The application of knowledge management (KM) to agri-food systems has been challenged by a lack of clarity in the overall goals for “system” performance, structural disconnects in the transmission of information, and dynamics that intentionally or otherwise dis-incentivise positive changes at different scales. These implementation barriers have hindered the ability of KM to bolster innovation and development in agriculture. Through the outlined principles and processes, the Agricultural Knowledge Management for Innovation framework can assist in closing the cycle of continually re-creating knowledge, evaluating and iterating upon innovations, building coalitions to democratize knowledge access and utilization to facilitate course-correction of all stages of KM.
Over 2 billion more people will need to be fed balanced and healthy diets by mid-century and substantial dietary shifts will be needed to improve both human health and environmental sustainability. Climate change, urbanization, and changing intensifying patterns will further increase the pressure on our productive ecosystems.
Human nutritional security is driven by complex and dynamic systems that interact with agriculture and food production, distribution, economic and physical access, consumption, health, and environmental issues, and affect the stability and sustainability of the food supply and of nutrition itself.
This paper provides a systemic conceptualization to illuminate functional elements of complexity in agri-food system processes and applications of Knowledge Management (KM) to meet agricultural and innovation goals. It builds on existing frameworks and integrates historical agricultural KM strategies while facilitating the targeting of locally adapted technology, and constructive and collaborative knowledge sharing for innovation.
The proposed AKM4I framework intends to
i) recognize that agri-food systems are complex adaptive systems and that KM must reflect this complexity;
ii) support the integration of explicit, implicit, and tacit knowledge through process-oriented “knowledge in action”;
iii) lay the foundation for reciprocal and collaborative relationships amongst diverse stakeholders;
iv) incorporate context specificity in agri-food systems across sites, actors, and processes; v) address the importance of communication channels for knowledge and innovation;
vi) move beyond traditional monitoring and evaluation to incorporate accountability and learning; vii) account for potentially obstructive power dynamics and knowledge ownership challenges; and
viii) create opportunities to integrate KM with decision-support systems and tools, for the benefit of all stakeholders in the agri-food systems.
Agri-food systems are an example of a planetary-scale complex adaptive system as they are composed of many heterogeneous pieces interacting in nonlinear ways that strongly influence overall outcomes. They operate at a range of temporal and spatial scales and are comprised of complex interconnections, which complicates the task of achieving synergistically positive developments across multiple priorities.
Meeting humanity’s current and future nutritional needs via food production and distribution, while simultaneously ensuring long-term environmental and economic sustainability along with human health and social equity is a “grand systemic challenge”.
While the term “sustainability” has eluded an adequate functional definition, there is general agreement that the goal is to steer food production and distribution systems to fully meet humanity’s present needs and other requirements indefinitely.
A sustainability space for any given system has n-dimensions defined by the multitude of social and ecological boundaries that represent the limits of acceptable conditions for a system. Hence, sustainability can also be defined as a measure of the extent to which systemic changes, over time, move components of the system within or beyond the limits of a non-static sustainability space.
The application of KM to agri-food systems has been challenged by a lack of clarity in the overall goals for “system” performance, structural disconnects in the transmission of information, and dynamics that intentionally or otherwise dis-incentivise positive changes at different scales. These implementation barriers have hindered the ability of KM to bolster innovation and development in agriculture.
Through the outlined principles and processes, the AKM4I framework can assist in closing the cycle of continually re-creating knowledge, evaluating and iterating upon innovations, building coalitions to democratize knowledge access and utilization, and using MEAL to facilitate course-correction of all stages of KM.
A complete establishment of this framework could allow to collect data from diverse sources (field, satellite, sensors), and use it to model complex agri-food systems, identify tipping points, elaborate relevant metrics and accompany intervention processes to guide positive innovation outcomes. The AKM4I provides a frame that considers some of the common pitfalls of KM for agri-food systems, to realize KM’s potential contributions to innovation and improved systemic outcomes.
Read the study:
Gardeazabal A, Lunt T, Jahn M, Verhulst N, Hellin J, Govaerts B (2021) Knowledge management for innovation in agri-food systems: a conceptual framework. Knowledge Management Research & Practice.
