Animal AgTech

Prime Future 87: Precision livestock management, so what?


Have you ever taken a work out class at Orange Theory Fitness? I’m a recent convert, mostly because of this:

The scoreboard inside Orange Theory Fitness gyms

That front & center, color coded, real time scoreboard. There are big screens at the front of the gym with everyone’s names in a block that changes color as your heart rate increases, relative to your resting heart. The color zones are based on your current heart rate as a % of your maximum heart rate, and it progresses from gray to blue to green to orange to red. The data is pulled from the heart rate monitor you wear during the workout.

So wherever I am in the gym, I can look up and quickly see how I’m performing in my workout…and how my performance compares to others in the class.

Orange Theory says that to maximize calorie burning, you should get at least 12 “Splat Points” per workout. A splat point is added for every 1 minute your heart rate is 84% and above your maximum heart rate. Each individual’s progress towards splat points is also displayed on the big screen.

This gives me a a real time quantifiable, measurable, trackable goal while I’m in the moment and can still dial up my effort to change the outcome of the workout.

After the workout, I open the OTF app to see how this workout compared to prior workouts. The app gives me a visual record of progress, of momentum. Which is wickedly motivating.

Before wearable fitness trackers, you only had documentation about whether the workout occurred, or what happened during the workout, if you wrote it down yourself. People just worked out for the sake of working out. <shudders>

That same shift, the same unlocking, is happening with the rise of precision livestock management. Let’s talk about what that means & why it matters.

There are three defining dimensions to precision agriculture technology, whether livestock or crops:

  1. Shrinking the unit of management. In crops, it is about going from field to acre, or acre to plant/tree. In livestock, it’s about going from herd to animal, or swine barn to pen, or poultry house to zone.
  2. Timing of the measurement. Real time data capture, or close to it.
  3. Actionable. What is the ‘splat points’ equivalent? The thing that gives the user something to GO DO differently, based on the data captured. Without a thing to GO DO differently, 1& 2 don’t matter and the whole concept falls down. There is near zero value created if the data cannot be actioned.

…and precision livestock management applies those 3 dimensions in a framework to optimize relevant inputs (animals, feed, grass, etc) and outputs (livestock, meat, milk). A critical caveat here is that ‘optimization’ is unique to each producer’s objectives and what they are optimizing for.

Contrast a future state with high value creating precision tools with the historical norm, in which livestock managers have had the most relevant data available at closeout with metrics like feed conversion, ADG, or profit/loss.

Quick timeout for 2 definition refreshers:

(1) Lagging indicators vs leading indicators. Lagging indicators tell us what happened in the past. Leading indicators tell us what’s happening now. And effective leading indicators predict outcomes.

(2) You’ve probably seen this a hundred times, as it’s a common framework to think about the progression of analytics in terms of both business value & degree of difficulty:

So, closeout data is both a lagging indicator and an example of descriptive analytics, the lowest value version of analytics. Closeout data can only tell us what happened – not why it happened or what’s likely to happen next, in time to course correct. 😕

In the absence of real time data around leading indicators, we rely on the combination of lagging indicators and qualitative evaluations. Like when a poultry vet walks into a poultry house and looks at litter conditions as an indication of bird health which is an indicator of how the flock will perform at close out.

But the promise of IoT is that with connected sensors, we can have real time data that enables more effective leading indicators of performance & outcomes.

It’s almost like there are 4 elements to make that promise of IoT hold up:

  1. What relevant data is captured?
  2. How is the data presented? (Think of the OTF scoreboard!)
  3. How does the ‘scoreboard’ change behavior?
  4. What is the value created by the change?

5 Considerations for livestock owners & managers

(1) Behavior change. Because precision livestock tools only create value when they drive a specific decision or set of decisions, these tools should change how we manage and/or what we manage. Full stop.

(And the more specific precision livestock products get about what they solve, the easier it is for decision makers to decide how to deploy the technology. Everything is the enemy of Something.)

(2) Scorecards motivate people. Humans like to see results, evidence of progress. It’s why every single management system in the world talks about the importance of keeping some sort of scorecard in front of people, whether it’s Andy Grove’s OKR system or Franklin Covey’s 4 disciplines of execution. Data turned into leading indicators turned into compelling scorecards can align work to be done & drive progress.

(3) New ways to get it right...or wrong. Incentivize the wrong metrics and you could end up with suboptimal results. Which potential metrics are simply ‘interesting’ and which potential metrics can move the needle on the most important outcomes? Getting really clear about vanity metrics vs useful metrics will be more important than ever.

(4) Linking leading indicators with lagging indicators. Find the leading indicators with Actual Predictive Value of lagging indicators. Not to mention that one segment’s lagging indicators at closeout, might be leading indicators as the product (livestock or meat) moves through the value chain.

(5) Find the right role for qualitative evaluation. I love the idea of human + machines, or qualitative + quantitative, or marrying what the data says and what the well trained human eye detects. Real time data doesn’t mean you don’t need people to manage livestock, obviously. But it should mean that you can allocate those human resources in higher value ways.

Long time Prime Future readers know that I am not interested in tech for the sake of interesting tech – it is always and only about value creation.

We are in the early early days of precision livestock management and aligning the right tech around the right business problems to create compelling value. I’m bullish on animal protein in the long run, and on precision livestock technologies as an enabler of that long run.

“The future is here, it’s just not evenly distributed.”


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