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Computer Model Predicts Crop Yields

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The new tool incorporates rainfall forecasts, groundwater level data, soil characteristics for each county, the water consumption of each crop, the cost of irrigation on a county-level basis, crop price data, and crop production budget data.

Credit: Steven Weeks

Scientists at North Carolina State University (NC State) and China's Zhejiang University built a computer model that predicts cotton, corn, sorghum, and soybean yields in the southeastern U.S. to complement climate change-era decisions by farmers and government water resource managers.

The regional hydroeconomic optimization modeling framework (RHEO) taps data including rainfall forecasts, county-level soil properties and irrigation costs, and U.S. Department of Agriculture-supplied crop prices.

NC State's Hemant Kumar said when fed 31 years' worth of historical data from 21 counties in southwestern Georgia, "RHEO was able to predict variability in each of our four target crops, as well as identify irrigation strategies that would reduce related costs."

From NC State University News
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Abstracts Copyright © 2023 SmithBucklin, Washington, D.C., USA


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