Transition through markets : Understanding diffusion of added-value markets among Dutch pig farmers

Dutch pig farmers face income challenges due to volatile international markets and societal challenges regarding the negative externalities of pig farming, such as poor air quality, impaired animal welfare and excess nitrogen. Added-value markets are proposed as a way out of this impasse. An added-value market is a market that decreases one or more negative externalities of production at the farm level, while offering farmers a price premium for their produce. In the past, however, stimulating policies for added-value markets had unintended consequences: a subsidy on conversion to organic farming resulted in over-supply and a decreased reputation of the organic pig sector among pig farmers. In addition, pig farmers feel peer pressure to remain conventional. In this thesis we, therefore, try to gain a better understanding of the diffusion of added-value markets by including farmer-to-farmer interaction and market price dynamics.Traditional research tries to gains insight in the diffusion of added-value markets by looking at individual factors that influence decision-making at one point in time, e.g., the role of age, motives or attitudes. In this thesis, we take a complex adaptive systems perspective. The Dutch pork sector can be seen as a system, because actors and components within the pork sector interact with each other, e.g., farmers with farmers and farmers with market prices. It is adaptive, because farmers react to changes in their environment and they do so heterogeneously. It is complex, because interaction between system actors and components can lead to non-linear and unpredictive macro-level patterns, such as the diffusion of added-value markets. An important difference between traditional approaches and a complex adaptive systems approach is that the latter includes a time component: interactions affect the systems output over time. The research questions addressed in this thesis are:What factors influence pig farmer decision-making over time? (Chapter 2)To what extent does social interaction influence diffusion of investment strategies? (Chapter 3)How does social interaction and context influence the diffusion of organic farming among the Dutch pig farmer population? (Chapter 4)The first research question is answered by means of literature research, social and social-psychological theory, and semi-structured interviews with pig farmers and pork sector experts. The factors that influence pig farmer decision-making are grouped in personal, social and contextual factors. The time-component makes it important to distinguish between static and dynamic factors that influence decision-making, and to include feedback mechanisms from the effects of farmer decision-making to the factors that influence decision-making. The semi-structured interviews pointed to the importance of contextual factors: a farmer’s investment rhythm and lack of trust in policies.The second research question is answered by means of a simulation game. A simulation game is an experimental setting that represents the real world while giving the researcher control over specific variables. The game was played with pig farmers and pork sector advisors. All players had two goals: to manage the societal acceptance of the Dutch pork sector and to avoid bankruptcy. Each player could choose between two main investment strategies: scale enlargement, which would decrease the societal acceptance score, and converting to an added-value market segment. The games were played with pig farmers and pig sector advisors. They sessions were recorded and transcribed to analyse the effect of social interaction on no-, low- or high adoption of investment strategies. The results showed that (1) only investments with a financial benefit did under influence of social interaction, result in high adoption, (2) for high adoption to occur, communication between participants was necessary, (3) opinion leaders played an essential role in the high adoption of investment strategies, (4) there was a common understanding among participants in favour of scale enlargement.The third research question was answered by means of agent-based modelling. Agent-based modelling is a computer simulation method that models heterogeneous agents (here: pig farmers) and interaction between these agents, to study the effect of micro-level interactions on macro-level diffusion. Pig farmers in the model differed in, e.g., farming style and market, and had the choice to change markets: from conventional to organic and back. Pig farmers in the model were influenced by their reference groups, i.e. social groups that can influence an individual’s behaviour through his/her self-concept. In addition, the model accounted for organic market price dynamics through the price elasticity of demand. An exploratory analysis was performed on social influence parameters. Based on these results, three social influence scenario’s were selected for a local sensitivity analysis. We performed expert validation on model results. The exploratory analysis showed that initialisation of two social influence parameters affected the number of organic farmers and organic pigs: the likelihood for influence in interaction. The sensitivity analysis showed that the most influential factor on diffusion of organic farming was the price elasticity of demand for organic pork meat in interaction with the likelihood for influence between farmers. This was followed by the importance of new entrants: successors stimulated diffusion of organic farming through a different farming style than their successor and through different peers. Expert validation argued that consumer demand, instead of the price elasticity of demand, was the most limiting factor for diffusion, followed by social influence between farmers or consumers, and successors.In this thesis we showed that the farmers social environment through reference groups can explain farmers’ reluctance to change. The reference group approach acknowledges different types of farmers and explains behaviour as a consequence of social motivations, rather than a traditional rational economic approach. In addition, the complex adaptive systems approach in this thesis showed the importance of going beyond the current farmer population to understand diffusion of added-value markets: it pointed to the importance of consumers and successors. Therewith, it points to a shared responsibility of pig farmers and consumers for decreasing the negative externalities of production. Finally, we argued that agent-based modelling is a useful tool for boundary-work on the black-box of the cognitive mechanisms behind behaviour change. Especially the process of agent-based modelling, instead of its results, serves this purpose