Recomendaciones

In the previous section, the factors that affected price volatility and some of the measures that were available to farmers to mitigate these effects were discussed. Of particular importance is the role of farmer decision-making in managing uncertainty, risk and adapting to market conditions. In this section this complex interaction is broken down into: attitudes, values and goals of the farmer; risk management strategies; and, attitudes towards new technology adoption, in particular FPRM tools.

2.3.1 Attitudes, values, goals and behaviour.

This section gives an overview of farmers’ attitudes, values and goals in general rather than specifically those regarding risk management and technology adoption. Ultimately, it is these attitudes, values and goals that form the basis for behavioural intention (BI) and actual

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intention (Ajzen, 1991). Whilst human behaviour is seen as a complex mix of reflex actions, impulses, habits, customs etc (Viner, 1925) it is the objective of research to construct theories that realistically portray the decision-making process and thus predict behaviour (McGuire, 1964).

Fundamentally, economic theory would suggest that individuals make decisions that increase their overall ‘utility’ or ‘well being’. This increase takes many forms, and the levels will differ between individuals and over their lifetime. However, utility is very difficult to quantify in real everyday situations as it’s so varied; the feeling of seeing the first daffodil in your garden, a child’s first step or making a profit. This utility concept is most commonly measured in business, in monetary terms, in terms of profit / loss from a decision.

However therefore, criticisms of the pure economic theory of profit maximisation because it fails to include either sufficient or the most relevant variables, and that firms do not in fact maximize profits (McGuire, 1964). As profit maximising is an unattainable procedure other guidelines are used, or needed, to determine what is ‘satisfactory’ (McGuire, 1964). ‘Satisficing’ (a satisfactory or adequate outcome is accepted, rather than the optimal one) is more likely (Simon, 1979). Individuals are more likely to be satisficing than optimising (Gasson and Errington, 1993) in terms of goals.

Yet it is important to acknowledge that there is a difference between farmers’ goals and their values. Gasson (1973) describes that ‘goals’ are ends or states to which the individual desires to be or wishes to achieve, now or in the more distant future. They often change over a person’s life, depending on circumstances. ‘Values’ are more permanent traits of an individual, less liable to change with time or circumstances than goals. They are governed by reason, ethics or aesthetic judgement.

Many agricultural models assume farmers are rational utility/profit maximisers (Norton and Schiefer, 1980; Moxey, 1995; Wallace and Moss, 2002). While this is not always true, it does give broad predictions. When the financial factors in a decision-making process decline and become less important, so too does the profit maximising assumption, and the usefulness of the models. Other ‘basic sociological constraints’, non-financial ones, take

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precedence as people try to maximise their utility instead (Edwards-Jones, 2006). In an agricultural context, this is important where government policies are often concerned with non-financial issues, such as environment, animal welfare and access to opportunities in rural areas. There has been substantial research into why farmers farm, and the results suggest it is not just based on profit and loss or financial reward. Such research has been undertaken in many countries including Scotland (Austin et al., 1996; Austin et al., 2001), in Ireland (Gillmor, 1986) and UK versus USA aspirations (Gasson, 1969). Sociology and psychology are increasingly being drawn upon within agricultural economics (Edwards- Jones, 2006).

It is these attitudes and goals that not only affect decision-making and business practices but also, as will be discussed in section 2.4.3, affect the adoption of technology. Pampel Jr and van Es (1977) suggests there are different factors influencing the adoption of commercial ‘profitable’ practices, than influence ‘unprofitable’ practices, such as conservation. Other studies have looked at the influence of the ‘relative advantage’ of a decision. (Nowak, 1987; Cary et al., 1989; Vanclay, 1992).

Why farmers farm and why people work has been the subject of literature from the 1920s (Ashby, 1926). There followed a myriad of research projects on farmer behaviour and adoption of new ideas and practices. A study of 80 farmers from North Carolina, Wilkening (1950a), revealed that a farmer’s decisions in his daily activities are influenced not only by the ideas, ethics, and beliefs to which he subscribes but also his social interactions. Values, with their degree of permanence, tend to underpin an individual’s goals and are more abstract in nature. Wilkening (1954) researched the techniques for studying values. They can only be studied indirectly through observed behaviour or verbal responses to questions. This has the disadvantage of interpretation of the answers by the questioner. The respondent may give answers they feel they ought to, in order to fit social norms they believe the questioner would like to hear. Careful question phrasing is required.

Gasson (1973), studying farmers in East Anglia, concluded that ‘dominant values’ associated with the occupation of farming are classified into four headings: instrumental, social; expressive; and, intrinsic. Instrumental is described as making money and having

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security in a pleasant working environment. Social is described as personal interactions with family, staff, community and other peers. Expressive is described as a means of personal fulfilment, pride and to be creativity. Finally, intrinsic is described as the value of farming in its own right, including, for example, the outdoor life and independence. How these are ordered, relative to one another, influences farmers’ decision choices. Her pilot studies suggest that East Anglian farmers have a predominately intrinsic orientation to work, valuing the way of life, independence and actual process of performing farming tasks above any other financial aspects (Gasson, 1973).

In a later study, Kerridge (1978) also describes these phenomena in a survey of 71 wheat and sheep farmers of Western Australia, which was carried out to explore farmer ‘value orientations’ in four classes of value, as related to farm performance and the personal characteristics of the farmers. The questionnaire used was based on earlier work (Gasson, 1973). Kerridge (1978) concluded that it was the older farmers and those on small farms that strongly valued farming as a ‘way of life’ and were most likely not to leave farming even when incomes are low. Larger, more efficient farms, once they reach a certain level of affluence, sought more aesthetic pleasures from farming and life than just money. This echoes the ‘Hierarchy of Needs Theory’ (Maslow, 1946).

This current research into the behavioural determinants of the use of FPRM tools in England will specifically use the principles of TRA (Fishbein and Ajzen, 1975), TPB (Ajzen, 1991), Diffusion of Innovations (Rogers, 1995), and an extension, The Decomposed TRB Model (Taylor and Todd, 1995a). They have been used to study previously the behaviour of farmers towards land conservation, in Western Australia, (Gorddard, 1991; 1992; 1993) and in Bedfordshire, England (Beedell and Rehman, 1999; 2000). They have also been used to study technology adoption in Florida (Lynne et al., 1995), tree planting in Pakistan (Zubair and Garforth, 2006) and the use of wool futures in Australia (Jackson, 2008). This research uses the above three theories, to understand the attitudes, values and goals of farmers in England towards FPRM techniques in their businesses to manage wheat price movements.

The literature above shows the various attitudes and goals that farmers have in relation to their businesses. From this research’s responses in-depth interviews, Appendix 2, and focus

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groups, Appendix 7, there is a clear influence on farmers’ decision-making process by social and economic factors, such as the family, their peers, education levels, farm size and indebtedness. However, there is divergence between what the theoretical economic literature suggests and what actually takes place, in practice, at farm level. There is no current literature on the use of FPRM, as applied to the farmer in England, nor on the behavioural determinants of the use of these hedging tools.

2.3.2 Attitude, risk and risk management strategies

A large change, such as a weather-related supply ‘shock’, increases the short-term volatility in commodity prices (Gilbert and Morgan, 2010). The farmer has to react by making decisions in the context of these changes and can use different tools, such as forward selling, futures markets and insurance (Moschini and Hennessy, 2001). How the farmer reacts depends upon their assessment of the risk involved, their attitude towards risk and the outcomes of any decision and subsequent action they take. This section gives an overview of risk, risk management strategies and attitudes of farmers towards risk and risk management.

Risk is often associated with adverse effects or loss of welfare (Bodie and Merton, 1998; Harwood et al., 1999). That is the probability of an undesirable event occurring through a particular set of circumstances and decision-making. Knight (1921) identified that risk can originate from both within and outside of a business and that it is not always possible to avoid all risk and as such states it is necessary to find a balance between the risks and rewards of different outcomes. A thorough discussion on risk and risk analysis is given by Boehlje (1998) and in particular with reference to agriculture by Moschini and Hennessy (2001).

Section 2.3 has already discussed the risk factors that can affect price volatility. However, in general, these factors also affect other aspects, such as yields, as well as price volatility. Harwood et al. (1999) identified five factors concerning agricultural risk. The first is yield and production risk that includes many random effects such as weather, disease and pests. As discussed previously, the farmer can have little control over weather events, but can introduce technology to mitigate disease and pests. The second, price risk, which has also

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been discussed previously, concerns the changes in prices, costs of the product and inputs for an agricultural enterprise and can significantly affect net margins (Tomek and Peterson, 2000). Similarly, the effects of imports and exports due to production levels can cause large price movements (Blandford and Schwartz, 1983). The third factor, policy risk is related to the consequences of government policy, although discussed in general in a previous section, specific policies, for example regulating the use of pesticides, can also have an immediate effect on the risk associated with an enterprise. Human or personal risk (the fourth factor) covers injury, death and changes in circumstances that have an impact on the farm business. Human and personal risk also encompasses theft and fire. Finally, financial risk is also cited as a key factor, and results from the way the farm’s capital is obtained and financed. This covers interest rates, borrowing and cash flow.

Analysing the risk in any sector involves identifying the risk factor, or multiple factors, that appear to be adversely affecting the business. Once risk is identified the effects can be measured and thus the risk can be systematically assessed. However, as discussed previously, account must be made for the situation when the risk becomes uncertainty and can no longer be measured. This uncertainty is based on the idea of inherent unpredictability of the factors in any business environment and not merely the fact of ignorance (Crouhy et al., 2006). Although risk can be disaggregated into its constituent components, it is often necessary to consider the wider agricultural environment as whole (Barker, 1981; Mehra, 1981; Hazell, 1984). For example, the adverse effect of weather and disease must take into account other factors such as how changes to input prices can affect their use. Further, Hartman (1972) says risk is not confined to one season and that inter-temporal decision- making must also be investigated.

To be successful, any (farm) business is required to understand and assess the risks involved and identify those that have the greatest impact on expected returns and other key objectives or goals. In the context of the wheat market in England the most significant risk factors with respect to financial returns over the past ten years have been the yield, as influenced by the weather, and price volatility. The weather is largely unpredictable and uncontrollable whilst price risk, seen as an added economic cost to the producer, can be controlled through FPRMs (Hardaker et al., 2004). However, because of the uncontrollable elements of risk

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and uncertainty associated with farming no strategy can completely mitigate the adverse effects.

Within any economically “optimal” management system, there is a set of alternatives that are only slightly less attractive than the optimum. Often these alternatives are large and with wide profit plateaus (Pannell, 2006). In economic production models with continuous decision variables, the width and flatness of the profit plateau varies, but the presence of a profit plateau is almost universal. Among production economists, the existence of flat payoff functions in agriculture was well recognized in the past (Hutton and Thorne, 1955; Doll, 1972; Bhalotra, 1998).

Relative to a risk-neutral decision maker, risk aversion on the part of a decision maker generally only makes a modest difference to optimal decisions. Modest differences to decisions often translate into very small benefits to the decision maker when payoff functions include wide flat regions. From the point of view of a decision analyst, this can mean that inclusion of risk aversion in models for decision support is of low priority (Pannell, 2000). Consideration of complexities such as risk aversion, which due to the ‘flatness’ phenomenon, only change the optimal strategy by moderate amounts, and does not greatly affect farmer welfare. Thus it is not the case that risk aversion does not affect the farmer’s optimal plan, but that the impact of the changes on farmer welfare is small. (Pannell, 2000).

The sources and consequences of risk with respect to price volatility have already been discussed in section 2.3, but how a farmer measures and perceives risk has not. It is this measurement, perception and subsequent behavior that were identified by Lin et al. (1974) as an important factor in economic decision-making. Decisions are based upon complete knowledge of the probability of any future outcome occurring and its consequences to the business. There are two parts to the decision; the action is only as good as the opinion of the decision-maker of that action and their confidence in that opinion (Ajzen, 1991). For example, Blandford and Currie (1975) discuss producer decision making under imperfect knowledge of future prices. These value judgment decisions are constantly being made. Because this complete knowledge is incomplete, it can vary over time as the market

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environment changes, but inevitably leads to problems because of this incompleteness (Blandford and Currie, 1975).

Typically, decision-makers use previous situations to estimate the likely consequences of any strategy followed. This type of decision, based on subjective experience and observation and not on real unbiased data, will give rise to a degree of deviation from the intended outcome, both positively and negatively. Crouhy et al. (2006) contended this would be different for each decision-maker and that risk management and risk taking is intrinsically related. This view was also shared by Drynan (1981), who suggests that the differences in views are due to personal characteristics. Coupled to this subjective assessment of the outcomes of a given decision or strategy are the influences and opinions of social referents such as family and peers as well as the perceived ability of the decision-maker to carry out a strategy (Fishbein and Ajzen, 1975; Ajzen, 1991). Gasson (1973) asserts that goals are ends or states to which the individual desires to be or wishes to achieve, now or in the more distant future.

How farmers actually manage risk is largely determined by their attitudes towards, and willingness to take, risks. Research has shown that these attitudes affect aggregate commodity supply response (Chavas and Holt, 1990; Holt and Moschini, 1992; Chavas and Holt, 1996), financial structure (Gwin, 1994), marketing decisions (Musser et al., 1996), the farm business and other agricultural characteristics (Barry, 1984; Hardaker et al., 1997). Knowing how farmers react to risk is important to all stakeholders including farmers, industry and policy makers (Bard and Barry, 2001). There have been many studies and research into addressing farmers’ attitudes towards risk and utility using different theories such as the modified von Neumann-Morgenstern and Ramsey procedures (Halter and Mason, 1978). Expected utility theory has been the most widely used technique for eliciting farmers’ attitudes towards risk (Lin et al., 1974). Dillon and Scandizzo (1978) classified the methods of measuring risk behaviours under different approaches as: economic anthropology; econometrics; farm risk programming; sectoral risk programming; and expected utility and safety-first theory. A more comprehensive discussion of the approaches to measuring risk are given by Antle (1987) and Just and Pope (1979).

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Farmers operate on a scale from risk averse to risk taking and which group farmers are in varies according to their inherent characteristics and are revealed through their decision making concerning risk (Drynan, 1981). Most studies suggest farmers are risk neutral to risk averse in their attitudes and actions. Roe (2013) asks how well farmers tolerate risk compared to non-farm business owners and the general population. The differing attitudes to risk were compared between German and USA farmers (Howard and Roe, 2011). Bond and Wonder (1980) used risk coefficients to measure attitudes and concluded Australian farmers were risk averse. They suggested farmers avoid low asset to debt ratios, are slow to adopt new technology/ideas and most look at on and off-farm diversification and forward selling and should increase the use of financial instruments. Similar conclusions were expressed by (Bond and Wonder, 1980; Austin et al., 1998b; Carter, 1999; Pennings and Leuthold, 2000; Tomek and Peterson, 2000; Tomek and Peterson, 2001; Jackson, 2008).

Of particular relevance to this thesis are behaviours towards price risk Barnard and Nix (1973); Tomek and Peterson (2001); Geman (2008) looked at the impacts of price uncertainty on USA wheat marketing margins and discusses the sources and modes of transmission of price risk. Brorsen (1995) also poses the question of whether this price volatility is desirable, or not, as does Adams and Klein (1978). Sandmo (1971) showed that, based on a hypothesis of expected utility maximisation, risk adverse farmers produce less output when there is a price risk. This was also the conclusion of Ishii (1977). In a study that examined the relationship between farmers’ attitudes and the future contracts to manage price risk it was shown that perceived risk reduction may differ from actual risk reduction (Pennings and Leuthold, 2000).

Risk management concerns managing unexpected variation in those factors that have a direct, or indirect, effect on the financial performance and viability of the business. Importantly some factors can be managed internally through the use of technology and diversification but some risks must be transferred out of the business, for example by the use of insurance or FPRMs. The literature on farm risk management strategies is large and reveals that farmers manage risk through production decisions and through the use of market-based and informal risk-management mechanisms.

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Varangis et al. (2002) states that the reward for risk-taking is profit, which implies decision- makers have to make a trade-off between risk and reward. Reward is measured as an average return of investment, whilst risk is measured as the variance about the average that