Corn yield model factors in growth stage

Corn yield model factors in growth stage

The ability to predict corn yields would benefit farmers as they plan the sale of their crops and the biofuel industry as it plans operations.

THE ability to predict corn yields would benefit farmers as they plan the sale of their crops and the biofuel industry as it plans operations, according to a new study.

Results of the study were published in the July-August issue of Agronomy Journal and describe a robust model that uses easily obtained measurements, such as plant morphology and precipitation, collected specifically at the silking growth stage of the plant. The new model could help both growers and industry maximize profits and efficiency.

Forecasting crop yield can be extremely useful for farmers because if they can estimate the yield expected, they can contract their corn prior to harvest, often securing a more competitive price than if they were to wait until after harvest. Likewise, the industry can benefit from yield predictions by better planning the logistics of the business, according to the report.

Still, dependable forecasts can be difficult to find.

Several methods of predicting and modeling crop yields have been used in the past with varying success. Statistical models often don't take into account characteristics of the plants, the weather or the management practices limiting their usefulness, the report explains. Some models are based on information from just a single year or location.

"When you develop a model using single location or year data, it will have limited practical applications," Spyridon Mourtzinis, lead author of the study, explained. "You don't include variability from multiple environments."

The new study from Mourtzinis and his co-authors from Auburn University found a more robust model for predicting both corn grain and stover yield. The model uses equations calculated with information about nitrogen fertilization rates, precipitation and plant morphology, such as plant height, stem diameter, height of the first ear, number of forming ears and plant population.

"Previous attempts were mainly looking at weather factors," said Francisco Arriaga, co-author of the study and now an assistant professor at the University of Wisconsin-Madison. "This approach has other factors included in the model, and that is an important strength."

The timing of the measurements is also an important aspect of the model. Mourtzinis took weekly measurements from more than 100 plots throughout the growing season to find the best window during which to collect data to be used in the equations.

"We looked at all the vegetative states to see which one was best, and it turned out to be the R1 growth stage," Arriaga explained. "Other models tried to take measurements earlier, but that may be why they had poor results. Things change as the season goes by, and the stage we found was the critical one."

The R1, or silking, growth stage, when silks are first visible outside the husks, occurs about two to two-and-a-half months before harvest. This model, then, would provide predictions early enough to affect crop prices and to allow industries to plan their operations.

While even earlier predictions might be possible, they would depend on better forecasting of weather, which can change significantly throughout the growing season and greatly affect yields.

In the new model, data were collected at two different sites in Alabama over three years. The equations used in the current model translate over six sets of data, suggesting that it could be used in a variety of environments, the report points out. That is a possible focus for future experiments.

"It would be interesting to test the equations across a lot more environments now that we know which growth stage to target," Arriaga said.

Future studies will also test the model with other corn hybrids and management practices. As more data are collected from a variety of environments and growing conditions, the authors are hopeful that the model will continue to accurately predict corn yield.

"We need to be open-minded," Mourtzinis said. "The equations might change a bit when we get more data from more environments, but I think we can build on the current model."

Volume:85 Issue:29

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