Dairy producers make decisions on cows every day. And some of those decisions are based upon milk production. Normally, this might be a monthly DHI test or data coming off an electronic metering system. But how do you really know what that cow means to your bottom line?
The most exact method is to calculate each cow’s daily production value based on milk payment methods. While that may sound interesting, in many cases it is fairly difficult to reflect all of the nuances in the pricing schemes. If milk pricing was as simple as X amount of dollars per hundredweight with a fat or protein differential, it would be quite simple. But almost every producer receives a different payment for milk depending on the buyer and the quality of milk.
FCM and ECM demystified
So the next best method is to take into account the components that a cow produces along with the volume. Fat-corrected milk (FCM) and energy-corrected milk (ECM) are the best tools that we have to estimate the value of her production along with her nutritional requirements.
The table below illustrates the effect that components have on both the FCM and ECM values. In the case of FCM, the calculations are standardized to 3.5 percent butterfat. Therefore, if a cow is testing at 3.5 percent butterfat, then the FCM is the same value as her actual milk (see Table 1).
In a similar fashion, ECM is standardized to 3.5 percent butterfat and 3 percent true protein. So the cow that tests at 3.5 percent butterfat and 3 percent true protein will have the same ECM as her actual milk production.
But how significant is the effect of the levels of percent fat and protein above the standardized values? In the data cited above, a cow that is producing 90 pounds of milk at 4.2 percent fat and 3.4 percent protein is producing the same amount of corrected milk as a cow that is producing 100 pounds of milk with 3.5 percent fat and 3.2 percent protein.
So when you are sorting cows, FCM and ECM really do make a difference. That cow with the higher components needs to have a diet that is at higher levels than a cow with similar milk production but lower components.
And so why is this important? It is not uncommon to balance rations for more milk than what the pen is averaging. But how much are you underfeeding the high producer with high components? And how much money are you leaving on the table if you are not meeting those nutritional requirements? While I don’t practice dairy nutrition, I do know that underfed cows are not nearly as profitable as they could be.
Using relative value to make herd management decisions
Ranking cows based on production, sorting cows by reproductive status and making sure you keep the good ones and get rid of the underachievers is all part of the normal process on a commercial dairy.
But how do you tell which animal is a keeper and which one goes? There is a plethora of data that can be used to make those decisions. There is usually a whole group of animals that are no-brainers. In the keeper group, the cow producing over 100 pounds of milk and is safe in calf would be such an example.
The potential culls would be the Holstein cow milking 35 pounds and not pregnant, the chronic high somatic cell count animal or the cow that has a hard time getting in and out of the milk barn.
But how about some of those animals that aren’t so obvious? How do you go about making some of those decisions? One of many tools that a producer has at his disposal is the relative value (RV).
Relative value is a tool that compares the 305 day-2X-mature equivalent (commonly referred to as the 305-2X-ME) of the cow against the average of the herd. For instance, a cow with a projected 305-2X-ME of 33,000 pounds of milk in a herd that averages 30,000 pounds of 305-2X-ME has a relative value of 110 (33,000/30,000 expressed as a percentage). Any cow that is above 100 is above average and a cow that is less than 100 is below average.
But what goes into the calculations for 305-2X-ME? First of all, as the name suggests, we are looking at production that is 305 days. That is either a projected number if the cow is less than 305 days in milk or an actual number if the cow is 305 days or more. That 305-day value is then adjusted for milking frequency, season of calving, age at calving and lactation number. The final value is an estimate of what the animal would produce if she was a mature cow calving in an “average” month and milking two times a day. When cows are compared across the country (as in U.S. genetic evaluations), there is a region of the country factor that is applied as well.
So now we have come up with an estimate of the pounds of milk that she would produce. These factors are also applied to pounds of fat, protein and solids-not-fat (if applicable). So with that, we are able to produce a 305-2X-ME for both fat and protein. Since we have values for milk, fat and protein, we are able to come up with values for both 305-2X-ME FCM and ECM.
A producer on official test has access to the data and has the option of choosing his ME (and therefore his RV) based on either ME milk, ME FCM or ME ECM. So which one should a producer choose? In my opinion, the producer should choose the option that most closely approximates how he is paid for his milk. The RV based on ME milk would be most applicable in a market that is geared to fluid production. One could argue that FCM is a better indicator. It really depends upon what the butterfat differential is for the milk being sold. In a cheese market, ECM or FCM would be the better indicators. For example, many California producers sell their milk based on the pounds of fat and solids-not-fat that is marketed. In that case, there is no doubt that ECM would be the ME option of choice.
So as you look at your records and as we see the role that cheese production has played in the dairy industry, it is probably in your best interests (and most profitable) to look at your cows’ production based on either fat-corrected or energy-corrected milk. It is a better indicator of the value of the milk that a cow is producing along with her nutritional needs.
- General Manager
- AgriTech Analytics