Most dairies I work with utilize dairy software to help organize daily activities such as what cows to breed, treat, etc. While this can be extremely helpful in staying organized, what can make data even more valuable to a dairy is its ability to help us solve mysteries.

Dschaak chris
Chris Dschaak

We’ve all heard the phrase, “You can’t manage what you don’t measure,” but what many are also learning is: You can’t identify the culprit if you don’t have the clues. Good dairy data gives us a detailed story of where a herd has been and where it’s going. Within that data story are a multitude of clues to help us solve a wide range of mysterious issues that can arise.

Whether those mysteries are production issues or health challenges, the dairies keeping good records are often able to pinpoint the underlying cause by carefully analyzing their data, making diet or management corrections and, most importantly, measuring the impact of the changes.

However, for many, analyzing their dairy data to solve a mystery can be a bit overwhelming, and they aren’t sure where to start. In this article, I will talk through a dairy mystery I recently helped our team solve and provide some tips for analyzing data to solve your own cases.

Our team of consultants recently brought me a case from a herd experiencing a high incidence of calves being born dead on arrival (DOA). This herd kept excellent records, and therefore I had a lot of data to compile and analyze from multiple data platforms, such as their herd management software and feed management programs. Each of these different software programs track different information and must be analyzed for different clues.

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Working with data from multiple programs can be challenging, and I find it easiest to compare the animal group in the herd management software to the same group in the feed management software to look for clues on the feeding side. For example, looking for patterns in intake that might cause a problem during calving (i.e., cows going off feed, erratic feed intake, inconsistencies in feeding).

I began this case by comparing the herd DOA rate to other herds we work with to better understand the severity of the problem. Unfortunately, the DOA rate wasn’t just high; it was double the rate we typically see on dairies of their size – something was clearly amiss. Next, I began to try to look for patterns in the data to see if I could narrow the issue to a specific group of animals.

Most management software programs already have a report where you can look at cows by month calved and cows that had a DOA in that month. To do this in DairyComp305, you can look in “Guide,” under “Reports,” and in DHI-Plus, you can look at the graphs in the cohort analysis reports. This will give you a table as well as graphs by lactation and month of calving.

After pulling this report, I was able to see the first-lactation animals were having the highest incident of DOAs. My first major break in the case. Now we could dig deeper into the records on this specific group of animals to narrow down the why.

I started with the heifer program to better understand why the first-lactation animals were having higher incidence of DOAs as compared to other groups. I sorted the data and looked at everything from age at calving, sire selection, calving ease scores, calf size, calf sex and calving technician to see if I could find a pattern and another break in the case.

To look at age at calving effects on DOA rates in DairyComp305, you can run a report looking at calving events for first-lactation animals by age at calving (i.e., type events by agefr for lact=1 into the command line and select option 3). This gives you a table, or you can click on “Graph” and see a graph. You can change the date range in options to look at DOA rates by a specific month or a range of dates.

In DHI-Plus, you can look at cohort group analysis in analysis reports. Choose the age at calving lact 1 in the drop-down menu, under “Cohort Groups,” and then select “% DOAs” in the data items. This gives you a table with the DOAs by age at calving. Similarly, with both programs, you can make custom reports to look a little deeper and evaluate sire selection, calf size, calf sex and calving technician to see if you can find a pattern and if there were effects of each on DOA rates.

After investigating these parameters, the data revealed the age at calving for the first-lactation animals experiencing the DOAs was younger than we typically prefer to see. The data also showed the animals calving a couple months later were older at calving and experiencing significantly less DOAs. This was the pattern I had uncovered, and I believed I had found the culprit. Every time the age at calving dropped, we would see a spike in DOAs in first-lactation cows.

I met with the Cargill advisory team, and to remedy the problem, we worked with the heifer breeders to help them better identify which animals to breed by analyzing heifer body condition, size and weight. In addition, the team also brought in our company’s maternity cow specialist to work with the maternity crew at the dairy on how to properly handle calving problems and when to intervene if a cow is having a problem.

After the changes were implemented, we noted the date on the calendar and continued to monitor the herd data to measure the impact. The DOA rate has since dropped in half and has continued on a positive trend for the last year. Another case solved by detailed records analysis and careful sleuthing.

Sooner or later, every dairy will need to look for clues to catch a culprit on their operation, so take care of how you track and enter data. When that time comes, I hope these insights will help you better analyze your data. However, if the trail goes cold, feel free to call for backup.  end mark

Chris Dschaak