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.
2003
Abstract
At present there are nearly 20 000 milk producers in Norway, and approximately 10 per cent of them are members of the Norwegian Dairy Financial Recording (NDFR). The NDFR is an important basis for production and financial advice given by the dairies. There is a great interest among milk producers and advisors in comparing results from different farms to find out why some are doing well and some are doing not so well, and to learn from those doing well. Gross margin (GM) per litre of milk produced is the traditional indicator for efficiency. This data, as other data on milk production, indicate that there is a wide variation in gross margin per litre of milk between farms with seemingly similar conditions for producing milk. This is interpreted as a potential for improving the efficiency of many producers. However, for many reasons gross margin per litre of milk is not an ideal indicator. A new version of the NDFR contains more information, for instance information on fixed costs of roughages produced on the farm. It is hoped that the new version of the NDFR makes it a better tool for improving the profitability of milk production. In an ongoing project we try to use the NDFR to analyse who are doing well and why. We use a combination of Data Envelopment Analysis (DEA) and statistical analysis. For each farm we produce an efficiency index, and then we apply statistical methods to find factors that can explain the index. So far we have only very preliminary results. Management factors are important, but the NDFR data-base have very little information on management factors. It is planned to collect such data for a sample of farmers and include that in the study at a later stage.
Authors
Harald Kvaalen Ola Gram Dæhlen Ulrika EgertsdotterAbstract
No abstract has been registered
Authors
Guillaume Besnard Virginie Acheré Sylvain Jeandroz Øystein Johnsen Tore Skrøppa Patricia Faivre Rampant Jean-Michel FavreAbstract
No abstract has been registered
Abstract
Areas near the Norwegian-Russian border are being strongly contaminated by heavy metal emissions from copper-nickel smelters in the Kola peninsula. The present report presents data for the four elements arsenic, chromium, cobalt, and selenium in vegetation sampled in eastern Finmark, obtained by neutron activation analysis. It is no doubt that the smelters in Nikel and Zapolyarny, constitute the main source of these elements in this area. Some chromium comes from local domestic sources. Still, however, the concentration of these elements in soil and vegetation are probably too low as such to represent any harm to the ecosystem.
Authors
Morten Ingerslev Inge Stupak Møller Karsten Raulund-Rasmussen Ingeborg Callesen Vivian Kvist JohannsenAbstract
No abstract has been registered
Abstract
As more data have been amassed and interest in working with the ensuing data sets have grown, methods for organizing and examining the data have evolved. The need to work with these larger amounts of data has led to the development of ‘data mining’ methods and software. Data mining has a somewhat skewed reputation, and has often been characterised as ‘data dredging’ or ‘fishing expeditions’ . However, most of us must admit that such ‘expeditions’ or what one also could call hypothesis-generating approaches where we look for both likely and less likely associations, has occurred within our own research. In principal, generating promising associations is what data mining is all about. In this paper we have applied one of many commercial software available (Enterprise Miner, SAS) on a small dataset merged from a questionnaire data set and the national dairy cattle health and production records. We investigated for patterns separating organic dairy farmers from the conventional ones. The main framework of the data mining approach, some of the core modelling methods and the data mining results are briefly described and assessed.
Abstract
No abstract has been registered
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No abstract has been registered
Authors
Per Otto Flæte Erlend Ystrøm HaartveitAbstract
No abstract has been registered
Abstract
No abstract has been registered