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
Young children have unique nutritional requirements, and breastfeeding is the best option to support healthy growth and development. Concerns have been raised around the increasing use of milk-based infant formulas in replacement of breastfeeding, in regards to health, social, economic and environmental factors. However, literature on the environmental impact of infant formula feeding and breastfeeding is scarce. In this study we estimated the environmental impact of four months exclusive feeding with infant formula compared to four months exclusive breastfeeding in a Norwegian setting. We used life-cycle assessment (LCA) methodology, including the impact categories global warming potential, terrestrial acidification, marine and freshwater eutrophication, and land use. We found that the environmental impact of four months exclusive feeding with infant formula was 35–72% higher than that of four months exclusive breastfeeding, depending on the impact category. For infant formula, cow milk was the main contributor to total score for all impact categories. The environmental impact of breastfeeding was dependant on the composition of the lactating mother’s diet. In conclusion, we found that breastfeeding has a lower environmental impact than feeding with infant formula. A limitation of the study is the use of secondary LCA data for raw ingredients and processes.
Authors
Anne-Grete Roer Hjelkrem Heidi Udnes Aamot Morten Lillemo Espen Sannes Sørensen Guro Brodal Aina Lundon Russenes Simon G. Edwards Ingerd Skow HofgaardAbstract
Fusarium graminearum is regarded as the main deoxynivalenol (DON) producer in Norwegian oats, and high levels of DON are occasionally recorded in oat grains. Weather conditions in the period around flowering are reported to have a high impact on the development of Fusarium head blight (FHB) and DON in cereal grains. Thus, it would be advantageous if the risk of DON contamination of oat grains could be predicted based on weather data. We conducted a functional data analysis of weather-based time series data linked to DON content in order to identify weather patterns associated with increased DON levels. Since flowering date was not recorded in our dataset, a mathematical model was developed to predict phenological growth stages in Norwegian spring oats. Through functional data analysis, weather patterns associated with DON content in the harvested grain were revealed mainly from about three weeks pre-flowering onwards. Oat fields with elevated DON levels generally had warmer weather around sowing, and lower temperatures and higher relative humidity or rain prior to flowering onwards, compared to fields with low DON levels. Our results are in line with results from similar studies presented for FHB epidemics in wheat. Functional data analysis was found to be a useful tool to reveal weather patterns of importance for DON development in oats.
Abstract
The availability of fresh vegetables grown in greenhouses under controlled conditions throughout the year has given rise to concerns about their impact on the environment. In high latitude countries such as Norway, greenhouse vegetable production requires large amounts of energy for heat and light, especially during the winter. The use of renewable energy such as hydroelectricity and its effect on the environment has not been well documented. Neither has the effect of different production strategies on the environment been studied to a large extent. We conducted a life cycle assessment (LCA) of greenhouse tomato production for mid-March to mid-October (seasonal production), 20th January to 20th November (extended seasonal) production, and year-round production including the processes from raw material extraction to farm gate. Three production seasons and six greenhouse designs were included, at one location in southwestern and one in northern Norway. The SimaPro software was used to calculate the environmental impact. Across the three production seasons, the lowest global warming (GW) potential (600 g CO2-eq per 1 kg tomatoes) was observed during year-round production in southwestern Norway for the design NDSFMLLED + LED, while the highest GW potential (3100 g CO2-eq per 1 kg tomatoes) was observed during seasonal production in northern Norway for the design NS. The choice of artificial lighting (HPS (High Pressure Sodium) or LED (Light Emitting Diodes)), heating system and the production season was found to have had a considerable effect on the environmental impact. Moreover, there was a significant reduction in most of the impact categories including GW potential, terrestrial acidification, and fossil resource scarcity from seasonal to year-round production. Overall, year-round production in southwestern Norway had the lowest environmental impact of the evaluated production types. Heating of the greenhouse using natural gas and electricity was the biggest contributor to most of the impact categories. The use of an electric heat pump and LED lights during extended seasonal and year-round production both decreased the environmental impact. However, while replacing natural gas with electricity resulted in decreased GW potential, it increased the ecotoxicity potential.
Authors
Samuel F. Kamga Frank T. Ndjomatchoua Ritter Atoundem Guimapi Ingeborg Klingen Clément Tchawoua Anne-Grete Roer Hjelkrem Karl Thunes Francois M. KakmeniAbstract
Despite substantial efforts to control locusts they remain periodically a major burden in Africa, causing severe yield loss and hence loss of food and income. Distribution maps indicating the value of the basic reproduction number R0 was used to identify areas where an insect pest can be controlled by a natural enemy. A dynamic process-based mathematical model integrating essential features of a natural enemy and its interaction with the pest is used to generate R0 risk maps for insect pest outbreaks, using desert locust and the entomopathogenic fungus Metarhizium acridum (Synn. Metarhizium anisoliae var. acridum) as a case study. This approach provides a tool for evaluating the impact of climatic variables such as temperature and relative humidity and mapping spatial variability on the efficacy of M. acridum as a biocontrol agent against desert locust invasion in Africa. Applications of M. acridum against desert locust in a few selected African countries including Morocco, Kenya, Mali, and Mauritania through monthly spatial projection of R0 maps for the prevailing climatic condition are illustrated. By combining mathematical modeling with a geographic information system in a spatiotemporal projection as we do in this study, the field implementation of microbial control against locust in an integrated pest management system may be improved. Finally, the practical utility of this model provides insights that may improve the timing of pesticide application in a selected area where efficacy is highly expected.
2021
Abstract
As the demand for proteins increases with growing populations, farmed seaweed is a potential option for use directly as an ingredient for food, feed, or other applications, as it does not require agricultural areas. In this study, a life cycle assessment was utilised to calculate the environmental performance and evaluate possible improvements of the entire value chain from production of sugar kelp seedings to extracted protein. The impacts of both technical- and biological factors on the environmental outcomes were examined, and sensitivity and uncertainty analyses were conducted to analyse the impact of the uncertainty of the input variables on the variance of the environmental impact results of seaweed protein production. The current production of seaweed protein was found to have a global warming potential (GWP) that is four times higher than that of soy protein from Brazil. Further, of the 23 scenarios modelled, two resulted in lower GWPs and energy consumption per kg of seaweed protein relative to soy protein. These results present possibilities for improving the environmental impact of seaweed protein production. The most important variables for producing seaweed protein with low environmental impact are the source of drying energy for seaweed, followed by a high protein content in the dry matter, and a high dry matter in the harvested seaweed. In the two best scenarios modelled in this study, the dry matter content was 20% and the protein content 19.2% and 24.3% in dry matter. This resulted in a lower environmental impact for seaweed protein production than that of soy protein from Brazil. These scenarios should be the basis for a more environmental protein production in the future.
Authors
Matthias KoeslingAbstract
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Abstract
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Abstract
This paper describes a tool that enables farmers to time harvests and target nitrogen (N) inputs in their forage production, according to the prevailing yield potential. Based on an existing grass growth model for forage yield estimation, a more detailed process-based model was developed, including a new nitrogen module. The model was tested using data from an experiment conducted in a grassland-rich region in central Norway and showed promising accuracy with estimated root mean square error (RMSE) of 50 and 130 g m-2 for dry matter yield in the trial. Three parameters were detected as highly sensitive to model output: initial value of organic N in the soil, fraction of humus in the initial organic N in the soil, and fraction of decomposed N mineralized. By varying these parameters within a range from 0.5 to 1.5 of their respective initial value, most of the within-field variation was captured. In a future step, remotely sensed information on model output will be included, and in-season model correction will be performed through re-calibration of the highly sensitive parameters.
Abstract
Late blight caused by Phytophthora infestans is a serious, worldwide disease on potato (Solanum tuberosum). Phytophthora infestans normally reproduces in a clonal manner, but in some areas, as the Nordic Countries, sexual reproduction has become the major determinant of the population structure. To improve the late blight forecasting in Norway, the process-based Nærstad model was developed. The model includes the structure of the underlying processes in the disease development, including spore production, spore release, spore survival and infection of P. infestans. It needs hourly weather records of air temperature, precipitation, relative humidity, leaf wetness and global radiation. The model contained 19 uncertain parameters, and from a sensitivity analysis, 12 were detected as weakly sensitive to model outputs and fixed to a nominal value within their prior boundaries. The remaining seven parameters were detected as more sensitive to model outputs and were parameterized using maximum a'posteriori (MAP) estimates, calculated through Bayesian calibration. The model was developed based on literature combined with field data of daily observed number of lesions on trap plants of the Bintje cultivar (late blight susceptible) at Ås during the seasons 2006-2008 and 2010-2011. It was further tested on daily observed number of lesions on trap plants of the cultivars Bintje, Saturna (medium susceptible) and Peik (medium resistant) at Ås during the seasons 2012-2015. For all three cultivars, the Nærstad model improved with a higher model accuracy compared to the existing HOSPO-model and the Førsund rules that both have shown relatively good correlation with blight development in field evaluations in Norway. The best accuracy was found for Bintje (0.83) closely followed by Saturna (0.79), whereas a much lower accuracy was detected for Peik (0.66).
Abstract
Leaf blotch diseases (LBD), such as Septoria nodorum bloch (Parastagnospora nodorum), Septoria tritici blotch (Zymoseptoria tritici) and Tan spot (Pyrenophora tritici-repentis) can cause severe yield losses (up to 50%) in Norwegian spring wheat (Triticum aestivum) and are mainly controlled by fungicide applications. A forecasting model to predict disease risk can be an important tool to optimize disease control. The association between specific weather variables and the development of LBD differs between wheat growth stages. In this study, a mathematical model to estimate phenological development of spring wheat was derived based on sowing date, air temperature and photoperiod. Weather factors associated with LBD severity were then identified for selected phenological growth stages by a correlation study of LBD severity data (17 years). Although information regarding host resistance and previous crop were added to the identified weather factors, two purely weather-based risk prediction models (CART, classification and regression tree algorithm) and one black box model (KNN, based on K nearest neighbor algorithm) were most accurate to predict moderate to high LBD severity (>5% infection). The predictive accuracy of these models (76–83%) was compared to that of two existing models used in Norway and Denmark (60 and 61% accuracy, respectively). The newly developed models performed better than the existing models, but still had the tendency to overestimate disease risk. Specificity of the new models varied between 49 and 74% compared to 40 and 37% for the existing models. These new models are promising decision tools to improve integrated LBD management of spring wheat in Norway.