Relationships between Body Measurements, Body Weight, and Productivity in Holstein Dairy Cows

Sieber, M; Freeman, A.E.; Kelley, David H.

doi:10.3168/jds.s0022-0302(88)79949-x

Cited by 69 publications

(48 citation statements)

References 19 publications

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“…Berry et al (2007) reported greater somatic cell counts in heavier cows (even following adjustment for differences in milk production) with the effect greater in Jersey compared with Holstein-Friesian cows. Sieber et al (1988) reported a positive correlation between WT and milk production of 0.20 while they also reported a weak positive correlation (0.09) between WT and milk fat concentration.…”

Section: Introductionmentioning

confidence: 97%

“…Previous studies have also related live weight (WT) to reproduction (Roche et al, 2007b), health (Berry et al, 2007) -E-mail: donagh.berry@teagasc.ie and milk production (Sieber et al, 1988) in dairy cattle. Associations with WT are generally similar to associations with BCS owing to the moderate correlations between BCS and WT (Berry et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the greater energy costs associated with heavier cows must be assessed with consideration of any additional benefits, economic or otherwise, accruing from heavier cows such as higher milk production (Sieber et al, 1988) or increased carcass weight.…”

Section: Introductionmentioning

confidence: 99%

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Body condition score and live-weight effects on milk production in Irish Holstein-Friesian dairy cows

Berry

Buckley

Dillon

2007

Animal

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The objective of the present study was to quantify the relationships among body condition score (BCS; scale 1 to 5), live weight (WT) and milk production in Irish Holstein-Friesian spring calving dairy cows. Data were from 66 commercial dairy herds during the years 1999 and 2000. The data consisted of up to 9886 lactations with records for BCS or WT at least once pre-calving, or at calving, nadir or 60 days post-calving. Change in BCS and WT was also calculated between time periods. Mixed models with cow included as a random effect were used to quantify the effect of BCS and WT, as well as change in each trait, on milk yield, milk fat concentration and milk protein concentration. Significant and sometimes curvilinear associations were observed among BCS at calving or nadir and milk production. Total 305-day milk yield was greatest in cows calving at a BCS of 4.25 units. However, cows calving at a BCS of 3.50 units produced only 68 kg less milk than cows calving at a BCS of 4.25 units while cows calving at 3.25 or 3.00 BCS units produced a further 50 and 114 kg less, respectively. Cows that lost more condition in early lactation produced more milk of greater fat and protein concentration, although the trend reversed in cows that lost large amounts of condition post-calving. Milk yield increased with WT although the marginal effect decreased as cows got heavier. Milk fat and protein concentration in early lactation also increased with WT pre-calving, calving and nadir, although WT did not significantly affect average lactation milk fat concentration.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Body condition score and live-weight effects on milk production in Irish Holstein-Friesian dairy cows

Berry

Buckley

Dillon

2007

Animal

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“…For this sensitivity analysis, we assumed a non-pregnant, lactating dairy cow at 90 days in milk, BW of 550±55 kg, MY of 32±3.2 kg/d with milk TP of 3.2±0.04% and milk fat of 3.7±0.1%. A correlation matrix from Sieber et al (1988) was used to take into account the intrinsic relationships among BW, MY, and milk TP and fat (%), as follows: BW and MY = 0.20; BW and milk TP = -0.20; BW and fat = 0.09; MY and milk TP = -0.17; MY and fat = -0.13; and milk TP and fat = 0.30. The correlations with solid non-fat from Sieber et al (1988) were used to represent the milk TP correlations in our analysis.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

“…A correlation matrix from Sieber et al (1988) was used to take into account the intrinsic relationships among BW, MY, and milk TP and fat (%), as follows: BW and MY = 0.20; BW and milk TP = -0.20; BW and fat = 0.09; MY and milk TP = -0.17; MY and fat = -0.13; and milk TP and fat = 0.30. The correlations with solid non-fat from Sieber et al (1988) were used to represent the milk TP correlations in our analysis. The BR-Corte was not included in this simulation, as it does not handle dairy cows.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

Models of protein and amino acid requirements for cattle

et al. 2015

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-Protein supply and requirements by ruminants have been studied for more than a century. These studies led to the accumulation of lots of scientific information about digestion and metabolism of protein by ruminants as well as the characterization of the dietary protein in order to maximize animal performance. During the 1980s and 1990s, when computers became more accessible and powerful, scientists began to conceptualize and develop mathematical nutrition models, and to program them into computers to assist with ration balancing and formulation for domesticated ruminants, specifically dairy and beef cattle. The most commonly known nutrition models developed during this period were the National Research Council (NRC) in the United States, Agricultural Research Council (ARC) in the United Kingdom, Institut National de la Recherche Agronomique (INRA) in France, and the Commonwealth Scientific and Industrial Research Organization (CSIRO) in Australia. Others were derivative works from these models with different degrees of modifications in the supply or requirement calculations, and the modeling nature (e.g., static or dynamic, mechanistic, or deterministic). Circa 1990s, most models adopted the metabolizable protein (MP) system over the crude protein (CP) and digestible CP systems to estimate supply of MP and the factorial system to calculate MP required by the animal. The MP system included two portions of protein (i.e., the rumen-undegraded dietary CP -RUP -and the contributions of microbial CP -MCP) as the main sources of MP for the animal. Some models would explicitly account for the impact of dry matter intake (DMI) on the MP required for maintenance (MPm; e.g., Cornell Net Carbohydrate and Protein System -CNCPS, the Dutch system -DVE/OEB), while others would simply account for scurf, urinary, metabolic fecal, and endogenous contributions independently of DMI. All models included milk yield and its components in estimating MP required for lactation (MPl) and calf birth weight and some form of an empirical, exponential equation to compute MP for pregnancy (MPp). The MP required for growth (MPg) varied tremendously among the original models and their derivative works mainly due to the differences in computing growth pattern and the composition of the gain. The calculation of MCP differs among models; some rely on the total digestible nutrient (TDN; e.g., NRC, CNCPS level 1) intake to estimate MCP, while others use fermentable organic matter (FOM; e.g., INRA, DVE/OEB), fermentable carbohydrate (e.g., CNCPS level 2, NorFor), or metabolizable energy (ME; e.g., ARC, CSIRO, Rostock). Most models acknowledged the importance of ruminal recycled N, but not all accounted for it. Our Monte Carlo simulation indicated the prediction of most models for required MPl overlapped, confirming uniformity among models when predicting requirements for lactating animals, but a large variation in required MPg for growing animals exists.

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Exploring dietary quality characteristics related to milk yield of crossbred cows in Ethiopian smallholder farms: a clustering and multivariate analysis approach

Alemayehu

Tesfay

Fievez

2022

Trop Anim Health Prod

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The study, conducted on 70 smallholder dairy farms in Northern Ethiopia, aimed to evaluate whether variation in milk yield (in early and mid-lactation) of multiparous Holstein-Friesian crossbred cows is related to diet composition and quality. At early stage (1-120 days in milk (DIM)), a total of 70 dairy farms were used, while at mid-lactation (121-240 DIM), 54 dairy farms continued to be part of the study. K-means clustering was applied to group the cows based on energy-corrected milk yield (ECMY) into three milk production farm clusters (MPFC): Low MPFC (5.7-9.3 L/day), medium MPFC (9.4-12.8 L/ day), and high MPFC (12.9-17.6 L/day). The dry matter intake (DMI) of cows during early lactation for high MPFC and low MPFC was 14.1 and 11.2 kg/day, respectively. The dietary proportion of crop residues in diets offered to crossbred cows tended to be lower in the high MPFC during early as well as in mid-lactation. Cows from the high MPFC consumed diets with higher (rumen degradable) protein levels both in early and in mid-lactation, while dietary fiber fractions and in vitro dry matter digestibility (IVDMD) only differed in early lactation. Multiple regression models indicated that DMI (kg/day) in combination with either neutral detergent fiber, crude protein, or IVDMD (g/kg DM) explained about 25% of the variation in daily ECMY expressed relative to body weight (mL/kg). Hence, higher milk production is linked to both increased DMI and better quality of diets.

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Relationships between Body Measurements, Body Weight, and Productivity in Holstein Dairy Cows

Cited by 69 publications

References 19 publications

Body condition score and live-weight effects on milk production in Irish Holstein-Friesian dairy cows

Body condition score and live-weight effects on milk production in Irish Holstein-Friesian dairy cows

Models of protein and amino acid requirements for cattle

Exploring dietary quality characteristics related to milk yield of crossbred cows in Ethiopian smallholder farms: a clustering and multivariate analysis approach

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