Dairy farmers are using new technology to give each cow a customized diet based on what that cow has eaten and produced in the past. Researchers wanted to see if this personalized approach actually helps cows eat more, make more milk, or make farmers more money. They tested 24 cows with different supplement strategies over several weeks. While cows that got extra supplements did eat more and produce more milk during training, the personalized feeding strategies didn’t improve results when tested. The cows on personalized diets didn’t earn farmers any extra money compared to cows on regular diets. This suggests that farmers may need better information and tools to make personalized feeding truly work.

The Quick Take

  • What they studied: Whether giving each dairy cow a customized diet based on its past eating and milk production habits would help it eat more, produce more milk, or make farmers more money.
  • Who participated: 24 lactating (milk-producing) Holstein dairy cows in a controlled research setting. The cows were divided into different groups to test various feeding strategies.
  • Key finding: During the initial training phase, cows given extra protein or energy supplements ate more and made more milk than cows on regular diets. However, when researchers used two different computer-based strategies to personalize each cow’s diet based on past performance, neither strategy improved milk production, feed intake, or profits compared to regular feeding.
  • What it means for you: If you’re a dairy farmer considering investing in precision feeding technology, this research suggests that simple computer algorithms based on short-term cow performance may not yet deliver the promised benefits. More advanced approaches may be needed before this technology becomes truly useful for improving farm profits.

The Research Details

This study had two main phases. In the first phase (training), researchers gave 24 cows four different types of supplements one at a time: soybean meal (high protein), corn grain (energy), corn gluten feed (protein and fiber), or no supplement. They watched how each cow responded to each supplement over 36 days. In the second phase (testing), the researchers used two different computer-based strategies to decide which supplement each cow should get based on what they learned during training. One strategy used the cow’s average past performance, while the other looked at whether the cow’s performance was improving or declining over time. They compared these personalized approaches to a control group that received a standard mixed diet.

The researchers measured several things: how much each cow ate, how much milk it produced, the quality of the milk, how efficiently the cow converted feed into milk, and whether farmers made more money. They tracked these measurements over four weeks of testing.

Understanding how individual cows respond to different supplements is important because dairy farming is becoming more automated and computerized. If researchers can figure out which supplements work best for each individual cow, farmers could save money on feed and get more milk production. However, this study shows that the simple methods tested so far don’t work as well as hoped, which means scientists need to develop better approaches.

This was a controlled experiment with a small but reasonable sample size (24 cows) using a Latin square design, which is a respected research method that helps control for variables. The study was published in the Journal of Animal Science, a peer-reviewed scientific journal. However, the study was relatively short-term (36 days of training plus 28 days of testing), which may not be long enough to see lasting effects. The findings were clear and honest about what didn’t work, which adds credibility. The researchers acknowledged limitations and didn’t overstate their results.

What the Results Show

During the training phase, cows that received any type of supplement (whether protein-rich or energy-rich) ate about 2.5 more kilograms of feed per day compared to cows on the control diet. These supplemented cows also produced more milk. However, they were less efficient at converting that extra feed into milk—meaning they ate more but didn’t produce proportionally more milk.

During the testing phase, when researchers used the two personalized feeding strategies based on past performance, the results were disappointing. The first algorithm (based on average past performance) showed only a slight tendency toward increased feed intake, but this wasn’t statistically significant. The second algorithm (based on performance trends) showed no improvement at all. Neither strategy improved milk production, milk quality, body weight, or feed efficiency.

Most importantly for farmers, the personalized feeding strategies did not increase profits. Feed costs, milk revenue, and income over feed costs were essentially the same between the precision-fed cows and the control cows. This means farmers would not have made more money using these personalized feeding approaches.

Milk composition (the amounts of fat, protein, and other components) did not differ between the groups. Body weight changes were similar across all feeding strategies. The study found no unexpected side effects or problems with any of the supplement types tested. These consistent results across multiple measures suggest that the lack of benefit from precision feeding was not due to measurement error but rather a genuine finding.

Previous research has shown that individual cows do respond differently to supplements and that some cows are naturally more efficient at converting feed to milk. This study confirms that individual variation exists. However, this research suggests that past performance alone is not a good predictor of future responses to supplements. This finding challenges the assumption that simple computer algorithms based on short-term data can effectively personalize dairy cow diets. It aligns with growing recognition in precision agriculture that more complex data and more sophisticated methods are needed.

The study was relatively short (only 36 days of training and 28 days of testing), so researchers couldn’t see if benefits might appear over longer periods. The study used only one breed of cow (Holstein), so results might differ for other dairy breeds. The algorithms tested were relatively simple and based only on past feed intake and milk production; they didn’t include other important information like cow health, genetics, or environmental factors. The study was conducted in a controlled research setting, which may not reflect real farm conditions. The sample size of 24 cows, while reasonable for a controlled study, is relatively small and limits how much the findings can be applied to all dairy farms.

The Bottom Line

Based on this research, dairy farmers should be cautious about investing in precision feeding systems that use only short-term performance data (low confidence level). If considering precision feeding technology, farmers should look for systems that incorporate more advanced data sources and more sophisticated analysis methods. Conventional total mixed ration (TMR) feeding remains a reliable approach until better precision feeding tools are developed (moderate to high confidence level).

Dairy farmers considering precision feeding technology should pay attention to these findings. Equipment manufacturers developing precision feeding systems should use these results to improve their algorithms. Agricultural researchers and veterinarians advising farmers should know that simple precision feeding based on short-term data may not deliver promised benefits. Farmers should NOT assume that precision feeding will automatically improve their profits without seeing strong evidence from their own operations.

In this study, researchers tested the algorithms over just four weeks. Even if precision feeding did work, farmers might not see significant financial benefits for several months as the system learns about each cow’s individual patterns. The fact that no benefits appeared even after several weeks of testing suggests that this particular approach may not work regardless of timeframe.

Want to Apply This Research?

  • If using a dairy management app, track daily feed intake, daily milk production, and feed costs for each cow over at least 8-12 weeks. Compare these metrics between cows on personalized supplement programs versus those on standard diets to see if personalization is actually improving your bottom line.
  • Rather than relying on automated precision feeding algorithms, use the app to manually monitor individual cow performance and work with a veterinarian or nutritionist to make supplement decisions based on multiple factors: the cow’s age, stage of lactation, health status, and overall herd performance—not just past production numbers.
  • Set up monthly reports in your app comparing income over feed costs (profit per cow) between different feeding strategies. Track not just milk production but also feed efficiency (milk produced per kilogram of feed eaten). This long-term monitoring will help you determine whether any feeding changes actually improve your farm’s profitability.

This research describes results from a controlled study with 24 cows over a limited time period. Individual farm results may vary significantly based on herd size, cow genetics, feed quality, management practices, and local conditions. These findings should not be considered a recommendation against precision feeding in general, but rather a caution that simple algorithms based on short-term performance data may not deliver expected benefits. Farmers considering changes to feeding programs should consult with a veterinarian, animal nutritionist, or agricultural extension specialist who understands their specific operation. This summary is for informational purposes and does not constitute professional agricultural or veterinary advice.