Putting AI into AG: EMILI's Ray Bouchard explains why Canada’s farms must go digital

CA: Where does Canada stand in the data revolution in agriculture?

RB: As a country, we've got a really rich history of agriculture. We're known as a global leader in quality production of commodities. We produce some of the safest, most nutritious foods in the world. Where we have this room to grow is to actually change our brand a little bit.

And the changing of the brand is around data. I really think we can pivot from being recognized as one of the best producers of quality, nutritious foods, to being a producer of the best quality, most nutritious foods with the most transparent integrated data strategy.

CA: What does an integrated agricultural data strategy mean?

RB: Right across the value chain, we have application of machine learning and AI technology. So whether that's the grower, whether that's the grain buyer, whether that's the producer, whether that's the retailer or the consumer. 

We're seeing more and more desire for stronger connectivity across the value chain. There is going to be more and more opportunity to produce premium products that have a premium attached to it, as long as they can provide transparency and prove out the process in terms of producing that specialty crop.

So what does an integrated data strategy mean? What it doesn't mean is having one data AgriFood strategy, but it means stronger collaboration amongst sectors of the value chain, and putting in place good data governance initiatives so that data, as it moves along the value chain, provides integrity, provides security, and allows the owners of those data sets to participate throughout that value chain where they can. 

We need to find a better way to do interoperability. So I would say one of the things as an industry that we really need to focus on is how to do a better job around data protocol and data interoperability.

CA: It seems daunting to get the tools to be so data savvy. Isn’t this for the big operators?

RB: When you're a smaller operation, you absolutely need to maximize productivity. So I think machine learning and AI tools probably help smaller growers more than, or as much as, larger growers.  

Most of the newer equipment is coming out technology enabled so the ability to collect the data is pretty easy. Companies like John Deere have a cloud storage system and there's zero cost to do that. So your data can be captured. It's your data up into that cloud environment.

In terms of getting support on the data cleansing and the data collection it's a minimal cost. It can be anywhere from 75 cents to a buck an acre, a buck 50 an acre. It is not that expensive.

CA: It sounds like you see a kind of fusion between the farmer and technology. Will they go for that?

RB: Around 20 years ago, the AI and the ML was the farmer. And so what a grower is going to need to know going forward, he's going to have a basic understanding of what machine learning and AI is. Machine learning and AI is not scary. All it is, is advanced mathematics.

It's been said that the most important sensor on a farm is the farmer himself or herself. No one knows a farm better than the farmer, in terms of historical performance and everything else. And so when you think about that, what we need to do now is we need to harness that with technology. So I think that's this evolution that we're talking about, and more and more insights are going to be garnered and gathered to allow farmers to really address those key areas, which is productivity, sustainability, and then profitability.

CA: Seems like a no-brainer. Why aren’t all farmers doing it?

RB: The challenge is a lot of growers today don't believe that they're getting full value for that data. But our argument and my argument is in five to 10 years from now, farm policy is going to demand data transparency, and so it's a minimal cost to get started and to get yourself in a position to really leverage the opportunities.

Growers really need to treat their data like intellectual property. If you think about it, production data is a grower's intellectual property. He or she has got some production data that is proprietary, and that is the grower's data. They may not want to share it at all, or may only share it with certain partners that are paying for it, or are required in the sale of a commodity. That'll be their decision.

CA: But won’t data and these tools make farming better and more profitable for farmers?

RB: I think they need to think about data in terms of how they can leverage like-minded growers, and how they might compare and do benchmark. Now you start to create deeper insights into how you can improve productivity, sustainability, or profitability. 

If we can get all growers to think about some of their data that should be open data, that they would want to share. Something like rainfall, just as simple as rainfall. If you allow that to be in the shared data pool now, all of a sudden, the industry has some pretty robust big data sets. I really think that's where we have an opportunity with growers.

CA:  Tell us about EMILI and its plans for Manitoba, one of Canada’s AG powerhouses.

RB: EMILI stands for the Enterprise Machine Intelligence Learning Initiative based here in Manitoba. And the reason that academia and industry initiated the EMILI not-for-profit group in 2016 is as a community we had a collective vision of how industry was going to be disrupted with new technology, specifically machine learning and AI. 

With my role in agriculture, I saw what was taking place there, and I was excited about having a community focus that would really let us scale out digital agriculture here in Manitoba. 

EMILI has got four key pillars that we focus on around digital agriculture. The first one is research and innovation. And so what can we do to better collaborate, better leverage startup ideas and concepts from a research and innovation perspective? 

The other area is intelligent technology integration. That's taking and bringing together startup technology companies with legacy companies so that again, in a collaborative environment, we can build out mid-tier and legacy companies with the collaborative activity of new tech startups.

The third pillar is skills and talent. We think there is a huge need from an industry perspective, and there's a need to have academia better connected to industry as it relates to what some of the new skill sets and talents that are going to be required as this evolves. 

And then the last pillar we've got is on the capital side. How do we help startups, not only from a financial perspective, but from a human capital perspective around the concept of mentoring and coaching?

CA: Give me a real example of what data can do on the farm?

RB: Right now today with the collection of the data on your farm, one of the big opportunities that we see with growers, and it is being done by some growers today, is what we call the application of a full farm research strategy. 

And so because you have the equipment that's technology enabled that collects the data that can automatically with the geo referencing turn on and turn off and increase rates and lower rates, you now have the ability on your farm to run research projects, everything from seed depth, to seed rate, to variable nutrient rate, to variable herbicide, pesticide, or fungicide rates. 

So you now have those tools to be able to quantify best management practices. Once you have those tools in place, you collect the data. After the season, you can take a look at those data sets and out of those insights, make some improved business decisions that should ultimately lead to improvements in productivity, better sustainability, or more profitability.

CA: Sounds like the future. What’s the risk to growers who don’t embrace it?

RB: In five to 10 years from now, the risk to the grower is he will not have the tools in place to really provide him with the business risk management he requires. So what does that mean? 

That can mean everything from how he deals with his lender and what his lender is going to be looking for when he goes to grow the farm and wants to expand. That institution is probably going to want some pretty solid data to be able to help make some risk profile decisions. 

I believe farming foreign policy is going to change. And in areas like crop insurance, it is still not nearly as advanced as it can be and should be, and I think you're going to see that evolve. You hear a lot about sustainability, and conversations taking place around where the next foreign policy is going to go. I think we're going to start to see a migration towards good stewardship, and at the foundation of that is going to be a solid set of data, data sets. So I think that's the risk for growers, is they will not be in a position to take advantage of all the opportunities coming to market.


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