Although our understanding of these forces remains somewhat limited, given our review of the literature, certain kinds of motivations and interventions appear to influence adoption rates, particularly positive feedback from within the social network, transition advisory services, marketing supports and transition subsidies. In many studies, it is the combination of these supports that accelerates adoption. There may also be other important supports but these have been less well studied.
One complicating factor is the nature of the studies undertaken. Ideally, ex-ante studies would exist that identify conventional farmer attitudes and understandings prior to conversion. But most studies are ex post facto because ex ante studies have proven to be too hypothetical, with too large a temporal gap to actual conversion (Knowler and Bradshaw, 2007). Most studies also pre-categorize producers based on behaviour and membership in certain organizations (Reimer et al., 2012) which can skew interpretations of the most important driving forces.
Another complication is determining optimal levels of these supports, especially when the objective is full conversion in a sub-watershed. The data tell us that multiple variables are in play and that the strength of each likely varies by producer and regional conditions. Regarding transition payments, for example, the Centre for Rural Economics Research concluded that 25% of conventional farmers would convert without payment, some 40% would be incentitivized with payment but that 33% might not convert at any price. As Sautereau (2009) indicated, some farmers will always so value their independence that they will not participate in courses and planning for the transition.
Determining the level of payment is also complex. In the UK, the payments in general agri-environmental schemes have often compensated for 20% of market losses, but at 40% for organic production schemes (Lohr, 2001). The shift to 40% in the UK in the 1999 period appears to have moved organic adoption quickly to 4% of national agriculture, but the curve to get to 30% above that level required, according to conventional farmers, payments of 150% of lost gross revenue.
Uptake related to other measures is less clear. Both Kaufman et al. (2009) and Deffuant et al. (2002) discussed the manner and the number of interactions between the sender and receiver of adoption attitudes. Their models found that as the frequency of the interactions increases, so often does the willingness to adopt. The frequency of contact with a “willing to adopt” agent or increased supports, especially when the agent is visited by a transition advisor, all appear to have a positive impact on transition rates. There may be a dynamic progression from unwillingness to convert to neutral to willingness, to seeking out information on one’s own (including market information), to engaging an advisor to converting and receiving a subsidy.
Based on this review, and given the limited quantification of these parameters and their interactions, we decided to design our model using the following (for further details on how these were integrated into the model and parametized, see Ghaffari et al., submitted). Our parameters represent a mix of motivations and characteristics (internal factors), clearly important as adoption drivers from an extensive number of studies, but also significant structural elements and social network elements (external factors) that the literature identifies as a brake on adoption.
1. Farm agent characteristics
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- Personal conviction and attitudes toward environment and sustainability
- Personal experience of negative effects of conventional farming
- Financial difficulties with the current model of farming (whether conventional or organic), defined as a farm agent’s net income $0 or 50% of mean farm incomes in the watershed
- Willingness to innovate
- Influence of the social network
2. Availability of government programs to support organic transition”
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- Marketing infrastructure support
- Existence of transition advisory services
- Access to information
3. Economic variables
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- Gross and net income of organic production, using enterprise budgeting
- Existence of government subsidy for organic transition, calculated as a percentage of lost gross revenue during transition
- Onetime avoided cost payment for environmental benefits associated with organic transition
Given uncertainty in the literature about what levels of support will optimize adoption, the model is designed to allow users to set levels and types of supports and levels of organic farming abandonment in the face of poor economic performance. Such numbers can be refined with an empirical study of conventional farmer attitudes to organic conversion, using for example an ethnographic decision tree approach (Fairweather, 1999; Darnhofer et al., 2005). In future work, we will set model parameters at different levels and report on a range of scenarios related to system shocks (e.g., drought, peak natural gas) and optimal transition supports and how these phenomena affect the trajectory of organic adoption in a subwatershed.
Acknowledgements
Funding for this research was provided by Agriculture and Agrifood Canada through the Organic Science Cluster, coordinated by the Organic Federation of Canada. Many thanks to Mark MacNeil for research support.
