Initial data required: –Macro data : For example, water availability, water quality, water use effectiveness, weather forecasts, salinity, current land use, sea level, soil mapping –Technical packages : Sowing and farm management practices that will enable water efficiency and resiliency –Technologies : Digital and non-digital, emerging equipment and machinery that can support farmers (e.g. precision agriculture, drip irrigation, etc.) –Policies and market mechanisms : guaranteed offtake, subsidies and other policy packages (e.g. carbon credit markets) –Financial mechanisms : Financial packages that can de-risk water-resilient practices –Historical data : Focused on agricultural practices and outcomes –Organizational data : Size of the organization, number of producers and their profiles, average and total size of farms, profitability over time, etc. The stack-based approach can provide insights into the food-water nexus (including some of the non-apparent interlinkages) and be a go-to tool for decision-making. It should be flexible and customizable to various scenarios, use cases and multistakeholder engagements. It is crucial to address constant variables like political dynamics, citizen engagement and historical policies. The stack should be inclusive, catering to both low- tech and high-tech solutions, with a focus on new incentivization and collaboration models. Data fragmentation remains a significant obstacle to building efficient intelligence infrastructure for food- water systems. While AI tools can help address several challenges, it is still critical to recognize the importance of authoritative data sources. The efficacy of the stack will depend on the quality of data. To ensure data readiness, the following design components need to be considered: Data collection and aggregation Data can be difficult to consolidate, as different sources provide it in various formats. In remote areas, the infrastructure needed for data collection is usually either insufficient or absent.15 Furthermore, guaranteeing data quality from a multitude of sources is also a challenge due to inaccurate equipment, gaps in coverage or human error. The stack will need to integrate the following: –A unified data integration layer: A set of connectors and technologies (e.g. IoT devices, satellite imagery, manual input) can help unify disparate data sources, improve infrastructure access, and ensure data quality. This layer enables different data formats (structured and unstructured) to coexist on one platform. –Aggregation tools: These include IoT gateways and edge computing devices that can collect and process data locally in environments with limited or intermittent connectivity.16 These devices can store and aggregate data locally and then synchronize with the cloud or central system when connectivity is restored. Another option is the use of satellite connectivity in areas where terrestrial internet infrastructure is insufficient to transmit data.17 –Data quality: Machine learning models can detect and flag outliers or erroneous data in real time, helping to clean and validate the data. Tools like Apache NiFi18 or Talend19 can be used to perform real-time cleansing and transformation as data is integrated. These can be implemented in the stack with protocols for regular calibration. Maintaining sensors to ensure they provide accurate data or incorporating self-diagnosing sensors (IoT devices that can flag performance issues) is also critical for consistent data quality. –Community engagement: Engaging local farmers, researchers, extension workers, governments, water managers and others through mobile apps to provide data, manual data validation, or feedback is critical for data efficacy. The data accuracy and density requirements will vary based on the specific use case (e.g. higher aggregation for policy-makers versus higher granularity for pre-season and in- season decisions by farmers).2.2 Design components to strengthen data-readiness Food and Water Systems in the Intelligent Age 14

Food and Water Systems in the Intelligent Age 2024