USDA ARS --
Research at Texas Tech is currently showing cattle on an all-forage diet produce more methane than cattle with grain mixed into the diet.
USDA ARS -- Research at Texas Tech is currently showing cattle on an all-forage diet produce more methane than cattle with grain mixed into the diet.


By CBW Staff


Beef cattle nutritionists at Texas Tech are in their second year of research into a project aimed at curbing cattle methane production. While curbing methane seems like a very “green” thing to do researchers are saying it makes sense on the production end as well. 

“When cattle releases methane, they’re losing carbon that could’ve stayed in the animal as meat, milk or fiber,” commented Darren Henry, a beef cattle nutritionist in the Animal and Food Sciences department at Tech, in a Lubbock Online interview. “Cattle are going to lose 2-to-12 percent of the energy they consume, and they’ll lose it to methane with the range depending on the type of diet.”

Research so far is showing that a forage only diet may not be the best option for lessening methane production. Grass-fed beef is shown to be creating more methane than grain-fed beef in the current research.

The study will continue for two more years. 

“What we may end up getting is there might not be much of a reduction of methane, there will be some,” says Chuck West, the Thornton Distinguished Chair for the Plant & Soil Science Department at Tech, who is also involved in the reseach. “But, even if the amount of methane given off every day is just a little bit less, the methane footprint for a pound of weight gain will be lower because we’re balancing their diet better and that translates into greater weight gain.”


UNL developing livestock monitoring

An interdisciplinary team from the University of Nebraska–Lincoln has developed precision technology to help producers continuously monitor animals and use the resulting data to improve animal well-being.

 The team includes Nebraska electrical and computer engineers Lance C. Pérez, Eric Psota and Mateusz Mittek, and animal scientists Ty Schmidt and Benny Mote, who developed the technology system using video footage of pigs.

 The system processes video footage from livestock facilities — day and night — and applies machine learning, which uses statistical algorithms to help computer systems improve without being explicitly programmed. It identifies individual pigs and provides data about their daily activities, such as eating, drinking and movement.

 Based on this data, the system can also estimate how much each pig weighs and how fast it is growing.

 “Our system provides a pattern of typical behavior,” said Psota, research assistant professor of electrical and computer engineering. “When an animal deviates from that pattern, then it may be an indicator that something’s wrong. It makes it easier to spot problems before they get too big to fix.”

 The team created their system using deep learning networks, a form of machine learning with millions of coefficients and parameters. To identify pigs from all angles, the networks processed images large and small, rotated, skewed and otherwise transformed. The team uses ear tags to help with identification but aims to rely on unique physical characteristics such as ear shape, saving producers the added work of tagging.

 Although the system has been developed to identify pigs, its algorithms can be used for other livestock, such as cattle, horses, goats and sheep.


FDA funding 

opportunity targets duration of 

use studies

The U.S. Food & Drug Administration announced April 1 a funding opportunity and request for applications (RFA) for studies that can help target and define durations of use for certain medically important antimicrobial drugs approved for use in the feed of food-producing animals. 

From April 1 to June 3, 2019, FDA’s Center for Veterinary Medicine (CVM) will accept research applications for the fiscal 2019 program, which will fund up to $1.5 million in support of applications for studies in fiscal 2019, with a maximum of $250,000 provided to any individual awardee, the announcement said. 

Subject to resources and awardee performance, awardees will be eligible for an additional year of support in fiscal 2020 of up to $250,000. 

The number of awards is contingent upon FDA funding availability and the number of suitable applications.

FDA said it is offering funding to help generate publicly available data that sponsor(s) of affected approved animal drug applications can use to update product dosage regimens to better target when and for how long the drug may be used.

Defining more targeted durations of use supports FDA’s ongoing efforts to slow the development of antimicrobial resistance by fostering the judicious use of medically important antimicrobial drugs in animals, FDA said, which helps preserve the effectiveness of these antimicrobials in both veterinary and human medicine.