A Stocheometric crop weather model to predict fingermillet growth and grain yield based on the dry matter accumulated at each stage has been developed. Multiple linear regression equations relating the GDD, SSH and AET with the accumulated dry matter during each growth stage and also the final grain yield were generated using the field experimental data for the period 1992-98. The coefficient of determinants indicate that the climatic parameters considered and the initial TDM used to estimate the final TDM in each stage could be able to predict the final to an extent of 93 per cent to 98 per cent (coefficients of determinants) in different stages. Comparison of the observed and the predicted yields indicate the close agreement between them in all the stages. There is a very good agreement between the observed and the predicted yield, which is revealed by the correlation coefficient of 97 per cent. Comparison of the observed and the predicted yields indicate the close agreement between them in all the stages. Considering the observed Total Dry Matter up to the first four stages and the predicted Total Dry Matter at the end of the harvesting stage, the model has been validated for the year 2001, and there is very good agreement between the observed and the predicted yield, which is revealed by the regression coefficients of 0.99 and 0.999 for two dates of sowing. Favorable influence of AET at the time of beginning of tiller and grain formation stage, and higher GDD during ear emergence and harvest stages was noticed. Increase in AET during pre-harvest stage did not favor good grain yield. Hence, this Stocheometric crop weather model could be used to predict the grain yield along with their dry matters well before harvest of the crop. This helps the planners for future action.
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