MILK FAT DEPRESSION UNDERSTANDING ITS CAUSES AND MECHANISMS

The long-term goal of this proposal is to develop a farm tool to predict milk fat depression and to prevent losses related with it. Within this proposal, we aim to develop robust, infrared-based models to detect risk of milk fat depression at the ration formulation stage. We hypothesize that full spectra infrared data from feeds (NIR) and milk (MIR) can be strong predictors of ruminal yield of milk fat depressing-CLA.The specific objectives are:1. Train one Ph.D. student on the multidisciplinary field of lactating dairy cow metabolism, high throughput data management, and machine learning-based modeling.2. Coupling feed NIR data and machine learning algorithms to predict rumen environmentsand outputs that favor milk fat depression.3. Develop adequate dietary strategies, through model optimization, that prevent partial biohydrogenation of dietary fatty acids and undesired rumen yield of CLA.4. Use milk mid-infrared spectroscopy to map milk fat depressing compounds in post ruminal fluid.The complementary profile of the co-PIs (molecular metabolism and empirical, big-data based modeling) will allow this project to address three scientific aims, all coordinated to address economical losses of Wisconsin and U.S. dairy farmers related with milk fat depression in lactating dairy cows. These are:1) Use milk mid-infrared spectroscopy to map milk fat depressing compounds in post ruminal fluid;2) Coupling feed raw NIR data and machine learning methods to predict rumen environments and outputs that favor milk fat depression; and3) Develop adequate dietary strategies through model optimization to prevent partial biohydrogenation of dietary milk fatty acids