Determinants of farmers’ attitudes and perceptions

There is, however, disagreement among previous studies about the consolidated determinants, which inevitably influence farmers’ risk attitudes, perceptions of risk sources and perceptions of risk management strategies. There are numerous variations of factors which can affect farmers’ risk attitude and perceptions. Also, a wide range of researches has been carried out in order to investigate how risk attitudes and perceptions vary from farmer to farmer and from farm type to another. It is worth mentioning that most of these researches rely on farm and farmers’ socio-economic characteristics to such variation. However, the classification of attitudes and perceptions, which was based on farmers’ socio-economic profile, was impossible in most of the previous studies (Patrick et al. 1985; Boggess et al.

1985; Wilson et al. 1988; Gunjal and Legault 1995; Patrick and Musser 1997; Meuwissen et al. 2001; Aditto 2011). Wilson et al. (1993, p. 99) pointed out that “results illustrate the highly complex and individualistic nature of risk perceptions and the selection of management tools”.

Regarding to farmers’ risk attitudes, Bardsley and Harris (1987) found that the wealth and income indicators played a significant role to form the Australian farmers’ attitudes toward risk. Conversely, Pålsson (1996) discovered that the Swedish farmers’ risk attitudes were constant with respect to the wealth indicators. Gender, age, experience and education level of the farm householders are found as important aspects to explain their attitudes toward risk (Pålsson 1996; Gómez-Limón et al. 2003; Olarinde et al. 2010; Aditto 2011; Menapace et al. 2013). The education level is a catalyzer factor which increases farmers’ willingness to take risks (Moscardi and Janvry 1977; Binswanger 1980; Anosike and Coughenour 1990;

Binici 2001; Aditto 2011; Roslan et al. 2012).

In addition to the socio-economic factors’ importance to explain farmers’ perceptions of risk sources, such perceptions can change over time. Patrick and Musser (1997) demonstrated that crop price and yield variability were the most important sources of risk in the first stage of the study in 1991 while human resources risk and the environmental regulations recorded the highest score in 1993. Furthermore, the production program plays an essential role in constituting farmers’ risk perceptions. Harwood et al. (1999) found that the cereal producers (wheat, corn, and soybean) were worried mostly about the yield and the price risks, whereas institutional risk was ranked first for the livestock farmers. Comparable results were reported by Ali and Kapoor (2008). They revealed that Indian fruit producers were more concerned about input prices than vegetables producers, although the fallen ground water levels and seasonality were perceived as the most important sources of risk in production of fruits and vegetables in India. Flaten and his team (2005) compared perceptions of risk sources among conventional and organic dairy farmers in Norway. They found that the institutional and market risks were ranked as the main risk sources threatening the organic dairy production, whereas the operating cost variability and animal welfare policy were perceived as having high relevance for the conventional dairy farmers. Lien et al. (2006) revealed significant differences between full-time and part-time crop and dairy farmers in Norway. Full-time crop’ farmers perceived risks of consumer preferences’ changes and human resources risks at a higher relevance than part-time farmers. Similarly, full-time dairy farmers were more concerned about animal welfare policy and production diseases than part-time farmers.

Regarding to the farmers’ preferences of risk management strategies, the scholars showed an extensive range of factors affecting such preferences. In Indiana (Shapiro and Brorsen 1988), it was displayed that the use of hedging was positively related to farm size.

Contrary to expectations, education was found to be inversely related to hedging. Farmers’

risk attitudes have no influence on selecting such a strategy. Makus’ working (1990) on farm willingness to adopt ‘futures and options marketing program’, reported that employing of forward contracts, value of gross sales, education level above bachelor degree and membership in a marketing club positively and significantly influenced the adoption of such a program (Makus et al. 1990). Similarly, the total years of formal education, marketing seminar participation, farm size and crop occupation, input intensity and the use of crop insurance had the greatest positive impact on the adoption of forward pricing techniques among Kansas farms (Goodwin and Schroeder 1994). Meuwissen’s study (2001) revealed that Dutch dairy farmers were more concerned about price risks, while pig and mixed farmers

more likely perceived production risks as very important. Furthermore, insurance was less relevant for mixed farmers compared with those in dairy and pig farms (Meuwissen et al.

2001). Ritchie et al. (2004) found that risk reduction by adjusting planted areas corresponding to the forecasted seasonal climate led to significant gains in gross margin returns for irrigated cotton farmers in Australia. However, the adoption of such a strategy was strongly influenced by farmers’ risk attitudes. Price risk management was an important practice among Australian cotton operators studied by Ada et al. (2006). However, demographic, agronomic, biophysical factors as well as farmers’ personality played a critical role in accepting price risk management instruments.

In fact, risk realization varies substantially from farmer to farmer. This variation is attributed to many personal aspects for instance, farmers’ goals, intents, experience and attitudes toward risk. Both business environment and the available instruments to cope with risks are also determining farmers’ risk perceptions. A number of researchers (Renn 1992;

Slovic 1992; Slovic 2001) illustrated that the general perception of risk was a mixture of many considerations like uncertainty, equity, controllability, fear and catastrophic future. This mixture is translated as a complex and qualitative perception by individuals. Legesse and Drake (2005) suggested that an extensive framework which includes psychometric paradigm, cultural theories of risk and farm structure model, should be followed, in order to provide comprehensive insights into factors determining risk perceptions. Psychometric paradigm includes aspects such as farmers’ attitudes, psychological and personal characteristics, and cognitive sources. A cultural theory is fundamentally a social theory which is considered as the most eloquent framework to investigate the relationships among human beings, as well as societal relationships. Farm structure theoretical model is concerned with the farm operation items, such as the size of the farming operation, which may influence such perceptions (Legesse and Drake 2005).

Murray-Webster and Hillson (2008) introduced a triple strand that influences perceptions and risk attitudes (Figure 3.5). This triple strand summarizes the factors that influence perceptions and attitudes under three headings: Conscious, subconscious and affective factors. Conscious factors represent the visible and measurable characteristics of the situation in which the decision is being made. Six typical conscious factors were suggested, for instance the familiarity, which investigates whether the individuals do something like before or in an adverse manner. Subconscious factors include mental short-cuts made to facilitate decision-making (heuristics); hence they provide mechanisms for making sense of complex or uncertain situations, and other sources of cognitive bias. Affective factors

represent the responses based on instinctive emotion or deep underlying feelings rather than rational assessments, such as fear, desire, love, hate, joy and sadness (Murray-Webster and Hillson 2008).

Figure 3.5: The triple strand of influences on perceptions and risk attitudes

Source: Murray-Webster and Hillson 2008

4. EMPIRICAL ANALYSIS BY QUESTIONNAIRES ON