From Event to Action: Accelerate Your Decision Making with Real-Time Automation
Stanford university
1. An abstract of the Rockefeller Foundation supported Project under the Climate Smart for Rural
Development Initiative
Project Title: Prioritizing Investments in Food Security under a Changing Climate
Grant number 2008 CLI 301
Name of the Organization: Stanford University
Country: USA
Name of the Contact Person: David Lobell
Email: dlobell@stanford.edu
Telephone: +1-650-721-6207
Key Objectives
1. Assessing climate threats to staple crops by 2030, at a country level.
2. Determining the relative importance of climate and crop models as a source of uncertainty.
3. Determining “climate-agriculture analogues” in 2050 and 2100.
4. Getting data on agricultural production and consumption in an easily usable and publicly available
form.
5. Translating crop productivity impacts into food security and/or poverty impacts.
Key Activities
1. Work with organizations in Africa to compile data on past crop production at various scales,
including field, state, and country level
2. Apply statistical modeling techniques to these datasets to understand crop responses to climate
3. Use process-based crop models as a comparison with statistical models
4. Access climate projections for Africa and apply crop models to identify impacts and associated
adaptation needs over the next few decades
5. Work with economic models to translate impacts into poverty or food security measures.
Key Deliverables
1. Country-level information on impacts for main crops, and existing analogues in different parts of
Africa, and other parts of the world where possible and appropriate.
2. Finer scale projections where possible.
3. Peer-reviewed scientific publications that give a clear description of the methods, results, and
sources of uncertainties.
Expected Outcomes
1. A more robust and quantitative understanding of how climate change will affect agriculture in
Africa over the next few decades.
2. Identification of key regions in need of rapid adaptation efforts.
3. Where possible, insights into which adaptation measures are likely to be most effective.