Don’t Fall for the Dairy Robo-Call Storyline

Just the other day, I was listening to my phone messages to get the time for an upcoming medical appointment.  A new robo call had entered the mix along with those wanting to extend my car warranty or get rid of my debt.  This new message stated, “you can get $50 off your electric and gas bill plus another 35% off your bill, to get your compensation press 1”.  Now who wouldn’t like a rebate on their utility bill, especially with all of the recent increasing amounts owed for many bills?  But, I think most of us quickly realize, this was a scam to get my hard earned money.  If the electric company was going to issue a rebate, it would have been credited to my next bill, not issued through a phone conversation.  The sad part is someone pressed 1 or stayed on the line and was their next victim.

Just like with this mentioned scam, we need to be diligent and do our homework to make sure claims are real as they relate to dairy businesses.  When evaluating a potential product, one should review actual data related to its performance.  Will it indeed deliver the results claimed?  Although there are no guarantees in life, using products that have sound research data behind them increase the probability they will perform as advertised.  The problem is that not all research and collected data meet the definition intended by scientists.  So what constitutes “good research” data for you to use to evaluate a potential product or feed additive?

If The Results Seem Too Good To Be True—They Probably Are. 

You need to be realistic in the response expected.  For many products fed with a good nutrition program, milk production increases of a ½, 1, or 2 lbs might be realistic, not 5 to 10 lb increases in production.  I still remember from many years ago, a feed salesperson claiming that if my husband fed his feed they would eat half as much and give more milk!  This claim ranks up there with the phone message of getting 35% off my electric/gas bill!

Testimonials Are Just That. 

Someone comments that they used product X and they saw such and such response.  The mentioned product may have increased milk production, reproductive performance, or saved money.  The response seen may have nothing to do with the product itself.  Management practices may have changed when starting to use said product or the cows responded regardless of whether you used the product.  Testimonials are often used in sales literature. 

Farmers tend to believe other farmers over scientists, sometimes to their betterment, but also their detriment.  Just because it works for your neighbor or another local farmer does not mean it will work for you.  Your cows, climate, forage base, and management style are different than the neighbor’s herd.  I still have to chuckle at the former practice where the multitude of feed salespeople traveled the countryside in the fall claiming their feed would increase milk production as seen with so-and-so.  For those that have been around for a while, understand that most milk companies used to have a financial incentive for fall-produced milk.  Thus, more cows calved in the early fall/late summer to meet this flush of milk and income.  Early lactation cows produce more milk than later lactation cows—no great news there!  Thus, expectations should be that milk production would increase, not because of the feed, but related to the stage of lactation of most of the cows in the herd.

What Data Should You Believe? 

Just because a salesperson shares what they call “data” which shows their product has a positive response, does not mean it should be trusted.  Data need to be collected in experiments that test whether the product really does have a beneficial response on an adequate number of animals or rows of a crop that were randomly assigned to each “treatment” group.  One of the treatments should be cattle or crops that did not receive the product and the average response is compared to cattle or crops receiving the product.

When scientists use the term “research study” or “trial”, they usually are referring to a specifically designed experiment(s) where a product/practice is tested on individual animals or crops.  To be considered an unbiased research study, animals or crop-rows receiving the new treatment, product, or practice must be managed identically within the same time frame as animals/crops not receiving the product or practice.  Those not receiving the new treatment or product are called the control group.  The only difference between the control group and the group receiving the treatment or product is the treatment itself. For example, in field crop experiments, all rows in the comparison would be planted at the same time, under similar soil characteristics, and would receive the same amount of rainfall.  With experiments utilizing lactating dairy cows, selected cows often are similar in number of days in milk or days till expected calving and each treatment group contains equal numbers of first lactation versus mature cows.  The most important point here is that the treatment group should be exactly the same as the control group and the animals or crop rows were assigned randomly. Each treatment, including the control group, contains multiple replications (not just one pen per treatment) of animals, crop rows, or pens of animals. 

Yes, this is a long answer to the question.  But, you may see that there is a list of criteria that needs to be met.  This increases the probability the response is real and not just due to chance.  So the next time you ask or are provided data on a product, ask yourself:

a) Were all animals or crops in the mentioned experiments treated the same, with the only difference being the product tested?

b) Do the data represent an average of many different cows or pens of cows on each of the treatments?

c) Did an equal number of the cows or rows of a crop receive each treatment and are the results compared to the no treatment or control group?

d) Were there the same kind of cows in both the control and treatment groups?  Same stage of lactation and number of lactations (first calf heifers vs mature cows)?

e) Were the data reviewed by other scientists not associated with the products company?

f) Also, scientists look for a response which is repeatable.  Was the response seen in multiple trials and locations or just one trial?

Reviewing this information on how to evaluate a product can prevent one from being the next victim of the “Dairy Robo Call”.

Author:  Donna M. Amaral-Phillips