Nutrition tweaks and adjustments are a necessary part of good ration management, and the right decisions made at the right time can pay big dividends. However, field assessments have found that for these ration tweaks to be effective, any underlying herd challenges must be identified and resolved first or the ration adjustment can be an expensive exercise in futility. It’s essential to make sure a dairy’s underlying herd management makes the grade before fine-tuning the ration.
Even the best rations can be undermined by management issues and unknown or unaccounted-for herd dynamics. For example, what’s the point of adding potassium to lactating rations to mitigate heat stress if a dairy doesn’t have a cooling strategy in place?
The first step
Don’t dive into an assessment without good information.
First and foremost, a herd monitoring system must be in place to accurately and consistently identify key data – like individual health incidents, milk/component production and reproductive events – as well as incidence trends. This system must track realistic numbers and offer more information than just total milk shipped.
Take the time to define which data to collect and how the information will be entered into your management software. Producers, their management team and key consultants must be confident that 10 cases of ketosis are 10 accurate cases, and not one cow entered into the system 10 times or 10 different ways.
If necessary, revisit herd protocols and provide employee training on protocol compliance to ensure policies are being followed and information is recorded appropriately.
Monitor parameters
Once proper monitoring is in place, producers and their team can then address performance bottlenecks holding the herd back and focus on solutions that aid in success.
Use the data gained through accurate monitoring to assess herd performance. Key metrics to evaluate include:
• Milk production parameters like production per cow per day, milk component production and milk production trends
• Reproduction parameters like 21-day pregnancy rate, conception rate and days to first insemination
• Herd health incidents and total incidence for transition diseases like metritis, ketosis and milk fever
Utilize expert advice
Bring in outside expertise when appropriate to offer a fresh perspective on the information. Then use the data and advice to make management decisions and/or needed changes.
These analyses should be done on dairies of all sizes on a regular basis to reach optimum performance.
The following case study is an example of how one dairy, working with outside experts, identified its herd’s bottlenecks to understand where appropriate changes needed to be made to maximize success.
Case study sidebar
A high-producing Midwestern dairy was recently looking to balance its lactating ration for amino acids. Before reformulating the ration, the dairy decided to bring a team in to evaluate herd performance and determine if there were areas to improve beforehand that would not allow the benefits of amino acid balancing to be fully realized.
Six basic data points were used as a jumping-off point for the herd’s performance assessments and ensuing herd investigation:
• 21-day pregnancy rate
• Conception rate
• Days to first insemination
• Dry period length
• First test-day milk and component production
• Peak milk and component production
While these parameters are not direct measurements of the herd’s nutrition program, they all are impacted by nutrition programs and demonstrate how most issues require a multidisciplinary approach.
Somewhat surprisingly, there were a number of bottlenecks lurking below the surface. Based on the six parameters, herd investigators concluded the following:
• Production – First test-day butterfat tests showed 35 percent of the lactating herd with butterfat levels greater than 4.5 percent – and many were above 5 percent. This indicated a potentially serious incidence of clinical/subclinical ketosis within the herd.
Figure 1: Butterfat production by days in milk
• Dry period – More than 60 percent of the herd had dry periods longer than the target of 50 to 60 days, with the average being 66 days dry. The extended dry periods put cows at risk for metabolic disorders during transition and are a potential cause for high incidence of ketosis.
Click here or on the image at right to view it at full size in a new window.
Figure 2: Dry period length
• Days to insemination – While the herd’s annual pregnancy rate was 16 percent, that performance was somewhat misleading because of the time it took to get cows into the breeding program. Instead of the goal of nearly 100 percent of cows inseminated by 85 days in milk, only 75 
percent of cows were inseminated by that timeframe.
• Delayed breedings – This meant that, instead of more than 50 percent of cows pregnant by 100 days in milk, it took until 125 days in milk for that percentage of cows to reach the goal. Conception rates also did not meet goals.
Click here or on the image at right to view it at full size in a new window.
Conclusions
As a result, the assessment provided the following areas for improvement:
1. Reproduction – Due to the dairy’s inconsistent reproduction program, cows had long days in milk before dryoff, as well as longer-than-desired dry periods. This most likely resulted in higher body condition scores. In addition, heat detection needed to be addressed by the dairy.
2. Transition – The herd’s high incidence of ketosis had lasting impacts, meaning transition management needed to be targeted.
Prior to the investigation, this well-managed, high-producing herd seemed to have everything right and be at a point where fine-tuning the ration was appropriate. However, bigger gains could be seen when underlying issues were fixed first. The dairy quickly modified a few key practices and improvements were seen in a very short time. Only then was it appropriate to fine-tune the herd’s amino acid nutrition to realize the full economic benefits of this technology. PD
Elliot Block
Senior Manager of Technology
Arm & Hammer Animal Nutrition
