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Hereford and Red Angus Heifers Recruited for Genomics Research

The University of Missouri is recruiting 2,500 Hereford heifers and 2,500 Red Angus heifers to participate in a heifer puberty and fertility genomic research project. Heifers should be registered Hereford, registered Red Angus, or commercial Hereford or Red Angus. Hereford x Red Angus crossbred heifers targeted for the Premium Red Baldy Program would also be a good fit for the research project. Producers must be willing to work with a trained veterinarian to collect the following data: ReproductiveTract Scores collected at a pre-breeding exam 30 to 45 days prior to the start of the breeding season. PelvicMeasurements (height and width) collected at the same pre-breeding exam 30 to 45 days prior to the start of the breeding season. Pregnancy Determination Using Ultrasound reporting fetal age in days. Ultrasound will need to occur no later than 90 days after the start of the breeding season. In addition, heifers must have known birth dates and have weights recorded eithe
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Beef Producers Could Have More Precise Way to Manage and Select Herds in the Future, Thanks to Collaboration Between Engineering, CAFNR

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by   Janese Heavin Beef producers in the future could have a more precise way to determine the productivity of their cattle, thanks to a collaboration between Mizzou Engineering and the College of Agriculture, Food and Natural Resources (CAFNR). “The goal is to maximize intake versus the output such as meat production,” said  Guilherme DeSouza . “From an engineering point of view, we want to optimize the process.” DeSouza is an associate professor of  electrical engineering and computer science  (EECS). He is working with  Jared Decker , an associate professor for CAFNR. Decker recently received a $500,000 grant from the U.S. Department of Agriculture’s National Institute of Food and Agriculture for the work. The project aims to provide a better understanding of how a herd’s environment and management impact productivity, Decker said. Researchers will predict factors such as a cow’s size—surface area to volume ratio—skeletal structure, fat resources and hair coat using 3-D image data.
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