Abstract
Statistical models were developed to predict the State average grain weight per ear using daily temperature and precipitation data, recorded from May 1 through late July. The required daily weather data was successfully obtained in an operational test of these models for ten major corn producing States in 1988. Relative forecast errors of ear weight averaged almost one-third smaller than those from a regular survey. Additional refinements of the models to make them more responsive to abnormally early adverse weather, as in 1988, are underway.
Keywords
modeling, weather, corn ear weight
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Warren, Fred B.
(1989).
"FORECASTING CORN EAR WEIGHTS FROM DAILY WEATHER DATA,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1462
FORECASTING CORN EAR WEIGHTS FROM DAILY WEATHER DATA
Statistical models were developed to predict the State average grain weight per ear using daily temperature and precipitation data, recorded from May 1 through late July. The required daily weather data was successfully obtained in an operational test of these models for ten major corn producing States in 1988. Relative forecast errors of ear weight averaged almost one-third smaller than those from a regular survey. Additional refinements of the models to make them more responsive to abnormally early adverse weather, as in 1988, are underway.
