Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first-lactation Holstein cows.

The large-scale recording of traits such as feed efficiency (FE) and methane emissions (ME) for use in genetic improvement programs is complex, costly, and time-consuming. Therefore, heritable traits that can be continuously recorded in dairy herds and are correlated with FE and ME traits could provide useful information for genetic evaluation. Rumination time has been suggested to be associated with FE, methane production (MeP; ME in g/d), and production traits at the phenotypic level. Therefore, the objective of this study was to investigate the genetic relationships among rumination time (RT), FE, methane and production traits using 7,358 records from 656 first-lactation Holstein cows. The estimated heritabilities were moderate for RT (0.45 ± 0.14), MeP (0.36 ± 0.12), milk yield (0.40 ± 0.08), fat yield (0.29 ± 0.06), protein yield (0.32 ± 0.07), and energy-corrected milk (0.28 ± 0.07), but were low and nonsignificant for FE (0.15 ± 0.07), which was defined as the residual of the multiple linear regression of DMI on energy-corrected milk and metabolic body weight. A favorable negative genetic correlation was estimated between RT and MeP (-0.53 ± 0.24), whereas a positive favorable correlation was estimated between RT and energy-corrected milk (0.49 ± 0.11). The estimated genetic correlation of RT with FE (-0.01 ± 0.17) was not significantly different from zero but showed a trend of a low correlation with dry matter intake (0.21 ± 0.13). These results indicate that RT is genetically associated with MeP and milk production traits, but high standard errors indicate that further analyses should be conducted to verify these findings when more data for RT, MeP, and FE become available.

Machine learning classification of breeding protocol descriptions from Canadian Holsteins.

Dairy farmers are motivated to ensure cows become pregnant in an optimal and timely manner. Although timed artificial insemination (TAI) is a successful management tool in dairy cattle, it masks an animal's innate fertility performance, likely reducing the accuracy of genetic evaluations for fertility traits. Therefore, separating fertility traits based on the recorded management technique involved in the breeding process or adding the breeding protocol as an effect to the model can be viable approaches to address the potential bias caused by such management decisions. Nevertheless, there is a lack of specificity and uniformity in the recording of breeding protocol descriptions by dairy farmers. Therefore, this study investigated the use of 8 supervised machine learning algorithms to classify 1,835 unique breeding protocol descriptions from 981 herds into the following 2 classes: TAI or other than TAI. Our results showed that models that used a stacking classifier algorithm had the highest Matthews correlation coefficient (0.94 ± 0.04, mean ± SD) and maximized precision and recall (F1-score = 0.96 ± 0.03) on test data. Nonetheless, their F1-scores on test data were not different from 5 out of the other 7 algorithms considered. Altogether, results presented herein suggest machine learning algorithms can be used to produce robust models that correctly identify TAI protocols from dairy cattle breeding records, thus opening the opportunity for unbiased genetic evaluation of animals based on their natural fertility.