Improved Program Planning Approaches Generates Large Benefits in High Risk Crop Farming

This paper examines whether there is room for the improvement of farm program decisions through the incorporation of mathematical optimization in the practical planning process. Probing the potential for improvement, we investigate the cases of four German cash crop farms over the last six years. The formal planning approach includes a systematic time series analysis of farmspecific single gross margins and a stochastic optimization model. In order to avoid solutions that simply exceed the farmer's risk tolerance, the apparently accepted variance of the observed program's total gross margin which represents an observable reflection of the individual farmer's risk attitude is used as an upper bound in the optimization. For each of the 24 planning occasions, the formal model is used in a quasi ex-ante approach that provides optimized alternative programs. The total gross margins that could have been realized if the formally optimized programs had been implemented are then ex-post compared to those that were actually realized. We find that the farmers could have increased their total gross margins significantly if - instead of using simple routines and rules of thumb - they had used the more sophisticated formal planning model. However, we also find that the superiority of formalized planning approaches depends on the quality of statistical analysis and the resulting forecasting model. Using our approach for practical decision support implies that farmers first specify their "own" production programs without the formal planning aid. Then, an alternative program can be provided which leads to superior expected total gross margins without exceeding the farmer's accepted total gross margin variance.production program planning, optimization, uncertainty, static distributions, stochastic processes, Crop Production/Industries, C1, C61, M11, Q12,

Real Options and Competition: The Impact of Depreciation and Reinvestment

International Relations/Trade,

IS THE "STANDARD REAL OPTIONS APPROACH" APPROPRIATE FOR INVESTMENT DECISIONS IN HOG PRODUCTION?

Applications of the real options approach hardly consider investment returns to be the result of competitive markets such as markets for agricultural products. The reason is probably that Dixit and Pindyck (1994, ch. 8) show in their very popular book "Investment under Uncertainty" that the investment triggers of firms in competitive markets are equal to those of firms with exclusive options. In this study, however, it is shown that this result is restricted to markets in which assets have infinite lifetime. If assets are subject to depreciation and subsequent reinvestment opportuni-ties, competition leads to significantly lower investment triggers. The reason is that depreciation of replaceable assets allows to compensate the potential decline in returns after negative demand shocks because of the non-replacement of depreciated assets. Accordingly, applications of the real options approach to investments in e.g. pig production should consider this effect. The results are obtained by an agent-based simulation approach in which a number of competing firms derive their investment triggers by a genetic algorithm. Since this method allows to understand the re-sulting price dynamics, an alternative method is presented that allows to simulate the identified price dynamics directly and which also can be used to determine investment triggers for specific conditions.Livestock Production/Industries,

Investment planning under uncertainty and flexibility: the case of a purchasable sales contract

Investment decisions are not only characterised by irreversibility and uncertainty but also by flexibility with regard to the timing of the investment. This paper describes how stochastic simulation can be successfully integrated into a backward recursive programming approach in the context of flexible investment planning. We apply this hybrid approach to a marketing question from primary production which can be viewed as an investment problem: should grain farmers purchase sales contracts which guarantee fixed product prices over the next 10 years? The model results support the conclusion from dynamic investment theory that it is essential to take simultaneously account of uncertainty and flexibility.dynamic programming, flexibility, investment, sales contract, stochastic simulation, uncertainty, Agricultural Finance, Risk and Uncertainty,

Improved Program Planning Generates Large Benefits in High Risk Crop Farming – A Profitable Application of Time Series Models and Stochastic Optimization

Agricultural production relies to a great extent on biological processes in natural environments. In addition to volatile prices, it is thus heavily exposed to risks caused by the variability of natural conditions such as rainfall, temperature and pests. With a view to the apparently lacking support of risky farm production program decisions through formal planning models, the objective of this paper is to examine whether, and eventually by how much, farmers’ “intuitive” program decisions can be improved through formal statistical analyses and stochastic optimization models. In this performance comparison, we use the results of the formal planning approach that are generated in a quasi ex-ante analysis as a normative benchmark for the empirically observed ones. To avoid benchmark solutions that would possibly exceed the respective farmer’s risk tolerance, we limit the formal search to a subset of solutions that are second-degree stochastically dominant compared to the farmer’s own decision. We furthermore compare the suitability of different statistical (time series) models to forecast the uncertainty of single gross margins.stochastic optimization, program planning, time series analysis, Crop Production/Industries,

Sophisticated Program Planning Approaches Generate Large Benefits in High Risk Crop Farming

Agricultural production relies to a great extent on biological processes in natural environments. In addition to volatile prices, it is thus heavily exposed to risks caused by the variability of natural conditions such as rainfall, temperature and pests. With a view to the apparently lacking support of risky farm production program decisions through formal planning models, the objective of this paper is to examine whether, and eventually by how much, farmers’ “intuitive” program decisions can be improved through formal statistical analyses and stochastic optimization models. In this performance comparison, we use the results of the formal planning approach that are generated in a quasi ex-ante analysis as a normative benchmark for the empirically observed ones. To avoid benchmark solutions that would possibly exceed the respective farmer’s risk tolerance, we limit the formal search to a subset of solutions that are second- degree stochastically dominant compared to the farmer’s own decision. We furthermore compare the suitability of different statistical (time series) models to forecast the uncertainty of single gross margins.stochastic optimization, stochastic processes, production risk, program planning, time series analysis, C1, C61, M11, Q12,

Optimal Timing of Farmland Investment - An Experimental Study on Farmers' Decision Behavior -

Replaced with revised version of paper 5/26/11.Experimental Economics, Investment, Real Options, Agribusiness, Agricultural Finance, Farm Management, Financial Economics, Institutional and Behavioral Economics, Risk and Uncertainty, C91, D81, D92,

An Interdisciplinary Approach to White-collar Crime in the Food Sector

The probability that buyers are deceived with regard to the quality or safety of purchased products (moral hazard) increases with the profits which suppliers can earn through opportunistic behaviour. It decreases with the probability and level of losses that result from disclosure of malpractice. It also decreases with protective factors rooted in the suppliers' social contexts - such as values, emotional bonds etc. - that shield them from yielding to economic temptations. This paper describes how a systematic analysis of economic incentives and social context factors can be provided through an interdisciplinary approach which combines the analytical powers of microeconomics (game theory) and criminology (control theories). The approach is discussed with regard to food quality and safety threatened by moral hazard. Its essentials are illustrated through a case study of grain farmers who might be tempted to infringe upon production-related regulations.asymmetric information, behavioural food risks, control theories, game theory, moral hazard, opportunistic malpractice, Agribusiness, Institutional and Behavioral Economics, A13, K32, K42,

Modeling and Hedging Rain Risk

In this article we price a precipitation option based on empirical weather data from Germany using different pricing methods, among them Burn Analysis, Index Value Simulation and Daily Simulation. For that purpose we develop a daily precipitation model. Moreover, a de-correlation analysis is proposed to assess the spatial basis risk that is inherent to rainfall derivatives. The models are applied to precipitation data in Brandenburg, Germany. Based on simplifying assumptions of the production function, we quantify and compare the risk exposure of grain producers with and without rainfall insurance. It turns out that a considerable risk remains with producers who are remotely located from the weather station. Another finding is that significant differences may occur between the pricing methods. We identify the strengths and weaknesses of the pricing methods and give some recommendations for their applications. Our results are relevant for producers as well as for potential sellers of weather derivatives.Risk and Uncertainty,

Does vertical integration reduce investment reluctance in production chains? An agent-based real options approach

This paper uses an agent-based real options approach to analyze whether stronger vertical integration reduces investment reluctance in pork production. A competitive model in which firms identify optimal investment strategies by using genetic algorithms is developed. Two production systems are compared: a perfectly integrated system and a system in which firms produce either the intermediate product (piglets) or the final product (pork). Simulations show that the spot market solution and the perfectly integrated system lead to a very similar production dynamics even with limited information on production capacities. The results suggest that, from a pure real options perspective, spot markets are not significantly inferior to perfectly integrated supply chains.real options, supply chain, agent-based models, genetic algorithms, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Institutional and Behavioral Economics, Productivity Analysis,