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Modelling: Predictive Yield

By Rob Mitchum // September 27, 2013

The 2012 drought in the United States was one of the worst in recent decades, hurting crop yields across the country and driving up food prices around the world. How can computer models help farmers better anticipate and prepare for the next serious drought? CI fellow Joshua Elliott talks about the successes and limitations of these models in this news feature by Nature Magazine. For more on Elliott’s work modeling crop yields during the 2012 drought, see our story from April.

Every day, NOAA’s models produce forecasts for up to 9 months. Accuracy over the longest time frames is poor, however, and the lack of regional detail renders them worthless for farmers. Elliott says that running these models 100 times as often might produce more useful results, but that’s hard to do with the existing computing resources. “Basically it’s just a huge computation and data challenge at this point,” he says.


Even with an accurate forecast, there is another part of the equation: predicting how crops will respond to drought. This depends on when in the growing season the drought occurs, how severe it is, and where in the world it is happening. Agricultural scientists have plenty of models — tailored to specific crops — that can simulate how plants react to variations in temperature, rainfall or about 40 other parameters that describe the characteristics of soil (see ‘Soil science comes to life’, page S18). These models work well, says Elliott, but they need better weather predictions to feed in.

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