Crops, climate, and change in the Corn Belt

Nov 4, 2010 - by Staff

November 4, 2010 | Crop yields are affected by many factors, including breeding, management, and climate. New research from NCAR seeks to better understand these factors and their contributions to historical yield increases, in order to anticipate future changes.

Across the U.S. Corn Belt, corn yields have steadily increased since the mid-20th century. Farmers have achieved these yield increases partly by lengthening the crop growth period, both by shifting planting to earlier dates and by adopting longer-season cultivars that increase the length of time devoted to yield accumulation.

Bill Sacks (NCAR) and Chris Kucharik (University of Wisconsin–Madison) analyzed weekly crop progress observations collected by the U.S. Department of Agriculture for corn from 1981 to 2005, documenting trends in planting dates, lengths of the vegetative and reproductive periods, and time between maturity and harvest. They input this data into a dynamic vegetation model to investigate the effects of changing planting dates and cultivars on both crop yields and fluxes of water and energy.

The simulations show that the trend toward using longer-season corn cultivars was responsible for 26% of the observed yield increase over the 25-year period, though the authors note that part of this effect may be attributable to the interactive relationship between longer-season cultivars and other management or climate changes. Simulations indicated that the trend toward earlier planting did not directly contribute to the yield increase, though earlier planting still plays an important role by allowing the use of longer-season cultivars while maintaining roughly constant maturity dates.

These management changes have also modified the surface water and energy balance, causing an increase in latent heat flux and decrease in sensible heat flux in June, and an increase in net radiation in October. Such changes can affect climate and also have implications for the development of regional and global crop models.

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