The influence of beef cow weaning weight ratio and cow size on feed intake behavior, milk production, and milk composition

Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis)

The objective of the present study was to evaluate the relationship between body weight (BW) and hip width (HW) in dairy buffaloes (Bubalus bubalis). HW was measured in 215 Murrah buffaloes with a BW of 341 ± 161.6 kg, aged between three months and five years, and raised in southeastern Mexico. Linear and non-linear regressions were used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). Additionally, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was assessed based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The relationship between BW and HW showed a high correlation coefficient (r = 0.96, P < 0.001). The chosen fitted model to predict BW was: -176.33 (± 40.83***) + 8.74 (± 1.79***) × HW + 0.04 (± 0.01*) × HW2, because it presented the lowest MSE, RMSE, and AIC values, which were 1228.64, 35.05 and 1532.41, respectively. Therefore, with reasonable accuracy, the quadratic model using hip width may be suitable for predicting body weight in buffaloes.

Predicting feed intake in confined beef cows

Six existing equations (three for nonlactating and three for lactating; NRC, 1987, Predicting feed intake of food-producing animals. Washington, DC: The National Academies Press, National Academy of Science; doi: 10.17226/950; NRC, 1996, Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791; Hibberd and Thrift, 1992. Supplementation of forage-based diets. J. Anim. Sci. 70:181. [Abstr]) were evaluated for predicting feed intake in beef cows. Each of the previously published equations are sensitive to cow-shrunk BW and feed energy concentration. Adjustments in feed intake prediction are provided for level of milk yield in NRC (1987. Predicting feed intake of food-producing animals. Washington, DC: The National Academies Press, National Academy of Science; doi: 10.17226/950) and NRC (1996 Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) equations. The equation published in 1996 used data generated between 1979 and 1993. Our objectives were to validate the accuracy of the published equations using more recent data and to propose alternative prediction models. Criteria for inclusion in the evaluation dataset included projects conducted or published since 2002, direct measurement of feed intake, adequate protein supply, and pen feeding (no metabolism crate data). After removing outliers, the dataset included 53 treatment means for nonlactating cows and 32 treatment means for lactating cows. Means for the nonlactating dataset were dry matter intake (DMI) = 13.2 ± 2.9 kg/d, shrunk body weight (SBW) = 578 ± 83.9 kg, body condition score = 5.7 ± 0.73, and Mcal net energy for maintenance (NEm)/kg of feed = 1.27 ± 0.15 Mcal/kg. Means for the lactating dataset were DMI = 14.6 ± 2.24 kg/d, SBW = 503 ± 73.4 kg, body condition score = 4.7 ± 0.58, and Mcal NEm/kg feed = 1.22 ± 0.16. Simple linear regression was used to determine slope, intercept, and bias when observed DMI (y) was regressed against predicted DMI (x). The NRC (1996. Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) nonlactating equation underestimated feed intake in diets moderate to high in energy density with intercept differing from 0 and slope differing from one (P ≤ 0.01). Average deviation from observed values was 2.4 kg/d. Similarly, when the NRC (1996. Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) equation was used to predict DMI in lactating cows, the slope differed from one (P < 0.01) with average deviation from observed values of 3.0 kg/d. New models were developed by pooling the two datasets and including a categorical variable for stage of production (0 = nonlactating and 1 = lactating). Continuous variables included study-average SBW0.75 and diet NEm, Mcal/kg. The best-fit empirical model accounted for 68% of the variation in daily feed intake with standard error of the estimate Sy root mean squared error = 1.31. The proposed equation needs to be validated with independent data.

Water and forage intake, diet digestibility, and blood parameters of beef cows and heifers consuming water with varying concentrations of total dissolved salts

The objective of this study was to investigate the effects of water quality on water intake (WI), forage intake, diet digestibility, and blood constituents in beef cows and growing beef heifers. This was a replicated 5 × 5 Latin square with five drinking water treatments within each square: 1) fresh water (Control); 2) brackish water (100 BRW treatment) with approximately 6,000 mg/kg total dissolved solids (TDS); 3) same TDS level as 100 BRW achieved by addition of NaCl to fresh water (100 SLW); 4) 50% brackish water and 50% fresh water to achieve approximately 3,000 mg/kg TDS (50 BRW); and 5) same TDS level as 50 BRW achieved by addition of NaCl to fresh water (50 SLW). Each of the five 21-d periods consisted of 14 d of adaptation and 5 d of data collection. Animals were housed individually and fed mixed alfalfa (Medicago sativa) grass hay cubes. Feed and WI were recorded daily. Data were analyzed with animal as the experimental unit. Age, treatment, and age × treatment were fixed effects, and animal ID within age was the random variable for intake, digestibility, and blood parameter data. Water and feed intake were greater than expected, regardless of age or water treatment. No treatment × age interactions were identified for WI (P = 0.71), WI expressed as g/kg body weight (BW; P = 0.70), or dry matter intake (DMI; P = 0.21). However, there was an age × treatment tendency for DMI when scaled to BW (P = 0.09) in cows consuming 100 BRW compared with fresh water. No differences were found for the other three treatments. Heifers provided 50 SLW water consumed less (P < 0.05) feed (g/kg BW) compared with heifers provided fresh water and 100 BRW. No differences (P > 0.05) in water, DMI, feed intake, or diet digestibility were found due to water quality treatment. In conclusion, under these conditions, neither absolute WI, absolute DMI, nor diet digestibility was influenced by the natural brackish or saline water used in this experiment. These results suggest that further research is necessary to determine thresholds for TDS or salinity concentration resulting in reduced water and/or feed intake and diet digestibility.

Cow efficiency: modeling the biological and economic output of a Michigan beef herd

In recent decades, beef cattle producers have selected cattle for biological traits (i.e., improved growth) to maximize revenue, leading to an increase in average cow body size. However, matching cow size to the production environment would allow producers to maximize productivity and economic returns per unit of land. This may help meet the goals of sustainable intensification, but environmental complexity and varying cow-calf production systems dictates a regional approach. The objective of this experiment was to examine the biological efficiency and economic returns of a Northern Michigan cow-calf system. We hypothesized that biological efficiency and economic returns would decrease with increasing cow body size. Data were collected from a Red Angus cow herd located at the Lake City AgBio Research Center in Lake City, MI from 2011 to 2018 on cow age, weight, and body condition score at weaning, and subsequent 205 d adjusted calf weaning weight (WW), sex, and yearling weight. Biological efficiency was defined as WW as a percentage of cow body weight (DBW). Enterprise budgeting techniques were used to calculate expected net returns from 2011 to 2018 after classifying cows into 11 BW tiers at 22.67 kg intervals beginning at 430.83 kg. Forward-looking net present value (NPV) was calculated using the same tier system, for a 10-yr production cycle with the baseline being a 200 d grazing season. Weaning weight increased with increasing DBW (P < 0.01), but the percentage of cow body weight weaned was reduced by −38.58 × Ln(DBW) (P < 0.01). This led to cows weaning 26.38 kg/ha more with every 100 kg drop in DBW. Expected net returns from 2011 to 2018 did not differ by DBW tier on a per cow basis but did on a per ha basis with a decrease in $10.27/ha with each increase in DBW tier (P < 0.01). Net present value was maximized in the baseline scenario at 453.51 kg DBW and decreased in value as DBW increased. These results suggest that for a Northern Midwestern cow-calf herd, comparatively lighter cows provide a higher economic value on a land basis.

The impact of cow size on cow-calf and postweaning progeny performance in the Nebraska Sandhills

Optimizing beef production system efficiency requires an understanding of genetic potential suitable for a given production environment. Therefore, the objective of this retrospective analysis was to determine the influence of cow body weight (BW) adjusted to a common body condition score (BCS) of 5 at weaning-influenced cow-calf performance and postweaning steer and heifer progeny performance. Data were collected at the Gudmundsen Sandhills Laboratory, Whitman, NE, on crossbred, mature cows (n = 1,607) from 2005 to 2017. Cow BCS at calving, prebreeding, and weaning were positively associated (P < 0.01) with greater cow BW. Increasing cow BW was positively associated (P < 0.01) with the percentage of cows that conceived during a 45-d breeding season. For every additional 100-kg increase in cow BW, calf BW increased (P < 0.01) at birth by 2.70 kg and adjusted 205-d weaning BW by 14.76 kg. Calf preweaning average daily gain (ADG) increased (P < 0.01) 0.06 kg/d for every additional 100-kg increase in cow BW. Heifer progeny BW increased (P < 0.01) postweaning with every additional 100-kg increase in dam BW. Dam BW did not influence (P ≥ 0.11) heifer puberty status prior to breeding, overall pregnancy rates, or the percentage of heifers calving in the first 21 d of the calving season. Steer initial feedlot BW increased by 7.20 kg, reimplant BW increased by 10.47 kg, and final BW increased by 10.29 kg (P ≤ 0.01) for every additional 100-kg increase in dam BW. However, steer feedlot ADG was not influenced (P > 0.67) by dam BW. Hot carcass weights of steers were increased (P = 0.01) by 6.48 kg with every additional 100-kg increase in cow BW. In a hypothetical model using the regression coefficients from this study, regardless of pricing method, cow-calf producers maximize the highest amount of profit by selecting smaller cows. Overall, larger-sized cows within this herd and production system of the current study had increased reproductive performance and offspring BW; however, total production output and economic returns would be potentially greater when utilizing smaller-sized cows.