Understanding Spatial Variability with Dr. Tracy Blackmer

January 26, 2016

Dr. Tracy Blackmer is the Senior Agronomist at FarmLogs and has over 20 years of experience incorporating precision agriculture technologies in agronomic management for growers. In this interview, he discusses his own first encounter with the idea of spatial variability, how it shaped his perspective on precision ag as an agronomist, and how—ultimately—it led him to FarmLogs.

Want to skip the video? You can read a full transcript below.  

 

 

What experiences led you to start thinking about spatial variability?

“In my background I’ve worked with farmers setting up research plots for over twenty years. My dad was a professor in agronomy doing a lot of nutrient work, so I grew up putting out small test plots all over the state, and that helped pay my way in high school and even as an undergrad in college.

“[That experience] taught me a lot about field variability and how we make recommendations. ...At that time, [when] putting out small plots, a lot of it [involved looking] for the most uniform part of the field. So, when you put out different rates of fertilizer or different planting populations, you [only typically] compared the yields between the two. [You didn’t want] soil variability or anything to mess it up. [You were just looking at] the rate difference.

“I remember how hard it was to find those nice flat level parts of the field because fields are so variable.

“And it never really occurred to me at the time that farmers really do farm the whole field, and maybe we ought to be getting data on [not just the flat parts of the field]. So, as we were collecting our data and plots (and at that time, you know, we’re putting out twenty to thirty sites around the state each year) it varied a lot where we were.

“And then, well, I remember the summer of ’88 [there] was a really big drought, and I remember [that we’d] take a [forty-foot row], [and] we’d hand harvest to measure the corn yields. Some years we’d have one ear because it was so dry, and other years, you know, we’d have over two hundred bushel of corn, which was a really big yield twenty years ago.”

 

How did remote sensing deepen your understanding of field variability?

“So, as I went from working as an undergrad and [then] into graduate school, I actually started working with some of the remote sensing. And so, actually, I’m [on] the first patent [for] crop sensors for nitrogen. And we learned that we wanted to monitor the crop, [and have it tell us] when it was stressed or not so we could work on it more, but we came into the problem of spatial variability, so we would put strips through the field.

“In the meantime, we’re using chlorophyll meters, and it was really good on small plots. The only problem is the variability in the field was so big that some people would say, “Yeah, we’re seeing a difference.” And another group out with the strip would say, “No, we aren’t seeing much [variance].” It all depended on where [the test was being performed.]   

“But, when we saw an aerial photo of the field, we saw all the variability, [and that] the field becomes N-stressed at different times.

field-variability-image.jpg

“The right plant population varies within the field. It was all of a sudden like, of course! So, even though we have great correlations on small plots and publish a lot of scientific papers saying, you know, this relationship [and] this tool works ... To go out in the real world of the farmer, [who’s] managing all that variability and having to deal with the weather interactions—they’re like, ‘We don’t care about the average. We want to know for our situation what we’re doing each year, because that’s what’s important to us.’

“And that was the time that precision ag was just really starting to come in. The GPS units started to come in, so we could start mapping.”

 

You began to adopt precision ag practices 20+ years ago. What was that like?

“I mean it wasn’t real easy. It was only the people who really were a glutton for pain [ who were doing it]—to try and get, you know, the GPS to talk to the differential GPS—or the correction, which was a different source to the computer logging it on, to the yield monitor with it, to calibrating it… . I don’t know how many times I heard farmers at first, when we were putting it in, saying, ‘This combine runs just fine without that stuff.’

“And so, as we were starting to record the data, it’s like okay, how is this useful? We know this is a higher yielding part of the field, [this part’s lower….]

“Even though growers knew it varied, when they can see just how many bushels it was changing, [they realized]: 'We've really got to manage these parts differently.'

“[So then], the first question [was] ...how do we make these yields higher everywhere? I want everything to be like my highest yield! [But] that's really not realistic in most fields.

“But then it was, Okay, I’m paying this much for these inputs. If [these inputs] are not going to produce more, can I at least save a lot of my inputs by not doing that? How do I develop that [new] recommendation because all our [current] recommendations on small plots were developed on good ground, nice flat stuff—not the lower wet spots, not the eroded side slopes, not all the variability they have.  

“And it became a real quandary. How do you possibly get enough data because even with a small plot, some universities—their budgets are limited….They’re never able to collect as much data as they wanted, and that was ignoring all the spatial variability questions.”

 

Good agronomic recommendations require good data. When did your pursuit for better data collection begin?

“I was working with the USDA, and we started [thinking about]: how do we go from small plots to putting in test strips in fields in ways we can use the field data to get feedback? If we can get farmers to do at least one trial—if every farmer just did one trial each year, [and] it could be a meaningful trial, and there would be little guidance [required]...[it would have to be] farmer friendly, so farmers [could] actually do it—we could pool all that data together and we can learn so many new things.

“I left the USDA to actually work for Monsanto for four years, [when] they were becoming a life science company, as opposed to just a chemical company. And as they were starting to acquire seed companies they wanted to know how do we manage this agronomy; how do we handle precision ag? So, I worked with setting up trials in multiple states on how do we learn? What’s the benefit from fertility? What’s the benefit from putting the right genetics in the right spot? Seed populations: what can they do? Which data is important?

“And I spent four years working for them, really setting up those protocols. How do we get that data and involve the growers in it?”

 

You helped create the On-Farm Network? Why?

“I was approached by a grower organization and some of the cooperators we had in Monsanto were also directors for commodity boards. And they said, okay, [we’re] investing a lot of money in research.

“And they really wanted to know, What is the value of precision ag?

“Because a lot of the projects they were funding—they called them coloring books, what they were getting back. They got maps, but [there was] nothing that told them really how to get value out of [those maps].

“That's when we set up what we called the On-Farm Network. So, I worked for thirteen years for the Iowa Soybean Association, setting up protocols, organizing literally hundreds of growers to set out meaningful data, put it together, analyze it to the point that we’re getting results that pass scientific standards ([because] we published in scientific journals), but more importantly, it gave benefits to the growers. Because the growers [got to] vote on if they want to spend their money on it. Are they happy? Are they willing to participate in it? And we actually expanded to eleven different states, helping to provide that infrastructure to do it.”

 

You’ve had a long career in spatial variability and precision agriculture. Why FarmLogs now?

“Of course, the focus of the Soybean Association is soybeans. Most growers grow more than one crop and so it was clear that that wasn’t really the best [career] path [for me] long term.

“[My goal was:] how do we develop the full agronomic system to really bring all the answers, and really set up the network across state lines and everything? How do we get that information to really bring it together?

“And that’s what really attracted me to FarmLogs.

“You know, the projects I had in the past, if we had one or two programmers that was considered a great luxury... To build something that [will] cross state lines and really, you know, sustain itself in time, it was something that I’d never really been involved in. [My experience was based] more on research or, you know, [it focused on] a small piece [of the whole puzzle.] But you need [to think about it] at a large scale.”

“You need to see the variability in dry areas and wet areas, and good soils and poor soils, and all these things to really understand the process.

“So, by coming to FarmLogs, where there’s tremendous capability to program and develop whatever we need [in order] to translate that information into practice—and having such a large base of [growers] already in the [FarmLogs] system...it’s truly an agronomer’s dream. If we have the data, we can collect the data, we can put it together to really have use. [Or…] I’m going to say to have impact.

“It’s one thing to say, “Here’s something of value.” It’s another to really make it happen and be utilized, and that’s what really attracted me to FarmLogs. It was an independent company [with great reach]. ...The time is right for growers and everyone to all come together, and to me, the stars have just lined up.”



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Ashley Rutter

Author

Ashley Rutter is the Content Marketing Writer at FarmLogs.