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.
2003
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.Precision agriculture (PA) may be defined as using the best available technologies to tailor soil and crop management to fit the specific conditions found within an agricultural field or tract (Johannsen, 2001). Knowledge about soil variation within fields is thus a prerequisite for optimum PA. The use of sensors which measure electrical conductivity (EC) has been introduced as a promising way of mapping within-field variation in soil properties. In this paper we present relationships found between EC and both clay content and ignition loss (SOM) of some morainic loam soils in SE Norway. Measurements of EC at Møystad (60°47"N, 11°10"E, altitude ca.150 m) correlated well with clay content. Despite the rather narrow range of clay content at Møystad (11-17%), EC measurements accounted for about 70% of the variation. The use of EM38 in the horizontal position gave the best prediction in the upper two layers, whereas measurements in the vertical position fitted best to data from the deepest layer (40-60 cm). This is reasonable, since the instrument has its deepest range in the vertical position. Ignition loss in the upper layer was 5-8% at Møystad. There was no significant relation between EC and ignition loss in the upper layer, when EC was measured with EM38 in the vertical position. When EC was measured horizontally, about 24% of the variation in ignition loss was reflected by the EC of the soil. One should, however, take into account that ignition loss and clay content were positively correlated with each other (r=0.382), so that the result may in fact have been due to variation in clay content. We also measured EC at Kise Research Station (60°46"N, 10°48"E, altitude ca.130 m), where we selected five points across a field with a particularly large gradient in ignition loss. Here EC correlated positively with clay content, but this was not statistically significant (R2=0.576, p=0.137). With ignition loss, however, EC showed a strong positive correlation (R2=0.878, p=0.019). Inclusion of both clay content and ignition loss in a two-predictor regression model, with EC as the dependent variable, showed that the conductivity measurements depended almost completely on clay content and ignition loss for the selected points at Kise (R2=0.981, p=0.019). However, the predictors were positively intercorrelated here as well (r=0.517), which may make the statistical approach questionable. We also admit that the number of data points was very low. Nevertheless, the results clearly illustrate the potential of EC measurements for mapping soil variation.
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Two trials have been established to study within-field variation in responses to N fertilizer. Preliminary results showed a close relation between soil organic matter and the amount of mineralised N in soil in spring. Both trials displayed considerable variation in responses to N fertilizer, even thought the yields increased up to the highest N level used. On the basis of responses in individual replicates, it was estimated that varying fertilizer levels across the field would have been positive in one of the trials. The studies will be continued to find out whether the pattern of variation remains the same, and to study the role of various soil factors.
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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.
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The objective of this study was to provide empirical insight into dairy farmers’ goals, relative risk attitude, sources of risk and risk management responses. The study also examines whether organic dairy farming, leads to important risk sources not experienced in conventional farming and, if so, how those extra risks is managed. The data originate from a questionnaire survey of conventional (n=370) and organic (n = 160) dairy farmers in Norway. The results show that organic farmers have somewhat different goals than conventional farmers, and that the average organic farmer is less risk averse. Institutional risk was perceived as the most important source of risk, independently of conventional or organic production system, while organic farmers indicated greater concern about forage yield risk. Keeping cash on hand was the most important strategy to manage risk for all dairy farmers. Diversification and different kinds of flexibility was regarded as a more important risk management strategies among organic than conventional farmers.
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The Norwegian Ministry of Agriculture (1999) has announced its goal of converting 10% of the total agricultural area to organic farming methods by the year 2009. Considerations of profitability and risk will be especially important, when the conversion of a farm is planned. Studies of risk and risk management in organic farming have been lacking in Norway. Only very few such studies have been carried out internationally, thus showing that there is a definite need for more risk and risk management research in organic farming. The project aims to increase knowledge about risks and risk management in organic farming systems. It is a co-operation between NILF, NORSK, and NVH. Both biological and economic aspects of risk will be taken into consideration. We wish to test and apply acknowledged statistical and risk analysis theories and methods on issues related to organic farming. The project will deal with the extent of risk in organic farming, strategies used by organic farmers to handle risk and whole-farm models to analyse optimal economic solutions under uncertainty in organic farming. The project will cover farms that are still in conversion and completely converted farms. Results from the project will directly benefit farmers and farm advisers. Politicians and public administrators will receive access to significant information for the design of future policies.
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Artikkelen beskriver vær og vekstforhold for kornproduksjon på Østlandet i 2002