Quantification of changes in body weight and body condition scores during lactation by modelling individual energy balance and total net energy intake

Current ration formulation systems used to formulate diets on farms and to evaluate experimental data estimate metabolizable energy (ME)-allowable and metabolizable protein (MP)-allowable milk production from the intake above animal requirements for maintenance, pregnancy, and growth. The changes in body reserves, measured via the body condition score (BCS), are not accounted for in predicting ME and MP balances. This paper presents 2 empirical models developed to adjust predicted diet-allowable milk production based on changes in BCS. Empirical reserves model 1 was based on the reserves model described by the 2001 National Research Council (NRC) Nutrient Requirements of Dairy Cattle, whereas empirical reserves model 2 was developed based on published data of body weight and composition changes in lactating dairy cows. A database containing 134 individually fed lactating dairy cows from 3 trials was used to evaluate these adjustments in milk prediction based on predicted first-limiting ME or MP by the 2001 Dairy NRC and Cornell Net Carbohydrate and Protein System models. The analysis of first-limiting ME or MP milk production without adjustments for BCS changes indicated that the predictions of both models were consistent (r(2) of the regression between observed and model-predicted values of 0.90 and 0.85), had mean biases different from zero (12.3 and 5.34%), and had moderate but different roots of mean square errors of prediction (5.42 and 4.77 kg/d) for the 2001 NRC model and the Cornell Net Carbohydrate and Protein System model, respectively. The adjustment of first-limiting ME- or MP-allowable milk to BCS changes improved the precision and accuracy of both models. We further investigated 2 methods of adjustment; the first method used only the first and last BCS values, whereas the second method used the mean of weekly BCS values to adjust ME- and MP-allowable milk production. The adjustment to BCS changes based on first and last BCS values was more accurate than the adjustment to BCS based on the mean of all BCS values, suggesting that adjusting milk production for mean weekly variations in BCS added more variability to model-predicted milk production. We concluded that both models adequately predicted the first-limiting ME- or MP-allowable milk after adjusting for changes in BCS.

Section: Resultscontrasting

confidence: 69%

Accounting for Energy and Protein Reserve Changes in Predicting Diet-Allowable Milk Production in Cattle

Tedeschi

Seo

Fox

et al. 2006

“…Treated cows had a lower BCS than control cows (3.13 and 3.17, respectively). Although the BCS difference between groups was significant (P < 0.001), the difference itself was not considered biologically or clinically important as it was only 0.04 of a BCS unit, which corresponds to approximately 3 kg of BW (Schwager-Suter et al, 2001). Therefore, the similar decline in BCS in both groups in the current study could be explained by the typical NEB and reduction in DMI that cows experience in the peripartum period rather than an effect of rbST.…”

Section: Healthmentioning

confidence: 56%

Effect of prepartum administration of recombinant bovine somatotropin on health and performance of lactating dairy cows

Gohary

LeBlanc

Lissemore

et al. 2014

A double-blind, randomized clinical trial was conducted in 5 commercial dairy herds in southern Ontario with 1,362 cows enrolled to evaluate the effect of prepartum administration of recombinant bovine somatotropin (rbST) on health and performance. Cows were randomly assigned to receive either 325 mg of sometribove zinc suspension (n=680) or a placebo injection (n=682; control) subcutaneously every 14 d until calving. Treatments started 28 to 22 d before expected calving, with a maximum of 3 treatments per cow. Serum samples taken at the time of enrollment, 1 wk before calving, and weekly for 3 wk after calving were analyzed for nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), glucose, aspartate aminotransferase, calcium, and haptoglobin. Diseases were recorded by farm staff. Incidences of clinical ketosis, clinical mastitis, displaced abomasum, metritis, retained placenta, milk fever, and lameness were similar between groups. Body condition score was lower for treated than for control cows at 3 wk after calving (3.13 and 3.17, respectively). Serum NEFA tended to be higher for treated than for control cows by 0.01 mmol/L. Overall BHBA was not different between groups, but BHBA for treated cows was higher in wk 1 after calving (750 and 698 μmol/L, respectively) and tended to be higher in wk 2 after calving (779 and 735 μmol/L, respectively). Incidence of hyperketonemia was similar between groups. Treated cows had higher serum glucose compared with control cows (2.8 and 2.7 mmol/L, respectively). We detected no differences in serum aspartate aminotransferase, calcium, or haptoglobin between groups. Milk yield was recorded daily for each cow for 63 d, and did not differ between groups (37.1 ± 0.5 kg and 36.7 ± 0.5 kg, respectively) but we detected a tendency for treated cows to produce 0.8 kg/d more milk than control cows in wk 1 after calving. We observed no difference between groups in the time from calving to first insemination or the probability of pregnancy at the first insemination. Groups did not differ in the proportion of anovular cows at 53 ± 3 d in milk based on serum progesterone measured from a subset of cows (38.0 and 34.3%, respectively, for treated and control groups). We found no difference between groups in dry matter intake from 21 d before calving to 63 d after calving in a subset of cows (17.4 ± 0.4 and 17.5 ± 0.4 kg/d, respectively). Based on results of the current study, biweekly (every 14 d) administration of rbST before calving to prevent disease and enhance performance is not recommended.

“…A method to do this would be to convert all inputs and outputs to energy equivalents before analysis using a random regression model and to use the smoothed values to calculate energy balance. This technique of analysis was used by Schwager-Suter et al (2001), who used the Net Energy system and polynomials to smooth the resulting energy balance. We chose not to conduct this type of analysis because fixed effects were thought to affect each trait differently.…”

Section: Discussionmentioning

confidence: 99%

Genotype and Diet Effects on Energy Balance in the First Three Lactations of Dairy Cows

Coffey

Simm

Oldham

et al. 2004

Dairy cows make strategic use of body energy to support early lactation and replenish this lost energy later in lactation, thereby creating body energy profiles that vary both within lactation and across lactations. The interaction between genotype and diet energy content is interesting from a management viewpoint and from a cow survival viewpoint. In this study, we modeled energy balance over 3 lactations using a multivariate random regression model, for cows from the Langhill Dairy Cattle Research Centre. This herd has been selected for maximum production or to remain at the UK national average for production (control group) and has been fed 2 diets of different energy density (high or low concentrate). Cows in the various groups differed in body condition score and the way they lost and regained body lipid. Cows selected for maximum production on a low-concentrate diet lost the greatest amount of body lipid (0.54 condition score units) after 3 lactations. Cows selected for maximum production lost more than control cows on either diet, indicating that selection mainly for yield has predisposed cows to utilize body energy to support lactation on diets spanning the range used here. Cows selected for maximum production were heavier at first calving than control cows but lost more weight and regained more weight so that at the end of each lactation, cows in the 2 groups did not differ significantly in weight. Cows use body lipid to support lactation over 3 lactations and the profile of that use varies according to genotype and diet.