Lactation Curve Pattern and Prediction of Milk Production Performance in Crossbred Cows

Data pertaining to 11728 test-day daily milk yields of normal and mastitis Karan Fries cows were collected from the institute herd and divided as mastitis and nonmastitis and parity-wise. The data of lactation curves of the normal and mastitis crossbred cows was analyzed using gamma type function. FTDMY in normal and mastitis cows showed an increasing trend from TD-1 to TD-4 and a gradual decrease thereafter until the end of lactation (TD-21) in different parities. The FTDMY was maximum (peak yield) in the fourth parity. Parity-wise lactation curve revealed a decrease in persistency, steeper decline in descending slope (c), and steeper increase in ascending slope (b) from 1st to 5th and above parity. The higher coefficient of determination and lower root mean square error (RMSE) indicated goodness and accuracy of the model for the prediction of milk prediction performance under field conditions. Clinical mastitis resulted in a significantly higher loss of milk yield . The FTDMY was maximum in the fourth parity in comparison to the rest of parity. It is demonstrated that gamma type function can give the best fit lactation curve in normal and mastitis infected crossbred cows. 1. Introduction Lactation curve provides valuable information about the pattern of milk production during lactation. It also depicts summary of the pattern of milk yield determined by the biological efficiency of the cow [1]. The cost of milk production depends to a large extent on the persistency of lactation, that is, the rate of decline in production after peak milk yield. High persistency is associated with a slow rate of decline in milk production, whereas low persistency is associated with a rapid rate of decline in milk yield. In general declining rate of milk production is about 7% per month after the peak yield [2]. Estimates of heritabilities for milk yield and persistency traits in HF cows have been reported [3, 4]. The lactation curve models have been used to predict the milk yield at any point of the lactation [5, 6]. This property of the model can prove beneficial in case of incomplete lactation records. Various models have been tried by different researchers to fit the lactation curve in indigenous as well as exotic cattle [7–10]. Further getting the test-day milk yield information from the field conditions is not easy and there is every chance of missing the data due to certain inevitable circumstances. In such case mathematical models may prove beneficial for prediction of milk production performance using. In view of this the lactation curve was composed for the