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
Abstract
Soil surfactants are applied to alleviate soil water repellency (SWR). The ability of surfactants to improve turfgrass quality under dry conditions is well documented, but less information exists about their role in situations with water surplus. Our objective was to study responses to monthly application of the surfactant Qualibra (20 L ha–1) under dry and wet conditions on a sand-based green covered with creeping bentgrass. Dry conditions implied irrigation to field capacity (FC) once a week (FC1) in 2014 (a year with warm and dry weather from May through July) and deficit irrigation to 60% of FC once a week (DEF1) in 2015 (May through July cool and wet). Wet conditions implied excessive irrigation twice a week (EX2) with 50% more water than needed to replenish FC. The surfactant decreased the average soil water content of the surface 7.5 cm of the root zone from 0.193 to 0.168 m3 m–3 in 2014 and from 0.191 to 0.171 m3 m–3 in 2015. In 2015, the reduction in SWC was stronger with EX2 than with DEF1 irrigation, and this was accompanied by less organic matter accumulation on plots receiving EX2 irrigation. The surfactant reduced the water drop penetration time (WDPT) regardless of irrigation treatments, but improved turfgrass quality only with DEF1 irrigation in 2015. A harder playing surface due to Qualibra was not observed in 2014 and only at one out of six observations with EX2 irrigation in 2015. We conclude that surfactants imply various benefits depending on water supply.
Authors
Hans Martin Hanslin Ellen Johanne Svalheim Harald Bratli J. Wissmann Geir Kjølberg Knudsen J. Kollmann Trygve S. AamlidAbstract
No abstract has been registered
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Trygve S. AamlidAbstract
No abstract has been registered
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Trygve S. AamlidAbstract
No abstract has been registered
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Trygve S. AamlidAbstract
No abstract has been registered
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Ishita AhujaAbstract
No abstract has been registered
Authors
Sophie Mentzel Merete Grung Knut-Erik Tollefsen Marianne Stenrød Petersen Karina S. Jannicke MoeAbstract
The aquatic environment is constantly exposed to various chemicals caused by anthropogenic activities such as agricultural practices using plant protection products. Traditional Environmental Risk Assessment is based on calculated risk estimations usually representing a ratio of exposure to effects, in combination with assessment factors to account for uncertainty. In this study, we explore a more informative approach through probabilistic risk assessment, where probability distributions for exposure and effects are expressed and enable accounting for variability and uncertainty better. We focus on the risk assessment of various pesticides in a representative study area in the south east of Norway. Exposure data in this research was provided by the Norwegian Agricultural Environmental Monitoring Programme (JOVA)/ or predicted exposure concentration from a pesticide exposure model and effect data was derived from the NIVA Risk Assessment database (RAdb, www.niva.no/radb). A Bayesian network model is used as an alternative probabilistic approach to assess the risks of chemical. Bayesian Networks can serve as meta-models that link selected input and output variables from several separate project outputs and offer a transparent way of evaluating the required characterization of uncertainty for ERA. They can predict the probability of several risk levels, while facilitating the communication of estimates and uncertainties.
Authors
Ishita AhujaAbstract
No abstract has been registered
Authors
Sophie Mentzel Merete Grung Knut-Erik Tollefsen Marianne Stenrød Roger Holten S. Jannicke MoeAbstract
In Northern Europe, future changes in land-use and weather patterns are expected to result in increased precipitation and temperature this may cause an increase in plant disease and insect pests. In addition, predicted population increase will change the production demands and in turn alter agricultural practices such as crop types and with that the use pattern of pesticides. Considering these variabilities and magnitudes of pesticide exposure to the aquatic environment still needs to be accounted for better in current probabilistic risk assessment. In order to improve ecological risk assessment, this study explores an alternative approach to probabilistic risk assessment using a Bayesian Network, as these can serve as meta-models that link selected input and output variables from other models and information sources. The developed model integrates variability in both exposure and effects in the calculation of risk estimate. We focus on environmental risk of pesticides in two Norwegian case study region representatives of northern Europe. Using pesticide fate and transport models (e.g. WISPE), environmental factors such as soil and site parameters together with chemical properties and climate scenarios (current and predicted) are linked to the exposure of a pesticide in the selected study area. In the long term, the use of tools based on Bayesian Network models will allow for a more refined assessment and targeted management of ecological risks by industry and policy makers.
Authors
Sophie Mentzel Merete Grung Knut-Erik Tollefsen Marianne Stenrød Roger Holten S. Jannicke MoeAbstract
Future weather patterns are expected to result in increased precipitation and temperature, in Northern Europe. These changes can potentially cause an increase in plant disease and insect pests which will alter agricultural practice amongst other things the used crop types and application patterns of pesticides. We use a Bayesian network to explore a probabilistic risk assessment approach to better account for variabilities and magnitudes of pesticide exposure to the aquatic ecosystem. As Bayesian networks link selected input and output variables from various models and other information sources, they can serve as meta-models. In this study, we are using a pesticide fate and transport models (e.g. WISPE) with specific environmental factors such as soil and site parameters together with chemical properties and climate scenarios that are linked to a representative Norwegian study area. The derived exposure of pesticide of the study area is integrated in the Bayesian network model to estimate the risk to the aquatic ecosystem also integrating an effect distribution derived from toxicity test. This Bayesian network model will allow to incorporate climate predictions into ecological risk assessment.