>> Live from San Francisco it's theCUBE. Covering IBM Think 2019, brought to you by IBM. (electronic beat music) >> Welcome back to theCUBE. We are live at IBM Think 2019 in soggy San Francisco. I'm Lisa Martin, with Dave Vellante. Dave, I hope you brought a big umbrella today. >> Well luckily the Marriott lent me one, so-- >> I got one from my hotel, too. And what a perfect day to day have the hybrid, multi-cloud open upon us, shower San Francisco with rain, and talk about weather with an IBM expert. Mark Gildersleeve, welcome to the Cube. You are Vice President, Head of Business Solutions, and Watson Media, The Weather Company. >> Thank you for having me. >> Our pleasure, so, we think IBM, this is the second annual IBM Think. There's about what, 30,000 people here, 2,000 plus business and technical sessions. There is a lot, a broad spectrum, no pun intended, of topics to cover, but excited to talk with you today about what IBM is doing in the agriculture industry. Let's talk about it from the growers perspective first, and we'll cover some other, other outlets. But, what are some of the challenges that growers are facing in 2019? >> So, first of all, if you think about it, this is a really sporty industry for growers to be in. They've got to worry about things that they can't have any control over: the weather, pest and disease, government regulation, trade, commodity pricing, there's a lot that they can't control. To make matters worse, they have very slim margins, okay, and they had to learn all these various aspects of technology to try to become better. And so, they're almost drowning in data, trying to figure out what do I do about it to get more yield, to get more profitability, to get better quality? There's a lot of challenges that they're wrestling with today. (people chattering) >> Well this is a huge problem, because the, the amount of farmable land isn't growing. It's essentially flat. >> It's flat. >> Maybe it's even shrinking. >> It's flat. >> They're talking with a multi-decade, 20, 30-year time frame. Population growth, we're talking about another two, two and a half billion people over the next three decades. So, something's got to give. What does the data say? >> So you're exactly correct, the estimates of population growth are 2.3 billion between now and 2050. That's 30% population growth. With zero incremental air-able lands, so, huge yeah. So we have to get yields, at least 30% higher. Okay, so if you think about that problem we're not going to get that yield increase status quo. We're not going to get that yield increase without having a much more data and an AI driven approach to agriculture, and that's exactly what we're doing. Our solution right now has 14 different AI and analytic capabilities inserted into it. Just to try to help growers, for one, make sense of their data and make better decisions to try and get their yield up, their profit up, their quality up. >> And is there enough in your estimation markers, is there enough head room actually to accommodate that population growth, given the constraints? >> Absolutely, taking a simple example of being a corn grower in the U.S. The average corn grower gets 175 bushels per acre, but the 70th percentile gets like 250, okay? So, if we got in, in the example of corn, every person that's at the 50th percentile, up to the 70th percentile, which is extremely doable. You can, you are, by definition, increasing the yield 30% in that case. So, it's doable, and we can see examples of growers doing it today. But what you have to understand is that 70% of the differences in performance between growers are just their farming practices. So, we have to get a handle on what farming practices drive better yield. We have to get those people at 50% to 70%. The people at 30% up to 50. We just have to get them about 20 points better in the benchmarking, and we will actually solve this problem from a U.S. perspective, then we have to do different things for other parts of the world. >> Now there's a multi-variable problem here as well though, because you got consumer patterns changing, people want, you know, more sustainable. You go into the grocery store now, you see all grass-fed, or free-range, and, so that takes up more land. Do consumer, how do consumer preferences, and the shifting consumer preferences factor in? >> It's the biggest change I think that's happened in this industry in the last 20 years. If you look at 20 years ago, 30 years ago, the tech chains were being driven kind of more from the ag-input side, and that's kind of the people that are selling to the growers. Now, we have the food companies hearing from consumers that they want sustainable, they want better quality, they want more nutrition, they want to understand how to have less chemicals going into their food. Okay, now we have the buyers of the growers, pushing on those growers to say you need to give me a better product. This change of consumers, and this ripple through the food eco-system is the big change. And the food companies are at the center of this revolution. And it's actually really interesting, and I think it actually will knit together this whole ag-eco-system, so that you now have to worry about the ag input people, the growers, the food companies, and the retailers, the bankers and the insurers, all kind of understanding, and coming together to figure out how to get better product to the consumers, and also, by the way, increase the yield so they can solve the food production problem. >> So, where do you start? Are you talking, what's the lowest hanging fruit? Is it going to the large-scale growers that have more resources, potentially resources that understand technology enough to start at that source? What about the smaller scale farmer growers? >> So, I think that, we have IBM clients that are interested in solving every aspect of the kind of size of foreign problem. So, I met with one organization from Africa today. In Africa, it's all a small farmer problem, right? And, and the vast bulk of growers in the world are small farmers, okay? But when we're looking at kind of solving the problem overall, we want to start with the food companies, and the people in finance. Because, right now, food companies, when they're trying to deal with their growers, they're trying to manage these growers with spreadsheets. Even though these are very sophisticated companies, very sophisticated. We need to help those food companies better understand what's going on the field. What chemicals that are going onto the land? When was the crop planted? When is it going to be harvested? When can I expect it in my storage facility? And they really want to understand, what are the farmers doing that are giving them the best quality crop? And how can they learn from the data, to get best practices for all the rest of their growers? If we start with the food companies, and have them work with their growers and the agronomists, that's going to be the best way to introduce change into this sector, I believe. >> And they're kind of the the pivot point between the consumer, they understand the consumer demand, they can feed that back to the farmers. Of course, they're ultimate goal is to make a profit. But look at it, if you give the people what they want, there's going to be a way to make money here. It's just, it's not going to be the same way that they've made money for the past 50 years. >> Exact, exactly right. But you know, take an example, in my house, we buy organic milk, okay? We're paying a premium for organic milk. We're willing to pay a premium. >> Happy to do so, yup. >> Happy to do it. We feel like it tastes better. We feel good about also the quality of it. So, I think in many cases, food companies are willing to pay a premium to growers to deliver a very specific crop to them. And so, this issue of food companies having more growers under contract, and working with those growers to deliver a better product, is of high interest to virtually every food company, every beer company that we've talked to. Every retailer that's worrying about the supermarket shelves. They're all worried about trying to get better product to the shelf, 'cause that's what the consumers are asking for. There is money, in this system, if you get the quality up. So that's really what we're focusing on with the food companies. >> People happy to pay for that and this eco-system is actually quite interesting. You talk a bit about, you talked about the banks. They're, even health care is part of the eco-system. >> It's the other constituent. >> They've said that people start making better food choices. It could ripple through to health effects. So, maybe you're paying more, as a consumer, for an individual product, but you could be living longer, having better health, maybe having lower health care costs. >> One analogy that I think you might find interesting, is that, just as all of us have an electronic medical record, that has all the images that would have been taken of our body, like an MRI, or our health history, our hospitalizations, what surgeries we've had. We're now, as IBM, bringing the electronic field record, which is an exact analogy to the electronic medical record, but it's about the field. What's been grown there? What have been the yields? What are the chemicals? When was the crop planted? What kind of tillage practices are being used? And we're trying to, essentially build that database of the electronic field record as the cornerstone for all the analytics for the AI that we're building, and running against, to help figure out benchmarks for all the corn growers in U.S.A., or the potato growers in the Netherlands. And beyond the benchmarks, best practices, so that we can say, what are the people that are 70th percentile doing, that the people that are 30th percentile aren't doing? We can bring all those people up. It's very cool. >> So we're talking about IBM, the computer company, right? So, what's the big picture of IBM's role? Obviously, there's a data angle. But what's the IBM story here? The holistic story. >> So, first pillar is data. Every piece of data coming off of a combine or a sprayer, so the equipment data, the machine data. All the environmental data, remotely-sensed data, soil-sensed data, stuff that's going on to the field, as well as the farm practices. So, there's a whole data story that, who better than IBM to handle massive amounts of data? Secondly, AI and analytics, right? So, we've got 13 or 14 different analytics and AI products embedded in our decision platform. All intending to give that grower a better first guess, a better recommendation of, here's what the data tells us about your field. It's still up to the grower and the agronomist to make the final call, but we can give them a much better guess than they have just based on their own personal fields experience. Then lastly, it's decisions that we can help that grower make. So, an example would be: we can help a banker understand exactly what crop is being grown on a piece of land without having the banker have to send somebody out and look at it. So, they can understand compliance-wise, Was a loan that I wrote being used in the purpose that was intended? But there are many enterprise examples of that. So it's data, AI, decisions. And that's then connected across the eco-system. It's a great IBM story 'cause we've been in business, we've been serving the USDA for 91 years. We've been in agriculture a long time. Lots of people in IBM don't know it, but we've been at this a long time. >> And if we look at the growers for a second, this is really kind of where it all starts, right? I understand this triangulation, and the constituents that are involved from the food companies, to the retailers, to the bankers. But, if we look at the growers, what are some of the benefits? Do you have a favorite success story where, whether it's a large-scale grower or something smaller, where their, maybe their loan terms are better? Or they have lower costs? Or they're actually making a better impact on the environment? What's your favorite grower impact story? >> There are lots actually, but let's pick a few. The first is, we have a lot of aspects of crop protection, where we can use satellite imagery to figure out where a crop is under stress. Where, what part of the field is under stress. Help them go out and scout that field. Take a picture with their smart phone and have Watson tell you what the disease is that's infecting that crop. And, essentially, be able to take faster action. When you're faster with crop protection, you are saving a lot of your crop. You get better yield, that's money in the bank. So crop protection is one. A second example is, with best practices, showing some of these growers what the 70th percentile growers are doing, that the 50th percentile guys are not doing. You can say, here are the four things that these 70th percentile guys are doing. You should try those four things. Or you might want to try two of them this year, two of them next year. But best practice is a huge impact. The last impact is, we help people with yield. So, we can now say okay, this is the projected yield that you're going to have at the end of the season. Here's what you can sell at the middle of the season. Here's what you're going to be able to sell at the end of the season. And we help them with market timing. Trading profitability can be easily 20, 30 bucks of incremental profit per acre. So, there's kind of a financial angle, there's a best practices angle, and there's a protecting your field angle, as the three examples I give you. >> Well, and that's huge from the standpoint of the debt loads that farmers face around the world. Over a trillion dollars in debt, in just, you know, a few countries. What does the future hold from that standpoint? What are the implications of that debt load? Obviously there's an imperative to improve yields and improve profitability, but your thoughts? >> So, first of all, you're correct that debt is a really enormous issue. So, for example, there's an article in the Wall Street Journal last week. Bankruptcies are at the highest level in the U.S. since the crash of 2008. So, this debt load, and the debt service is a really large problem. Here's how I'd like to try to focus it. Many growers have been taught to worry about better yield. When we should have been focusing more on better profit per acre. There are two ways you can get out that profit per acre. One is, you can do things with new chance fertilization, seed type, plant date, that can drive your yield better. But the other aspect is, there are parts of your land that are going to be lower productivity potential. Your smartest move is to put less inputs on those portions of the land and double down on the inputs on the highest productivity areas of the land. Because most farmers don't understand that there's 25% of their land, where they're actually losing money, and they'd be better to actually not be planting. But instead the idea is, plant at a lower population rate, put less input costs in, and then you can even make that area of less productive land profitable. If we improve the profitability of these growers, they can afford the debt service, and that's kind of the way to do it. The other aspect is that, everybody that's doing contract growing for a given food company is getting a premium on their crop. Oftentimes, 10%, or even 15% premium. That 10%, or 15%, solves the problem of the debt service for almost every grower, in the U.S. that's doing zero crops. >> That focus on profitability versus pure yield per acre. That's potentially involves a a different crop? And a shifting strategy? >> Usually it's a different farming practice. So, it's applying variable rate technology. It's essentially understanding how to treat each aspect of your field differently so that you're not treating it homogeneously. But you're actually saying, I'm going to do this practice, and with this level of input costs down over here, in this section of the land. And do a different practice over here. Because, every piece of land has low productivity areas, high productivity areas, and areas that are either high or low, depending on the weather. Understanding how the land varies is a huge data insight that we give growers with our data insights using AI. >> And that can drop right to the bottom line, obviously. >> It's all bottom line, baby. >> Last question before we have to wrap, this is, I feel like we're scratching just the surface here, of such an interesting topic of, and the massive global implications of IBM and agriculture can have on all of us. Where can people go on the IBM website for example, to learn more about this? >> You can go to the, well, so at the Think, there are a number of sections actually that we have right now. Talks that we're giving later on Friday morning. All related to the Watson Decision Platform for Agriculture. And there's material at the Think exhibit stuff that you can go to. We're also exhibiting in the Watson Media and Weather section downstairs. We'd ask everybody to come there. >> Excellent, well Mark, thanks so much for joining Dave and me on the program today, really interesting conversation. >> Great story. >> Thank you for having me. >> Our pleasure. We want to thank you for watching the Cube, I'm Lisa Martin, with Dave Vellante. Live, from IBM Think 2019. Stick around, we'll be right back shortly with our next guest. (electronic music beat)