This document intends to provide an analysis of the outcomes of the application of the TAP Common Framework in the eight countries of the Capacity Development for Agricultural Innovation Systems (CDAIS) project.  The TAP Common Framework (TAP CF) was developed at the global level as an initial activity of the CDAIS project in order to guide capacity development (CD) and strengthening of Agricultural Innovation Systems (AIS). The project then tested this framework in eight pilot countries (Guatemala, Honduras, Burkina Faso, Angola, Rwanda, Ethiopia, Lao PDR, Bangladesh).

The evidence base on agri-food systems is growing exponentially. The CoSAI-commissioned study, Mining the Gaps, applied artificial intelligence to mine more than 1.2 million publications for data, creating a clearer picture of what research has been conducted on small-scale farming and post-production systems from 2000 to the present, and where evidence gaps exist.

A range of approaches and financial instruments have been used to stimulate and support innovation in agriculture and resolve interlocking constraints for uptake at scale. These include innovation platforms, results-based payments, value chain approaches, grants and prizes, incubators, participatory work with farmer networks, and many more.

Innovation for sustainable agricultural intensification (SAI) is challenging. Changing agricultural systems at scale normally means working with partners at different levels to make changes in policies and social institutions, along with technical practices. This study extracts lessons for practitioners and investors in innovation in SAI, based on concrete examples, to guide future investment.

A huge increase in investment in innovation for agricultural systems is critical to meet the Sustainable Development Goals and Paris Climate Agreement. Most of this increase needs to come from reorienting existing funding for innovation. However, understanding whether an investment will fully promote environmentally sustainable and equitable agri-food systems can be difficult.