Farm Level Risk Assessment Using Downside Risk Measures

Recent and presumable future developments tend to increase the risk associated with farming activities. This causes an increasing importance of risk management. Farmers have a wide variety of possibilities to influence the risk exposure of their operations. Among them are the choice of the production program as well as marketing activities including forward pricing and hedging with futures and options. In total all these opportunities comprise a portfolio of activities which must be selected as to match the resources of the farm as well as the farmer's attitudes towards risk. The paper addresses this issue using a whole farm stochastic optimisation approach based on a risk-value framework. The paper starts with a discussion of risk-value models and the relationship between them and the expected utility hypothesis. In the second part the approach is incorporated in a whole farm model that optimizes a portfolio of production activities and risk management instruments. A case study is used to analyse the possibilities and limitations of the approach and to illustrate the effects of yield and production risk on decision making.downside risk, risk management, risk measure, risk-value models, stochastic optimisation, Risk and Uncertainty,

Weather Derivatives as an Instrument to Hedge Against the Risk of High Energy Cost in Greenhouse Production

In many areas agriculture is exposed to weather related risks. Weather derivatives that get more and more in the focus of interest can reduce these risks. In this study we develop a temperature based weather derivative and analyse how it can reduce the weather-related energy cost risk in greenhouse production. We base this study on a temperature index whose stochastic characteristics are analysed. Finally we simulate the heating demand for energy of a horticultural firm.Environmental Economics and Policy, Risk and Uncertainty, C22, D8, Q14,

Weather derivatives as an instrument to hedge against the risk of high energy cost in greenhouse production

In many areas agriculture is exposed to weather-related risks. Weather derivatives that get more and more in the focus of interest can reduce these risks. In this study we develop a temperature based weather derivative and analyse how it can reduce the weather-related energy cost risk in greenhouse production. We base this study on a temperature index whose stochastic characteristics are analysed. Finally we simulate the heating energy demand of a horticultural firm.Risk and Uncertainty,

Wetterderivate: Ein Instrument im Risikomanagement für die Landwirtschaft?

The risks associated with farming activities are likely to increase in the future. It, therefore, appears worthwhile to analyse new risk management instruments. This paper investigates weather derivatives for which a market has already emerged in the USA. Contrary to traditional financial derivatives, their payoff is determined by future weather events, such as temperature or precipitation. Thus, they hedge risks which result from climate. Since they address production risks they are complementary to instruments that hedge price risks, such as future markets. The objective of the paper is to evaluate the economic impacts of weather derivatives and to assess their potential as farm level instruments of risk management. After outlining the main characteristics and the functioning of weather derivatives and their emergence, emphasis is placed on model calculations to quantify farm level impacts. The potato farm is used as a case study. Empirical data on yields and weather variables are taken from an experiment station of the Chamber of Agriculture at Hanover, Germany. After studying the relationship between yields and weather variables, the findings are used to design an option based on a precipitation index. Stochastic simulation is then used to assess the effects on the probability distribution of revenues. The results show that weather derivatives can be useful instruments of risk management in agriculture. Since there is still a lack of knowledge with respect to some of their economic impacts, further research is needed. This refers to the choice of suitable commodities and weather indexes, the contractual design and methodological aspects of pricing and of integrating weather derivatives into the risk management of farms. Last but not least, the question has to be answered, as to which partners would be willing to accept the risk that farmers intend to reduce by means of weather derivatives.weather derivatives, weather risk, risk management, stochastic simulation, Financial Economics, Risk and Uncertainty,

Wetterderivate: Ein Instrument im Risikomanagement für die Landwirtschaft?

The risks associated with farming activities are likely to increase in the future. It, therefore, appears worthwhile to analyse new risk management instruments. This paper investigates weather derivatives for which a market has already emerged in the USA. Contrary to traditional financial derivatives, their payoff is determined by future weather events, such as temperature or precipitation. Thus, they hedge risks which result from climate. Since they address production risks they are complementary to instruments that hedge price risks, such as future markets. The objective of the paper is to evaluate the economic impacts of weather derivatives and to assess their potential as farm level instruments of risk management. After outlining the main characteristics and the functioning of weather derivatives and their emergence, emphasis is placed on model calculations to quantify farm level impacts. The potato farm is used as a case study. Empirical data on yields and weather variables are taken from an experiment station of the Chamber of Agriculture at Hanover, Germany. After studying the relationship between yields and weather variables, the findings are used to design an option based on a precipitation index. Stochastic simulation is then used to assess the effects on the probability distribution of revenues. The results show that weather derivatives can be useful instruments of risk management in agriculture. Since there is still a lack of knowledge with respect to some of their economic impacts, further research is needed. This refers to the choice of suitable commodities and weather indexes, the contractual design and methodological aspects of pricing and of integrating weather derivatives into the risk management of farms. Last but not least, the question has to be answered, as to which partners would be willing to accept the risk that farmers intend to reduce by means of weather derivatives

Predictive performance of multi-model approaches for model-informed precision dosing of piperacillin in critically ill patients

Objectives: Piperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA). Methods: Precision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicentre dataset (561 patients, 11 German centres, 3654 TDM-samples). Results: The results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, best single models: inaccuracy ±3%, ±10%, ±8%; imprecision: 77%, >71%, >73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 h. Conclusions: In conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimisation of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice

External evaluation of linezolid population pharmacokinetic models

These are the R-scripts used for external model evaluation of linezolid pharmacokinetic models

Minimal Inhibitory Concentrations of Linezolid and Empirical Target Attainment in an Intensive Care Setting

Repository for sharing R scripts developed for the evaluation of empirical linezolid dosing strategies in critically ill patient

External evaluation of linezolid population pharmacokinetic models

These are the R-scripts used for external model evaluation of linezolid pharmacokinetic models