Canada Digital Agri-Food
leadership for the digital transformation of Canadian agri-food


The vision of Canada Digital Agri-Food (CPAF), a Canada-wide common infrastructure evolved from the Ontario Precision Agri-Food (OPAF) and Canadian Precision Agri-Food (CPAF) vision: of developing a platform for secure data collaboration amongst all members of the Agri-Food value chain, addressing issues of data standards, data interoperability, data security, seamless sharing of data and information between stakeholders in complex environments, and providing an ecosystem for open innovation.

The OPAF initiative is collectively defined by the activities and outcomes of Phase One, Two and Three projects.

The activities and findings of Phase One and Two provided the foundation for next steps, to develop a comprehensive open Canadian Agri-Food data collaboration and innovation platform. This is a long-term initiative with participation from all stakeholders to build the functional components (e.g., reporting, analytics and visualization tools), to integrate existing data assets with emerging data assets (e.g., farm, input suppliers, service providers, government, academia, private enterprise and open-data repositories) and enable the implementation of industry-wide prescriptive analytics capabilities (by employing such techniques as machine learning) that will drive the next generation of decision support system capability. The proposed platform allows for ‘living’ data; where data is not connected once, but integrated across time and space, where reporting and analysis will pull from the most recent, near real-time data.

Phase Three was the implementation and test drive of the envisioned platform.

Phase 1 Reports

The goal of Phase 1, was to identify opportunities for Ontario to take a leading role in developing strategy, vision and necessary infrastructure to facilitate and accelerate the validation, adoption and innovation of Precision Agri-Food Technologies (PAT).  

Phase 1 of this study is complete and studied the following aspects of PAT in Ontario.

User Needs Assessment

  • Establish Ontario Precision Agri-Food (OPAF) as a non-profit institute dedicated to the facilitation of advanced PAT capability within Ontario based on an organizational and collaborative model similar to models employed by LEI Wageningen UR.  OPAF will act as a pilot for a broader national program.
  • Employ cutting edge data management tools and concepts in the scoping and definition of OPAF pilot projects to ensure that a hybrid data environment platform developed is and will continue to be relevant within the rapidly evolving global ICT landscape.  Such infrastructure will capture, store and enable appropriate access to large data sets to multiple stakeholders. 
  • Include a focus on the incubation and validation of PAT technology that can be marketed globally.  Focus on ROI for various stakeholders and ICT standards that enable/accelerate further innovation, collaboration and amplification of data value for all users.



Ontario IT Capacity Assessment

  • We must increase rural and academic IT capacity
  • Create infrastructure to facilitate access (academic and otherwise) and collaborations with big data sets
  • Identify, create, customize and standardize software tools of suitable performance to handle the ‘4 V’s’ of data sets (volume, variety, velocity and veracity).

Global Precision Agriculture and Big Data

  • A facilitator/integrator for the Precision Agri-food sector is a critical need for an Ontario initiative for the long-term and multi-stakeholder benefit of big data implementations.
  • Support for and development of multi-disciplinary teams to address limitations outlined in R&D section below.

IT Standards, Security and Sharing

  • Establish an independent organization dedicated to the facilitation and guidance for PAT implementations and innovation.
  • Develop protocols and standards relating to data, metadata, web services and data exchange for the PAT sector.
  • Adopt a service oriented architecture strategy to best enable appropriate and multiple stakeholder access to data.
  • Leverage best practices in data privacy and security.  Identity and trust models as well as encryption should be developed where necessary.  User training must be available.
  • Enable broad sensor webs and use of cloud services (third party audits may be necessary).