Hello, and welcome to this demonstration of Colostrum Monte Carlo, or CMC. My name is Jim Quigley. I’m the Managing Director of Calf Notes Consulting and the author of the Monte Carlo simulator. Let’s take a look at the program and see how it works.
We’ll begin with the input screen. The program consists of two tabs, Input and Results, which you can see in the lower left-hand corner of the screen. Below those tabs is the name of the current working file. We’ll begin with the Default.CMC file, which contains the basic information the program uses at startup. Along the top of the screen are the available menu options, and the version number appears in the lower right-hand corner.
The Input tab contains the information needed to define and run the simulation. Just below the menu are three settings related to the simulation itself. The first is the AEA curve. The default is the base exponential curve, which you can see plotted on the right-hand side of the screen. The form of the equation is also shown here. Several coefficients and variables in this equation influence the calculation of apparent efficiency of absorption, or AEA. The calculated AEA is then used to estimate serum IgG concentration for each calf in the simulation.
The number of simulations can be selected from 1,000 to 100,000, with a default of 10,000. Ten thousand simulations is a reasonable number for most situations. We can also select one, two, or three colostrum feedings. The default is two feedings, and the number selected determines how much feeding information must be entered.
In the middle left-hand section of the screen, we enter information describing the calves and their feeding program. The first row is birth weight of the Holstein calves. Here, we’re using a normal distribution with a mean of 41 kilograms and a standard deviation of 5 kilograms. Notice that the Minimum, Maximum, and Mode columns are shown in light gray. Those values are not used with a normal distribution, so they aren’t available for entry.
Plasma volume, expressed as a percentage of body weight, is also initially defined using a normal distribution, with a mean of 8.9% and a standard deviation of 2%. I’m going to change this to a truncated normal distribution. That allows me to specify minimum and maximum values so the program doesn’t select values that are biologically unreasonable.
For age at the first and second feedings, I’m using a triangular distribution. A triangular distribution uses a minimum, maximum, and mode. The mode represents the most common value in the distribution. For the first feeding, for example, I’ll use a minimum age of one hour and a maximum of six hours, with a mode of two hours. Most calves will therefore be fed at about two hours of age, with progressively fewer calves fed closer to the minimum and maximum ages.
For liters fed, I’m assuming the calves are fed by esophageal tube, so I’ll use a fixed value for each feeding. The maternal colostrum IgG concentrations are currently set at 68 grams per liter. I’m going to change those to an average of 50 grams per liter and use a truncated distribution so that the simulated concentrations remain within the range I’ve specified. Truncating a normal distribution changes its theoretical shape somewhat, but it prevents the simulation from selecting values outside the biologically reasonable range or outside the range expected on the farm.
Once the inputs have been defined and we’ve selected the AEA curve, we’re ready to run the simulation. There are six AEA curve options available, with the exponential curve being the default. I’ll click Run, and CMC will perform 10,000 iterations, calculate serum IgG concentrations, and display the results on the Results tab.
At the top of the Results tab is the simulated serum IgG distribution. The graph shows the percentage of calves across the range of serum IgG concentrations. In this example, we have an approximately normal distribution, with the largest proportion of calves having serum IgG concentrations somewhere in the range of about 23 to 27 grams per liter.
The Monte Carlo results are summarized in the table at the top. The first row is body weight. Our input mean was 41 kilograms, and the simulation produced a mean of 41.1 kilograms with a standard deviation of about 5 kilograms. The table also shows the coefficient of variation, median, and 5th and 95th percentiles.
Maternal colostrum IgG was set at an average of 50 grams per liter. The simulated result is slightly higher because truncating the distribution eliminated some of the lower values that otherwise could have been selected. As I move down the table, the graph at the bottom changes to show the simulated distribution for each variable.
Here, for example, is the triangular distribution for age at first feeding. Most calves are fed at about two hours of age, with values extending from the minimum of one hour to the maximum of six hours. Liters fed at the first feeding was fixed at 3 liters, so every simulated calf receives exactly 3 liters. The same is true for the second feeding.
AEA at the first feeding averaged about 33%, with the 5th to 95th percentile range extending from approximately 27 to 39%. AEA at the second feeding averaged about 21%. When I select serum IgG concentration in the results table, the two graphs show the same variable. In this simulation, serum IgG averaged 24.9 grams per liter with a standard deviation of 7.7 grams per liter, and the 5th to 95th percentile range is shown here. A few calves have serum IgG concentrations below 10 grams per liter, but relatively few fall into that category.
The table in the middle of the screen is perhaps the most important part of the Results tab. It shows the proportion of calves within the consensus serum IgG categories. Lombard and colleagues recommended that fewer than 10% of calves in a herd have serum IgG concentrations below 10 grams per liter. In this simulation, only 1.8% of the calves, or 178 out of 10,000, had serum IgG concentrations below 10 grams per liter.
Another 16.5% of the calves were between 10 and 17.9 grams per liter, 35% were between 18 and 24.9 grams per liter, and 46.6% had serum IgG concentrations of 25 grams per liter or greater. In this example, therefore, the simulated calf population meets or exceeds the recommendations proposed by Lombard and colleagues.
The AEA calculations in this simulation included an adjustment for cumulative IgG intake. As total IgG intake increases, AEA generally declines, so the amount of IgG consumed at an earlier feeding influences AEA at subsequent feedings. We did not adjust AEA for prepartum heat stress or pasteurization in this example. I’ll demonstrate those options in another video.
Once the simulation is complete, we can print a report containing the results. The report includes the simulation inputs, the numerical results, the serum IgG categories, and the graphs generated during the simulation. This provides a permanent record of the assumptions used and the results obtained.
We can then return to the Input tab, change any of the assumptions or distributions, and rerun the simulation to evaluate a different feeding program or set of conditions. That gives you a brief introduction to Colostrum Monte Carlo. There’s much more that we can do with the program, but this should give you a good start on how to set up and run a simulation. Thanks for watching, and we’ll see you soon.