To evaluate the importance of non-linear relationships between Relative net income per day of productive life (RNI/DPL) and individual traits, 921,915 potential offspring were simulated from all possible matings of 20,487 Jersey cows and 45 active AI sires. Predicted milk yield, fat yield, and 13 linear type traits of potential progeny were used to predict RNI/DPL of all potential progeny.

Five methods of mate selection and pairing were evaluated for their effectiveness in choosing mates and the amount of computer time required to choose those pairings. Results of a linear programming (LP) method were used as a comparison for the other four more easily applied methods. Two of the other four methods were not significantly (P > .01) different from the LP method. Although the random pairing method was significantly different, similarity of results, for this method indicated non-linear relationships between RNI/DPL and individual trait scores are of minor importance. A11 four methods used considerable less computer time than the LP method.

Analysis of variance for predicted RNI/DPL (all possible offspring) indicated herd, dam within herd, sire, and inbreeding class to be significant (P < .01) variables in determining RNI/DPL. However the sire by dam within herd interaction did not significantly affect RNI/DPL, again indicating non-linear relationships between traits and RNI/DPL were not very important.

Regressing PD's, Cl's, and their crossproducts for milk yield, fat yield, and 13 linear type traits showed the relative importance of crossproducts to be minimal in comparison to the linear effects of parental genetic evaluations.