By Tyler Thomas in Newsletter — Oct 25, 2021

Oh, Ho, Ho, It's Magic You Know 🎸

In this week's R2R Edition: Simple solutions are often the best solutions. Also, "smart" devices and related technology have started their move into animal agriculture.

For those who did not get the title, please click play below. For those who got the title and now have the song stuck in your head, please click the play button below. And for those who are also fans of one-hit-wonders, you're welcome.

In this week's R2R Edition:

R2R will now be sent out twice a week with two stories apiece! This change will make it easier for our readers to get through each of the interesting stories, and we get to share more research in the publication–it truly is a win-win! So watch your inboxes this Friday!
Simple solutions are often the best solutions. Today, we get a taste of just how simple of a solution, a "magic" ingredient, for feed intake could be.
The move towards "smart" devices and technology has been alive and well in consumer electronics like Amazon Alexas, iPhones, and smart cars like Tesla. However, this technology has now started its move into animal agriculture–enter, smart farms.
As always, feel free to forward this to others who would also enjoy bridging the gap between discovery and deployment. Here is a link to subscribe!

Nutrition

Could it really be the magic ingredient?

What you need to know: A TMR, or total mixed ration, is a term used to describe various feed ingredients mixed into a singular and consistent product. Nearly all feedlots and dairies utilize this method of feeding. But why do we do this? What is the purpose of mixing the feedstuff together?

The answer is twofold. First, we mix the ingredients so that each animal gets a consistent proportion of nutrients. And second, we mix so that animals eat the less palatable feeds instead of "sorting" the good stuff out and leaving the rest in the feed bunk.

Mixing feedstuffs into a TMR does accomplish these two things, for the most part. However, there is still some level of sorting occurring, which researchers attempted to solve in this paper. They focused on what could be added into silage to hinder sorting.

What was their magic ingredient? Water. They added water (12.5 or 14 liters per cow) to some of the rations while mixing feedstuffs in the feed truck. Cows were then fed either a standard dry TMR or the new wet TMR, and were monitored for their sorting and intakes.

The water method worked rather well at decreasing sorting, with ratios of fine, intermediate, and large particle feedstuffs staying similar through the day. There was a 16% higher large particle proportion in the dry TMR versus the wet TMR. Think Chex-mix–it was like the cows had picked out all the good stuff, like the Chex pieces and the cracker-sticks, but not the gross pretzels (I mean, who likes pretzels anyway?). They would be left with mostly pretzels or, in this case, the large particles from the grass silage, corn silage, and hay.

Based on the numbers reported, the wet ration seemed to do the trick and decrease sorting. This method worked because water essentially clumped the feedstuffs together, where cattle were forced to eat all the ingredients in each mouthful. There was little chance for the cows the eat the small particles without eating large ones as well.

Important to Note:

This research was conducted on dairy cattle, which are fed different ingredients than beef operations. However, the findings are still applicable to beef operations that have small particles in their ration.
Some earlier research studying the same concept found either no effect or the opposite effect as this paper. This, however, does not take away from the paper's validity.
The first trial went for 90 days, and the second went for 70 days. Although this is adequate time, cattle are clever creatures and could learn to better sort over time. I would be interested in the presence or absence of learned ability over a few seasons.

Industry Application: Adding water to your TMR could drive feed intake and decrease sorting behavior from animals eating out of feed bunks. This could, in turn, better regulate the animal's digestive system and improve feed efficiency and animal health.

Animal Health

Smartwatches, smart cars, smart ranching?

What you need to know: You may have heard the term "smart" used to describe things like cars (i.e., smart cars like Tesla) or devices like Amazon Alexa, and Siri on the iPhone. Although not new in 2021, these smart products have already impacted industries like consumer goods, vehicle safety, and social media. One sector, however, that has not been made "smart" yet is agriculture, and to an even lesser extent, animal agriculture.

In animal agriculture, tracking and maintaining the health and well-being of our animals is both time and money-intensive. At a feedlot, this looks like pen-riders tasked with checking pens for sick or lame animals each morning. At a ranch, it looks like calving checks throughout the night and day, or estrous checks every few hours during the breeding season. Regardless of what it looks like for your operation, agriculture is a labor-intensive and manual-centric industry. And more manual labor means more money spent.

Although agriculture has looked like this historically, it does not mean this is what agriculture will look like in the future. This future is especially relevant when margins are slimmer, and prices are erratic for those producing agriculture products. This new era of agriculture might be closer than expected, especially when reading a recent review discussing the smart-agriculture that already exists.

Current advancements in animal monitoring like pedometers (Fitbit for cows), LiDAR (a 3-Dimensional camera system), and artificial intelligence allow us to monitor animals around the clock with more accuracy and less labor required. These systems can evaluate the animal's gait, time spent eating versus lying, and changes in disposition, and output actionable information to the manager based on observations.

For example, many papers demonstrated accurate detection of lameness in cattle using 3D camera systems, which could be used to detect hoof infections early and prevent animal loss and treatment costs. Other examples are using AI to evaluate calving-related behaviors, measuring an individual's feed consumption with cameras, or tracking animal feeding and standing behaviors throughout the day.

Although wide-ranging, there is much interest and the potential for these technologies to solve current issues in animal production, like early detection of disease and the opportunity to lower labor requirements for previously manual tasks.