About This Theme
Many technologies that have been developed to address food insecurity are not adopted due to gaps in policy, the regulatory environment, research, distribution, and marketing systems. As P2IRC develops its own suite of technologies and processes to address global food security, this research will help to determine their pathways to approval, acceptance, and widespread adoption.
The social license theme will work with the phenometrics, image acquisition, and computational informatics themes to gain a detailed understanding of P2IRC technologies and processes and their potential future applications. This knowledge will inform research to identify the regulatory and commercialization barriers that the P2IRC-developed tools face and also to evaluate their positions in the evolving global intellectual property framework.
P2IRC-developed tools are intended to have a profound impact on global food security and this potential is realized only when breeders worldwide can use these tools effectively. This research will assess how designed crops may secure freedom to operate and will help determine which pathways to adoption have the greatest potential to advance global food security.
Securing Social License and Clearing Regulatory and IP Hurdles for Widespread Adoption of P2IRC Innovations
Project 4.1 will determine a path to social license for P2IRC innovations that will mitigate the social, economic, legal, and regulatory pushback that is often experienced in this product space. The suite of technologies and applications embodied in P2IRC, while not fundamentally transforming foods in overtly controversial ways, will be a catalyst for significant changes in the way that crop breeding is done and how it is organized.
This research will investigate data-intensive machine learning in the global crop system, focusing initially on the breeding, regulatory, and commercialization pathways for three important large-area crops; wheat, canola, and lentil. Traditional decision analysis will be supplemented by innovative qualitative assays, interactive surveys, dynamic economic modeling, social networking probes, and behavioural experimentation to unpack the contingencies that underpin conventional plant breeding social license. This knowledge will then be utilized to anticipate how data-intensive machine learning may follow a path to the same widespread adoption that conventional breeding has garnered.