Panhandle Perspectives - November 14, 2017

Field Studies – Blowing the whistle on marketing claims

By Sara Berg, South Dakota State University; John Thomas, University of Nebraska Lincoln; Josh Coltrain, Kansas State University;: Lizabeth Stahl, University of Minnesota;

Part 4 of a four-part series on agricultural research and interpretation by University Extension Educators in the North Central Region. Part 1 discussed the importance of arranging research plots in replicated patterns instead of simple side-by-side comparisons. Part 2 discussed setting up an in-field trial so the data are statistically valid. Part 3 explained the concept of statistically significant differences in data.

With technology surrounding today’s culture, data and marketing information has become a key part of life. Farmers, especially, have been targeted with large quantities of new technology created to generate more efficient farming systems promising easy real-time data access.

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With large amounts of data and fast access to information and product marketing, producing a commodity requires many decisions.

As the number of U.S. farms has dropped, average farm size has risen 23 percent from 2009 to 2016 (USDA, 2017). At the same time, producers have seen a shift in the types of ag services available. With such a wide scope of products and options available, it can be difficult to determine what products or technologies to invest in and what to leave on the shelf.

The best way to determine if a product or practice is effective is to ask for the data and research backing a company’s claims. However, before a producer makes a decision, understanding the data and statistics is key.

Sometimes, companies leave this vital information off of advertising because many view it as confusing and unnecessary. However, collecting unbiased data from well-designed research can make the difference in millions of dollars of decisions made on ag products each year. 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.

False research claims, or partial truths are found alongside accurate claims about quality products in marketing around the world. Separating falsified or misleading claims from those that are not is crucial.

The accompanying graphic shows several methods that marketers use that can be confusing or misleading to potential customers, as well as one example of relevant and accurate information. One method 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. 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.

If questions should arise, contact your nearest extension office for data interpretation assistance.

For more information on research trials and statistics see parts 1, 2, and 3 of this 4-part article series.

Field study result examples
These four fictional examples show three false or misleading ways of reporting research data, and one example of a table with relevant and helpful information. Figure 1 (left): Yield trial results – 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 (center top): Alfalfa yield trial results – there is no background information about how or where the data was collected and 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 (center bottom): Hybrid characteristic advertisement – 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 (right): This comprehensive table includes relevant background information about the trial and statistics to help in interpretation of the information provided.