ARS | Publication request: Quasi-biennial corn yield cycles in Iowa

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: November 4, 2010
Publication Date: November 4, 2010
Citation: Malone, R.W., Meek, D.W., Hatfield, J.L., Ma, L. 2010. Quasi-biennial corn yield cycles in Iowa. American Society of Agronomy Annual Meetings [abstract]. ASA-CSSA-SSSA Annual Meeting. Oct. 31 - Nov. 4, 2010, Long Beach, CA. CD-ROM.

Technical Abstract: Quasi-biennial cycles are often reported in climate studies. The interannual El Niño Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) are two phenomena containing quasi-periodicities of approximately 2.5 and 2.2 years. It is known that ENSO affects corn yield through weather patterns; NAO affects surface temperature and cloudiness; and surface temperature, rainfall, and radiation affect corn yield. However, a quasi-biennial pattern in corn yield and the combined effect of several climate signals on long-term U.S. corn yield are not known. Multi-taper spectral analysis was used to show statistically significant 2-3 year periods in long-term corn yield from one of the world's most important corn producing regions. High (low) yields are due in part to high (low) surface radiation and low (high) temperature early in the corn growing season coupled with sufficient (insufficient) rainfall later in the growing season. A statistical model we developed using three climate indices accounts for 54% of the interannual variation in Iowa corn yield. The most significant periodicities found in the model's spectrum are similar to the quasi-biennial periodicities in observed corn yield. We classify Iowa corn yield from several regional datasets (1960 to 2006) for 'low yield' and 'high yield' conditions as predicted by the model. The difference between observed corn yields for 'high' and 'low' yielding years was 19% (p = 0.0001). The results demonstrate a quasi-biennial pattern in long-term Iowa corn yield related to large-scale climate variability. This knowledge could lead to models that help guide springtime agricultural management decisions that improve profitability and reduce nitrate flux to groundwater, streams, rivers, and coastal oceans.