Farming Video | Can AI Finally Tell Us Exactly How Much Nutrition a Crop Needs?

00:00:00 Hey, I'm standing in the XtremeAg field day plot, and specifically the AgroLiquid trial. The AgroLiquid trial is really interesting to me this year because there's no 00:00:10 protocols. The reason there's no protocols is what we are doing with AgroLiquid this year is we're applying 00:00:17 their products, we're then running sap analysis. We are then going to use the calibrated proprietary AI 00:00:26 tool that we will be able to make recommendations. But before we can make the recommendation, we've got to calibrate the response of the product. And so AgroLiquid has agreed 00:00:37 and is interested to know what their products do, and a sap analysis is the best way to figure that out. So a couple of days ago, we sprayed a quart of Liberate Calcium and 00:00:48 a quart of Micro 1000. We took a sap analysis, made the application. Twenty-four hours later, we took another sap analysis to see the 24-hour response from the product. 00:00:58 And now, six days later, we'll take another sap analysis, and we will see, is the Liberate Calcium and the Micro 1000, is it still 00:01:06 in the crop, and to what levels? By doing this, by calibrating this response, we will then be able to put that data into the calibrated agronomy 00:01:17 AI tool, and we'll be able to use it to make recommendations. We've done this with all the calibrated ag products that we have thus far, and 00:01:24 we're still building the database. And now here's an example of a company like AgroLiquid really wanting to show you the value of their products. I commend this because we're using the 00:01:33 technology to actually know what the product does. And so from a grower standpoint, you're going to know what products do I need and how much of each product do I need to use. 00:01:43 This calibration of the response that I'm talking about of the products, we're doing it in corn and soybeans. I think as growers, we all pretty 00:01:51 much know what our crop needs. But are you positive that you know exactly how much? Are you using enough? Are you using too much? 00:01:57 I really believe there's going to be a savings on inputs here, and I really believe there's going to be a yield response here because on some things you're using too 00:02:05 much and you can cut back, and on other things, you might be missing it entirely and you're leaving bushels on the table. 00:02:10 So what we're really doing here is trying to generate the biggest ROI possible, 41 00:02:17.180 --> 00:02:18.560