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). 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. 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.
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. 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.
0 kommentar(er)
