Predictive formulas for different measures of cheese yield using milk composition from individual goat samples

Area of Science:

• Dairy Science
• Animal Genetics
• Food Chemistry

Background:

• Accurate prediction of cheese yield (CY) is crucial for optimizing dairy goat farming and breeding programs.
• Understanding the influence of milk composition and udder health on CY is essential for developing effective predictive models.

Purpose of the Study:

• To develop and validate formulas for predicting cheese yield (CY) traits (fresh curd, milk solids, water retained) based on individual goat milk composition.
• To assess the contribution of major milk components (fat, protein, casein) and udder health indicators (lactose, somatic cell count, pH, bacterial count) to CY traits.
• To evaluate the impact of cheese-making methods and goat breeds on the accuracy of CY prediction formulas.

Main Methods:

• Utilized a laboratory cheese-making procedure (9-MilCA method) on 1,200 milk samples from 600 goats across 6 breeds.
• Employed linear regression models incorporating milk components and udder health indicators.
• Implemented cross-validation (CrV), within-animal validation (WAV), and stratified cross-validation (SCrV) to assess prediction accuracy (R²_VAL, RMSE_VAL).

Main Results:

• The most accurate prediction formula for all CY traits included fat, casein, and udder health indicators (UHI), achieving R²_VAL of 0.65 (%CY_CURD), 0.96 (%CY_SOLIDS), and 0.23 (%CY_WATER) in CrV.
• Within-animal validation (WAV) yielded higher R²_VAL than CrV, indicating the high repeatability of the 9-MilCA method.
• Stratified cross-validation (SCrV) revealed significant differences in CY_CURD and CY_WATER among breeds, emphasizing breed as a key factor.

Conclusions:

• Formulas incorporating milk fat, casein, and UHI effectively predict CY traits in goats.
• Breed is a critical factor influencing cheese yield, necessitating breed-specific considerations in predictive formulas and selection indices.
• These findings support the monitoring of milk composition and genetic improvement for enhanced cheese production in dairy goats.