Feed additives are a multi-billion international business, part of the greater animal nutrition industry. Each year, millions are spent of research, yet very few data are being published in credible scientific journals.
Thus, for most additives, animal performance results under practical conditions remain largely inconclusive and quite often difficult to evaluate. When reading a research report, most often furnished by the supplier of the additive, it is always a good to start looking for two things: the Negative and Positive controls!
Negative control treatment. It is commonly believed that any well-known additive (for example, antibiotics used to promote growth) will always be working when used under standard conditions. In a farm trial where such product is replaced by a new additive (for example, a phytogenic additive) and there is no loss in animal performance, it is easy to conclude that these two additives are interchangeable.
But, if this trial does not contain a diet-treatment devoid of both additives, such research data would not be admissible for publication to any serious scientific journal as there is no value in comparing two additives without a negative control. This is essential to safeguard against the possibility that in any given trial, the standard additive does not work at all as expected, for example, because the animals were too healthy in the above trial.
In such rare but possible cases, the inclusion of a negative control treatment would reveal that all three treatments were equal-that is no positive animal performance even for antibiotics. In other words, only when the "old" additive treatment gives a positive response over the negative control (no additives), then we can safely discuss the performance (or lack of) of the "new" additive. Otherwise, the trial cannot be considered with any seriousness.
No positive control treatment. On the other hand, without positive controls, it is difficult to evaluate the return on investment from the use of a novel additive that may or may not support performance equal to the standard additive. Let's use again the example of antibiotics and design a trial that would enable us to replace antibiotics with butyric acid. First, we need a diet without antibiotics and any organic acids (negative control), then we need a diet with butyric acid (the novel additive), and finally we need a diet with antibiotics (the positive control).
Ideally, it would even include a fourth diet with antibiotics and butyric acid (but that is another article!) Evaluating the results, we should see that animals receiving the negative control treatment will have the lowest growth performance, whereas animals receiving antibiotics will (hopefully) grow more (or the trial is automatically invalidated).
If butyric acid achieves the same level of (positive) response as antibiotics, then we can conclude this product can replace antibiotics. Nevertheless, as butyric acid is very expensive, we might want to include a second positive control, using a well-known organic acid that is less expensive. In this case, we could use benzoic acid, for example, that has an established good record. With such design, we can evaluate the return on investment safely as we get to compare two similar products (butyric versus benzoic) used against each other under the same (well-designed) trial.
So, the next time you are presented with a trial involving additives, why not make sure the design contains those so-much needed controls. Otherwise, it is as good as anyone's educated guess, as any university professor would say!
