RESUMEN
Selection for more feed efficient dairy cows is key to improving sustainability and profitability of dairy production; however, underlying mechanisms contributing to individual animal feed efficiency are not fully understood. The objective of this study was to identify circulating metabolites, and pathways associated with those metabolites, that differ between efficient and inefficient Holstein dairy cows using targeted metabolite quantification and untargeted metabolomics. The top and bottom fifteen percent of cows (n = 28/group) with the lowest and highest residual feed intake in mid-lactation feed efficiency trials were grouped retrospectively as high-efficient (HE) and low-efficient (LE). Blood samples were collected for quantification of energy metabolites, markers of hepatic function, and acylcarnitines, in addition to a broader investigation using untargeted metabolomics. Short-chain acylcarnitines, C3-acylcarnitine, and C4-acylcarntine were lower in HE cows (n = 18/group). Untargeted metabolomics and multivariate analysis identified thirty-nine differential metabolites between HE and LE (n = 8/group), of which twenty-five were lower and fourteen were higher in HE. Pathway enrichment analysis indicated differences in tryptophan metabolism. Combined results from targeted metabolite quantification and untargeted metabolomics indicate differences in fatty acid and amino acid metabolism between HE and LE cows. These differences may indicate post-absorptive nutrient use efficiency as a contributor to individual animal variation in feed efficiency.
RESUMEN
The objective of this study was to investigate the relationships between postpartum health disorders and mid-lactation performance, feed efficiency, and sensor-derived behavioral traits. Multiparous cows (n = 179) were monitored for health disorders for 21 days postpartum and enrolled in a 45-day trial between 50 to 200 days in milk, wherein feed intake, milk yield and components, body weight, body condition score, and activity, lying, and feeding behaviors were recorded. Feed efficiency was measured as residual feed intake and the ratio of fat- or energy-corrected milk to dry matter intake. Cows were classified as either having hyperketonemia (HYK; n = 72) or not (n = 107) and grouped by frequency of postpartum health disorders: none (HLT; n = 94), one (DIS; n = 63), or ≥2 (DIS+; n = 22). Cows that were diagnosed with HYK had higher mid-lactation yields of fat- and energy-corrected milk. No differences in feed efficiency were detected between HYK or health status groups. Highly active mid-lactation time was higher in healthy animals, and rumination time was lower in ≥4th lactation cows compared with HYK or DIS and DIS+ cows. Differences in mid-lactation behaviors between HYK and health status groups may reflect the long-term impacts of health disorders. The lack of a relationship between postpartum health and mid-lactation feed efficiency indicates that health disorders do not have long-lasting impacts on feed efficiency.
RESUMEN
Predicting dry matter intake (DMI) and feed efficiency by leveraging the use of data streams available on farm could aid efforts to improve the feed efficiency of dairy cattle. Residual feed intake (RFI) is the difference between predicted and observed feed intake after accounting for body size, body weight change, and milk production, making it a valuable metric for feed efficiency research. Our objective was to develop and evaluate DMI and RFI prediction models using multiple linear regression (MLR), partial least squares regression, artificial neural networks, and stacked ensembles using different combinations of cow descriptive, performance, sensor-derived behavioral (SMARTBOW; Zoetis), and blood metabolite data. Data were collected from mid-lactation Holstein cows (n = 124; 102 multiparous, 22 primiparous) split equally between 2 replicates of 45-d duration with ad libitum access to feed. Within each predictive approach, 4 data streams were added in sequence: dataset M (week of lactation, parity, milk yield, and milk components), dataset MB (dataset M plus body condition score and metabolic body weight), dataset MBS (dataset MB plus sensor-derived behavioral variables), and dataset MBSP (dataset MBS plus physiological blood metabolites). The combination of 4 datasets and 4 analytical approaches resulted in 16 analyses of DMI and RFI, using variables averaged within cow across the study period. Additional models using weekly averaged data within cow and study were built using all predictive approaches for datasets M, MB, and MBS. Model performance was assessed using the coefficient of determination, concordance correlation coefficient, and root mean square error of prediction. Predictive models of DMI performed similarly across all approaches, and models using dataset MBS had the greatest model performance. The best approach-dataset combination was MLR-dataset MBS, although several models performed similarly. Weekly DMI models had the greatest performance with MLR and partial least squares regression approaches. Dataset MBS models had incrementally better performance than datasets MB and M. Within each approach-dataset combination, models with DMI averaged over the study period had slightly greater model performance than DMI averaged weekly. Predictive performance of all RFI models was poor, but slight improvements when using MLR applied to dataset MBS suggest that rumination and activity behaviors may explain some of the variation in RFI. Overall, similar performance of MLR, compared with machine learning techniques, indicates MLR may be sufficient to predict DMI. The improvement in model performance with each additional data stream supports the idea of integrating data streams to improve model predictions and farm management decisions.
