Adoption of Agri-Environmental Measures by Organic Farmers: The Role of Interpersonal Communication

Abstract

Purpose: The purpose of this study is to investigate the impact of interpersonal communication on the adoption of agri-environmental measures (AEM) by organic farmers in Germany.

Methodology: The study used the logit model to predict the probability of adoption behaviour, and Social Network Analysis (SNA) was conducted to analyse the question of whether validating information about organic farming provided by interpersonal information sources is associated with communication frequency.

Findings: Our findings demonstrate that being an early adopter of organic farming practices and frequent contact with young and highly educated farmers increases the probability of adoption of other AEM. However, contact frequency in interpersonal networks was found not to be a significant determinant for explaining adoption decisions. Frequently communicating farmers in the network are more likely to attribute higher levels of importance to organic farming information received from formal actors than to information received from informal actors.

Practical Implications: If young and highly educated farmers, who can be considered as informal opinion leaders, are approached by the extension services, then an effective diffusion of information on AEM can be expected. To support the AEM adoption, a platform should be provided by state agencies that would enable organic farmers to understand the environmental benefits achieved over the time.

Originality/value: This study contributes to the scientific discussion on the role of interpersonal communication on AEM adoption. A new aspect is our consideration of organic farmers adopting additional AEM. Moreover, we highlight organic farmers' validation of the importance of formal and informal information sources on organic farming.

Key Words:

• Communication frequency
• Interpersonal network
• Innovation adoption
• Agri-Environmental measures
• Organic farmers

Acknowledgements

This research used dataset collected during the FOODIMA Project (EU Food Industry Dynamics and Methodological) that has been funded by the EU 6th Framework Programme, contract nº 044283, www.eng.auth.gr/mattas/foodima.htm. We thank Karin Kataria, Zhanli Sun, Anne MacKinnon and several anonymous reviewers for comments that substantially improved this article.