BROOKINGS, S.D. — Collecting unbiased data from well-designed research can have a large impact on farmers’ bottom.
“Farmers spend millions of dollars on agronomy products each year. The best way to determine if a product or practice is effective prior to purchase or implementation, is to ask for the data and research backing a company’s claims,” explained Sara Berg, SDSU Extension Agronomy Field Specialist.
Berg is part of a multi-state team of Extension personnel working together to clear up confusion among producers when it comes to research. Together they have published a series of articles which delve into four research topics including: replicated vs. side-by-side comparisons, how to set up on-farm research, interpreting research terms and data, and the topic of this article, interpreting and clarifying ag product marketing claims.
This is the fourth and final article, written by this team, to help producers see legitimate research from biased information produced to sell inputs. To view past articles, visit iGrow.org and search by Sara Berg’s name.
In addition to Berg, the team includes: Lizabeth Stahl, University of Minnesota; Josh Coltrain, Kansas State University; John Thomas, University of Nebraska-Lincoln.
See through marketing ploys
New on-farm technology provides many farmers with real-time data access. “With large amounts of data and fast access to information and product marketing, producing a commodity requires many decisions,” Berg added. “Knowing that a product has been tested and shown to make a difference should be a deciding factor when making purchases. Yet, it is not that simple in most cases.”
The reason? Berg explained that although data may be included on packaging, sometimes companies leave vital information off when advertising because many view it as confusing and unnecessary.
“False research claims or partial truths are found alongside accurate claims about quality products in marketing around the world,” Berg said. “Separating falsified or misleading claims from those that are not is crucial.”
One method Berg said some marketers use is to display limited data in a skewed or biased manner by changing the scale of a graphic (Figure 1). Another method is to add disclaimers (Figure 2), or provide vague information and/or nothing to compare the product claims to (Figure 3). However, some companies and institutions provide excellent data with honest results for farmers to choose from; even in these cases, one must understand how to interpret the data (Figure 4).
“When a product is falsely promoted, often the customer is provided only baseline information needed to make a sale. It is vital that farmers take time to look over product information, ask questions and understand data presented to them,” Berg said. “Marketing claims are not always falsified or skewed, but knowing how to spot poorly-backed claims can provide farmers peace of mind in knowing they are investing in products or adapting practices that have been properly tested.”
Figure 1. Yield trial results (fictional example). The scale on the Y-axis begins at ’40’, which can create an optical illusion for the reader and skew the appearance of data. When the axis does not begin at ‘0’, results can be misleading. In addition, no statistical analysis and little background information is provided, so the reader has no way of knowing if, for example, yields are from strips in fields or replicated trials.
Figure 2. Alfalfa yield trial results (fictional example). There is no background information about how or where the data was collected and there are no statistics for the reader to determine if significant differences were found. In addition, the disclaimer at the bottom of the table could nullify any findings should the company choose to do so.
Figure 3. Hybrid characteristic advertisement (fictional example). This figure describes a corn hybrid with highly enticing descriptive words that may catch the reader’s attention. No data is provided and there is nothing to compare the above product claims against.
Figure 4. Comprehensive table (fictional example). Table includes relevant background information about the trial and statistics to help in interpretation of the information provided.
For more information on research trials and statistics see parts 1, 2, and 3 of this 4-part article series. If questions should arise, contact an SDSU Extension agronomy team member for data interpretation assistance. A complete listing can be found at iGrow.org under the Field Staff icon.
— SDSU Extension
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