Field Studies: Blowing the whistle on marketing claims Back »

Written collaboratively by John Thomas (University of Nebraska Lincoln), Lizabeth Stahl (University of Minnesota), Josh Coltrain (Kansas State University), and Sara Berg (SDSU Extension).

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

Making Educated Management Decisions

As the number of US farms has dropped, average farm size has risen 23% 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.

Check the data
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.

Beware of misleading claims
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. 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 (Table 1), or provide vague information and/or nothing to compare the product claims to (Table 2). 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 (Table 3).

In Summary

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. For more information on research trials and statistics see Part 1, Part 2, and Part 3 of this 4-part article series. If questions should arise, contact your nearest SDSU Extension office for data interpretation assistance.

This article is part four in a four-part series of articles on agricultural research and interpretation by University Extension Educators in the North Central Region.


Figure 1. (Below) 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.


Table 1. (Below) 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.
Effects of XXX on Alfalfa
Alfalfa Component Before Treatment 1st Cutting 2nd Cutting 3rd Cutting
Crude Protein 23.70% 25.46% 23.75% 28.11%
Fat (EE) 1.89% 1.76% 1.80% 2.11%
Calcium 1.25% 1.42% 1.53% 1.42%
Sodium 0.24% 0.29% 0.22% 0.20%
Chloride 1.31% 1.36% 1.20% 0.60%
TDN 53.96% 63.35% 61.51% 61.41%
Actual results may vary

Table 2. (Below) Hybrid characteristic advertisement (fictional example). This table 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.
Hybrid XY Summary
Variety Characteristics
Ear Type Semi-Determinate
Cob Color Red
High yield potential with great roots. Early flower lends to quick dry-down. Handles different types of soils and responds superbly to intensive management.
Management Tips
A strong performer for its maturity. Likes higher populations for the area and adequate fertility to really shine. Outstanding late season plant health leads to fast dry-down and topmost yields.

Table 3. (Below) Comprehensive table (fictional example). Table includes relevant background information about the trial and statistics to help in interpretation of the information provided.
Soybean grain yield response to XX company fertilizer product application at Someplace, SD1 in 2014.
Fertilizer Applied
Oct. 2013 Soil Test2
  P K Zn  
-----ppm 0-6”-----
Product A 13 150 11.5 34.1a
Product B 18 145 13.9 34.9a
Product C 3 177 11.0 20.0c
Product D 12 115 8.5 29.6b
Pr>F       0.01
CV (%)       8.7
LSD (0.05)       4.0
1Site in corn/soybean/small grain rotation since 1995.
2Nutrients applied: N= 90lbs/a in 2013. Previous nutrients applied since 1997 except for 2013 were: P2O5 = 40 lbs/a/yr, K2O = 50 lbs/a/yr, Zn = 5 lbs/a/yr
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