Dr. Kononoff’s lab evaluated retrospective feed mixing records collected from eight commercial dairy farms. Data was divided into 28-day periods. Daily TMR nutrient deviation was automatically calculated from feed mixer data as the actual amount of a nutrient fed minus the target amount from the original diet formulation, divided by the target amount. (5:43)
Crude protein, NDF, fat, and starch were the nutrients evaluated in the study. (13:40)
Variation was positive for every nutrient on the vast majority of days. Dr. Kononoff attributes that to more feed being delivered than the diet formulation predicted animals would consume. Dry matter intake decreased with increasing positive deviation days in starch and increased with increasing positive deviation days in crude protein. NDF deviation did not impact dry matter intake. A narrow range of diets was used in the dataset and the main byproduct feed was high in NDF, so Dr. Kononoff speculates that there was not a wide enough range in NDF to have an impact on intakes. (17:04)
Milk yield increased with increased positive deviation days in starch and decreased with increased positive deviation days in NDF. The pregnancy rate increased with increasing positive deviation days in fat and decreased with increasing positive deviation days in crude protein. Unfortunately, milk urea nitrogen data was not available in the dataset to further investigate the crude protein/pregnancy rate relationship. (20:44)
There was little farm-to-farm variation in the data. (25:08)
As positive deviation days for starch increased, so did feed conversion. The opposite effect was noted for NDF. As positive deviation days for fat increased, feed conversion decreased. This result was a little surprising, as delivering more energy usually improves feed conversion. However, the dataset did not specify the source of fat or fatty acid profile, so there may have been some rumen fermentation interference from fat. (27:08)
Dr. Kononoff thinks it would be interesting to track individual cows through lactation and collect nutrient variation data. Dr. Weiss asks if the correlation between daily farm milk yield and nutrient variation was evaluated; it was not. Dr. Kononoff agrees that there may be some additional correlations that would be interesting to run. (33:22)
In closing, Dr. Zimmerman commends Dr. Kononoff’s work in tackling such a large dataset and looks forward to follow-up research. Dr. Weiss agrees and encourages more data extraction from the dataset. He was also very surprised at the low farm-to-farm variation observed and speculated if that would hold up if there were more variation in diets. Dr. Kononoff reminds the audience that taking a look at the TMR beyond the paper ration and digging into mixing techniques and TMR consistency is as important as evaluating bulk tank information or the amount of milk shipped. (37:20)
You can find this episode’s journal club paper from the Journal of Dairy Science Communications here: https://www.sciencedirect.com/science/article/pii/S2666910224000760
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By Balchem Animal Nutrition & Health
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Dr. Kononoff’s lab evaluated retrospective feed mixing records collected from eight commercial dairy farms. Data was divided into 28-day periods. Daily TMR nutrient deviation was automatically calculated from feed mixer data as the actual amount of a nutrient fed minus the target amount from the original diet formulation, divided by the target amount. (5:43)
Crude protein, NDF, fat, and starch were the nutrients evaluated in the study. (13:40)
Variation was positive for every nutrient on the vast majority of days. Dr. Kononoff attributes that to more feed being delivered than the diet formulation predicted animals would consume. Dry matter intake decreased with increasing positive deviation days in starch and increased with increasing positive deviation days in crude protein. NDF deviation did not impact dry matter intake. A narrow range of diets was used in the dataset and the main byproduct feed was high in NDF, so Dr. Kononoff speculates that there was not a wide enough range in NDF to have an impact on intakes. (17:04)
Milk yield increased with increased positive deviation days in starch and decreased with increased positive deviation days in NDF. The pregnancy rate increased with increasing positive deviation days in fat and decreased with increasing positive deviation days in crude protein. Unfortunately, milk urea nitrogen data was not available in the dataset to further investigate the crude protein/pregnancy rate relationship. (20:44)
There was little farm-to-farm variation in the data. (25:08)
As positive deviation days for starch increased, so did feed conversion. The opposite effect was noted for NDF. As positive deviation days for fat increased, feed conversion decreased. This result was a little surprising, as delivering more energy usually improves feed conversion. However, the dataset did not specify the source of fat or fatty acid profile, so there may have been some rumen fermentation interference from fat. (27:08)
Dr. Kononoff thinks it would be interesting to track individual cows through lactation and collect nutrient variation data. Dr. Weiss asks if the correlation between daily farm milk yield and nutrient variation was evaluated; it was not. Dr. Kononoff agrees that there may be some additional correlations that would be interesting to run. (33:22)
In closing, Dr. Zimmerman commends Dr. Kononoff’s work in tackling such a large dataset and looks forward to follow-up research. Dr. Weiss agrees and encourages more data extraction from the dataset. He was also very surprised at the low farm-to-farm variation observed and speculated if that would hold up if there were more variation in diets. Dr. Kononoff reminds the audience that taking a look at the TMR beyond the paper ration and digging into mixing techniques and TMR consistency is as important as evaluating bulk tank information or the amount of milk shipped. (37:20)
You can find this episode’s journal club paper from the Journal of Dairy Science Communications here: https://www.sciencedirect.com/science/article/pii/S2666910224000760
Please subscribe and share with your industry friends to invite more people to join us at the Real Science Exchange virtual pub table.
If you want one of our Real Science Exchange t-shirts, screenshot your rating, review, or subscription, and email a picture to [email protected]. Include your size and mailing address, and we’ll mail you a shirt.
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