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Simulating Kernel Production Influences Maize Model Accuracy

Date:
September 23, 2007
Source:
American Society of Agronomy
Summary:
By combining two approaches to model maize productivity, researchers have increased the accuracy of maize yield predictions. These findings may help to improve yield predictions throughout the world. Researchers are also predicting pollen movement from GM crops with this new model.

Recently, researchers at Iowa State University discovered a way to increase the accuracy of a popular crop model. By zeroing in on early stages leading up to kernel formation, scientists believe they can help improve yield predictions across a variety of environmental conditions.

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The Crop Environment Resource Synthesis (CERES)- Maize model is used worldwide to predict maize yield each growing season. CERES-Maize predictions are based upon simulations of plant growth and the amount of carbon and nitrogen maize plants accumulate each day. While this approach provides growers with ballpark estimates of maize production, the accuracy decreases when growing conditions affect kernel formation more than plant growth.

Unlike most crop plants, maize has separate male and female flowers. Pollen from male flowers must travel to and fertilize female flowers located on ear. Each successful fertilization of a female flower leads to the production of a kernel.

"Pollination success depends on the amount of viable pollen produced, the presence of the pollen receptive part of the female flower, and close synchrony in male and female flower development," says Mark Westgate, Iowa State University professor of agronomy. "CERES-Maize does not consider these critical aspects of the pollination process."

To overcome the limitations of CERES-Maize, Westgate and his colleagues developed algorithms for a Flowering Model to simulate maize flowering dynamics. Once they were convinced the Flowering Model was properly imitating maize flowering patterns, they coupled it to CERES-Maize. The Modified version of CERES-Maize then was calibrated against two years of field data involving three hybrids, eight population densities, and seven nitrogen levels. The Modified version of CERES-Maize generated more accurate predictions of maize yield across a wide range of growing conditions.

"There are many situations in which kernel number is not limited by the ability of the plant to supply carbon and nitrogen to the ear," Westgate said. "By taking into account other factors influencing kernel number, CERES-maize is much more sensitive to biological factors that can affect yield."

What's up next for Westgate and his research team? A model they are developing to predict pollen movement from one field to another. They're using it along with the Flowering Model to predict the genetic purity of harvested seed. According to Westgate, genetic purity is a concern in hybrid seed production as well as for corn grown for non-genetically modified markets.

The team of scientists reported their findings in the September-October issue of Crop Science.


Story Source:

The above story is based on materials provided by American Society of Agronomy. Note: Materials may be edited for content and length.


Cite This Page:

American Society of Agronomy. "Simulating Kernel Production Influences Maize Model Accuracy." ScienceDaily. ScienceDaily, 23 September 2007. <www.sciencedaily.com/releases/2007/09/070921071649.htm>.
American Society of Agronomy. (2007, September 23). Simulating Kernel Production Influences Maize Model Accuracy. ScienceDaily. Retrieved December 21, 2014 from www.sciencedaily.com/releases/2007/09/070921071649.htm
American Society of Agronomy. "Simulating Kernel Production Influences Maize Model Accuracy." ScienceDaily. www.sciencedaily.com/releases/2007/09/070921071649.htm (accessed December 21, 2014).

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