scholarly journals 127 Predicting Dry Matter Intake of Gestating and Lactating Beef Cows

2020 ◽  
Vol 98 (Supplement_2) ◽  
pp. 58-58
Author(s):  
Megan A Gross ◽  
Claire Andresen ◽  
Amanda Holder ◽  
Alexi Moehlenpah ◽  
Carla Goad ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Milk Yield ◽  
Feed Intake ◽  
Beef Cows ◽  
Multiple Regression Equation ◽  
Validation Data ◽  
Data Set ◽  
Lactating Cows ◽  
Downward Bias ◽  
Protein Supply

Abstract In 1996, the NASEM beef cattle committee developed and published an equation to estimate cow feed intake using results from studies conducted or published between 1979 and 1993 (Nutrient Requirements of Beef Cattle). The same equation was recommended for use in the most recent version of this publication (2016). The equation is sensitive to cow weight, diet digestibility and milk yield. Our objective was to validate the accuracy of this equation using more recent published and unpublished data. Criteria for inclusion in the validation data set included projects conducted or published within the last ten years, direct measurement of forage intake, adequate protein supply, and pen feeding (no tie stall or metabolism crate data). The validation data set included 29 treatment means for gestating cows and 26 treatment means for lactating cows. Means for the gestating cow data set was 11.4 ± 1.9 kg DMI, 599 ± 77 kg BW, 1.24 ± 0.14 Mcal/kg NEm per kg of feed and lactating cow data set was 14.5 ± 2.0 kg DMI, 532 ± 116.3 kg BW, and 1.26 ± 0.24 Mcal NEm per kg feed, respectively. Non intercept models were used to determine equation accuracy in predicting validation data set DMI. The slope for linear bias in the NASEM gestation equation did not differ from 1 (P = 0.07) with a 3.5% positive bias. However, when the NASEM equation was used to predict DMI in lactating cows, the slope for linear bias significantly differed from 1 (P < 0.001) with a downward bias of 13.7%. Therefore, a new multiple regression equation was developed from the validation data set: DMI= (-4.336 + (0.086427 (BW^.75) + 0.3 (Milk yield)+6.005785(NEm)), (R-squared=0.84). The NASEM equation for gestating beef cows was reasonably accurate while the lactation equation underestimated feed intake.

Genetic parameters for feed intake and efficiency in lactating beef cows

1996 ◽  
Vol 76 (1) ◽  
pp. 81-87 ◽  
Author(s):  
L. Q. Fan ◽  
J. W. Wilton ◽  
P. E. Colucci
Keyword(s):  
Milk Yield ◽  
Feed Intake ◽  
Feed Efficiency ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
National Research Council ◽  
Beef Cows ◽  
Feed Consumption ◽  
Research Centre ◽  
Total Data

Genetic parameters of feed intake and efficiency and production traits for lactating beef cows were estimated from data collected from 1980 to 1988 at the Elora Beef Research Centre, Guelph, Ontario. Estimates were obtained using restricted maximum likelihood (REML) with an individual animal model with year–season–treatment, sex of calf, parity, breeding system, covariate daily change of backfat depth and direct genetic and permanent environmental effects. The data included 1174 observations, 511 cows, 369 dam–maternal grand dam pairs and 245 sires of cows. Feed efficiency for milk was calculated as milk yield relative to energy consumed for milk and maintenance and residual feed consumption as estimated energy intake minus energy requirements as estimated by the National Research Council. Heritabilities for Herefords alone and total data, respectively, were estimated to be 0.02 and 0.11 for cow's daily ME intake (MEI), 0.26 and 0.26 for daily milk yield (DMY), 0.45 and 0.33 for milk fat percentage (MFP), 0.29 and 0.40 for metabolic body weight (MBW), 0.21 and 0.10 for calf weaning weight as a proportion of cow weight at weaning (PPW), 0.18 and 0.11 for feed efficiency for milk (FE), and 0.23 and 0.03 for residual feed consumption (RFC). Genetic correlations of output (DMY) and input (MEI) were 0.31 for Hereford and 0.75 for the total data. Genetic correlations of RFC with both output (DMY) and input (MEI) were low. Genetically, PPW was positively associated with FE and DMY and negatively associated with MBW. Key words: Genetic parameters, feed efficiency, lactation, beef cow

scholarly journals FACTORS INFLUENCING MILK YIELD OF BEEF CATTLE

1971 ◽  
Vol 51 (3) ◽  
pp. 551-560 ◽  
Author(s):  
H. B. JEFFERY ◽  
R. T. BERG ◽  
R. T. HARDIN
Keyword(s):  
Weight Loss ◽  
Weight Gain ◽  
Birth Weight ◽  
Beef Cattle ◽  
Milk Yield ◽  
Average Daily Gain ◽  
Negative Relationship ◽  
Beef Cows ◽  
Positive Influence ◽  
Hybrid Breeding

The joint and separate effects of several cow-calf variables on milk yield were studied with 176 and 201 beef cows from the University of Alberta beef breeding herd for 1966 and 1967, respectively. The dams consisted of Hereford, Aberdeen Angus, Galloway and hybrid breeding. Independent variables considered were: breed, post-calving weight, winter weight loss, summer weight gain and age of dam, and weaning age, sex and birth weight of calf. Total variance of milk yield explained by all variables together was only 40 and 52% for 1966 and 1967, respectively. Breed and age differences of dam accounted for 82 and 87% of explained variance in milk yield respectively for 1966 and 1967. Holding cow age constant, post-calving weight of cow explained 0.0 and 8.5% additional variance in milk yield for 1966 and 1967, respectively. Summer weight gain of cow was negatively associated with milk yield. Winter weight loss of cow had little influence on milk yield. There appeared to be a negative relationship between early parturition and milk yield. The effect of calf sex on milk yield of dam was inconsistent; cows suckling male calves vs. female calves yielded more milk in 1966 but less in 1967. Birth weight of calf had a small positive influence on milk yield of dam. Association between milk yield of dam and preweaning performance of progeny was high. It appeared that the quickest way to improve milk yield in beef cattle would be the introduction of breeds noted for high milk yield and by indirect selection, through selection of dams with progeny that have high average daily gain to weaning.

PSX-A-29 Late-Breaking: Use of machine learning algorithms to predict residual feed intake value and classification groups in commercial beef cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 377-378
Author(s):  
Ghader Manafiazar ◽  
Mohammad Riazi ◽  
John A Basarab ◽  
Changxi Li ◽  
Paul Stothard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Beef Cattle ◽  
Feed Intake ◽  
Phenotypic Variation ◽  
Genomic Analysis ◽  
Residual Feed Intake ◽  
Machine Learning Algorithms ◽  
Genomic Information ◽  
Data Set ◽  
Better Than

Abstract The objective of this study was to explore the potential of Machine Learning (ML) algorithms to predict residual feed intake (RFI) classification group (high or low RFI) and individual RFI using performance records and genomic information. A total of 4145 animals from research and commercial herds with RFI performance records were included in the study from which 3899 cattle had genomic information (genotyped using Illumina Bovine 50k SNP BeadChip). Different libraries based on R and Python including Lazy Predict, Scikit-learn, PyCaret, and H2O Flow were used to test various ML models. Genomic information was subjected to quality control by removing SNPs with an allele frequency less than 0.05 or with a call rate lower than 0.95. A total of 42,689 SNPs remained for further analysis and accounted for 34% of phenotypic variation (heritability of 0.34±0.07) in RFI. Different numbers of SNPs were selected based on their contribution to phenotypic variation (500 SNPs, 1K, 5K, 10K, and 15K) then were included in the ML models. The GLM Stacked Ensemble model with 15k SNPs performed better than the other models to predict RFI classification group (R2 = 0.54). Regardless of the number of SNPs included in the model, GLM Stacked Ensemble performed better than other models to predict individual RFI. This model’s performance improved with increasing SNPs (MAE=0.39 for 500 SNPs; 0.31 for 15k SNPs). In the test data set, an increasing number of SNPs did not change the performance of the model and had a MAE of 0.39). The results demonstrate the potential for ML to improve predictions for feed efficiency compare to genomic analysis in beef cattle without measuring feed intake.

scholarly journals Milk yield and composition in first-cross and backcross beef cows

1994 ◽  
Vol 74 (2) ◽  
pp. 209-216
Author(s):  
R. M. McKay ◽  
G. W. Rahnefeld ◽  
G. M. Weiss ◽  
H. T. Fredeen ◽  
J. A. Newman ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Key Words ◽  
Milk Yield ◽  
Beef Cows ◽  
Milk Composition ◽  
Weight Changes ◽  
Pasture Management ◽  
Semi Arid

Milk yield and composition from three distinct milkings (spring, August, and fall) were evaluated on first-cross and backcross cows maintained under two contrasting environments. The dam crosses at Brandon (semi-intensive cultivated pasture management) were HA, SN, CN, ACA, CCA, ASA, SSA, HCH, CCH, HSH, SSH, NCN, CCN, NSN, and SSN with H = Hereford, A = Angus, N = Shorthorn, C = Charolais, S = Simmental and a SSA cross was 3/4 Simmental-1/4 Angus. At Manyberries (semi-arid and short grass rangeland) the dam crosses were HA, SN, ASA, SSA, HSH SSH, NSN, and SSN. All cows were bred to Limousin bulls and milkings took place from 1981 to 1985, inclusive. Definitive differences among the backcrosses and between the backcrosses and the F1 crosses were not present. Possible genotupe × environment interactins were observed for milk yield and composition. Milk yield and composition were affected by age of cow and influenced cow and calf weight changes. Key words: Beef cattle, crossbreeding, backcrosses, milk yield, milk composition

Purine derivatives in urine and plasma of lactating cows given different levels of feed intake

1994 ◽  
Vol 1994 ◽  
pp. 105-105 ◽  
Author(s):  
Z. M. Daniels ◽  
X.B. Chen ◽  
D.J. Kyle ◽  
K. Sinclair ◽  
E.R. Ørskov
Keyword(s):  
Beef Cows ◽  
Molar Ratio ◽  
Purine Derivatives ◽  
Fresh Weight ◽  
Spot Urine ◽  
Lactating Cows ◽  
Before And After ◽  
Beet Pulp ◽  
Protein Supply ◽  
Relationship Of

It has been demonstrated in previous work (Chen et al 1993) that the molar ratio of purine derivatives (PD) to creatinine (C) in spot urine samples can be used as an index of microbial protein supply in sheep. This study was to examine the feasibility of using this approach in cattle.Twelve lactating beef cows were allocated into three groups and each group was fed one of the following levels of restricted feeding: (1) 13 kg fresh weight (FW)/d (high), (2) 10 kg FW/d (medium); (3) 7 kg FW/d(low), given in two equal meals. The diet was pelleted and contained (on fresh weight basis): 0.40 sodium hydroxide treated straw, 0.24 barley, 0.10 soya, 0.25 molassed sugar beet pulp and 0.01 urea. Daily urinary excretion of PD was measured for a week. Two-hourly collections of urine (regarded as ‘spot urine’) were also made over the same period for measurement of PD/C molar ratio. Plasma PD concentration was measured in three jugular blood samples taken on the last day of urine collection. Milk yield was indirectly measured by calf weights before and after suckling. The results were analysed to examine the variability of PD/C in spot samples and the relationship of the spot PD/C measurement with the daily PD output.

Prediction of the Growth and Composition of Beef Cattle

1984 ◽  
Vol 1984 ◽  
pp. 49-49
Author(s):  
B.L. Baldwin ◽  
J.W. Oltjen ◽  
A.C. Bywater ◽  
D.J. Thomson
Keyword(s):  
Body Weight ◽  
Beef Cattle ◽  
Body Fat ◽  
Feed Intake ◽  
Feed Additives ◽  
Metabolizable Energy ◽  
Synthesis Rate ◽  
Protein Synthesis Rate ◽  
Net Energy ◽  
Data Set

A dynamic model of beef cattle growth and composition was developed based upon concepts validated in detailed modelling analyses of the growth of individual organs. Concepts that cell number (DNA) and cell size are primary determinants of organ (animal) size are included. Initial body weight (BW) and composition (percentage body fat), mature body weight, feed intake and feed net energy values for maintenance (NEM) and growth (NEG) are inputs to the model. Alternative inputs descriptive of feed can be metabolizable energy (ME) concentration and efficiencies of ME use for maintenance and fattening. Current body DNA and protein content and ME intake are primary determinants of rates of protein accretion. Net synthesis of body fat is calculated after accounting costs of maintenance and protein synthesis. Rate constants defining DNA synthesis, and protein degradation and synthesis, and effects of feed additives and implants upon these were deduced using data from comparative slaughter (energy balance) trials with over 1,000 cattle (mainly Hereford) fed mixed diets. No systematic errors in prediction of body weight or composition due to mature body weight, ration or feed intake (FI) were evident within this data set.

scholarly journals A mathematical nutrition model adequately predicts beef and dairy cow intake and biological efficiency

2021 ◽  
Author(s):  
Phillip A Lancaster ◽  
Michael E Davis ◽  
Luis O Tedeschi ◽  
Jack J Rutledge ◽  
Larry V Cundiff
Keyword(s):  
Milk Yield ◽  
Feed Intake ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Beef Cows ◽  
Biological Efficiency ◽  
Production Cycle ◽  
P Value ◽  
Energy Yield ◽  
Beef Cow

Abstract The beef cow-calf sector accounts for 70% of feed consumed and greenhouse gases emitted for the beef industry, but there is no straightforward method to measure biological efficiency in grazing conditions. The objective of this study was to evaluate a mathematical nutrition model to estimate the feed intake and biological efficiency of mature beef cows. Data from dams (N = 160) and their 2 nd and 3 rd progeny (312 pairs) were collected from 1953 through 1980. Individual feed intake was measured at 28-d intervals year-round for dams and during 240-d lactation for progeny. Body weights of progeny were measured at 28-d intervals from birth to weaning, and of dams at parturition and weaning each production cycle. Milk yield of dams was measured at 14-d intervals. Dam ME intake (DMEI) and milk energy yield (MEL) of each cow was predicted using the Cattle Value Discovery System beef cow (CVDSbc) model for each parity. Biological efficiency (Mcal/kg) was computed as the ratio of observed or predicted DMEI to observed calf weaning weight (PWW). Pearson correlation coefficients were computed using corr.test function and model evaluation was performed using the epiR function in R software. Average (SD) dam weight, PWW, DMEI, and observed MEL were 527 (86) kg, 291 (47) kg, 9584 (2701) Mcal/production cycle, and 1029 (529) Mcal, respectively. Observed and predicted DMEI (r = 0.93 and 0.91), and observed and predicted MEL (r = 0.58 and 0.59) were positively correlated for progeny 2 and 3, respectively. The CVDS beef cow model under-predicted DMEI (mean bias = 1120 ± 76 Mcal, 11.7% of observed value) and MEL (mean bias = 30 ± 25 Mcal, 2.9% of observed value). Observed and predicted progeny feed intake were not correlated (r = 0.01, P-value = 0.79). Observed and predicted biological efficiency were positively correlated (r = 0.80 and 0.80, P-value ≤ 0.05) for parity 2 and 3, respectively, and the CVDSbc model under-predicted biological efficiency by 11% (mean bias = 3.59 ± 0.25 Mcal/kg). The CVDSbc provides reasonable predictions of feed intake and biological efficiency of mature beef cows, but further refinement of the relationship between calf feed intake and milk yield is recommended to improve predictions. Mathematical nutrition models can assist in the discovery of the biological efficiency of mature beef cows.

Effect of Bypass Protein on Milk Production and Economic of Lactating Crossbred Cows

2020 ◽  
Vol 7 (01) ◽  
Author(s):  
RATNESH K CHOUDHARY ◽  
A Saran ROY ◽  
N K SINGH3 ◽  
SANJAY KUMAR ◽  
RAUSHAN K SINGH
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Crossbred Cows ◽  
Lactating Cows ◽  
Feed Cost ◽  
Total Diet ◽  
T1 And T2 ◽  
Daily Milk Yield ◽  
On Farm ◽  
Daily Milk

An On-Farm Trial was conducted on 24 lactating crossbred cows for assessment of feeding formaldehyde treated mustard cake (bypass protein) on milk production and economic analysis of lactating cow. Cows were divided into three groups having 8 cows each, treatments were farmers’ practice (FP); (Control): The lactating animals under this group were fedas per the feeding schedule of the farmers (5 kg. dry roughage as rice straw + 6 hrs grazing as local grass and 4 kg. commercial concentrates), T1: The lactating animals under this group were fed as per farmers practicewith 12% mustard cake of total diet was provided to the cow by replacing the same amount of commercial concentrates andT2: The lactating animals under this group were fed as per farmers practicewith 12% formaldehyde treated mustard cake of total diet was provided to the cow by replacing same amount of commercial concentrates. The average daily milk yield of lactating cows under FP, T1 and T2 was 8.58, 8.82 and 9.85 kg per cow, respectively. Differences between FP and T2 were significant. The daily increase in milk yield was 1.27 kg and 1.03 kg in cows fed T2diet over the cows fed FP and T1diet, respectively. The B: C ratios for FP, T1 and T2 groups were 2.6, 3.0 and 3.3, respectively. The feed cost reduced in T2 group by Rs. 8.64 and increased milk production by 1.27 kg in respect to FP group.

Sign in / Sign up

Export Citation Format

Share Document