Multi-Step Assessment of Lactation Curve Functions of Iranian Simmental and Jersey Cows with Emphasis on Relative Information Criteria

Dairy herd improvement (DHI) programs and national genetic evaluation at the individual and/or herds levels rely on adjusted 305 d lactation performance predicted by lactation curve functions. These functions are approximations of real curves. To find the best function, multi-step assessment of predicted lactation curves is required. The purpose of this study was to investigate four-step examination of two-parameter Pollott mechanistic function and compare it with two empirical functions (Wood and Wilmink) to choose the one that best suited individual lactation curves in Jersey and Simmental cattle populations, independently. Wilmink had the lowest BIC for both breeds, while the Pollott had the lowest AICc value (although the difference with other functions is negligible) and produced the most typical curves, so could be the best fit. Moreover, the correlations between curve parameters in the Pollott function were the lowest for both breeds; demonstrating the independence of the evaluated parameters and the strength of that. In the best function (Pollott), the mean and annual trends for the estimated total lactation milk yield were 6082.1 kg and 48.01 kg for Simmental, and 6747.9 kg and 148.33 kg for Jersey cows, respectively. Overall, our results confirm that the Pollott’s mechanistic function outperforms the other two functions for fitting individual lactation curves. It is more robust in terms of: (1) maximum number of standard curves, (2) lowest AICc, (3) independent curve parameters, and (4) biological interpretation of typical curves. Therefore, it could be recommended for practical implications of fitting and standardization of test-day yield of these two breeds.