This paper (Part I) present a case study of work conducted by the International Centre for Tropical Agriculture (CIAT) to adapt network mapping techniques to a rural and developing country context. It reports on work in Colombia to develop a prototype network diagnosis tool for use by service providers who work to strengthen small rural groups. It is complemented by a further paper in this issue by Louise Clark (Part II) which presents work to develop a network diagnosis tool for stakeholders involved in agricultural supply chains in Bolivia.

This paper (Part I) present a case study of work conducted by the International Centre for Tropical Agriculture (CIAT) to adapt network mapping techniques to a rural and developing country context. It reports on work in Colombia to develop a prototype network diagnosis tool for use by service providers who work to strengthen small rural groups. It is complemented by a further paper in this issue by Louise Clark (Part II) which presents work to develop a network diagnosis tool for stakeholders involved in agricultural supply chains in Bolivia.

Participatory Impact Pathways Analysis (PIPA) is a practical approach to planning, monitoring and evaluation, developed for use with complex research-for-development projects. PIPA begins with a participatory workshop where stakeholders make explicit their assumptions about how their project will make an impact, and produce an ‘Outcomes logic model’ and an ‘Impact logic model’. These two logic models provide an ex-ante framework of predictions of impact that can also be used in priority setting and ex-post impact assessment.

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.

Finance is a key lever for turning agriculture from a potential source of environmental harm and social inequity to a driver of conservation and social inclusiveness. Private and public sector funding for farmers to combat climate change and protect and restore nature (‘Paying for Nature’) is rapidly increasing. Yet this new funding may not reach its aims without drastically improving farm-level reward mechanisms.