Designing Reputable & Valuable Research Studies
Research
May 23, 2024

Designing Reputable & Valuable Research Studies

Research studies require a lot of work before the seed hits the soil. Here’s an inside look at how agronomists design research studies.

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Designing a research study involves hours and hours of work before the seeds hit the soil. Nate Firle, owner of AgRevial, conducts strip trials for numerous companies in the ag industry that want to test various products or even practices, such as tillage methods and timing. In this article, Nate details for us how AgRevival designs and conducts its research studies. The need for an end result—the data and the story behind the data—drives every step along the way. 

Strip Trials: Designed for Real-World Results

The first step in research is to design the trial. If you’ll remember from our first discussion, we mostly do strip trials here at AgRevival. There are also small-scale replicated trials and larger on-farm studies. Our goal here at AgRevival with our strip studies is to expose each product or practice we test to some variability. So, in all of the blocks we have set up here, we have at least two different growing environments. This isn’t the same thing as simply saying different environments. Because we’re only located in south central Minnesota, all our blocks are part of the same “environmental environment,” for lack of a better term. For example, if one of our plots receives one inch of rain, they all receive one inch of rain. 

What will vary between blocks and within blocks are things like soil conditions and soil types. That’s the benefit of having strips that stretch two hundred and fifty feet or more. We want to have some variability within a block, but not with extremes if the study doesn’t call for that. Although we do have some studies, like iron product testing with IDC on soybeans, where we do seek out extremes on purpose.  In a study like this, the data we collect could help a farmer decide whether it pays to apply a treatment to entire fields or only pockets of certain fields.

The First Steps in Study Design

I should back up a little bit. Before we even start designing a study, we have at least one meeting with the company that wants to test a particular product or practice. There are a few points we have to cover during the meeting: What is the product? What results have you seen in prior testing? How do you, as a company, see this product working on a farm? Where do you see it fitting into a farmer’s agronomy program? 

Then, we need to take in this information and decide if it’s something we want to test on our own research farm. Obviously, if something is too far out in left field or we don’t feel it’s a good fit for testing in the Midwest, we have to decline. In other cases, a company may tell us that we have to avoid testing on certain blocks within our research farm. We provide our historical soil data and yield maps to companies planning research studies and sometimes they’ll let us know that they’re not going after a particular market, say sandy soils, so we need to avoid testing their products on this type of ground. 

If we do move forward, the next step with any study is to figure out what ground we’re going to put it on within our research farm. Is there any specific soil type or other conditions that would be best for testing the product? 

In addition to study placement, we also have to determine what we’re going to test the product or practice against. When we conduct our studies, we make sure that we’re comparing each new product or practice to a current product or practice that’s in common use with farmers. For example, we wouldn’t test a fertilizer product against zero nitrogen; there are zero farms out there applying zero nitrogen. I want to make sure every product or practice we test can be compared to something that is currently relevant in the industry. 

Fine-Tuning the Study Details

Once we’ve identified a test block for the study and what we want to benchmark the product against, we get down to the real specifics of the study. Blake is our research operations lead—his talent is thinking about and identifying all of the various details we have to consider when designing a study. 

After we know what block we’re going into, we figure out how many replications we should incorporate. We may do as little as three or up to six. Once we know this number, we can go ahead and assign numbers to the test. Every treatment on the farm is assigned a GPS number and location and range. So, it would go something like this: Titus, which is one of our research farms, block B, GPS line 63, North—that would be one data point. Every single block is tracked this way so when we come to harvest, we match the right data with the right locations. 

The final step in study design is to go through the protocols of application timing and rates (for products). Sometimes we determine this during meetings with the company, especially when we feel they need to increase or decrease the recommended rate depending on what technical data they’ve seen and what we think would be most practical for farming in our location.

A study’s protocol also details when we’re taking notes, pictures, and samples during the growing season. It also details the final stage, harvest, and whether we’re going to harvest just the center two rows or all four rows of a study, for example. If we’re studying a product on foliar application, we typically prefer to only harvest the center rows to eliminate the possibility of skewed results from an overspray.

Tallying the Final Results

Once harvest is complete, it’s time to compile all the data and observations and package them for delivery to the customers. The data we provide include yield, test weight, moisture by individual treatments, as well as the summary from the replications. Something we do a bit different than others is that we provide yield maps, so someone can easily see if a product’s performance changed at all from one end to the other of one of our strips. These are helpful visuals—summaries if you will—as most of us in agriculture are used to looking at yield maps today. 

Strip Trial Setbacks

Of course, the difficulty with strip trials is that sometimes an outside variable will drastically affect a dataset. When compared to a small-scale replicated trial, which is concerned with controlling for variables as much as possible, a strip trial will naturally introduce some variables, such as soil types, which we’ve already discussed. But we can also be impacted by larger forces—drought or torrential rain. For example, if water stands on the back seventy feet of a block for two days and it doesn’t on the front, that may be the factor that, when you average it all out, affects the performance of the product. So the companies involved in the trial are a bit disappointed. 

This is why we also provide summaries with our research data—the story that goes along with the numbers. And when you think about it, this is often what a farmer will experience himself. You typically don’t lose a whole field; you lose a portion of the field to extremes. When we publish our research book at the end of the year, we also make a point to include these details with each study, so people know whether there were significant events that had an impact on the various research studies. 

The Importance of Staying Active During the Growing Season

One thing that’s unique on our research farm is the network of grass alleys. I maintain them so we can access all our studies throughout the growing season. If a farmer tries to scout a field after a two- or three-inch rain, he’s going to be looking at his field from the road. The alleys give us access to our test blocks any time of day, any time of year, in any field conditions. And we put a lot of thought into where we located our alleys; in some cases, we strategically placed them to section off various soil environments. 

So, yes, I lose quite a few acres on the farm, but our alley network is crucial to the success of our trials. It all goes back to staying active out there. Because, at the end of the day, we want to deliver more than just charts of data; we want to tell the stories behind what we’ve observed in the field. It’s these stories that help farmers make informed decisions about the suitability of a particular product or practice for their farms.

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