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
Authors
Lampros LamprinakisAbstract
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A model is presented to investigate the optimal economic life cycle of grass leys with winter damage problems in northern Norway and to determine the threshold of winter damage before it is profitable to reseed. A two‐level hierarchic Markov process has been constructed using the MLHMP software (the MLHMP software and the plug‐in constructed for this model are available for download at http://www.prodstyr.ihh.kvl.dk/software/mlhmp.html). The model takes uncertainty concerning yield potential, damage estimation and weather‐dependent random fluctuations into account. A Kalman filter technique is used for updating the knowledge of yield potential and damage level. The application of the model is demonstrated using data from two commercial Norwegian farms. As parameter estimates vary considerably among farms, it is concluded that decision support concerning optimal economic life cycle of grass leys should be done at farm level. The results also show the importance of using a flexible dynamic replacement strategy. Use of the model for specific farm situations is illustrated.
Abstract
Farmers in northern Norway have experienced severe winter damage on grassland rather frequently, especially on flat areas and peat soils in regions with an unstable winter climate around zero degrees Celsius. Traditional drainage with drainpipes is normally not sufficient to prevent such damage in these areas. During the past two decades the use of open ditches and surface grading has become the main method of reclaiming and draining peat land. A new heuristic stochastic dynamic analysis method for problems like this, combining simulation and optimisation, is used to explore the profitability of surface grading of peat soils. This analysis indicates that the year in which a ley should be reseeded depends on stage in the growth curve when eventually the winter damage happens as well as on the severity of the damage. Given the present acreage subsidy payment, surface grading is normally profitable from a farmers point of view.
Abstract
No abstract has been registered
Authors
Ola FlatenAbstract
Norwegian dairy farmers are facing changes in the economic environment. Prices of products and concentrates are falling, while area and headage payments are increasing. The availability of grasslands has become more abundant. Impact of changes in economic conditions on production systems and profitability are examined. Linear programming models of dairy farms, with grain and beef as alternative enterprises, are designed to analyse the adjustments. Optimal production systems are largely determined by a combination of economic factors associated with the various inputs, outputs and support schemes together with availability of farm resources. The typical Norwegian dairy farm has a small quota compared to other farm resources. Producing a fixed milk quota with moderate yielding cows is then most profitable (1999-conditions). Early cut silage offered ad libitum is most profitable. Changes in the milk price have no effects on production as long as the quota is effective. If all of the land is utilised and grassland is the only possible land use, increased area payments have no production effects. If some grassland is not in use, area payments increase land utilisation as cows are fed less concentrate. If grain is also grown, increased grassland area payments result in more land allocated to grass. Forage and milk production become less intensive. By increasing headage payments, milk yield falls, as it is optimal to have more cows to produce the fixed quota output. This contributes to keep more grassland in production and in a more intensive forage production. Lower concentrate prices lead to increased use of concentrates and higher milk yields.
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.
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
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.