6 STEPS TO A SUCCESSFUL ON-FARM RESEARCH TRIAL Written By Laura Barrera and first published on AgFuse.com If you want to make smart, well-informed decisions on your operation, you need relevant, high-quality data to support those decisions, says Tim Laatsch. A great way to achieve this? On-farm research.
An Illinois-based farmer, Laatsch knows first-hand the importance of on-farm research. As the Director of Agronomy, North America for Koch Agronomic Services (Koch), he leads a team of agronomists responsible for developing and executing field research strategy.
Illinois farmer and Director of Agronomy, North America for Koch Agronomic Services, Tim Laatsch. Photo provided by Koch Agronomic Services.
Here he shares the steps to follow for conducting on-farm research and the best strategies for ensuring it’s a successful endeavor.
1. Frame Your Research Question The first step to getting started with on-farm research is to ask yourself why you’re doing it, Laatsch says. “If profitable decision-making is their goal, it’s worth the time and effort to do the job the right way,” he explains. “Because the decisions they make as an outcropping of the research could potentially impact their profitability for years to come.” The key is to keep it simple and only focus on one management change at a time, as one of the initial mistakes 62 DIRECT DRILLER MAGAZINE
farmers make with on-farm research is trying to answer too many questions at once. Laatsch highly recommends working with a qualified expert on this. In addition to helping you come up with the right question to ask, they can also provide guidance and support throughout the trial. Once you’ve determined the one question you’re trying to answer, then you need to come up with your hypothesis. Laatsch recommends the following format: • Start with a basic problem statement: “The problem is X…” • Come up with a potential solution: “I believe that X…” • Determine what you hope to achieve: “Will result in X…” For example: “The problem is I apply twothirds of my nitrogen prior to planting and I’m concerned I may be losing nitrogen and sacrificing yield opportunity. I believe that using a nitrification inhibitor will reduce nitrification loss, which will result in higher corn grain yields.” The last part of creating your hypothesis is to define the outcome. Laatsch says this part is most often neglected when framing the question. “Not making an educated guess about the results before the research begins is a mistake that’s made at times,” he says. “What we should do is define on the front end our standard of success.” It needs to be more specific than just “higher yield” or “reduced fertilizer inputs.” Laatsch suggests that you include your level of confidence in the treatment, and specify the number of environments it needs to be tested across, as there
may be some environments where you expect more or less change. For example: “We will have the confidence to make a decision when the yield increase is X bushels per acre, with a minimum of 80% statistical confidence across three locations.” Statistical confidence is simply an outcropping of being able to measure the variation around the average, Laatsch says. In other words, what are the odds that the result is due to your treatment and not by chance or another factor? So what should your confidence level be? Often 95% is used in university research and scientific literature, but for farmers, that probably sets the standard too high. “There’s a very good chance you will reject a viable technology [at 95%],” he says. “Maybe 80% would be a more reasonable starting point. I’ll take a bet that 80% probability of a product will create more yield for me.” Again, working with an expert who is familiar with using statistics in on-farm research would be helpful in determining what the confidence level should be.
2. Design the Trial The lack of statistical confidence is also why single side-by-side or field-by-field comparison research trials are flawed,
ISSUE 15 | OCTOBER 2021
