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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Agroecosystems Management Research » Research » Publications at this Location » Publication #224032
Title: Quasi-Biennial Corn Yield Cycles

Author
item Malone, Robert - Rob
item Meek, David
item Hatfield, Jerry
item MANN, MIKE - PENN STATE UNIVERSITY
item Jaquis, Robert - Bob
item Ma, Liwang

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/21/2009
Publication Date: 3/27/2009
Citation: Malone, R.W., Meek, D.W., Hatfield, J.L., Mann, M., Jaquis, R.J., Ma, L. 2009. Quasi-Biennial Corn Yield Cycles. Agricultural and Forest Meteorology. 149(6&7):1087-1094.

Interpretive Summary: Quasi-biennial cycles are commonly observed in climate studies. The El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Quasi-Biennial Oscillation (QBO) are three phenomena containing cycles of approximately 2.5, 2.2, and 2.3 years, and these phenomena have been found to be associated with variation in weather variables that affect corn yield such as precipitation, cloudiness, surface temperture, and surface UV radiation. But little, if any, research reports: 1) a clear quasi-biennial pattern in corn yield for the U.S. and 2) the combined effect of several climate signals on long-term corn yield. Our results clearly show that long-term corn yield in central Iowa has a temporal pattern with statistically significant 2.3 and 2.6 year periods. Additionally an empirical model we developed using three climate indices (Southern Oscillation Index - SOI, NAO, and QBO) accounts for 54% of the annual variation in corn yield. This model shows a similar temporal pattern as the observed corn yield with significant periods of 2.4 and 2.7 years. Categorizing long-term corn yield for "low" and "high" conditions as predicted by the model results in a difference of 19% between observed high and low yielding years. Our results suggest that use of prediction models such as those developed in this study or refined versions thereof will help guide springtime agricultural management decisions that improve profitability and reduce nitrate flux to streams, rivers, and coastal oceans. This research will help agricultural and climate scientists understand the effect of weather patterns on corn yield and help them to design more effective systems that maintain crop production while protecting the environment.

Technical Abstract: Quasi-biennial cycles are commonly observed in climate studies. The interannual El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) are two phenomena containing quasi-periodicities of approximately 2.5 years and 2.2 years. It is known that ENSO affects corn yield; NAO affects surface temperature and cloudiness; and surface temperature, precipitation, and radiation affect corn yield. However, a quasi-biennial pattern in corn yield and the combined effect of several climate signals on long-term corn yield are not known. Here we show statistically significant 2.3 and 2.6 year periods in long-term corn yield from one of the world’s most important corn producing regions. An empirical model we developed suggests that relatively high (low) surface radiation and low (high) temperature early in the corn growing season coupled with sufficient (insufficient) rainfall later in the growing season result in high (low) annual corn yield. Additionally, we developed a statistical model using three climate indices that accounts for 54% of the annual variation in corn yield. The most significant periodicities evident in the model's spectrum (2.4 and 2.7 years) are similar to the 2.3 and 2.6 year periodicities in observed corn yield. By categorizing corn yield from several regional datasets (1960 to 2006) for 'low yield' and 'high yield' conditions as predicted by the model, we observe a substantial difference of 19% (range of -3% to +3%) between observed corn yields for model predicted high and low yielding years. We believe that use of prediction models such as those developed in this study will help guide springtime agricultural management decisions that improve profitability and reduce nitrate flux to streams, rivers, and coastal oceans.