La formación de gobierno en 1996: el gabinete Aznar

A model may be as simple as a verbal description of a system or a complex mathematical representation. Wegener (1994, p.18) quoted Radford (1968), who said that:

“Models can aid in understanding of some particular aspects of a real object because they are simplifications of reality. The essential element in their construction is a degree of abstraction of particular features of the object being modelled. The essential features are presented uncluttered by all other features which are not essential. It is a way to see the wood, unconfused by the trees”.

In definitional terms, any analytical method meant to imitate a real-life system might be referred to as a simulation model, especially when other analyses are mathematically complex, or difficult to reproduce.

According to Wegener (1994), models used to simulate farming systems could essentially be placed in two broad categories: biophysical models and economic models. Wegener (1994) suggested that biophysical models could be used to describe almost any aspect of the agricultural production system while models that had an economic orientation included those used to achieve economic efficiency in the allocation of resources and those used for risk analysis.

In 1997, an International Seminar, that addressed ‘Risk Management Strategies in

Agriculture: State of the Art and Future Perspective’, was held at Wageningen Agricultural University, The Netherlands (Huirne et al., 1997a). The purpose of the seminar was to examine the state of the art in agricultural risk management and to assess the future

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prospects for improving the way that risks are managed by farmers, agribusinesses, and rural policy makers. One of the key conclusions of the seminar was that recent

developments in software packages for risk analyses could be the theme of a future

conference. User-friendly decision aids could play a vital role in educating decision makers about risk analysis and in bridging the still wide gap between theoretical and practical applications. Seminar participants concluded that significant progress in software development was occurring and further communication about these technologies was needed (Huirne et al., 1997a).

It is acknowledged universally that risk and uncertainty play an important role in agriculture worldwide, and it is not surprising that a substantial focus on techniques for analysing risky decisions has emerged in the literature (Martin, 1996). Furthermore, much research has been devoted to developing and refining these techniques. Some examples include various econometric models and production functions, or more sophisticated formulations such as quadratic programming models or specific linear alternatives such as MOTAD (Hazell, 1971), separable linear programming (Thomas et al., 1972), marginal risk constrained linear programming (Chen and Baker, 1974), whole-farm mathematical programming (Kingwell and Pannell, 1987), utility-efficient programming (Patten et al., 1988), and linear programming model (Pannell and Nordblom, 1998). In general, these models help to identify risk-efficient farm plans from a range of alternatives with specific resource constraints.

While these risk programming approaches can help identify risk-efficient outcomes at the farm level, concern has been expressed that the solutions from some models such as

MOTAD and quadratic programming may be very sensitive to the model’s structure (Mapp and Helmers, 1984). A failure to specify the resource situation and constraint set

completely may contribute to solution sensitivity. This may occur because farmers operate in a multi-attribute environment in which many forces, choices, preferences, and events influence behaviour and performance (Mapp and Helmers, 1984). Such complexities are increasing as farming systems in many countries face a wider range of external forces (Tyler and Lattimore, 1990; Eidman, 1994), and are likely to make it increasingly difficult to model farmers’ behaviour. In addition to inadequate specification of resources and constraints, modelling bias could also arise if an incomplete set of risk management alternatives is considered in the analysis. The nature of risk modelling implies

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or strategies which are difficult to quantify. For example, marketing activities such as forward contracting are amenable to risk analysis whereas the use of market information is not (Martin and McLeay, 1998).

Models based on multiplying production risk and price variation have generally been used to address the more realistic case of both price and yield risks (Pope and Kramer, 1979; Newbery and Stiglitz, 1981; Innes, 1990). McKinnon (1967) and Grant (1985) showed, in a variance minimization framework, the correlation between price and yield significantly affected the hedging decision. Expected utility theory has been used as the theoretical base for much risk analysis work. Sakong et al. (1993) and Moschini and Lapan (1995) used it to analyse yield risk. They found that some options were no longer dominated in the choice set, particularly when there was significant expected correlation between yield and price variables.

While theoretical models for planning and decision making under uncertainty have been treated extensively in the economic literature on farm management, the application of these models in extension work and practical planning and decision making has been rather modest. One of the main reasons, as suggested by Rasmussen (1997), was that no empirical background for efficient application of such risk models was available.

Just and Pope (2001) and Just (2003), using mainly an econometric focus, have assessed possibilities and proposed sound principles for research on decision analysis and risk in agriculture. Recently, Hardaker and Lien (2005) proposed sixteen principles of good practice for decision analysis in agriculture. These principles were based on reasoned argument and many relevant findings in the literature. The main principles identified that all decisions are risky, and that risk analysis is ‘the art of the possible’. Measuring the risk attitudes of decision makers is difficult, and the importance of risk aversion has generally been exaggerated (Hardaker and Lien, 2005).