Driving Innovation in a swine production system with Dr. Aaron Gaines @ The Swine it Podcast Show

"You can have a state of the art research facility or even the best ideas in the world, but if you don't have a good research staff at the slat level, you don't have much. It's about people... the role of research staff is to accurately collect data at the farm and to transfer that information timely to key stakeholders." - Dr. Aaron Gaines

I had the chance to chat with Dr. Kara Stewart about gilt development. If you are on the go, you can hear the episode at Apple Podcast, Spotify, or subscribe to our Youtube Channel. Otherwise, read on:

How to prioritize research ideas in a production system?

With researchers, there's typically no shortage of good ideas. However, there's usually limited resources, whether that's facilities, time or money, or maybe all three. Thus, we have to be disciplined, prioritize those ideas, and focus on the ones that matter to the business. Looking back on how I tackled this in my past life, we attempted to put these into a prioritization matrix that considered three critical pieces:

1) profit potential - what did the idea bring in terms of profit per animal? That could either be through cost reduction or through revenue generation of that technology or idea;

2) What's the probability of success: a low, medium, or high? And, some of that would be based on if there was previous research done or not. If there are previous findings that showed a positive response to the idea, the probability of success would rate higher in that scenario.

3) Difficulty level for implementation within the production system. Certain technologies are easier to implement than others. And we would rate the difficulty level for implementation. For example, a lot of the things that we put through the feed are easier to implement than perhaps, a reproductive technology that had to go across multiple farms, from a training standpoint.

Once we worked through profit potential, and rating that in terms of low, medium, or high probability of success, and difficulty level we basically focused on the ideas that had the highest score for the business.

How to tie those research ideas with the company's profitability?

I think the research team has to be viewed as a valuable resource to the organization. They just can't be the department that just does research without any impact of the company. The implementation of those ideas has to be built into the annual financial budget as well as individual performance goals. This forces the accountability of the research team to ensure the delivery of those ideas back into the business that helps company profitability. Now, the research team becomes a valued resource to the production team in terms of driving company profitability within the system.

How to measure the mortality and morbidity effectively from a large-scale research standpoint?

We put a lot of focus on growth and feed conversion measurements and research trials. However, there's a lot of instances where we miss out on measuring other economically important traits such as mortality, morbidity. We probably have seen examples where the technology may yield a positive result in a research trial with respect to growth performance but when you put that technology in the field, it fails, as it relates to higher mortality and morbidity. Those are very economically important traits that can be significant dollars for the production system. 

The challenge with measuring mortality or morbidity in a research setting is the number of experimental units needed to pick up differences across treatments. And this is where a power test calculation comes in handy for binomial traits like mortality. If you're looking at nursery mortality for example, and you typically run 3% nursery mortality and you're trying to pick up a 1% difference in mortality, it's going to require almost 4,000 pigs per treatment group to pick up differences amongst those treatments.

The other piece of this is that most commercial research facilities don't have enough experimental units to attack these smaller differences, that makes you set up field research trials within the production system where there's a large enough sample size to detect those differences and that can be done on the same production site, whether that's replicated within the room or across rooms or even replicated across multiple barns. I think it's a very important measurement that we need to be looking at more closely. Particularly with an industry that has significant mortality losses from birth to weaning.

Those field research trials can be set up in a controlled manner to measure mortality, morbidity if done right. They can be very important pieces to the equation on measuring mortality, morbidity, and not just growth performance data.

What's the role of the research staff?

Good research staff at the farm level will determine whether you have research or not. You can have a state of the art research facility or even the best ideas in the world. But if you don't have good research staff at the slat level, you don't have much. The role of the research staff is: to accurately collect data at the farm and transfer that information timely to key stakeholders. 

You need to make sure those people that are running the facility have the right technical and behavioral competencies needed to be successful in that role. We need to make sure that the research staff that's collecting the data understands the importance of their role to company profitability. Additionally, the other people that are part of the research department also need to have performance goals tied to certain research metrics and company profitability.

What would be the two or three most important key traits for the research staff?

The technical competencies you'd want somebody that has:

1) Some experience in running trials in terms of understanding experimental design and the importance of data collection. 

2) If they've had previous experience collecting data on farms, that'd be a technical competency you want to look for. 

3) Attention to detail. 

4) How effective are they at communicating with others in the organization?

5) Problem-solving: when you're doing a data collection on-farm, there are things that are not may be identified in the research protocol and that person that is running that trial needs to be in a position to problem-solve and have the technical acumen to "fill in the blanks" if you will, the protocol only tells you so much. Thus, it becomes critical to make sure you have a person that can make those decisions and have a successful trial. There are certainly several more examples, but that just gives you a flavor of what type of people that you want to run in these facilities.

What are your thoughts on it? What did we miss and what has been your experience?