Prediction of annualized lactation yield from partial lactations

Area of Science:

• Animal Science
• Dairy Science
• Agricultural Economics

Background:

• Accurate estimation of annualized lactation yields is vital for dairy herd management and economic assessment.
• Existing models may not fully capture the complex factors influencing lactation performance.

Purpose of the Study:

• To develop and validate a predictive model for annualized lactation yields in Israeli-Holstein cows.
• To identify key factors influencing milk, fat, and economically fat-corrected milk yields.

Main Methods:

• Utilized a large dataset of 747,904 partial lactations from 105,379 Israeli-Holstein cows.
• Incorporated factors including last test yield, days pregnant, days in milk, farm type, calving season, and days remaining in lactation.
• Analyzed primiparous and multiparous cows separately for milk, fat, and economically fat-corrected milk yields.

Main Results:

• The developed model demonstrated improved prediction accuracy for fat yield (0.04 higher) and milk/economically fat-corrected milk yield (0.01 higher) compared to an alternative model.
• Last test yield was the most significant predictor, but incorporating additional factors enhanced prediction accuracy, particularly for fat yield.
• Correlations between actual and predicted lactations were slightly higher for fat yield in partial lactations under 4 months.

Conclusions:

• The proposed model effectively predicts annualized lactation yields in Israeli-Holstein cows.
• Inclusion of multiple factors beyond last test yield significantly improves prediction accuracy, especially for fat yield.
• This enhanced predictive capability can aid in better dairy herd management and economic evaluations.