Variation in nutrients formulated and nutrients supplied on 5 California dairies

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Computer models used in ration formulation assume that nutrients supplied by a ration formulation are the same as the nutrients presented in front of the cow in the final ration. Deviations in nutrients due to feed management effects such as dry matter changes (i.e., rain), loading, mixing, and delivery errors are assumed to not affect delivery of nutrients to the cow and her resulting milk production. To estimate how feed management affects nutrients supplied to the cow and milk production, and determine if nutrients can serve as indexes of feed management practices, weekly total mixed ration samples were collected and analyzed for 4 pens (close-up cows, fresh cows, high-milk-producing, and low-milk-producing cows, if available) for 7 to 12. wk on 5 commercial California dairies. Differences among nutrient analyses from these samples and nutrients from the formulated rations were analyzed by PROC MIXED of SAS (SAS Institute Inc., Cary, NC). Milk fat and milk protein percentages did not vary as much [coefficient of variation (CV). = 18 to 33%] as milk yield (kg; CV. = 16 to 47 %) across all dairies and pens. Variability in nutrients delivered were highest for macronutrient fat (CV. = 22%), lignin (CV. = 15%), and ash (CV. = 11%) percentages and micronutrients Fe (mg/kg; CV. = 48%), Na (%; CV. = 42%), and Zn (mg/kg; CV. = 38%) for the milking pens across all dairies. Partitioning of the variability in random effects of nutrients delivered and intraclass correlation coefficients showed that variability in lignin percentage of TMR had the highest correlation with variability in milk yield and milk fat percentage, followed by fat and crude protein percentages. But, variability in ash, fat, and lignin percentages of total mixed ration had the highest correlation with variability in milk protein percentage. Therefore, lignin, fat, and ash may be the best indices of feed management to include effects of variability in nutrients on variability in milk yield, milk fat, and milk protein percentages in ration formulation models.

Original languageEnglish (US)
Pages (from-to)7371-7381
Number of pages11
JournalJournal of Dairy Science
Volume96
Issue number11
DOIs
StatePublished - Nov 2013

Fingerprint

dairies
Milk
Food
nutrients
Fats
Lignin
milk protein percentage
cows
lignin
Milk Proteins
milk yield
total mixed rations
lipids
milk fat
milk production
milk
milk fat percentage
nutrient management
Rain
Micronutrients

Keywords

  • Milk production variability
  • Nutrient variability
  • Ration formulation variability

ASJC Scopus subject areas

  • Animal Science and Zoology
  • Food Science
  • Genetics

Cite this

Variation in nutrients formulated and nutrients supplied on 5 California dairies. / Rossow, Heidi A; Aly, Sharif S.

In: Journal of Dairy Science, Vol. 96, No. 11, 11.2013, p. 7371-7381.

Research output: Contribution to journalArticle

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abstract = "Computer models used in ration formulation assume that nutrients supplied by a ration formulation are the same as the nutrients presented in front of the cow in the final ration. Deviations in nutrients due to feed management effects such as dry matter changes (i.e., rain), loading, mixing, and delivery errors are assumed to not affect delivery of nutrients to the cow and her resulting milk production. To estimate how feed management affects nutrients supplied to the cow and milk production, and determine if nutrients can serve as indexes of feed management practices, weekly total mixed ration samples were collected and analyzed for 4 pens (close-up cows, fresh cows, high-milk-producing, and low-milk-producing cows, if available) for 7 to 12. wk on 5 commercial California dairies. Differences among nutrient analyses from these samples and nutrients from the formulated rations were analyzed by PROC MIXED of SAS (SAS Institute Inc., Cary, NC). Milk fat and milk protein percentages did not vary as much [coefficient of variation (CV). = 18 to 33{\%}] as milk yield (kg; CV. = 16 to 47 {\%}) across all dairies and pens. Variability in nutrients delivered were highest for macronutrient fat (CV. = 22{\%}), lignin (CV. = 15{\%}), and ash (CV. = 11{\%}) percentages and micronutrients Fe (mg/kg; CV. = 48{\%}), Na ({\%}; CV. = 42{\%}), and Zn (mg/kg; CV. = 38{\%}) for the milking pens across all dairies. Partitioning of the variability in random effects of nutrients delivered and intraclass correlation coefficients showed that variability in lignin percentage of TMR had the highest correlation with variability in milk yield and milk fat percentage, followed by fat and crude protein percentages. But, variability in ash, fat, and lignin percentages of total mixed ration had the highest correlation with variability in milk protein percentage. Therefore, lignin, fat, and ash may be the best indices of feed management to include effects of variability in nutrients on variability in milk yield, milk fat, and milk protein percentages in ration formulation models.",
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