Quantifying unintended effects of an agroecological research project on farmers' practices and social network in Papua New Guinea

Abstract

Abstract Agroecological researchers and advocates often make assumptions about the social impact and dissemination of their work: researchers may assume that their work has impact through postresearch dissemination, while advocates may assume that new agroecological practices can be effectively spread through existing social networks. Here, we test these assumptions by quantifying the effects of an agroecological research project on farming practices and the social network in a village community in Papua New Guinea. The project aimed to test the effect of applying banana peel compost, chicken manure and NPK fertiliser on sweet potato yields. Local farmers were involved in the research as project garden owners or research assistants. Using stochastic actor‐oriented modelling, we tracked changes in farming practices and the social network. Over the course of the research project, more people started to use food waste on their farms, while animal manure and NPK fertiliser were not frequently adopted. Farmers also took up practices that were not directly researched, such as mulching and planting the specific variety of sweet potato that was used in the project. This suggests that local farmers created meaning from the project, despite the researchers not intending to give advice until the end of the project. The research project also affected the community's social network. Research assistants became more often sought‐after for advice, while knowledge about the project did not flow far from those directly involved. These results indicate that who gets involved in a project may have social consequences, and show the importance of understanding existing social networks before they are relied upon for spreading farming practices. Overall, this work challenges often‐held assumptions about the social impact and dissemination of agroecological research, provides insights into the types of agricultural innovations more likely to be accepted among farmers, and explores how new practices may most effectively be promoted within a community. Read the free Plain Language Summary for this article on the Journal blog.

Keywords

• behaviour change
• fertiliser
• knowledge exchange
• science‐policy gap
• social network analysis
• stochastic actor‐oriented modelling