Probability of working days for various agricultural operations
Solorzano-Campos, Edwin G
MetadataShow full item record
A mathematical model was developed to predict the number of days with rain for the continental United States. The model was developed on a cumulative basis using super-positioning techniques and published weather data from 89 weather stations. Prediction of rain-days using the model requires only a limited number of readily available data inputs, which are day of year, mean annual precipitation, mean annual temperature, and climatic region. Mathematical relationships found for the model coefficients were explained by the distribution patterns of precipitation. Ten different areas were defined based on the coefficients determined for the model. The model was applied to generate probability prediction data for incorporation of chemicals by rain, probability of having no rain for hay, and probability of non-wet days for machinery operations and dry soil surface days use in the prediction of wind erosion.