Mathematical Models Help Predict Corn Production

Information could be used to adjust production practices.

Scientists have developed new mathematical models that could eventually help farmers use climate patterns to predict corn yields.  Agricultural Research Service scientists and colleagues believe farmers could use this information, which indicates yield cycles of about two years, to adjust their production practices. For instance crops grown in low-yield years may require less fertilizer.

Agricultural Engineer Rob Malone works at the ARS National Soil Tilth Laboratory in Ames, Iowa. He and other colleagues at USDA and Penn State found an indication that high surface radiation and low temperatures early in the growing season often produce high yields when followed by sufficient rainfall later in the growing season. This model accounted for 89% of the variation in annual corn yields.


Changes in these weather variables are often associated with long-term climate trends. Their model detected an average difference between high- and low-yielding years of 19% and identified an approximate two-year cycle between high- and low-yielding years. They said this helps explain the combined effect of several long-term climate trends on long-term U.S. corn yields.

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