Physiological-climatological model to predict Texas rice yields

Typescript (photocopy).The 14 years of daily and hourly data from three selected stations in Texas were used to determine if they could assist in predicting the Texas rice yields per planted hectare. Minimum temperature in the first stage and solar radiation in the third stage showed strong positive correlation with the grain yield, while hot and dry conditions during the third stage of growth is favorable for a bumper yield. Texas rice yields indicated a strong upward trend when plotted against year. A third-degree polynomial curve was found to depict best this time-trend of agriculture technology in the Texas rice belt. The thermal unit approach was found to predict the time of maturity and the developmental stages of the crop. The goal of this research was to develop models that can predict rice yield. First, a physiological and climatological simulation model called GLOBALRICE, which was driven by weekly weather parameters, was developed. The simulation model was used to parameterize an agro-meteorological model based on the physiological processes related to the stage of rice growth and development. The simulation model was tested for its predictive ability in Texas data. Second, a multiple regression model was developed to predict rice yield and also to guide in the adjustment of the parameters in the simulation model. The models' accuracy gave errors of from 1% to 16% in prediction of rice yields in Texas. The highest values of error were due to disease. Future developments were recommended

Physiological-climatological model to predict Texas rice yields

Typescript (photocopy).The 14 years of daily and hourly data from three selected stations in Texas were used to determine if they could assist in predicting the Texas rice yields per planted hectare. Minimum temperature in the first stage and solar radiation in the third stage showed strong positive correlation with the grain yield, while hot and dry conditions during the third stage of growth is favorable for a bumper yield. Texas rice yields indicated a strong upward trend when plotted against year. A third-degree polynomial curve was found to depict best this time-trend of agriculture technology in the Texas rice belt. The thermal unit approach was found to predict the time of maturity and the developmental stages of the crop. The goal of this research was to develop models that can predict rice yield. First, a physiological and climatological simulation model called GLOBALRICE, which was driven by weekly weather parameters, was developed. The simulation model was used to parameterize an agro-meteorological model based on the physiological processes related to the stage of rice growth and development. The simulation model was tested for its predictive ability in Texas data. Second, a multiple regression model was developed to predict rice yield and also to guide in the adjustment of the parameters in the simulation model. The models' accuracy gave errors of from 1% to 16% in prediction of rice yields in Texas. The highest values of error were due to disease. Future developments were recommended