For the high threshold scenario this is exactly the opposite. This indicates that when there is a higher likelihood of influence, craftsmen and entrepreneurs become more positive towards organic, while idealists less. A lower likelihood of negative social influence leads to more organic idealists in the low threshold scenario, and it leads to more organic craftsmen and organic entrepreneurs in the high threshold scenario. In the baseline scenario, a higher likelihood of negative social influence leads to more organic idealists, organic craftsmen and organic entrepreneurs. This indicates that disassociation actually stimulates organic farming diffusion. All experts thought market factors to be the most important factor for the size of the organic market. Their explanations were different: one argued that farm gate price stability in the organic market is the most important factor , and two thought demand by consumers would be most important . Experts differed on the second most important factor. One expert thought that this was social influence among farmers, another expert thought social influence among consumers , and the third expert thought that shocks or events in the conventional market was the second most important factor. The third most important factor, according to all experts, was farm succession. According to these experts, most recently converted organic farmers are young farmers who think differently than their predecessors. This research aimed to gain insight in what factors influence the size of organic pig farming and the diversity of farming styles among organic pig farmers, by considering social influence mechanisms, market price dynamics and farmer heterogeneity. The exploratory analysis showed that when there is a higher likelihood of influence in interaction , the numbers of organic farmers and organic pigs increase, as well as the diversity of organic farmers’ farming styles. The latter also happens when similarity in farmers’ farming style does not affect the credibility of another farmer. The sensitivity analysis showed that the most influential factor on the size of the organic market is the trend in demand.

The most influential factors on the distribution of farming styles in the organic market were similar to the factors that affect the size of the organic market, except for the successors’ farming style which was important for the diversity of farming styles, mobile vertical grow tables while trend in the demand was not. In addition, the importance and effect of parameters on outputs differed between number of organic idealists versus number of organic craftsmen and organic entrepreneurs. Experts categorised and confirmed that demand is the most important factor stimulating diffusion. They furthermore pointed to the importance of successors in the diffusion of organic farming, a trend also found in the model. One of the innovations in this model, compared to previous agent based models on organic farming diffusion is inclusion of market factors in the farmer decision-making process in addition to their personal and social factors. The importance of including demand is in line with previous research by Smith and Marsden and Rose et al. who argued that the wider agricultural system should be taken into account , including consumer demand , to understand diffusion processes of alternative farming strategies in general , or organic farming in particular . They both argued that considering other actors and components in the system, diminishes, though does not kill, the importance of individual farmer behaviour. Both our model results and experts judgement confirm that an increase in the trend in demand is the most important factor to stimulate organic market diffusion. In addition, increase in demand is related to an increase in the diversity of organic farmers’ farming styles according to model results and expert discussion. Another component of demand is the price elasticity of demand for organic meat, which we used to account for volatility in organic meat price as a consequence of imbalances in supply and demand. Interviewed experts, however, argued that gains or losses caused by imbalances in supply and demand are currently taken by supply-chain actors instead of consumers, which basically hints towards a low price elasticity.

To this end, further research focusing on dynamic price mechanism in an agent-based modelling framework might either be to model stakeholders, such as the role of slaughterhouses in the formation of prices as outlined by experts, or confirm the current level of price elasticity of demand for organic meat through empirical studies. Additionally, it would be interesting to identify and quantify factors that affect price elasticity of demand for organic meat. In particular, the growing supply of substitutes might affect both demand for organic meat and its price elasticity . Due to more available environment-friendly alternatives, e.g., plant-based meat, it can be hypothesized that the current level of price elasticity of demand for organic meat is higher than the one we adopted from Bunte et al. , meaning that an increase in price of organic meat would force more consumers to switch to substitutes. Another point which affect the organic meat price is when organic farmers do not have a successor: the organic meat price will go up as these organic pigs disappear from the model. In reality, they might have been taken over by another farmer, which could be an interesting addition to the model. Although one of the experts believed risk preferences related to meat price volatility to be an important factor for organic market conversion, we did not consider it in the model. In line with the observation of the expert, an hypothesis could be that risk-averse farmers in a less volatile organic market context, would have more incentives to convert to organic farming. Yet, quantification of the effect usually requires quite strong assumptions on the level of risk aversion, which is rather difficult to estimate. Furthermore, even in the farm-level context, research on dynamic decisions under uncertainty and risk preferences is limited . Hence, including risk preferences in agent based modelling requires, first of all, further methodological research. The same applies to learning mechanism linked to organic farming, which has proven to be an important factor in conversion . Instead, we assumed that intrinsic motivation shaped by the Social Identity Approach in interactions is the most important prerequisite to consider conversion to organic farming, discussed further below. The social identity approach was the theoretical backbone of the social interaction mechanism between farmers in the model.

Four reference groups were distinguished which differed in importance between innovative and conservative farmers: farmers who have a high status compared to one’s own status were more credible for innovative farmers, while similar farmers were more credible for conservative farmers. In addition, following the social identity approach, farmers who were respectively low in status or dissimilar were considered out groups and disassociation followed through negative social influence. Model parameters were operationalised to determine how much credibility was necessary before influence takes place , the magnitude of influence when influence takes place, and a threshold for the value of attitude before a farmer continues to calculate the expectation income in the alternative market. Model results showed that the likelihood of influence in interaction affected organic market diffusion, and that similarity in farming styles obstructs diffusion of organic farming and diversity of organic farmers’ farming styles.It also leads to fewer organic idealists, and more organic craftsmen and organic entrepreneurs in the organic market. This indicates that when different types of farmers influence each other more easily, i.e. are more open to each others’ ideas, it favours the attitude towards organic of craftsmen and entrepreneurs, but disfavours the attitude of conventional idealists. Taking expert observations into account,mobile vertical farm a more positive attitude of entrepreneurs and craftsmen regarding organic is indeed the case. But, probably more importantly, the effect of threshold values on output supports the argument made by Flache et al. that in agent-based models attention should be paid to the technical implementation of interaction between agents and the sensitivity of the outcome on the parameterisation of the interaction mechanism. In other words, model operationalisation of a conceptual model on influence affects model outcomes. An important model assumption, following from the social identity approach, was negative social influence to avoid association with out groups. In this research, the effect of disassociation between farmers on number of organic farmers and organic pigs depend on the likelihood of influence: when there was a higher likelihood of influence , negative social influence was actually a motivation for craftsmen and entrepreneurs to convert, while when there was a smaller likelihood of influence , negative social influence was a motivation for conventional idealists to convert to organic farming. This means that disassociation was an incentive for craftsmen and entrepreneurs to stay conventional in the scenario with a smaller likelihood for influence , which is in line with empirical research that showed that craftsmen and entrepreneurs oppose idealistic motivations .

It also means that disassociation with out groups was an incentive for craftsmen and entrepreneurs to convert to organic farming in the scenarios with a higher likelihood for influence. This is in contrast with empirical research, which showed that craftsmen and entrepreneurs oppose idealistic motivations for organic farming . The question is whether disassociation can be an incentive to consider market conversion in real life. So far, empirical evidence for or against negative social influence is poor . A way forward on this discussion would be to further explore model results with only positive social influence in accordance with the critique by Flache et al. . Given the results, it is likely that this would lead to fewer organic farmers in the scenarios with less likelihood for influence , and it would lead to fewer organic craftsmen and entrepreneurs in the scenarios with more likelihood for influence . According to the social identity theory, influence occurs through similarity . The results of the model showed that the three factors that defined similarity hardly affected the number of organic farmers, whereas similarity in farming style did affect the organic farmers’ farming styles: if similarity in farming style did not influence the credibility of another farmer, the number of organic craftsmen and organic entrepreneurs increased, while the number of organic idealists decreased. Similarity in farming style, therefore, obstructs diffusion of organic farming from idealists to craftsmen and entrepreneurs, given that the initial organic farmers were idealists. This social identity mechanism can, therefore, explain the finding by de Rooij et al. that entrepreneurs and craftsmen oppose organic farming because of idealist organic farmers. According to the experts, this mechanism mainly applies to the older generation farmers, since young farmers seem to have another rationale for converting than existing farming styles. The rationale used by the young farmers, mentioned by experts, fits the constructivist discourse identified by de Rooij et al. : they accept changes in societal norms regarding animal welfare and recognise the need to respond to this. Young farmers are, therefore, not obstructed by organic farmers’ farming styles. In addition, it is likely that similarity in farming styles does not weigh heavily within the decision-making process for some entrepreneurs and craftsmen, since they did enter the organic market according to the experts. Finally, experts also mentioned that most organic idealists changed from a dominant idealist rationale to a dominant craftsmen rationale to professionalise organic farming. This is interesting input for further refinement of the social influence mechanism: e.g., multiple farming style reference groups exist within one farmer , and the dominant farming style is flexible to the context.The effect of status on the number of organic idealists was seen by the relatively high number of organic idealists in the scenarios with a lower likelihood for influence: organic farmers had a high idealist status compared to the idealist status of conventional farmers. This triggered especially idealist farmers in the higher credibility threshold scenarios to convert. The effect of the status mechanism was, furthermore, seen in the relative importance of the price elasticity parameter on the number of organic entrepreneurs whose status is dependent on income: in the scenario with a small likelihood for influence, a decrease in price elasticity increased organic entrepreneurs ; in the scenarios with a larger likelihood for influence, a decrease in price elasticity resulted in fewer organic entrepreneurs .