Modeling Lactation curve in Jersey crossbred cows
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Date
2017
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Abstract
The present study was conducted to fit lactation curve models for Jersey crossbred dairy cattle in organised dairy farms, so as to find the best fitting model. Lactation curve modeling of Jersey crossbred cows was carried out using daily milk yield data of 259 lactations at two farms of Tamil Nadu Veterinary and Animal Sciences University. Various models were fitted to find the best model of fit for prediction of milk yield, based on four diagnostic parameters viz. coefficient of determination (R2), adjusted R2, Root Mean Square Error (RMSE) and Durbin-Watson (DW) coefficient. Highest rank obtained by Friedman’s test was chosen as best model. The results of the study revealed that the highest value of Coefficient of Determination (R2 - 0.74) was obtained in both Ali and Schaeffer and Cubic models, while the lowest R2 (0.45) was in Inverse Quadratic Polynomial modified model. Average Root Mean square Error was the lowest in Inverse Quadratic Polynomial modified model (0.08 kg) and highest was in Quadratic model. Durbin-Watson coefficients in different models ranged from 0.76 (Inverse Quadratic Polynomial modified model) to 1.09 (Ali and Schaeffer model), indicating positive auto-correlation of residuals for the models considered. Ali and Schaeffer, Cubic, Quadratic cum log, Quadratic and Mixed log models were found to the best lactation curve models for Jersey crossbred cows in that order. Lactation parameters estimated through this study can be used in genetic evaluations and in selection of cows to improve persistency and yield of milk production.
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Veterinary Science, Animal Husbandry Statistics and Computer Applications