62 research outputs found
Risk programming analysis with imperfect information
A Monte Carlo procedure is used to demonstrate the dangers of basing (farm) risk programming on only a few states of nature and to study the impact of applying alternative risk programming methods. Two risk programming formulations are considered, namely mean-variance (E,V) programming and utility efficient (UE) programming. For the particular example of a Norwegian mixed livestock and crop farm, the programming solution is unstable with few states, although the cost of picking a sub-optimal plan declines with increases in number of states. Comparing the E,V results with the UE results shows that there were few discrepancies between the two and the differences which do occur are mainly trivial, thus both methods gave unreliable results in cases with small samples
The prospects for whole-farm risk management: evaluating the impact of a deregulation of agricultural markets
Percepção e medidas de gestão de riscos por produtores de arroz irrigado na Fronteira Oeste do Rio Grande do Sul
Whole-farm planning under uncertainty : impacts of subsidy scheme and utility function on portfolio choice in Norwegian agriculture
This paper addresses the impacts of degree of risk aversion, subsidy scheme and choice of utility function on optimal farm plans in Norwegian agriculture. Data from a farm business survey (1991-1997) are combined with subjective judgements to formulate a two-stage utility-efficient programming model. Under existing policy and market conditions, the ex ante expectation was that farmers' risk attitudes are unlikely to have a large effect on choice of enterprise mix. The results tend to confirm this view, and a farmer who is hardly risk averse at all would choose the same farm plan as a very risk averse farmer. Factors such as subsidy schemes, market conditions for the products and available labour on the farm are found to be more important determinants of the optimal plans than farmers' risk attitude or the form of the utility function
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