Abstract
The object of this research was to identify and evaluate alternatives when building mathematical models to measure the impact of weather on crop yields. Alternatives exist relative to selection of: (1) observational units with attention to size and coverage (areal and temporal), (2) observational periods for defining weather variables, and (3) mathematical forms and types of weather variables to measure impacts of moisture and temperature. The study involved an analysis of four weather-yield functions for winter wheat. The functions represented combinations of levels of two factors: (1) size and coverage of the observational units (plot yields from a multi-state area vs. average farm yields over Agricultural Statistics Districts in Kansas) and (2) weather variables used to represent moisture impacts (precipitation vs. evapotranspiration). From an eight-year test, using data from Kansas, we concluded that functions developed from a broad coverage (plot yields from a multi-state area) may have had a slight edge in precision.
Keywords
weather, wheat
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Feyerherm, Arlin M. and Paulsen, Gary M.
(1989).
"MODEL BUILDING TO MEASURE IMPACT OF WEATHER ON CROP YIELDS,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1460
MODEL BUILDING TO MEASURE IMPACT OF WEATHER ON CROP YIELDS
The object of this research was to identify and evaluate alternatives when building mathematical models to measure the impact of weather on crop yields. Alternatives exist relative to selection of: (1) observational units with attention to size and coverage (areal and temporal), (2) observational periods for defining weather variables, and (3) mathematical forms and types of weather variables to measure impacts of moisture and temperature. The study involved an analysis of four weather-yield functions for winter wheat. The functions represented combinations of levels of two factors: (1) size and coverage of the observational units (plot yields from a multi-state area vs. average farm yields over Agricultural Statistics Districts in Kansas) and (2) weather variables used to represent moisture impacts (precipitation vs. evapotranspiration). From an eight-year test, using data from Kansas, we concluded that functions developed from a broad coverage (plot yields from a multi-state area) may have had a slight edge in precision.
