A team led by FSE fellow David Lobell has found a valuable, untapped resource in historical data from crop yield trials conducted across sub-Saharan Africa. Combined with weather records, they show that yield losses would occur across 65 percent of maize-growing areas from a temperature rise of a single degree Celsius, even with sufficient water.
Over much of the world, the growing season of 2050 will probably be warmer than the hottest of recent years, with more variable rainfall. If we continue to grow the same crops in the same way, climate change will contribute to yield declines in many places. With potentially less food to feed more people, we have no choice but to adapt agriculture to the new conditions. New approaches are needed to accelerate understanding of climate impacts on crop yields, particularly in tropical regions.
This project is studying the potential effects of climate change on agriculture and adaptations options in African agriculture. The work will seek to assess climate threats to staple food crops at a country level, quantify the sources of uncertainty inherent in these assessments, and determine what implications shifts in crop climates have for agricultural adaptation and genetic resources preservation - with the end goal of helping prioritize investments in agricultural development and food security under a changing climate.
- Walter Falcon, Helen C. Farnsworth Professor of International Agricultural Policy, Emeritus
- Rosamond Naylor, Professor of Environmental Earth System Science and Senior Fellow at the Woods Institute for the Environment and at the Freeman Spogli Institute and Professor, by courtesy, of Economics
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I study the interactions between food production, food security, and the environment using a range of modern tools. Current work focuses on three main areas of research: how to effectively adapt agriculture to climate change, how to reduce yield gaps in major cropping regions, and how to quantify environmental consequences of biofuel and food crop production. A common theme is the use of large datasets to constrain and improve models that represent our understanding of how the world works. Prospective students interested in food security, climate change, and/or how to combine...