 Hi, I'm Mark Hall with the Alabama Cooperative Extension System here with Ohio State's Brighton Shining Star, Dr. John Fulton. Our next lesson on advanced precision agriculture series is digital agriculture. John, as we look ahead to precision agriculture, the importance of data is growing and has a larger significance. Can you tell us how this evolution is going to work out? Mark, what farm press doesn't report today on something about not only technology but it has the word data in it. And as we think, not only today, but really what's happening kind of in front of us, just to kind of keep things progressing here, we're starting to call it digital ag. And so, you know, we had talked about just kind of thinking ahead, where we're at in thinking ahead in digital agriculture is where I think we're heading and so just kind of going through. But as we look, you know, this is kind of, there's not a company out there, doesn't matter what continent or type of company, Brighton Ag, that doesn't have a, you know, something like this that they've shown. And so this gives you insight about where the things are going and for some growers, you know, that maybe they've already got quite a bit of connectivity on their farm. So perhaps leveraging consumer devices like our smartphones and iPads, I mean, that's just growing. I mean, I can't think of the number of apps today that you can utilize either for information or connect right to the machine or visualize data or get access to data. So, you know, but one of the questions not only for farmers, I mean, Mark, you know, you've been in extension for many years. Where do you see yourself, you know, in that picture? Looking at a smart phone, talking to farmers about what they're doing when I go to party and farmers are there, you know, what we'll do if we get out our smart phone, hey, Mark, if you're seeing this, it shows the right, it shows what the weather's doing wherever you want to look. It shows this, it shows that. Boy, this technology is making life better, John. And the old guys, young guys, everybody just give it to me. There's not a probably a commodity meeting that you don't go to that they probably got smartphones and 70% of them have a tablet or iPads with them. And so, you know, this is real. This whole idea around digital ag is real. We're at the forefront of this. I pinned 2012 as a real key where the industry said, you know what? We've talked about it. We've tried this multiple times, but today in 2012 or that year, they made a commitment and the industry's just kind of followed it. And so I don't think the idea around data and the components of technology being embedded in machines is going away. This thing's here and it's here to stay. And so I think as whether we're extension, we're in the industry, you know, you've got to be thinking about where you're going to plug in and how either you're going to supply information or help growers make money in a digital way. John, the data, one thing that just baffles me is the amount of data. I was talking to Christian yesterday, and he was talking to the IT guy for EuroSystem Biosystems, talking about terabytes of information needed a thumb drive or a drive, external drive with terabyte. Man, that, wow, so much data. How are we going to manage that? And that's the thing. It's a bit overloaded today, and if you take that terabyte and try and upload it, we're talking about some of this cloud technology, it takes a while to do that. And so we've got some things to still overcome. We think about rural coverage of wireless and even cellular connectivity. I mean, once that happens, I think this will even accelerate. But you know, this is the ability that, like I said, we've talked about this. I mean, as people were connected almost 24-7, these machines are now connected, and it's just a matter of time where we start to see more sensors, moisture, temperature, and similar type devices that are going to be in the field connected in the field. But I think the thing is, is apps and kind of thinking, if you're going to serve this industry, whether it's here in the US, South America, Europe, or wherever, I mean, there's some countries today in Africa that we've worked with that they don't have all the nice machines and things, but you know what they have? They have smartphones, and they have cellular connectivity, and they're taking advantage of apps. And so this thing's worldwide. And so, but let's just explore it. This is how I kind of based upon a report out of Iowa a couple of years ago kind of pulled this out. And this is how I kind of begin to explain what digital lag is. Let's just step through and talk about this. And so I see it as kind of four buckets. And we talk a lot about big data, but you know, in reality, when we look at some of the things in the medical profession, the retail profession, they're utilizing big data, but big data is really not here in Ag yet. We just are not aggregated. And we'll talk a little bit about that in a second, but we'll start with precision agriculture. We've been at that for a long time, Mark. We've been at that since, you know, the early-mid-90s in a lot of areas of the country. But precision ag, the other thing that's probably the fastest growing area in this country is prescriptive agriculture. If you go and you listen to the precision seeding, prescriptive seeding, we touch on that a little bit. It's really utilizing the data to be informed or make recommendations. The other third part of the lag, and you're seeing companies provide this, is really enterprise agriculture. And that's really bringing the business. And in that case, I think about it as not enterprise level, but it's field by field understanding. It's management of logistics of your machines, your grain, and bringing all that in. And an important ingredient to that is it's being informed by the precision ag technology, pulling data from it to do a better job of characterizing fuel and things of that nature at a true cost on a field by field basis. And the prescriptive agriculture. This is what I'm doing now, and the feeding that in. And so it's kind of a field, what I would say is it's business, but it's really getting down to the detail of field by field. What kind of money am I making? And then, as I mentioned, big data is where we're heading. There's promise. We'll talk lightly on that in a little bit. But those are the four buckets. As I explained, that when you put them all together, that's the world of digital agriculture. Kind of to bring some definition. To me, it's precision agriculture is quite simply this technology that we've talked about in many of our sessions, Mark, that just the technology itself, we've been at this for nearly 20 years of bringing technology to the farm. Now it's embedded in these machines. Farmers are used to doing it. They're comfortable with it. And whether I buy or release a new machine, it's already there. It's readily available for many years, just like we talk about air conditioning in a car. You know, I don't check that box anymore. It just comes. Yes. And then the other thing is really basic site specific services. And as an example, it's going out in either grid or zone sampling and then putting a recommendation of that. That's very basic. I measure and I react or I determine how much. And so that's precision agriculture, Mark, in just very simple terms. Today we talk about this. This is in the North America market. And I think even in Australia and Europe, you're seeing growth, but this prescriptive agriculture. But that's where we're really utilizing data, collecting it to drive information, drive recommendations. A lot of times we think about prescriptions, but also being able to make those site specific decisions. And so really it's getting down to that subfield level of being able to manage variability. And I would say a grower in a lot of areas of the country today are probably sharing data with three people in their area. They've got their seed person. They've got their retail or cooperatives that's providing potentially fertilizer. They could have their dealership. And they could have their agronomists. I mean, there's easily three or four people that are beginning and are taking that data and are generating prescriptions or some kind of information from it, utilizing it. And that's what I would call prescriptive agriculture today. Kind of the third. And this is just as an example, in the United States it's very common that folks that are implementing verbrate seeding in the prescriptive world, verbrate nitrogens becoming popular, spacing in corn. And then adapting nutrient management. Maybe as an example, as I'm taking my yield maps and generating removal maps, I'm coupling that with my precision sampling schemes and looking at that. And so all of a sudden I'm using the data and I'm refining my fertility application program at a site specific level. So those are just some very common. There's many more out there, fungicides and et cetera that we see. But those are, to me, those are the three main ones that you see folks are really starting to utilize out there as relates to prescriptive agriculture. The other thing in enterprise agriculture, we talk about ERP and again, this is kind of the business field level management at the farm. Bringing that to the forefront, I can look at things like not only cost and if I add, you know, if I mix up my crop rotation, I can model that. I can look at scenarios and decide what I want to do. If I had a planter, I had a combine, I had another truck. I mean, these are things that I see again that we can do. But this is a heavily growing field and at least in the US you're starting to see larger farmers take advantage of this for them to better manage their bottom line. And so that's kind of the enterprise agriculture. And then the thing that gets a lot of attention, especially since 2012 is this thing about big data, Mark. We've got a lot of data. In fact, you were saying, well, how much data are we generating? Well, I can tell you, based on some of our projections, today a corn grower can contribute a half kilobyte of data per corn plant per year. Wow. You say kilobyte, but well, how many plants are out there? 42,000 an acre. You know, or 35,000. You know, and I'm doing 2,000 acres. You know, you're talking about thumb drives earlier and about the size. Well, you take a two-gig thumb drive, I can fill her up real quickly, just annually. And so, but big data is this idea that where it's just not my farm, it's aggregation of data and then beginning to query and learn from it in different ways than never been. If you can pull it up in a spreadsheet like Excel, that's not big data. Using some of our traditional databases, that's not big data. This requires all new kinds of cloud technology and such. But big data, as an example, the retail sector, you know, uses it to see trends and the likes of Google, the medical profession, beginning to do it, but it brings a whole higher order of learning and value potentially, but we're not quite there today. I thought this would be one good, you know, kind of answer to that is that officially when we get to the big data, you know, you think about when we work on projects, Mark, I send you the data, right? Hey, you give me the summary and the analysis and send it back. Well, once the data gets aggregated and it gets so big, you're gonna have to bring those analytic pieces to the data because you just can't efficiently and effectively transmit that anymore. And so we're gonna have to take the analytical tools to the data versus what we've traditionally done is taking the data to the, you know, loading it in Excel or loading it into R, SAS, or whatever we were, GIS. So that's a good way to measure what the difference is. John, do you see artificial intelligence coming in and looking at all this big data and be able to say that that's problem A and that's problem B and that's this and get that back to the farmer because it's just so much data. How's anybody or how are we gonna deal with it? Yeah, absolutely. And I think you're seeing companies trying to do that, but the requirement there is you gotta have an aggregated data set in order to explore and begin to learn. And so it's gonna take time. I think we see those tools being built today by companies, we call it machine learning. Yeah. Those are the processes, the analytical pieces that are being implemented on it. But we're still, I think, trying to get it to a level and learn how to really draw value out of some of these data sets once they are built. So that's, you know, the four components as I see it, the digital ag. And so that kind of brings us to this, you know, again, I think about this. So now farmers, as they look at it, they're using all this technology. They could be using like the telemetry pieces technology. And now all of a sudden I got all this data. So where am I gonna store it? How am I gonna share it? And if I am sharing and I'm doing it in a big data scheme, how do I remain anonymous if I don't want others? You know, I'm willing to share and I'm willing to learn from it. You know, these are now the questions. And you know, I talk even now that a farm, it's a business, you know, needs to sit down and talk about what's your digital ag strategy? You need to, you know, where am I gonna store it? We're talking about using like Dropbox and Box and you know, there's all these cloud storage, but you know, we've got companies offering their own platforms, but you know, that's something that you gotta kind of sit down and think about today. Even as an individual, right? As a consumer out there. So, but that kind of, you know, as the first part of this discussion comes in, I think we've kind of covered at least as I define it what digital agriculture and now we're starting to move in to all these issues around data today. This is exciting stuff, John. Thank you so much for sharing with us and please watch all our Precision Ag videos and we're gonna have another one on dealing with data. So watch that. Thank you.