India India’s initiative promoting the use of digital technologies in agriculture aims to boost productivity, sustainability and resource-use efficiency while delivering actionable insights on value chains for farmers, markets and governments. It seeks to combine various data sets, including soil health and weather patterns, with advanced technologies like AI and the internet of things (IoT). A few examples include: –The Agricultural Development Trust in Baramati, Maharashtra, in partnership with Microsoft, Oxford University and Click2Cloud, sought to enhance crop productivity and sustainability by integrating real-time crop, weather and market information through IoT- and cloud-hosted data points. Initial use of this technology in farming has led to 20% increases in production and 8% reductions in water uses.7 –The 2030 Water Resources Group (2030 WRG), the World Bank and the Government of Uttar Pradesh (UP) worked with Microsoft to pilot a holistic solution for data-driven sustainable agriculture. The advisory support, powered by AI, aims to markedly lower carbon and water footprints and production costs while boosting the stagnant yields of rice and sugarcane crops. Once validated, this AI-assisted model is anticipated to aid the Department of Agriculture in refining policies and enhancing the efficiency of resource planning and management. Significant enablers influence the deployment and scale of these programmes. Macro-level pro- innovation policies have driven the rapid adoption, mainstreaming and acceptance of AI-based solutions to public policy issues. The establishment and scale-up of pilot projects rely on a strong, responsive multistakeholder partnership. Partnerships at the grassroots level in Maharashtra and Uttar Pradesh have highlighted how crucial institutions are in providing resources and data- driven support to train and validate models. The UP-Accelerator platform by 2030 WRG has facilitated a private-sector-driven ecosystem designed for public/private partners (e.g. the government and Microsoft). There is a significant need to enhance AI literacy among stakeholders, including the government, to effectively conceive, create, enable and deploy solutions in agriculture and water management. These examples illustrate how data and a stack framework can effectively help a diverse set of stakeholders, including different government agencies, farmers and the private sector, make data-driven decisions to improve food and water outcomes while supporting development and livelihoods. Applications and use cases of the food-water stack Countries are using digital tools to enhance sustainable agriculture and water management, supporting resilient food systems.2 2.1 Country-based use cases for the food-water stackThree cases illustrate how the stack framework can bring together stakeholders for more sustainable management of water resources. Food and Water Systems in the Intelligent Age 9

Food and Water Systems in the Intelligent Age 2024