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<p>The production of dairy animals through energy-oriented genetic evaluation criteria may result in increased shares of milk and milk products in fulfilling dietary energy requirements of the ever-rising human population. The present study was thus conducted to predict the first lactation energy value (E<sub>p</sub>) of milk and energy corrected milk yield (ECMY) based on fat (F) and solids-not-fat (SNF) content of milk in Murrah buffaloes. For developing prediction models, data on 6314 first lactation monthly test-day records of 756 Murrah buffaloes were analyzed. The E<sub>p</sub> of milk was predicted using simple and multiple linear regression analysis, using F % and SNF % as independent variables. The coefficients of determination (R<sup>2</sup>), mean square of the error (MSE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to assess the accuracy of each model. The outcomes showed that the model based on both F % and SNF % (model III) had the highest accuracy (R<sup>2</sup> = 99.99%) of prediction. Furthermore, F % alone contributed very highly (R<sup>2</sup> = 99.32%) to the E<sub>p</sub> of milk, indicating that the&#xa0;model based on F % only (model I) can also be used efficiently for accurate prediction of the E<sub>p</sub> of milk. Prediction models for ECMY (6% F) and ECMY (6% F and 9% SNF %) were also developed in the present study. The developed prediction models may be applied for the genetic evaluation of buffalo bulls as well as for the selection of buffalo cows to produce energy-oriented dairy animals.</p>

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Prediction Models Developed For Energy Value of Milk of Murrah Buffaloes

  • 期刊论文

  • 发表日期：

    2025年1月2日

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  • Arpan Upadhyay,
  • Atish Kumar Chakravarty,
  • Soumya Dash

摘要

The production of dairy animals through energy-oriented genetic evaluation criteria may result in increased shares of milk and milk products in fulfilling dietary energy requirements of the ever-rising human population. The present study was thus conducted to predict the first lactation energy value (Ep) of milk and energy corrected milk yield (ECMY) based on fat (F) and solids-not-fat (SNF) content of milk in Murrah buffaloes. For developing prediction models, data on 6314 first lactation monthly test-day records of 756 Murrah buffaloes were analyzed. The Ep of milk was predicted using simple and multiple linear regression analysis, using F % and SNF % as independent variables. The coefficients of determination (R2), mean square of the error (MSE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to assess the accuracy of each model. The outcomes showed that the model based on both F % and SNF % (model III) had the highest accuracy (R2 = 99.99%) of prediction. Furthermore, F % alone contributed very highly (R2 = 99.32%) to the Ep of milk, indicating that the model based on F % only (model I) can also be used efficiently for accurate prediction of the Ep of milk. Prediction models for ECMY (6% F) and ECMY (6% F and 9% SNF %) were also developed in the present study. The developed prediction models may be applied for the genetic evaluation of buffalo bulls as well as for the selection of buffalo cows to produce energy-oriented dairy animals.