ΔDBH and ΔHT estimates, greater prediction accuracy in individual tree aboveground live carbon mass estimation was also found in general but performance varied with dataset and accuracy metric examined. Overall, this analysis highlights the importance and challenges of developing robust ΔDBH and ΔHT equations across broad regions dominated by mixed-species, managed forests.
Abstract
No abstract has been registered