What's Happening?
The International Food and Agribusiness Management Review (IFAMR) has issued a call for papers for a special issue focusing on how farmers' organizations facilitate the adoption of green agricultural technologies. This initiative seeks to gather research
on how cooperatives, producer organizations, and other forms of collective action can help farmers access sustainable technologies, overcome adoption barriers, and participate more effectively in agricultural development. The special issue aims to examine the influence of these organizations on the adoption of green agricultural technologies, assess how collective action improves access to information, training, services, inputs, and markets, and investigate whether they help reduce inequalities in technology adoption. It also seeks to evaluate the economic, environmental, and social outcomes associated with these technologies and generate evidence to inform agricultural policies and development programs. Contributions are particularly encouraged from researchers examining technology adoption among vulnerable and underserved farming groups.
Why It's Important?
This call for papers is important because it addresses a critical aspect of agricultural sustainability and inclusive development, particularly in the U.S. and globally. By focusing on farmers' organizations, IFAMR highlights the collective power and potential of these groups to drive the adoption of green agricultural technologies, which are essential for mitigating climate change, conserving resources, and ensuring food security. The research generated will provide valuable insights for policymakers, practitioners, and development agencies, enabling them to design more effective agricultural policies and programs. This can lead to improved agricultural productivity, enhanced environmental sustainability, and more inclusive rural development, benefiting smallholder farmers, women, young farmers, and remote communities who often face significant barriers to technology access. The emphasis on reducing inequalities in technology adoption is crucial for fostering equitable growth within the agricultural sector.
What's Next?
Researchers and practitioners are invited to submit full papers by December 15, 2026. The submitted manuscripts will undergo a double-blind peer-review process, with an initial screening for relevance and quality. Accepted papers may be published online ahead of the completed special issue, which is expected to be published in 2027. The findings from this special issue are intended to inform agricultural policies, development programs, and institutional strategies globally. This will likely lead to increased awareness and potentially new initiatives aimed at strengthening farmers' organizations and their role in promoting green agricultural technologies. The insights gained could also influence funding priorities for agricultural research and development, encouraging further innovation in sustainable farming practices and support for farmer-led initiatives.
Beyond the Headlines
The initiative delves into the deeper societal and economic implications of agricultural technology adoption. It implicitly acknowledges that technological advancements alone are insufficient without robust social structures to support their implementation and equitable distribution. By focusing on farmers' organizations, the call highlights the importance of community, collective action, and knowledge sharing in fostering sustainable agricultural practices. This approach moves beyond a purely technical view of agriculture, recognizing the cultural and social dimensions that influence how technologies are adopted and integrated into farming systems. It also underscores the ethical imperative to ensure that the benefits of green agricultural technologies are accessible to all farmers, particularly those in marginalized communities, thereby promoting social justice and economic equity within the agricultural sector. The long-term shift could be towards more community-centric and ecologically integrated agricultural models.
