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Publikasjoner

NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.

2011

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Sammendrag

Specific PCR primers were developed for identifying two post harvest pathogens, Mycocentrospora acerina and Fibularhizoctonia carotae, which cause liquorice rot and crater rot respectively, during prolonged low temperature storage of carrots. The methods allow routine detection of less than 0.3 pg of M. acerina DNA and less than 0.03 pg F. carotae DNA, even in the presence of large excess of plant or soil DNA. Standard PCR and quantitative PCR gave similar results and either method could be used in a practical situation.  Experiments were carried out testing these methods on different types of carrot tissue- and soil- samples. Soil was sampled before sowing, and soil adhering to the roots or root tissue was sampled at different times during the growing season or at harvest. Soil adhering to the carrots at harvest had the best predictive ability for liquorice rot development during storage (R2predicted 74.9 % using standard PCR), but samples taken during the growing season also gave reasonably good predictive ability values.  PCR data from soil samples taken in the spring were not as good as a predictor for thisdisease. A dense sampling strategy using 20 m between sampling points generally gave better orrelation between PCR data and disease data than using 40 m between the sampling points.  Use of the developed methods in an IPM strategy for liquorice rot is discussed. For crater rot the correlation between PCR data and disease data was generally poor for all types of samples. These results are discussed in relation to the biology of F.carotae.  

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Sammendrag

The aim of this study was to use energy-dispersive X-ray spectroscopy (EDX) to localize chitosan in the cell wall of chitosan-impregnated Scots pine. It was of interest to investigate the concentration of chitosan in wood to gain further knowledge and understanding of the distribution of chitosan in the wooden matrix. After deacetylation, chitosan was re-acetylated with chloroacetic anhydride to achieve a covalent bonding of chloride to the chitosan polymer. Chloride-labelled chitosan was measured by EDX using a scanning electron microscope and described as chloride intensity. Analysis of free chloride anions was performed by dialysis and inductively coupled plasma atomic emission spectroscopy. There was a significant correlation between the molecular weight of chitosan and the intensity of covalentbonded chloride to the chitosan polymer. High molecular weight chitosan showed a better interaction with the cell wall structure than low molecular chitosan.

Sammendrag

Forecasting models for prediction of diseases and pests in crop plants are helpful tools in decision support systems for crop management.  Correct use of pesticides may result in optimal effect, increased yield and better quality of the crop, while minimizing the environmental strain and costs. In Norway, a range of decision support systems for diseases, pests and weeds are available through the internet service VIPS (www.vips-landbruk.no). The reliability of disease and pest forecasts depends on robust forecasting models and relevant weather data. Although weather data are collected from a network of 80 weather stations located in agricultural production areas in Norway, many farms are remotely located from a weather station. The accuracy of forecasts relies on distance and geographical variation from the farm site to the nearest weather station. Forecasts for pest or diseases can be tailored to fit the local conditions at a farm site by use of weather forecasts and radar measured rainfall. The use of this system will be of particular interest to farms located far from the nearest weather station. Also, locally adapted forecasts for pest or diseases promote a sense of ownership and personal interest in the forecasting systems provided. The Norwegian Meteorological Institute provide weather forecasts on a 4x4 km spatial resolution in rural areas on a 1 hour timescale, while radar measured rainfall has a 1x1 km spatial resolution on a 15 min time scale. These data are currently connected to the existing weather stations to predict warnings ahead of time. The new approach is to adapt these data to individual farm sites. Previous tests have shown that weather prognosis for rainfall is less accurate than weather prognosis for temperature, wind, air humidity and radiation. Estimated rainfall will therefore be based on radar measurements. As part of a pilot project, the use of farm scale forecasts to predict development of plant diseases were tested at 35 farms in the Solør-Odal district in Norway in 2010 and 2011. Preliminary results show that late-blight forecasts produced on a farm scale often differ from forecasts based on data from the nearest weather station, proving the significance of the local approach in farm scale forecasting. Predictions of DON (deoxynivalenol) concentration in oats at harvest based on farm scale weather data, compared to predictions based on weather data from the nearest weather station will also be studied. Future aspects will be to work towards an improved system where farmers throughout Norway can register their farm and automatically have access to a range of pest and disease forecasts based on site specific weather data.