Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account @entity1267 ( @entity1267 ), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the @entity1267 was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the @entity1718 (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for models which included a @entity1267 effect. The corresponding correlation values ranged from 96.4% to 97.6% when the daily yields were estimated from the AM milkings and ranged from 96.9% to 98.3% when the daily yields were estimated from the PM milkings. The simplicity of these proposed models should facilitate their use by breeding and milk recording organizations.

Predictions of Daily Milk and Fat Yields, Major Groups of XXXX , and C18:1 cis-9 from Single Milking Data without a Milking Interval.

@entity1267 :: ('MESH:D010319', 'Disease') :: ['milking intervals', 'MI'] @entity1718 :: ('MESH:D005227', 'Chemical') :: ['fatty acids']
@entity1718 :: (MESH:D005227,Chemical) :: ['Fatty Acids']
