Scenarios to avoid the worst case

Foresight and impact models must be accessible to everyone to increase their reliability for decision making. Photo: P.Casier (CCAFS)

The pessimists in our midst are seeing a future of mass starvation. Worst-case scenarios for climate impacts, GDP growth and population increase point to a world where the majority of people in low-income developing nations are living at an intake of just around 1,800 calories a day.

In fact, says Gerald Nelson of the International Food Policy Research Institute (IFPRI), “if you’re thinking about the future and you’re not thinking about climate change,” says “you’re making a huge mistake.”

Speaking to participants and partners in the CGIAR research program for Climate Change, Agriculture and Food Security (CCAFS) at the Global Conference on Agricultural Research for Development (GCARD2) in Punta del Este, Uruguay, Nelson highlighted the important role that foresight must play in connecting CCAFS science to its use in society.

If IFPRI’s climate impact projections (part of the Global Futuresresearch project) are correct, we could see drastic price increases for maize and a resulting blow to human well-being. But that “if,” says Nelson, is precisely the challenge. To what extent can we depend on these projections for creating foresight-driven policy? Why do such broad disconnects exist between different models, and what does that mean for their reliability?

The key to making scientific foresight models useful is to make them, well, not so scientific. They must be accessible to everyone, says Nelson. We must train technical advisors to use them so that policy makers can customize the results to their interests. Regional actors need to put them to use for planning and adaptation strategies. Farmers need to be able to understand them to make farm-level management decisions.

The feedback that would result from that sharing process is the key. If a regional user implements the computer model and realizes that the data for his region are not relevant or don’t work, then those data can begin to be refined.

The feedback process is how the discrepancies between predictive models are closed; otherwise, policy makers won’t know who to listen to. “They end up just listening to the guy with the nicest powerpoint presentation” rather than the best science, says Nelson.

On the other hand, an informative, useable and understandable modeling tool that can be used by anybody – not just an IFPRI scientist – enables the making of that elusive species of “informed” decision.

The hope is that truly informed decisions, ones that don’t make the mistake of ignoring climate change, could help us avoid the worst case scenarios.

See more about IFPRI’s work on Global Futures and climate scenarios.

Stay updated with happenings at the GCARD2 conference on the GCARD2 Social Reporting Platform

Blogpost by Caity Peterson, one of the GCARD2 social reporters.

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