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 New mathematical models developed by ARS scientists and colleagues could eventually help farmers use climate patterns to predict corn yields. Click the image for more information about it.For further reading
High-quality corn for low-input farming systems
Scientist pinpoints when weeds are most meddlesome
Curbing what goes down the drain Clues on Corn Yields, Weather Conditions and Climate Patterns Ann Perry  New mathematical models developed by Agricultural Research Service (ARS) scientists and colleagues could eventually help farmers use climate patterns to predict corn yields. 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. These adjustments, in turn, could reduce the flow of excess nitrate from crop fertilizers into the surrounding watershed, which may help control hypoxia downstream in the Gulf of Mexico. Corn yield variability affects nitrate loss because small changes in corn yield may have greater effects on nitrate loss in fields with subsurface tile drainage systems. Agricultural engineer Rob Malone works at the ARS National Soil Tilth Laboratory in Ames, Iowa. He and other colleagues in ARS and at Penn State gathered more than 50 years of data on corn production from six high-yield corn-producing counties in Iowa to see if they could identify key correlations among yield, weather conditions and climate indices. Malone’s modeling results indicated that high surface radiation and low temperature early in the growing season often produce high yields when followed by sufficient rainfall later in the growing season. This model accounted for 89 percent of the variation in annual corn yields. Changes in these weather variables are often associated with long-term climate trends. So the team used established climate indices derived from the large-scale flow of high- and low-pressure air masses and equatorial stratospheric winds to develop models that accounted for the variability in corn yields. This model detected an average difference between high- and low-yielding years of 19 percent and identified an approximate two-year cycle between high- and low-yielding years. Malone’s research helps explain the combined effect of several long-term climate trends on long-term U.S. corn yields. In addition, these results provide information about how the annual variation in ground-level solar radiation during the growing season affects long-term corn yield in the United States. This research was published in the journal Agricultural and Forest Meteorology. ARS is the principal intramural scientific research agency of the U.S. Department of Agriculture.


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