Open Ag Data : Landscape Analysis ●Who is involved in collecting data on agricultural investments, and from whom? ●How is data publicly shared? Which sources could be usefully opened up to wider access? ●What is the nature of data being collected? How is data collected, and how frequently? ●How is data being classified and structured? ●Where are the areas of duplication, or the gaps, in existing data collection efforts? ●What are the common challenges in collecting and using agricultural data?

Three kinds of investment data: Aggregate Project Outputs Country or region level data on investment, including data from national statistics, national budgets, and regular or ad-hoc surveys. Projects funded or implemented. Drawn from donor systems, country aid management platforms, crowdsourcing, and company portfolios. Results data, reports and research outputs, held in custom platforms, internal systems and research repositories. ASTI: Agricultural Science and Technology Indicators (IFPRI) ADAM - Agricultural Development Assistance Mapping (FAO) Digital Green Analytics Dashboard (Digital Green)

A wider landscape Infrastructure data Extension service data Geodata Taxonomies

Data sources

Data sources 1. OECD DAC Reporting [ Project Level ] 2. IATI Publication [ Project & Activity Level ] 3. National Aid Management Platforms [ Project, Activity & Output level ] 4. Non-aid project databases [ Project, Activity & Output level ] 5. Organisation, project and network websites [ Project, Activity & Output level ] 6. Transactional websites & project reporting tools [ Project, Activity & Output level ] 7. Surveys, administrative and reporting processes [ Aggregate & Project ] 8. Crowd-sourcing and desk research [ Aggregate & Project level ] 9. Contracting data [ Activity & Output level ] 10. Research repositories [ Output level ]

The International Aid Transparency Initiative (IATI) ●IATI represents a dynamic and growing set of organisations openly sharing data in a standardised method and format, providing a key platform ( There are over 550,000 IATI “activities” published by 450+ organisations.)450+ organisations ●Information on agricultural investments is readily and openly available via IATI datasets and interfaces. ●Nearly all of the major bilateral and multilateral donors in agriculture publish IATI data ●The IATI community is active and open, providing conditions for discussions and adoption of agriculture investment specifics

IATI & ag data: who ●The IATI sector codelist provides a mechanism to discover agriculture investments - eg: describes “Agricultural services” ●Of the 40 IATI sectors codes relevant to agriculture investments, there is a greater take-up of the more detailed five-digit codes ●A small core set of codes have the most traction right now, although a long tail exists to represent adoption of more niche and specific classifications ●A small number of IATI publishers account for most of the agriculture IATI data ●These publishers all tend to be larger institutions and established IATI / DAC publishers

IATI & ag data: what Broadly speaking, these IATI activities are of “general” quality - detailing where (at country level), who (organisations) and what (descriptive text and sector codes) A much smaller subset includes data on sub-national geography & results

IATI & ag data: Whilst the data is standardised, it is also important to consider that IATI is not a database. In practice, IATI is made up of distributed open datasets maintained by hundreds of organizations that have different data practices, and accessed through a range of platforms and entry points. Understanding this context is key when analyzing and using IATI data.

Tools

Recommendations QualityCollectionCuration Develop a quality framework for agriculture investment data. Which IATI fields are essential and how should they be used? Provide quality assurance tools. Enhanced validators. Provide publisher support. Helping to improve data quality over time. Multiple entry points. Not all data will come from IATI. Alternative inputs will be needed for full coverage. Pilot enhanced survey processes. Drawing on existing data, rather than starting from scratch. Deploy data enrichment tools to classify location and subject based on documents. Build a corpus of agriculture investment data, ready for use.

Where next?