David Wildy, owner of Wildy Farms in Manila, Arkansas, is truly one of the pioneers of precision agriculture in the Cotton Belt. A decade ago, he acquired his very first cotton yield monitor, admitting that “we did not know exactly what we were going to do with it,” effectively launching his farm’s use of precision. What they did do is use the unit to begin collecting valuable data about the farm’s fields, which has served as a platform for adding on more products and strategies. Today, few growers in the Cotton Belt have more personal experience with precision technology for cotton production.
Cotton Grower magazine editors sat down with Wildy recently to talk about his perspective on the state of precision technology in cotton, and what has him excited about the future.
Editors: Are you optimistic about the future of precision technology in cotton?
David Wildy: I think it’s the future for cotton, and I think precision agriculture has a bright future in the ag industry. I don’t want to come across as not pro-precision agriculture, but we have to figure out where it fits and where it pays. It might not always show an economic advantage, but as long as it’s not hurting yield or costing us more to use, then it’s the right thing to do, for example, to put fertilizers and insecticides in the right place.
Precision agriculture has helped us in a big way, but all this technology is not free. So we have to figure out how to make it pay for us either in savings on input items or increased yield. And that’s what is taking some time to figure out, and we will probably work on it for years to come.
Editors: What did you do after purchasing that first yield monitor?
DW: We documented a lot of history with yield maps, but again, we spent money on that technology but it didn’t really make us any money. We also did some grid sampling back in the early days, and at that time we had to hire someone to pull those grid samples. And application costs were high then, too. Variable rate application of potassium and phosphorus we were not showing very much savings, and it’s very hard to quantify whether you are actually increasing yield. We suspected we did, but we were trying to make up expenses by saving on product.
We found that we were allocating where that product went, and while we did not save a lot of fertilizer costs, we were putting less in some parts of the field and more in other parts. We did save enough to pay for the costs, but it was lots of trouble to do, so we decided not to expand that part of the operation.
Where we did see a big advantage was with the application of limestone — soil types in this area are variable from deep sands to clay, and they can be in the same fields. We can save quite a bit in limestone by grid sampling for it.
Editors: What about [[variable rate]] inputs?
DW: We began to use plant growth regulators and defoliants and boll openers (in a precision system), and what we found was that while we really haven’t been able to show an increase in yield, we did have some savings in chemical costs. Fortunately, [[PGRs]] have become less expensive over the years. And although we were saving some product, we weren’t saving enough money to overcome the cost to collect the data that enables us to do this and pay for the equipment.
We may continue to do [[variable-rate defoliants]] and [[PGRs]] to some extent, which will allow us to justify the equipment and the expense we will have with variable rate on more high cost input items such as fertilizer, lime and seed.
Editors: Seed was a big variable rate topic at InfoAg. How do you feel about it?
DW: Seed cost is up so much that if it’s possible for us to nail down a proper seeding rate — figuring average weather conditions — across different soil types, that is something that could be a big savings for us. In general, we feel like we need to step back and look at the highest input items and see how we can make precision agriculture fit those operations, along with some of the other things we feel are just the right thing to do. As far as insecticides or [[PGRs]], maybe we can make it all work together and it will be good for the environment and good for us as well.
Back to [[variable rate seeding]] — the technology is certainly there. You can write a prescription for what seeding rate you want to use, and the planter seems to do a very good job of making those changes and planting the proper rate. The problem is, we don’t know what is the best seeding rate by soil type, so we are just going to have to be pioneers in trying to figure out what is the proper seeding rate for our conditions. We’re spending somewhere in the neighborhood of $100 per acre on seed, so if we can cut our seeding rate in certain areas that will yield just as well — or more by cutting rates in certain areas — we’ll save a lot of dollars over our whole farm.
One specific thing I should mention in our experience — while three seeds per foot made the most yield in all different soil type [[zones]] based on both [[imagery]] and [[Veris]] mapping, the most return was at two seeds per foot. Even in the high, medium and low zones, two per foot gave the best return. We still have a lot of work to do in this area.
Editors: Who are you working with on precision practices?
DW: We do a lot of on-farm testing on our own. We generally want to evaluate for ourselves the things we work on. We pick out and work with the university, cooperative extension, etc. that we feel will help us. We also test products with manufacturers — for example, we have worked with [[John Deere]] over the last few years on precision agriculture.
Editors: What works best for your operation in terms of imagery?
DW: There’s no doubt that we are going to have to have imagery of some sort, whether it’s with a plane (which can be a little cumbersome with the cloudy weather we have in the summer) or the [[Greenseeker]] technology, where there is actually a real-time image of the field being taken as you drive across it with the sprayer. And there’s also [[Veris]] technology — all three pieces of data are going to help you combine certain production zones in the field, and I think the combination of all of that data is where we are headed.
Editors: But imagery is not the end all, is it?
DW: Originally, I thought that grid sampling was going to be a little too expensive, and my opinion was that if we could define certain zones that we would be able to reduce the sampling costs and take fewer samples. I do get a better idea of what’s going on in the field using zones, but what we are finding is that often the zones do not correlate well with themselves. I am beginning to see that grid sampling, while expensive, can make a difference. The smaller you can make those grids, the more accurate the zone.
Eventually, I hear that there will be an automatic sampling machine that will take the sample for you, georeference it and get many samples done per acre, which will create more accurate samples and zone maps that we can utilize.
The bottom line is you always give up something when you try to save labor and sampling costs, and I am still in the middle of how this is going to play out. It looks like to get the most accurate map for potassium, phosphorus. pH and nematodes, a closer grid sample is going to be a real advantage.
Editors: What else is promising as far as technology?
DW: Where we have run the [[Veris]] machine to define soil type and we have found it to be very accurate, but we have not yet run a Veris with the pH sensor technology. It is something we are really interested in doing, and we may possibly purchase one of those.
[[Variable-rate nematicide]] application also looks real promising. We do have root knot nematodes, and the nematicides are really expensive. It’s expensive to put out as a flat rate across the field, so we are looking at ways to put nematicides where the pests actually are.
We are working with the University of Arkansas on a project — if we know we have nematodes and are able to rule out negative variables on a poor performing field, such as a nutrient problem, we can identify the sandy areas of the fields that we know nematodes like using Veris data. We may not end up with the highest yield, but variable rate vs. flat rate might give us an economic advantage in the end.
We’re still doing more testing, and we are building a machine to apply nematicide automatically based on soil type. And we are going to look more at it in the future.
Editors: You mention the challenge of compiling and interpreting data — any solutions for you on the horizon?
DW: With all the data we collected in the last few years, there is no real program out there into which you can put this data that, for one thing, allows you to just be able to find it and look at, and for another, that provides some kind of data evaluation. I am on the verge of being able to talk about a program that I think will be able to do it for us.
Editors: Finally, any advice for new precision adopters?
DW: There’s much out there and it is a challenge to get into if you’ve done nothing at all. But certainly [[georeferencing]] all fields, getting good boundaries and [[yield maps]] and acquiring some images or doing some [[Greenseeker]] work would be a beneficial starting point.