| Autor: |
Kebreab, E., Cant, J.P., Knapp, J.R., Souza, V.C., Bougouin, A., Archimede, H., Balehegn, M., Adegbola, A., Choi, E., Mueller, N.D., Olthof, L.A., Briggs, K.R., Bradford, B.J., Pressman, E.M., Cabezas-Garcia, E. H., Waddell, J., Hu, H., Reed, K. F., Muñoz-Tamayo, R., Tedeschi, L. O. |
| Předmět: |
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| Zdroj: |
Canadian Journal of Animal Science; Jun2024, Vol. 104 Issue 2, pS1-S7, 7p |
| Abstrakt: |
This document contains three separate articles related to dairy cattle research. The first article explores the use of depth imaging as a noninvasive method to measure daily dry matter intake (DMI) in dairy cattle. The researchers found that depth imaging showed promise as a constant measure of DMI on an individual cow basis, potentially providing a more accurate and efficient method for monitoring feed intake. The second article compares different nutrient requirement models and their effects on dairy cattle diets and methane emissions. The study found that using the NASEM (2021) model resulted in lower methane emissions compared to the NRC (2001) model. The third article focuses on optimizing diets for automatic milking systems using mixed-integer, nonlinear programming. The study developed optimized diets that reduced cost and predicted group performance. [Extracted from the article] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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