Comparison of different models to describe the lactation curve of Holstein cows in a small-scale dairy system

Modeling lactation curves with mathematical equations is useful to describe and predict the productivity of dairy cows. However, studies analyzing the performance of different models to describe lactation curves in small-scale dairy systems are sparse. Therefore, the objective of this study was to evaluate the capacity of five models to describe lactation curves of cows in a small-scale dairy system. Monthly milk weighins from 936 lactations were conducted in 23 farms in western Mexico. The Bayesian information criterion (BIC) and mean square prediction error (MSPE) were used to assess the fit of the models to the observed data, and the root mean square prediction error as a percentage of the observed mean (%RMSPE) was used to evaluate the accuracy of the models. The Gaines model had the best fit (lowest BIC and MSPE) for the first, second, and third or higher lactations. Regarding accuracy, the Sikka and Gaines models had the lowest RMSPE and %RMSPE. When classified by production level, the Gaines model had the best fit. The Gaines and Sikka models had the best accuracy across all three production levels. It was concluded that the Gaines model is the best option for representing lactation curves in a small-scale milk production system.