As the planet warms, China’s wheat crops will be threatened by more frequent epidemics of ‘fusarium ear blight’ (FEB), scientists in the UK and China have projected. Bruce Fitt from the University of Hertfordshire in Hatfield, UK, and his teammates forecast levels of the disease in the Anhui and Hubei provinces from 2021-2050. Whereas in the worst affected regions in 2001-2010 around one-sixth of all ears were infected, this was the lowest disease level the researchers found in their future scenario. In the worst-hit areas, FEB infected more than a third of all ears. “This has implications for crop breeding because it takes 10-15 years to breed a new cultivar,” Bruce told me. “If you know the disease is going to become more important then you need to get on and start breeding now rather than waiting until the disease hits you.”
Today, over a billion people don’t have enough to eat, and further population growth and climate change are set to put the world’s food supplies under even greater strain. To help ease that pressure, Bruce and other scientists are working to understand and help improve control of crop diseases like FEB. While some crop diseases will worsen in the future, not all will, he stressed. “For example, you might have a disease that is spread by rainsplash in summer and then it’s predicted that there will be far less rainfall in summer,” he explained. “Then you would expect that with climate change the importance of that disease would diminish.” If governments, farmers and seed suppliers know which diseases are likely to get worse, they can prioritise developing strategies to contol them, like breeding disease resistant varieties.
To make useful forecasts for which diseases will worsen, scientists build models that include weather data, how crops grow and how the disease pathogen spreads through the crop. “In this particular instance the wheat is susceptible only at flowering,” Bruce said. “It may be in flower for a few days. If it doesn’t get the pathogen inoculum and the right weather conditions at that time it will not get the disease.” Climate change can both alter flowering times and the chances of warm, wet weather that make infection more likely. When wheat gets infected, even if it can be harvested it is more likely to contain poisonous mycotoxins. “If it’s full of mycotoxins then it can’t be eaten by man or beast, so it’s just wasted,” Bruce added.
Digging up data
Bruce had developed a FEB model for the UK, and was also part of the China-UK Sustainable Agriculture Innovation Network (SAIN) studying how climate change will affect farming in China. People in northern China eat wheat in mantou, or steamed buns, as a staple food, as well as in noodles. “FEB is a particular problem in central Chinese provinces along the Yangtze river, where you’ve got winter wheat, because there’s suitable weather and pathogen inoculum at flowering,” Bruce said. “There’s wheat grown in northern China but it’s far too dry for fusarium ear blight.”
Although Bruce and other researchers have already made FEB models for other countries, they’re only relevant to the specific country they’re developed for. To build a reliable model for China, they would therefore need direct local measurements as a foundation. “Getting the Chinese data was a big challenge,” Bruce admitted. He already knew Chinese scientists who were able to persuade their colleagues to share disease and crop data for 15 places in Anhui and Hubei provinces. However, only ten of these sites had direct weather measurements recorded nearby.
When he met ChangGui Wang from the UK’s Met Office through the SAIN network that shortfall turned into an opportunity. As part of the Adaptation to Climate Change in China project ChangGui had been working with Chinese collaborators to develop a high resolution regional climate model, called PRECIS 2.0. That let her estimate the missing data by downscaling a ‘global reanalysis’ dataset to local scale. Bruce’s team could take disease and measured weather data for ten sites and adapt an existing wheat growth model, known as SIRIUS, creating a tool to use in China. Then they could test how well their final model worked using PRECIS 2.0 reanalysis data for 2001-2008 across all 15 sites.
Can we change in time for climate change?
Having satisfied themselves that their new model did represent how FEB spread in China, Bruce’s team could then look at future climate with PRECIS 2.0. To do that they input climate projections based on the UN Intergovernmental Panel for Climate Change’s A1B scenario, where greenhouse gas emissions peak in the mid-21st century. Putting the resulting temperature forecasts into the SIRIUS wheat growth model gave flowering dates in 2021-2050 that were 3-6 days earlier than in 2001-2010. Forecast temperature and rainfall leading up to these flowering periods also contributed to increased risk of serious FEB epidemics, more in the north of the region than the south.
These latest results, published in Annals of Applied Biology earlier this year, are similar to earlier findings for the UK. However, the implications for China are much more serious, because ‘FEB is already a cause of substantial losses in wheat yield and quality’, they wrote. Bruce hopes that those making choices affecting the country’s farmers take note of these findings. “Final decisions have to be made by people who are breeding crops and developing strategies for controlling disease, what they’re going to put money into, what the priorities are,” he observed. “As a scientist you can’t take into account all the factors that they might have to. But they need our information, otherwise it’s very hard for them to make decisions.”
Journal reference:
Zhang, X., Halder, J., White, R., Hughes, D., Ye, Z., Wang, C., Xu, R., Gan, B., & Fitt, B. (2014). Climate change increases risk of fusarium ear blight on wheat in central China Annals of Applied Biology, 164 (3), 384-395 DOI: 10.1111/aab.12107
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It looks like they are making forecasts of FEB based on forecasts of climate change, based on models that have already proved unable to model current climate change (or lack of change) accurately.
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