Behind every product in the agriculture market is a claim. “Bust your bins with an extra 10 bushels per acre!” But what’s behind the claims? Hopefully, research. Nate Firle, owner of AgRevival in Gibbon, Minnesota, has spent his career evaluating agricultural products and practices. He was kind enough to sit down with us to discuss the different types of research performed in the industry and what it can—and can’t—tell farmers about the viability of a product for their operations. 

Three Types of Farm Research

I place research studies into three different buckets. Number one, you have your small-scale replicated trials, which some companies call small-block research. Second, you’ve got your replicated strip trials, which is what we do here at AgRevival. Around ninety percent of our work are these trials. And third, you have on-farm studies. You might hear these referred to as on-farm comparisons. 

1. Small-Scale Replicated Trials

These are the real technical studies. The goal in mind with these studies is to get a true comparison between product A and product B. For example, a fungicide maker might be trying to identify whether twenty-eight percent or thirty-eight percent active ingredient is a better formulation to have in the product. In these trials, you’re trying to eliminate as many variables as possible. You’re trying to place a treatment on the same exact soil and conditions. With these trials you wouldn’t go much larger than four rows wide and forty feet long. Trials like this are also randomized; depending on the number of treatments you’re testing, the amount of space for any one product could range from one hundredth of an acre all the way up to one and a half acres per study. 

These studies are very important. The seed industry uses them to compare hybrids, for example. Since they’re smaller, you’re eliminating environmental factors but yet have the ability to situate individual plots to test the hybrids in very specific environments. The sell sheet data that farmers encounter often comes from this type of research trial. If a company claims, for example, that a seed variety has an eighty-eight percent win ratio, the company may have used one hundred to two hundred data points to create that percentage. 

Small-Scale Downsides

These studies are most helpful for decision making by the company itself. As I mentioned in my earlier example, a company may need to decide exactly how to formulate its product to achieve the best results for the farmer with the minimum of cost to the company. You don’t want to pack your product with more active ingredient than necessary. For farmers, these studies can be a little bit misleading because, by their nature, they work to eliminate so many variables. And there are so many variables that farmers experience on a real, working farm. For instance, in a half-mile-long stretch of land, it’s very rare that you’re going to have the same exact soil type, the same pH, and so on. These studies generate helpful and necessary data, but as far as applying that information to real life, buyer beware. 

2. Replicated Strip Trials

Strip trials comprise most of our work at AgRevival. In these trials, the plots are stretched to two hundred and fifty (or more) feet and they can be four or six rows wide, replicated and randomized. Along that two hundred and fifty foot strip you’re placing various products or practices under multiple environments—introducing them to the natural variability that you’re going to find on any farm. We find that the data from this type of study is much more aligned with what a farmer may experience on his farm. 

In small-scale trials, you might see big swings in data between test plots—ten, fifteen, twenty bushel swings. When you then place these same products or comparisons in the larger strip trials, oftentimes we’re seeing five, ten, maybe fifteen bushel differences at the top end.

For companies interested in testing products, strip trials can help them identify who their customers are. And, for the farmers, these studies give them confidence that yes, a particular product may be a wise investment for their farms and give them an idea of the realistic scale of improvement they can expect. 

3. On-Farm Trials

These are much larger studies where we’re taking one product or practice and putting it on a minimum of ten acres and comparing it to a minimum of ten acres of something else, whether that’s two different products or an old practice versus a potential new practice. For example, we might want to compare two tillage practices. These large trials are an important step in validating a product or practice. You first see a response in the small-scale trials, you confirm it in the strip trials after introducing a bit of variability, and then you test on a larger scale on-farm to see if you see those same responses pass through to the real world. 

Small-Block University Studies

Universities are typically set up to perform technical research—those small-scale or small-block studies. Their goal is obviously to provide learning opportunities, both for students and about the products or practices being tested. And a good way to do this is to focus on eliminating a lot of variables to really drill down into a particular product. A good university trial should identify in its publications and summaries what the goal of the study was or what question they were trying to answer. And then, if a university is interested in the practical application of this knowledge, the research team can provide insights as to where they see the product working—in a certain region or environment. However, the one thing universities won’t typically provide is an ROI calculation. 

Identifying the Sources of Data

From the FCLG: If you’ve spent any time working in agriculture, you probably know that every product (or practice) is sold using bushel claims. And that’s understandable: every farmer wants more bushels per acre, as that number has a significant impact on a farm’s productivity. But where do these bushel claims come from? Will a farmer know if a company has invested in multiple research studies to gather the data? Will a farmer know if a company has only used one small-scale study to produce its data? 

It’s really up to the company. I’ve seen companies clump data together into one data set. I’ve seen our strip trial data blended with small-scale replicated data. Again, there’s nothing essentially wrong with that. From a marketing standpoint, this aggregation of data can help tell a story. However, I can also see the situation from the farmer’s perspective. This sort of combo data can be confusing because it’s hard (or impossible) to identify what stage that data came from. And then you have the other end of the spectrum, where a company will identify that the data came from a university study or from AgRevival or another independent researcher. And they’ll even go so far as to detail how the research was conducted. 

That’s our goal at AgRevival—to provide not only a data set but a story to go along with that data set. This gives the farmers confidence in the data you’re supplying them for a particular product or practice. 

Are Researchers Under Pressure to Deliver Results?

In the research world, everything costs money. Think about a university research program: you have the facilities, the equipment, the grad students, the assistants, and the professors. That’s a huge program to fund, and it would be natural to feel a little bit of pressure: “Company X paid twenty-five thousand dollars for this study, so we better make sure that the company is getting a positive result out of this.” And Company X probably feels the same way—we better get something out of this trial that we can stick on our sell sheets and go to market with. People may not realize this, but product trials aren’t cheap; on the low end they can cost five thousand dollars, but they can stretch up to thirty-five thousand dollars or more.

And this pressure to deliver results doesn’t just exist at the university level. I have had to have those same conversations with companies. A company may hint that if they give us enough money for “a comprehensive enough study” that we’ll get the results that they’re looking for. But that’s not science and not the point of research. I’m firm with people: you’ll get data, and the data is what it is. As I said earlier, we also try to provide the story behind the data, and this in itself can be valuable for companies as to why something did or did not perform. 

Why Do Some Studies Re-Answer the Same Old Questions?

One phenomenon you also run into in the research industry is answering the same old question again and again. Think again about the pressure to maintain a research department at a university. As a small business owner, I’m well aware that it takes effort and money to do research. Now, take that pressure and scale it up to a big research department and you can imagine that it’s a tall order to keep the machine running.

So, you have big fertilizer companies funding research, as one example. They may know that one hundred and sixty units of nitrogen is the right amount to apply. And then you might begin a three-year trial that hints at, well, maybe we should be applying one hundred and ninety units. And then after three years you’re back at one hundred and sixty. I believe that was Einstein’s definition of insanity—doing the same thing over and over and expecting different results. 

However, there’s a secondary benefit to this phenomenon: Constant studies keep these companies in front of the next generation of employees. Many of these grad students and assistants can move into positions with the large ag companies; it’s not a bad avenue for recruiting purposes. 

True Influence: People vs. Numbers

I’ve brought this up several times, but at AgRevival, as an independent research company, we provide more value than just a set of numbers or data. Our competitive edge is that we’re actually farming our ground. If a product performs, that helps my profitability because I’m selling the grain off our farm. I’m not a land-grant university where the funding goes back to somebody else; this is a farm that has to have a positive income line from the grain sales. So when we do see fifteen or twenty bushel losses from a product or practice, it comes right out of my pocket.

This is why I like to tell the stories behind the data. At the end of the day, something has either helped or hurt my balance sheet, and those are the experiences we can share. When you stop to think about it, it’s people who influence people, and I think the farmers of the future want to know what experiences others have had with a product or practice. As far as the data, it just validates the experience from a factual standpoint. People want confirmation beyond just “this thing gave us nine extra bushels per acre.” 

Farmers are no different than any other consumer: it’s much easier to invest in something when you can connect it to another farmer—someone in your shoes who has had a certain experience with the product or practice. People buy from people and because of people, not from tables, charts, and graphs. This is why relating my experience from a farmer’s perspective, as well as the data, is so important to me. Those stories are what can help farmers make truly informed decisions for their operations. 

Next Up: Research by Design

In Nate’s next article, he draws us a picture of the many elements that go into a research study’s design and execution at AgRevival.