By Sara Berg
With technology being so prevalent in 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 to generate more efficient farming systems and easy real-time data access. With large amounts of data and fast access to information and product marketing being the new norm, producing a commodity requires many decisions.
While the number of US 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. Before a producer makes a decision, it is key to understand the data and statistics involved. However, companies sometimes leave this vital information off of advertising because many view it as confusing and unnecessary. Using unbiased data from well-designed research can make the difference in millions of dollars of decisions 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 always that simple to sort through the information available.
False research claims, or partial truths are found alongside accurate claims in marketing around the world. Separating false 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 (Figure 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 2).
Table 1. 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|
| ||percent (%)|
|Actual results may vary|
Table 2. 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 Somewhere, SD1 in 2014.|
|Fertilizer applied||Oct. 2013 soil test2|| |
| ||ppm 0-6 inches||bu/ac|
|Product A||13||150||11.5||34.1 a|
|Product B||18||145||13.9||34.9 a|
|Product C||3||177||11.0||20.0 c|
|Product D||12||115||8.5||29.6 b|
|Pr>F|| || || ||0.01|
|CV (%)|| || || ||8.7|
|LSD(0.05)|| || || ||4.0|
|1Site in corn/soybean/small grain rotation since 1995.|
2Nutrients applied: N=90 lb/a in 2013. Previous nutrients applied since 1997 except for 2013 were P2O5=40 lb/a/yr, K2O=50 lb/a/yr, Zn=5 lb/a/yr.
When a product is falsely promoted, the customer is often provided with only baseline information needed to make a sale. It is vital that farmers take time to look over product information, ask questions, and understand the 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 practices that have been properly tested.