Global Open Data for Agriculture and Nutrition (GODAN) and The Haller Foundation joined forces in 2016 when the UK based charity released version one of the Haller Farmers App.

Digitalization and internet use are transforming every aspect of our lives. Digital technologies are profoundly changing how we grow food, pack it, transport it and even shop for food. Digitalization and use of digital data, applications, and platforms are opening new possibilities for developing and restructuring the agrifood system. Digital agriculture is turning to digitalizing agrifood, rural economy, and rural societies. This report introduces the FAO Digital Village Initiative, which aims to facilitate through knowledge and information.

This report aims at inspiring strategic thinking and actions to transform agrifood systems towards a sustainable, resilient and inclusive future, by building on both previous reports in the same series as well as on a comprehensive corporate strategic foresight exercise that also nurtured FAO Strategic Framework 2022–31. It analyses major drivers of agrifood systems and explores how their trends could determine alternative futures of agrifood, socioeconomic and environmental systems.

Urban-rural gap and regional inequality are long-standing problems in China and result in considerable number of studies. This article examines the dynamic behaviours of incomes for both urban and rural areas with a prefectural data set. The analysis is conducted by using a distribution dynamics approach, which have advantages in examination on persistence, polarization and convergence clubs. The results show that persistence and immobility are the dominant characteristics in the income distribution dynamics.

Much has been written on the determinants of technology adoption in agriculture, with issues such as input availability, knowledge and education, risk preferences, profitability, and credit constraints receiving much attention. This paper focuses on a factor that has been less well documented: the differential ability of households to take on risky production technologies for fear of the welfare consequences if shocks result in poor harvests. Building on an explicit model, this is explored in panel data from Ethiopia. Historical rainfall distributions are used to identify consumption risk.