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<br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br />I <br /> <br />n"''''....~Q <br />$.) '_'.1 ~~ ,) V ) <br /> <br />Crop Yiel_ds <br /> <br />The crop yield subcomponent used time series data for each state's <br />yield, by commodity and by cropping practice. Using time as the independ- <br />ent variable, linear, Cobb-Douglas, and Spillman functions were estimated <br />using Ordinary Least Squares regression. The regression results which had <br />a combination of best fit and best yield projection characteristics were <br />chosen. These yield equations and their projected values were then sub- <br />jected to peer review and revised in situations where required. In addi- <br />tion, the yields in the six High Pl ains states were further checked and <br />adjusted with the State A-l Coordinators to gain additional consistency <br />between the linear programming models and the NIRAP Model. <br /> <br />It is again stressed that crop yields in the NIRAP Model are not used <br />to determine changes in national supply and consequently national prices. <br />The production and consequential effect on national prices are determined <br />by supply shifters which are derived from productivity indices. Yields in <br />NIRAP are used to determine the amount of cropland used given a state's or <br />region's projected share of national production. However, consistency is <br />maintained between the productivity estimates and the crop yield projec- <br />tions. Appendix Tables 1 list the summary yields for the various crops for <br />the U.S. <br /> <br />II -5D <br />