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With research staff from more than 60 countries, and offices across the globe, IFPRI provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries.

Liangzhi You

Liangzhi You is a Senior Research Fellow and theme leader in the Foresight and Policy Modeling Unit, based in Washington, DC. His research focuses on climate resilience, spatial data and analytics, agroecosystems, and agricultural science policy. Gridded crop production data of the world (SPAM) and the agricultural technology evaluation model (DREAM) are among his research contributions. 

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IFPRI currently has more than 600 employees working in over 80 countries with a wide range of local, national, and international partners.

How will crop yields change with an uncertain climate future?

Open Access | CC-BY-4.0

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IFPRI researchers recently received some attention for research on how climate change will affect croplands in the future. Using HADGEM2, a climate prediction model, Research Fellow Richard Robertson was able to predict what kinds of yields we might see under climate change for potatoes, rice, wheat and corn in 2050.

What does the model show us?

If we use the model, we can provide predictions for how the climate might change in any given place to be able to say that if the climate looks like this, then we are going to get yields that are like this. So if it gets two to three degrees warmer the yields are going to change.

National Geographic called the model aggressive. How so?

Aggressive means that of the three or four models that we have data for, this is the most pessimistic. This is the one that by and large tends to predict the largest temperature changes, which in general are up. And the outcomes aren’t exactly happy. That’s what we meant by aggressive.

Why did you choose the crops that you did?

The crops we chose are the major starchy staples that we can get our hands on easily. The one that’s really missing, that we did a little bit with but didn’t make it into the final cut, is soybeans. Collectively, rice, maize, wheat, soybeans, and potatoes comprise almost half of people’s diets. Basically, that’s the way the world gets fed.

According to the model, is there a region that will benefit from climate change?

If you’re in the mountains or up north where it is already cold, climate change is good because now you go from being on the cold end of the range where it is reasonable to grow the crop to the temperatures moving more into the hump. The temperature is higher, the chemistry works faster, living things are happier. But if you are in India growing spring wheat during the winter season, because that’s the only time it is just barely cool enough that the wheat can stagger through, if you raise the temperature by 3 degrees, now instead of being on the edge of reasonable, you’re headed toward destruction.

Did the results of the modeling tell us anything we didn’t already know?

What showed up on those two pages is basically the same thing that IFPRI has been discovering since it embarked on global crop modeling about seven years ago. By and large, the conclusions coming out are pretty much the same. Now, we have a lot more confidence in the way that we do it because it is a lot more refined and there is better weather data. We are still coming up with the same numbers, we’re just a little more confident in them. Now, I don’t know how widely this is known, but as far as the very small community of people who do this kind of thing, this isn’t earth shaking. So in that sense, yes, we are learning something new because now we also can get geographic differences and spatial disaggregation.

Are any there misconceptions that contribute to how the public thinks about climate change?

Well, the problem with only talking about climate change in relation to rising temperatures and global averages is that the world is a big place! So what we are saying is that we are going to try to get more detailed. It’s important for people to understand that climate change affects agriculture differently depending on where you live. We sought to better understand how climate might change in different places, and our model predicts that with this temperature change here, yields in this place will be affected by X amount.

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