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Yielding of milk is of great economic importance for milk processors in dairy industry and for consumers. Also, milk composition has a major role in determining the price of milk. Protein amount is a major constituent in milk so this study focused on predicting its amount from total milk yield. Generally, the total milk yield and protein amount are linearly correlated, so it is important to study this relationship with other nonlinear models. This work attempted to: investigate the relationship between protein amount and milk production, predict protein amount from total milk yield and choose the best fit model for this purpose. Beside the linear model, ten nonlinear regression techniques were used such as power, quadratic and cubic modelling technique and others. Data of 1300 animal from lactation records of Holstein dairy cattle which belongs to Dina farms at Alexandria-Cairo desert road Egypt were used. The regression models (curve estimation regression method) were applied using SPSS software packages version 26. The goodness of fit measures for the best fit model are the highest value of R square and adjusted R square (inadequate or intuitive measures) with the lowest values of standard error of estimate and AIC values (more accurate measure). The results showed that from the 11 regression models, the power model was the best fit model to predict the amount of protein from total milk yield depending on R Square (0.856) and Adjusted R Square (0.856) that were the highest values between the models, smaller standard error of the estimates (0.230) and AIC value (-13135.84) which were the lowest values between the models. The power model could be used for prediction through this equation (protein amount = 0.130 * (total milk yield ** 0.815) after 15 iteration criteria.
Abdallah et al. (Mon,) studied this question.