Se reconoce que los procesos de innovación suceden entre un conjunto heterogéneo de actores, donde el Análisis de Redes Sociales (ARS) es una herramienta prometedora para su análisis y comprensión y, así, diseñar intervenciones basadas en red para catalizarla. Las intervenciones en red describen el proceso a través del cual se usan datos relacionales para acelerar el flujo de información entre los actores que la conforman.
Increasing investment and spending in agricultural innovation is not enough to meet Sustainable Development Goal (SDG) targets of ending poverty and hunger because the effectiveness of investments in low- and middle-income (LMI) countries is affected by the low quality of infrastructure and services provided, and by different norms and practices that create a considerable gap between financing known technical solutions and achieving the outcomes called for in the SDGs.
Desde los años 70, los estudios sobre adopción de innovaciones agrícolas han estado dominados por una perspectiva según la cual la decisión de adoptar es un asunto individual, centrado en la utilidad percibida por el productor. En años recientes ha crecido el interés por comprender el papel de la interacción social en estos procesos. Poco a poco, conceptos como capital o aprendizaje social han ganado terreno entre los analistas. Sin embargo, casi ningún estudio ha utilizado el Análisis de Redes Sociales.
The Commission on Sustainable Agriculture Intensification (CoSAI) and the Foreign, Commonwealth and Development Office (FCDO) jointly commissioned a gap study to determine how far away innovation investment is from helping agri-food systems achieve zero hunger goals and the Paris Agreement while reducing impacts on water resources in the Global South. The results show that the world can come much closer with some well-placed investments.
Considering the new opportunities that ICT innovations bring to improve performance of financial and extension services, this study looks at the potential contribution of financial and extension services to the Sustainable Development Goals (SDGs). The approach used extends the standard Data Envelopment Analysis (DEA) model to include longer-term management goals and find a solution that balances the efficient use of innovation investments and the achievement of policy goals, making this approach well suited for the analysis of the SDGs.
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.