When was the last time you saw a global dataset that could actually explain 60 years of government-backed agricultural innovation? If you’re a policy analyst, data scientist, or food systems researcher, you know how rare that is. The Global Agricultural R&D Database (GRAPE database) is a valuable resource.
This is more than an open data project. This is a carefully-curated dataset that is global in scope, providing a history of public agricultural R&D obligations across 190 countries from 1960 to 2022. It provides not only real and comparable numbers behind some of the most significant structural changes in global agriculture, from the Green Revolution to today’s sustainable food security policies.
What Is the GRAPE Dataset in the Global Agricultural R&D Database?
GRAPE is the acronym for Global Research on Agriculture: Personnel & Expenditures. GRAPE was developed jointly by Wageningen University & Research (WUR), and the U.S. Economic Research Service (ERS). The full dataset is available to the public through Zenodo, and was also fully described in a peer-reviewed article in the journal Scientific Data (Van Dijk et al., 2025).
Key Features:
- Coverage: 190 countries
- Time Span: 1960–2022
- Metrics Included:
- Number of public agricultural researchers
- R&D expenditure (in local and internationally comparable terms)
- Purchasing power parity (PPP) adjustments
- Country-level macro indicators
- Formats: Excel, RDS, and PDF documentation
- Open license: Creative Commons BY-NC-ND 4.0
GRAPE is more than just a database. It’s a tool for decoding the often-overlooked public investment patterns that shape everything from crop yields to rural livelihoods.
Why the Global Agricultural R&D Database (GRAPE) Matters
Agricultural research has always served as an unheralded engine of economic growth, food security, and rural transformation. But until now, there has been no single credible source of longitudinal, internationally comparable data, to enable us to analyze these trends at a global scale.
Use Cases:
- Comparative R&D investment by country (i.e. Brazil vs. India vs USA)
- Charting historical patterns in public funding of agricultural science
- Connecting research investments with productivity growth and variabilities of sustainability outcomes.
- Historical policy shifts relating to investments right after the Cold War in Eastern Europe or the uptick in agricultural investments in Sub-Saharan Africa in the 2000’s.
- Evidence based development of approaches to and understanding of global planning of food systems.
Open, aligned data is needed in this paradigm more than ever, particularly given the nature of things like climate change, geopolitical turbulence, and population growth. GRAPE provides a stable baseline for modeling and decisions.
Global Agricultural R&D Database: Methodology and Data Integrity
The GRAPE dataset isn’t scraped together from press releases or patchy national statistics. It’s built from structured, vetted sources that follow the Frascati Manual (OECD) standards for R&D data collection, and harmonized with inputs from the IFPRI/ASTI initiative.
Data Curation Process:
- Data collection from government agencies, statistical offices, and research institutions
- Adjustment for exchange rate fluctuations and inflation
- Inclusion of metadata files explaining methodology and country codes
- Incorporation of PPP data and macro-level economic indicators to allow comparability
The dataset is versioned and comes with a full change log (see grape_version_history.pdf on Zenodo), making it trustworthy for longitudinal studies. Transparency and reproducibility are built into its design.
Some Notable Findings
In considering the data where there are some more surprising patterns:
- China’s public agricultural R&D spending increased dramatically between 2000 and 2014, eventually exceeding spending levels in the U.S. and France.
- Several African countries increased their research personnel since 2005 by a factor of three, which contradicts the conventional narrative of stagnant investment.
- The post-Soviet states have remarkable extremes in trajectories in research capacity which may implicate varied national food and land management strategies.
- Public agri-research budget in high-rich countries was stagnant or decreasing which could have implications on long-terms abilities to innovate for competitiveness.
These findings from GRAPE highlight that the state of the global agricultural research and research capacity and workforce is not just a dataset. It can also be considered a diagnostic tool of sorts for global development analysis usage.
Practical Tips for Using GRAPE: Exploring the Global Agricultural R&D Database
If you’re diving into the dataset for the first time, here’s how to get started:
1. Start with the Documentation
Download the grape_documentation_v1.0.0.pdf. It outlines variable definitions, data sources, and how to interpret null values and interpolated data.
2. Use the Pre-Processed R Files
For R users, the .rds files like rd_pre_imp_v1.0.0.rds or hr_pre_imp_v1.0.0.rds contain pre-imputed values, which are ideal for time series analysis.
3. Normalize Your Comparisons
To compare countries accurately, use the ppp_2017_db_v1.0.0.rds to adjust expenditures into PPP terms. This is essential when analyzing economic trends or making cross-country comparisons.
4. Explore Long-Term Trends
The rd_v1.0.0.xlsx file contains raw and adjusted R&D expenditures. Charting these over time can uncover decades-long structural shifts in agricultural R&D commitments.
5. Match R&D with Output
Use external datasets (e.g., FAO production data or World Bank development indicators) to correlate R&D investment with agricultural performance or GDP growth.
Why GRAPE’s Key Strengths Set the Global Agricultural R&D Database Apart
Unlike many sectoral datasets, GRAPE is:
- Truly global in coverage
- Historically deep (62 years!)
- Transparent and replicable
- Well-documented with clear licensing
- Built to support comparative and causal analysis
GRAPE will continue to exist in an active GitHub repository where users can view the coding they apply while processing the data, and contribute ideas for improvement. By using a committed and open approach we can enable the dataset to continue living, evolving, and being useful.
Who Should Care About GRAPE and the Global Agricultural R&D Database?
- Policy analysts evaluating the ROI of agricultural innovation
- Development economists studying growth patterns in rural economies
- Food systems researchers modeling future food security
- Educators and students looking for real-world data on public investments
- Think tanks and NGOs crafting global development strategies
GRAPE’s design exposes its relevance for not just academics but for stakeholders across the public sector and NGOs dependent on accurate historical and cross-country comparable data!
Final Thoughts: Data That Tells a Story
Agricultural R&D often lurks in shadows cast by other more visible debates broadly related to food policy, trade, or environmental sustainability. But the figures in GRAPE suggest that public investment in agricultural science is one of the cornerstones of long-term development.
We finally have a dataset that doesn’t just report numbers, but allows us to ask better questions:
- What happens when a country triples its agri-research staff?
- Does more spending always lead to better outcomes?
- How do geopolitical shifts influence national R&D priorities?
These aren’t just academic questions. They’re central to how we plan for the future of food, land use, and equitable development.
Interested in more datasets like GRAPE? Browse our curated collection of science databases covering global research, open data, and long-term trends.
References
- Van Dijk, M., Fuglie, K., Heisey, P., et al. (2025). A global dataset of public agricultural R&D investment: 1960–2022. Scientific Data. https://www.nature.com/articles/s41597-025-05331-y
- GRAPE Dataset Repository. (2025). Zenodo. https://zenodo.org/records/15507361
- GitHub Repository (Code & Development): https://github.com/michielvandijk/GRAPE
- OECD. (2015). Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development.
- ASTI/IFPRI. Agricultural Science and Technology Indicators. https://www.asti.cgiar.org
