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.
2017
Abstract
Mapping and valuating ecosystem services has gained increasing attention over the last years and remains high in the research agenda. In this paper, a mixed methods approach is used to valuate ecosystem services provided by the Divici-Pojejena wetland in Romania. A qualitative part relied on focus group discussions and interviews to identify key stakeholders and the ecosystem services provided by the wetland site. The benefit transfer (BT) method was used for the monetary valuation of the identified ecosystem services that the wetland provides. Bird watching opportunities, water quality, and flood prevention services are among the highest valued services, while the amenity services are the least valued among all wetland services.
Authors
Monica Matei Lucian Laslo DEÁK György Ciobotaru Nicu Mădălina Boboc Marius Raischi Cristina Mușat Theodor Lupei Simona Raischi Andreea Moncea Diana Dumitru Gabriel Badea Lampros Lamprinakis Divina Gracia P. Rodriguez Anne Prestvik Asbjørn Veidal Björn KlimekAbstract
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Authors
Tomas Persson Henrik Eckersten Oleif Elen Anne-Grete Roer Hjelkrem Joel Markgren Mats Söderström Thomas BörjessonAbstract
Deoxynivalenol (DON) in cereals, produced by Fusarium fungi, cause poisoning in humans and animals. Fusarium infections in cereals are favoured by humid conditions. Host species are susceptible mainly during the anthesis stage. Infections are also positively correlated with a regional history of Fusarium infections, frequent cereal production and non-tillage field management practices. Here, previously developed process-based models based on relative air humidity, rain and temperature conditions, Fusarium sporulation, host phenology and mycelium growth in host tissue were adapted and tested on oats. Model outputs were used to calculate risk indices. Statistical multivariate models, where independent variables were constructed from weather data, were also developed. Regressions of the risk indices obtained against DON concentrations in field experiments on oats in Sweden and Norway 2012–14 had coefficient of determination values (R2) between 0.84 and 0.88. Regressions of the same indices against DON concentrations in oat samples averaged for 11 × 11 km grids in farmers’ fields in Sweden 2012–14 resulted in R2 values between 0.27 and 0.41 for randomly selected grids and between 0.31 and 0.62 for grids with average DON concentration above 1000 μg kg–1 grain in the previous year. When data from all three years were evaluated together, a cross-validated statistical partial least squares model resulted in R2 = 0.70 and a standard error of cross-validation (SECV) = 522 μg kg–1 grain for the period 1 April–28 August in the construction of independent variables and R2 = 0.54 and SECV = 647 μg kg–1 grain for 1 April–23 June. Factors that were not accounted for in this study probably explain large parts of the variation in DON among samples and make further model development necessary before these models can be used practically. DON prediction in oats could potentially be improved by combining weather-based risk index outputs with agronomic factors.
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In this study, we investigated the potential of airborne imaging spectroscopy for in-season grassland yield estimation. We utilized an unmanned aerial vehicle and a hyperspectral imager to measure radiation, ranging from 455 to 780 nm. Initially, we assessed the spectral signature of five typical grassland species by principal component analysis, and identified a distinct reflectance difference, especially between the erectophil grasses and the planophil clover leaves. Then, we analyzed the reflectance of a typical Norwegian sward composition at different harvest dates. In order to estimate yields (dry matter, DM), several powered partial least squares (PPLS) regression and linear regression (LR) models were fitted to the reflectance data and prediction performance of these models were compared with that of simple LR models, based on selected vegetation indices and plant height. We achieved the highest prediction accuracies by means of PPLS, with relative errors of prediction from 9.1 to 11.8% (329 to 487 kg DM ha−1) for the individual harvest dates and 14.3% (558 kg DM ha−1) for a generalized model.
Authors
Eivind Uleberg Sigridur Dalmannsdottir Hilde Halland Bjørg Helen Nøstvold Rune Rødbotten Ingrid KvalvikAbstract
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