Access the full text.
Sign up today, get DeepDyve free for 14 days.
Translational Animal Science, 2022, 6, 1–9 https://doi.org/10.1093/tas/txac120 Advance access publication 25 August 2022 Ruminant Nutrition Retained energy in lactating beef cows; effects on maintenance energy requirement and voluntary feed intake † † † † Emma A. Briggs , Amanda L. Holder , Megan A. Gross , Alexandra N. Moehlenpah , ‡ †, †, || †,1, Jared D. Taylor , R.R. Reuter , Andrew P . Foote , Carla L. Goad , and David L. Lalman Department of Animal and Food Sciences, Oklahoma State University, Stillwater, OK 74078, USA Department of Veterinary Pathobiology, Oklahoma State University, Stillwater, OK 74078, USA || Department of Statistics, Oklahoma State University, Stillwater, OK 74078, USA Corresponding author: firstname.lastname@example.org ABSTRACT The objectives of these experiments were to determine the relationship between maintenance requirements and energy partitioned to maternal tissue or milk production in limit-fed Angus cows and to determine the relationship between retained energy during the lactation period to dry- period voluntary forage intake (VDMI). Twenty-four mature fall-calving Angus cows were used in a 79-d study during late lactation to establish daily metabolizable energy required for maintenance (ME ). Cows were individually fed daily a mixed diet (2.62 Mcal MEl/kg, 18.2% crude pro- tein) to meet energy and protein requirements of 505 kg beef cows producing 8.2 kg milk daily. If cow BW changed by ±9 kg from initial BW, daily feed intake was adjusted to slow BW loss or reduce BW gain. Milk yield and composition were determined on 3 occasions throughout the study. Maintenance was computed as metabolizable energy intake minus retained energy assigned to average daily maternal tissue energy change, average daily milk energy yield, and average daily energy required for pregnancy. After calves were weaned, cows were fed a low-quality grass hay diet (8.2% crude protein, 65% NDF) and VDMI was measured for 21 days. Lactation maintenance energy was 83% the default value recommended by NASEM (2016. Nutrient Requirements of Beef Cattle: Eighth Revised Edition.) for lactating Angus cows. Increasing lactation- period retained energy (decreasing BW loss and increasing milk energy yield) was associated with lower maintenance energy requirements (P < 0.01; R = 0.92). Increased residual daily gain during lactation was associated with lower lactation maintenance energy requirements (P = 0.05; R = 0.17). Post-weaning VDMI was not related to late-lactation milk energy production, although sensitive to lactation period BCS and BW loss. These results contradict previous reports, suggesting that maintenance requirements increase with increasing milk yield. Key words: efficiency, maintenance, milk yield, milk composition, residual gain INTRODUCTION (Capper, 2011; Kuehn and Thallman, 2016). At the same time, some breeds have aggressively selected for increased calf The cow/calf sector uses 74% of the total feed energy required weaning weight through milk expected progeny differences to produce one pound of carcass weight (Rotz et al., 2019). (Kuehn and Thallman, 2016). Numerous reports suggest a Furthermore, the cow/calf sector accounts for 77% to 81% of positive relationship between maintenance energy require- enteric CH emissions per unit of carcass weight (Baber et al., ment and genetic capacity for milk yield, mature size, and 2018; Rotz et al., 2019). Therefore, improvements in energy growth (Ferrell and Jenkins, 1984; Ferrell and Jenkins, 1987; utilization efficiency by the cow herd would result in both Solis et al., 1988; Laurenz et al., 1991). However, these reduced cost of beef production and carbon footprint. studies were structured to determine differences in mainte- The maintenance requirement for energy is defined as the nance requirements among breeds and breed crosses rather energy needed to achieve no net loss or gain of energy retained than within a breed, i.e. it is difficult to separate potential in the tissues of the animal’s body (NASEM, 2016). For per- effects of breed vs. milk yield and other traits. In a recent spective, average annual energy requirement for 550-kg beef study with sheep (Yang et al., 2020), authors suggested that cows producing 8 kg of milk at peak lactation is about 4,875 long-term selection for increased productivity may be respon- Mcal NE , with 73% partitioned to maintenance, 10% to sible for a 40% increase in net energy required for mainte- pregnancy, and 17% to lactation (NASEM, 2016). Similarly, nance compared with recommendations of AFRC (1993), Ferrell and Jenkins (1987) reported 70% to 75% of total which were developed using data that is now over 40 years annual energy expenditure is used for maintenance. These old. In the current energy system for beef cows (NASEM, authors also noted that variation in maintenance requirement 2016), productivity (or performance) can be quantified as en - is greater than variation in requirements for growth, gesta- ergy retained in the form of body tissue, milk, and conceptus tion, or lactation. tissue. The objective of this experiment was to determine the Over the last several decades, most beef breeds have been relationship between maintenance requirements and energy selected for increased growth, carcass weight and mature size Received April 5, 2022 Accepted August 23, 2022. © The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 2 Briggs et al. Table 1. Ingredient and chemical composition of the diets in experiments partitioned to maternal tissue or milk production in limit-fed 1 and 2 Angus cows using a long-term feeding approach. A second objective was to determine the relationship between retained Item Experiment 1 Experiment 2 energy during the lactation period to dry period voluntary forage intake. Ingredient, % DM basis Bermudagrass hay 48.86 Native grass hay — 94.5 MATERIALS AND METHODS Corn distiller’s grains 25.45 All procedures and protocols were approved by Oklahoma Rolled corn 16.55 State University Animal Care and Use Committee (#AG-17- 1 2 Liquid supplement 3.98 5.5 26). Experiments were conducted at the Range Cow Research Center near Stillwater, OK. Twenty-four fall-calving cows and Soybean meal, 44% CP 2.39 their calves were used in two consecutive experiments to eval- Limestone 2.20 uate the relationship of energy partitioned to milk produc- Salt 0.56 tion and maternal tissue to maintenance energy requirements. Chemical composition, DM basis From January 7 (day –18) to March 26 (79 days), cows CP , % 18.2 8.2 were individually fed a total-mixed ration (TMR) at a rate NDF , % 33.85 65.1 approximating each cow’s daily energy requirements ac- ADF , % 19.6 42.4 cording to NASEM (2016). Subsequently, milk energy yield, TDN , % 71.6 53.4 maternal tissue energy change, and energy used for preg- nancy were subtracted from daily metabolizable energy in- DE , Mcal/kg 3.20 2.36 take (MEI) to estimate maintenance requirements. Following ME , Mcal/kg 2.62 1.94 weaning, a voluntary feed intake trial was conducted from June 12 to July 19 to determine dams’ voluntary feed intake Liquid supplement (Quality Liquid Feeds, Dodgeville, WI) chemical composition, DM basis = 15% CP, 2.3% NaCl, 0.5% P, 0.9% Ca, 70,500 (experiment 2) during the dry (nonlactating) period. IU vitamin A/kg. All cows were managed as a contemporary group prior to 2 Liquid supplement chemical composition, DM basis = 42.1% CP, 2.75 the initiation of the restricted feeding experiment (experiment Mcal ME/kg, 2.5% NaCl, 0.84% P, 0.72 % CA, 66,000 IU vitamin A/kg. CP = crude protein, NDF = neutral detergent fiber, ADF = acid detergent 1). Cows calved during September and October 2018 while fiber. grazing native tallgrass prairie pastures. Dried distiller’s grains TDN = total digestible nutrients determined as DE/4.409 (NASEM, 2016). DE = digestible energy, computed using the summative equation (NRC with solubles were fed at the rate of 1.5 kg/d throughout the 2001) with modifications recommended by Weiss and Tebbe (2018). The calving season and the feeding rate was increased to 2.5 kg/d contribution of NDF to DE was determined using 48 h in vitro NDF through November and December. The 7-d Co-Synch protocol digestibility. ME = metabolizable energy, calculated as DE × 0.82. (Stein et al., 2015) was initiated on November 8, followed by timed artificial insemination performed 10 days later. Cows were then exposed to a fertile bull for an additional 50 days. At 0700 hours on January 7, bulls were removed, cows were individual feed intake units were located (Table 1). Feed was fed in the individual feeding facility for the first time, and placed in the creep area intake units at 0730 hours each day pairs were subsequently transferred to one of two pens. and if necessary, again at 1600 hours to ensure calves had ad libitum access to feed with a minimum of 10% daily orts. Experiment 1: lactation performance and Cows never had access to the creep feed area or its feed in- maintenance requirements take units. Cows and their calves were randomly assigned to one of The Beef Cattle Nutrient Requirements Model (BCNRM; the two pens (12 cows and 12 calves each). Each pen was NASEM, 2016) was used to estimate the daily TMR allow- 32.9 × 32.9 m, dirt-surfaced, and was equipped with: fence-line ance for each cow that would provide the amount of feed feed bunks, a windbreak on both north and south perimeters, energy required to maintain body weight (BW) and support an automatic livestock watering system (MiraFount A3465, 8.2 kg daily milk production (Andresen et al., 2020) and preg- 0.75 Miraco Automatic Livestock Waterers, Grinnell, Iowa), and nancy during late lactation (20.8 Mcal ME/d or 71 g/kg BW an 80-m creep feeding area equipped with two individual TMR, DM basis). For the first 18 days, cows were adapted feed intake measurement units (C-Lock Inc, Rapid City, South to the TMR, limit-feeding strategy, and the feeding facility Dakota). Pens were stocked to provide approximately 90 m in the following manner. Starting on day –18, cows were fed surface area per cow–calf pair. 35% TMR and 65% chopped bermudagrass hay at the rate 0.75 Cows were fed a TMR (Table 1) at 0700 hours daily in a of 95 g/kg initial BW for 4 days. Subsequently, the feeding 0.75 stall barn equipped with individual feeding stanchions. Before rate was reduced by 6 g/kg initial BW at 4-day intervals. feeding, calves were penned in the creep feeding area. Cows At the same time, dietary proportion of TMR was increased were then moved to the stall barn one pen at a time. The order and the hay was decreased by 16.25 percentage units at 4-day pens were brought into the stall barn was rotated daily to intervals. This resulted in cows being fed 100% TMR and 0.75 minimize any potential confounding effect of time of feeding. 71 g/kg BW on the morning of day –`2. Cows and calves Cows were loaded into the stalls individually and offered were weighed using a hydraulic squeeze chute equipped with TMR, allowing approximately 1 h to consume their ration. electronic load cells (Tru-Test HD5T; Datamars, Mineral They were then returned to their pen. At that time, creep area Wells, TX) and an electronic weigh scale indicator (Tru-Test gates were opened to allow calves access to the entire pen XR5000; Datamars). The experimental period began on the and to the cows. Calves were fed the same TMR as the ma- morning of January 25 (day 0) and continued for 61 consec- ture cows and had continual access to the creep area where utive days. Retained energy and feed intake in beef cows 3 Cow BW was recorded at 0700 hours prior to feeding on Milk yield was measured and milk samples were collected 10 days at approximate 6-day intervals, beginning on day 0 on days 5, 33, and 62 by the procedure described by Wiseman and continuing through day 61. Cows had ad libitum access et al. (2019). On the day before milking, calves were removed to water throughout the experiment. Because cow BW was from their dams at 1400 hours. Calves were not allowed ac- recorded at least 18 h after the previous day’s feeding event, cess to creep feed during this period. At 2000 hours, calves our BW data represent shrunk BW (NASEM, 2016). Cow were returned to their dams and were allowed to suckle until body condition score (BCS; 1 to 9, Wagner et al., 1988) was satiated. At the conclusion of the suckling period (2045 hours), recorded on approximately 14-day intervals at the same time calves were again removed from their dams. Milking began BW was recorded. Two experienced technicians recorded BCS, the next morning at 0500 hours allowing for an average 8.5-h and these two scores were averaged within animal for each separation. Cows were milked with a portable milk machine date. Daily feed allotment was adjusted by ≤0.45 kg DM if (Portable Vacuum Systems, Springville, UT). To determine milk an individual’s BW fluctuated by ≥9 kg above or below initial composition, a subsample was taken, preserved with 2-bromo- (day 0) shrunk BW (Cooper-Prado et al., 2014). Subsequent 2nitropropane-1,3-diol and shipped to the Heart of America adjustments (≤0.45 kg DM) were made if weight change con- Dairy Herd Improvement Association laboratory (Manhattan, tinued to increase or decrease ≥9 kg above or below initial KS). To adjust for differences in dam-calf separation time, rate study shrunk BW. of milk production (g/min) was determined by dividing milk Samples of TMR were collected weekly. Dry matter was yield (g) by separation time (min). The rate of production was determined by oven drying at 60 °C for 4 h. Dried samples then multiplied by 1,440 min to calculate 24-h milk yield (Yn). were ground through a Wiley Mill grinder (Model-4, Thomas Milk energy concentration was calculated as (NASEM, 2016) Scientific, Swedesboro, NJ, USA) using a 2-mm screen and later analyzed for concentrations of ash (combusted 6 h in E =(0.092 × MkFat)+(0.049 × MkSNF) − 0.0569 a muffle furnace at 500 °C), CP (N×6.25; CN628, LECO Corporation, St. Joseph, MI, USA), neutral detergent fiber where E is the energy content of milk (Mcal/kg), MkFat is (aNDF, Van Soest et al., 1991) and acid detergent fiber (ADF, milk fat content (%), and MkSNF is milk solids non-fat con- AOAC, 1990, #973.18) were analyzed using an ANKOM Delta tent (%). Daily net energy partitioned to milk (NE , Mcal/day) Automated Fiber Analyzer (Ankom Tech Corp, Fairport, NY, was calculated as USA). Neutral detergent fiber was assayed with alpha amylase and sodium sulfite. Both aNDF and ADF are expressed inclu- NE = Yn × E sive of residual ash. Fat content was determined utilizing the ether extract method (AOAC, 1990). The summative equa- The average of the three NE estimates were used to determine tion (NRC, 2001 with modifications recommended by Weiss daily net energy partitioned to milk production. and Tebbe, 2018) was used to determine digestible energy Net energy required for pregnancy (NE , Mcal/day) was (DE) by multiplying the digestible masses of CP, NDF, fat, calculated retrospectively using calf birth BW and calf birth and nonfiber carbohydrate by their enthalpies (5.6, 4.2, 9.4, date from the subsequent calving season as follows (NASEM, and 4.2, respectively; Weiss and Tebbe, 2018). The mass of di- 2016): gestible NDF was determined using 48-h in vitro digestibility (0.03233 × DP−0.0000275 × DP ) NE = CBW × (0.5855 − 0.0000996 × DP) × e /1, 000 (NRC, 2001). Feed consumed from days 0 through 61 was y multiplied by feed ME (Mcal/kg) to determine the total feed energy consumed during the experimental period. Linear or quadratic regression equations were calculated where CBW is calf birth BW, kg, and DP is days pregnant. for each cow using BW and BCS regressed over time (Ferrell Metabolizable energy required for pregnancy (ME , Mcal/d) and Jenkins, 1996) and these equations were used to deter- was converted to an ME basis using the fixed partial effi- mine initial (day 0) and final (day 61) BW and BCS. Initial and ciency of 0.13 (NASEM, 2016): final cow BW was adjusted to a non-pregnant basis retrospec - tively using subsequent calving season birth date and birth BW (NASEM, 2016). Cow BW, adjusted to a non-pregnant ME = NE /0.13 y y basis, was used to calculate average daily gain (ADG) and metabolic mid-point BW. Total retained energy (NE ) was obtained by summing en- Total body energy for each cow was computed retrospec- ergy partitioned to or produced by maternal tissue (NE ), NE , t l tively for days 0 and 61 using the methods described by and NE . Retained energy from maternal tissue gain or loss NASEM (2016). Briefly, equations first published by NRC and lactation were converted to an ME basis using the par- (1996, 2000) use BCS to compute the proportion of empty tial efficiency coefficient from the Garrett (1980) equation. BW that is fat and protein. Next, body protein and fat pro- Finally, maintenance energy requirement (ME , Mcal/d) was portion are multiplied by empty BW to determine total body estimated by subtracting retained energy pools (ME basis) fat and total body protein. Finally, total body fat (kg) and from MEI: total body protein (kg) are multiplied by their biological en- ergy value (9.4 and 5.7 Mcal/kg, respectively). Calculated ME = MEI − ME − ME − ME m t y total body energy for day 0 was subtracted from calculated total body energy for day 61 to determine body net energy Experiment 2: voluntary forage intake change (Mcal NEm). If BW loss occurred during the 61-day experimental phase, the loss in energy was multiplied by 0.8 Following the conclusion of experiment 1, cows and their to estimate Mcal NEm available for maintenance during mo- calves were turned out to pasture. On May 15, calves were bilization (NASEM, 2016). weaned, and cows were palpated to determine pregnancy 4 Briggs et al. Table 2. Summary statistics of production and feed intake traits for limit- status. The voluntary forage intake study (experiment 2) was fed Angus cows (N = 24) initiated on June 12 (day –21). Twenty-four gestating cows were assigned to similar dry lot pens as described for exper- Item Mean Min Max SD iment 1. Three pens were used, each equipped with two in- dividual feed intake units (C-Lock, Inc., Rapid City, South Avg DMI, kg/day 7.94 7.23 8.79 0.43 Dakota). The diet (8.2% CP, 1.94 Mcal ME/kg; DM basis) 0.75 Avg DMI, g/kg BW 74.8 71.1 78.7 2.04 is shown in Table 1 and consisted of 94.5% (DM basis) 0.75 Day 0 MEI, kcal/kg BW 194.9 182.3 212.1 7.6 chopped native tall-grass prairie hay and 5.5% (DM basis) 0.75 Day 61 MEI, kcal/kg BW 196.4 180.7 217.4 9.4 sugarcane molasses-based liquid supplement shown in Table 0.75 Avg MEI, kcal/kg BW 196.5 189.6 203.7 3.9 1. The liquid supplement was sprayed onto the processed hay Day 0 BW, kg 506.5 426.1 562.7 35.8 and thoroughly mixed. Subsequently, 5% (as-fed basis) water was sprayed onto the diet and thoroughly mixed prior to Day 61 BW, kg 500.8 419.9 562.3 37.0 feeding. Cows were fed twice daily to maintain at least 10% BW change, kg -5.72 -31.0 16.8 11.9 daily orts in the feed intake units to ensure ad libitum access SADG, kg/day -0.09 -0.51 0.28 0.20 to feed. The intake units were stocked at 4 cows per feeder, BCS 4.9 3.9 6.0 0.47 i.e., 8 cows per pen. Weekly feed samples were collected and Avg days in milk 177.5 139 201 17.2 analyzed for chemical composition as previously described Milk yield, kg/day 8.4 6.9 13.2 1.23 for experiment 1. Cows were adapted to the diet and feeding 0.75 Milk yield, g/kg BW 79.3 63.3 118.3 12.1 system for the first 21 days and daily feed intake was recorded Milk energy, Mcal/kg milk 0.70 0.58 0.82 0.06 for the following 21 days. Body weights were recorded at 0700 hours on days –21, Milk protein, % 2.95 2.39 3.62 0.30 0, 1, 20, and 21 using the same scale system described for Milk fat, % 3.61 1.23 5.40 0.72 experiment 1. Because cattle were provided access to feed on Milk solids-not-fat, % 8.75 5.4 9.47 0.30 an ad libitum basis prior to and throughout the experiment, 0.75 NE , kcal/kg BW 54.9 44.9 73.6 7.6 all weights were adjusted to a shrunk BW basis (BW × 0.96; 0.75 NE , kcal/kg BW -5.0 -19.3 9.4 8.6 NASEM, 2016). For each BW recorded, non-pregnant BW 0.75 NE , kcal/kg BW 1.55 0 3.0 0.86 was calculated by subtracting the estimated BW of the con- 0.75 NE , kcal/kg BW 51.3 34.2 69.0 9.9 ceptus as described for experiment 1 (NASEM, 2016). Fetal 0.75 ME , kcal ME/kg BW 118.0 91.5 148.2 15.1 age was determined retrospectively based on calving date the following year. Non-pregnant BW was then used to determine MEI = metabolizable energy intake; BW = study-average cow body weight ADG and metabolic mid-point BW. adjusted for pregnancy; BCS = study-average body condition score; SADG = shrunk average daily gain; NE = net energy for lactation; NE = net l t Statistical Analyses energy provided by (weight loss) or partitioned to (weight gain) maternal tissue; NE = net energy for pregnancy; NE = total retained energy; ME = y r m Pearson correlation coefficients were calculated (SAS 9.4; metabolizable energy for maintenance. SAS Inst. Inc., Cary, NC) to determine the relationships be- tween late-lactation performance characteristics, energy (2020) reported similar late-lactation milk yield in limit-fed partitioning, and subsequent nonlactating voluntary dry mature cows from this herd, although in that study, greater matter intake (VDMI). Dependent variables used to compute milk fat concentration (3.8%) resulted in greater milk en- ME were investigated for multicollinearity using multiple ergy concentration (0.73 Mcal/kg). Considering cows in the linear regression and evaluating variance inflation factor, tol - current experiment had lower mean BCS, daily BW gain erance, and collinearity diagnostics (SAS 9.4; SAS Inst. Inc.). 0.75 and daily MEI/kg BW , lower milk energy concentration Forward stepwise linear regression was used to explore the in- is not surprising. After experiment 1 was completed, three fluence of each of the four independent variables used to com- cows were determined to be nonpregnant. Data from these pute ME . At each step, variables were chosen according to three cows remained in the data set with no adjustments for their contribution to the model’s coefficient of determination estimated weight change associated with fetal tissue and zero (R ). Residual average daily gain (RADG) was computed for energy partitioned to NE (pregnancy). Mean estimated daily each cow as the residual from mixed model regression (SAS NE in pregnant cows was minimal, averaging 3.0% of total 9.4; SAS Inst. Inc.) of shrunk BW average daily gain (SADG) NE . on MEI, study-average BCS, and milk yield (kg/day). The av- Although cows were initially assigned uniform calculated erage number of days each cow was pregnant during the trial 0.75 feed energy intake scaled to BW , weight change associ- was included as a random variable. The effects of time on calf ated with the adaptation period resulted in modest variation feed intake, scaled to BW, were characterized using a spline 0.75 in day 0 calculated ME intake per kg BW (CV = 3.9%). regression model (NLIN procedure, SAS 9.4; SAS Inst. Inc.) Considering minimal mean BW change during the experi- to determine whether a break point in time existed, and if so, mental period (–5.7 ± 11.9 kg), the BCNRM provided a rea- the slope of the two resulting regression lines. sonably accurate estimate of energy requirements to achieve BW stasis (on average) for this group of cows. Variation in RESULTS AND DISCUSSTION BW change during the experimental period was expected due to potential differences in efficiency of feed conversion to In experiment 1, mean days in milk was 177 ± 17 (Table 2). DE, ME, and NE (NASEM, 2016), as well as differences in Late-lactation milk yield averaged 8.4 ± 1.23 kg/day while NE and NE . In an effort to achieve BW stasis for each cow, milk energy concentration averaged 0.70 ± 0.06 Mcal/kg. l m adjustments in daily feed allowance were made when a cow’s Mean daily milk energy yield did not differ by month (P = BW gain or BW loss exceeded 9 kg. These adjustments were 0.21; 5.89 ± 0.9 Mcal/day; data not shown). Andresen et al. Retained energy and feed intake in beef cows 5 only marginally successful because there was a wide range in Mudgal, (1977) estimated k of 0.65 in Brown Swiss × final calculated BW change (–31.0 to 16.8 kg). This is likely Sahiwal crossbred lactating cows. due to the combination of modest adjustments in daily feed The resulting estimate of mean NE was 83% (77.1 kcal/ 0.75 allowance (≤0.45 kg of feed DM) combined with the experi- kg SBW ) of the default value used for lactating Angus cows 0.75 mental period being limited to 61 days. In fact, the first four in the BCNRM (92.4 kcal NE /kg BW ). Similarly, previous cows requiring feed allowance adjustment did not meet the reports from this herd (Andresen et al., 2020; Wiseman et ± 9 kg criteria until day 27. Overall, daily feed allowance al., 2019) estimated NE requirements in limit-fed beef cows adjustments were made for 14 cows between days 27 and 54. lower than the BCNRM default value. Freetly et al. (2006) The BCNRM assumes equal efficiency of ME use for and Trubenbach et al. (2019) also reported lower estimates of NE , NE , NE , and NE . Efficiency of ME use is computed NE when cows are limit fed an energy-dense diet. m t l y m using diet ME concentration (Garrett et al., 1980) or using As Freetly et al. (2019) described, maintenance requirements a fixed value of 0.6 (NASEM, 2016). To compute NE , we and efficiency of ME utilization for maintenance and (or) pro - first converted NE to an ME basis using a fixed value for duction are not independent. At the same level of MEI scaled K (0.654; Garrett, 1980). Subsequently, ME was subtracted to BW, increased NE leads to a lower estimate of NE when m r r m from MEI to compute ME . The k value generated by the k is fixed. However, if NE is fixed, increased NE leads to an m m m m r Garrett (1980) equation did not differ substantially from that increased estimate of k . Overall, default values for NE and m m reported by Reynolds and Tyrrell, (2000; 0.64) and Freetly k used in the BCNRM resulted in a reasonably accurate pre- et al., (2006; 0.69) using primiparous beef cows. Patle and diction of the amount of feed energy required for these cows. However, the lower estimate of NE could also indicate that k was underestimated by the Garrett (1980) equation. For example, increasing k to 0.80 results in the same NE used m m Table 3. Summary statistics of cow performance and voluntary forage in the BCNRM for lactating Angus cows. intake (N = 24), experiment 2 Mean, minimum, maximum, and standard deviation for performance and VDMI characteristics for experiment 2 are Item Mean Min Max SD shown in Table 3. Late-gestation VDMI of this low-quality diet was considerably greater (13.8 ± 2.8 kg) than predicted Days pregnant 223 191 243 14.7 by the model used in BCNRM (11.5 ± 0.55; NASEM 2016, BW, kg 580.7 515.3 634.8 33.9 Eq. 10-5). This equation is sensitive to cow BW and diet en- BCS 5.1 3.6 6.4 0.63 ergy concentration. Previous feed restriction of an energy- SADG, kg 0.32 -1.44 0.83 0.48 dense diet, experiment 2 forage particle size (chopped), and VDMI, kg/day 13.8 9.1 20.0 2.8 added molasses-based liquid feed and water to forage in ex- 0.75 VDMI, g/kg BW 117.2 79.8 182.9 24.3 periment 2 may contribute to excessive feed intake in this experiment. Days pregnant = study-average days pregnant for pregnant cows (n = 21); Pearson correlation coefficients for performance traits BW = study-average shrunk body weight adjusted for fetal tissue weight; and energy partitioning are presented in Table 4. Cows with BCS = study-average body condition score; SADG = shrunk average daily gain adjusted for fetal tissue weight; VDMI = voluntary dry matter intake. greater study-average SBW produced less NE (r = –0.44, Table 4. Pearson correlation coefficients between late-lactation body weight, body condition, weight gain, and energy partitioning (experiment 1) and nonlactating voluntary dry matter intake ( experiment 2) Item SBW BCS SADG MEIv NE NE NE ME t l y m BCS 0.31 0.14 SADG 0.10 0.64 0.64 < 0.01 MEI -0.27 0.32 0.23 0.21 0.12 0.28 NE 0.19 0.40 0.65 -0.17 0.38 0.05 < 0.01 0.43 NE -0.44 -0.31 -0.40 0.42 -0.26 0.03 0.14 0.05 0.04 0.22 NE -0.05 0.12 -0.02 -0.29 0.10 -0.02 0.83 0.59 0.94 0.17 0.65 0.94 ME 0.11 -0.03 -0.20 0.11 -0.72 -0.43 -0.23 0.63 0.88 0.35 0.60 < 0.01 0.04 0.27 VDMI -0.14 -0.35 -0.46 0.10 -0.27 0.30 -0.12 0.16 0.51 0.09 0.02 0.64 0.20 0.15 0.57 0.45 SBW = experiment 1 pregnancy-adjusted shrunk body weight, kg; BCS = expriment 1 body condition score; SADG = expriment 1 pregnancy-adjusted 0.75 shrunk average daily gain, kg; MEI = expriment 1 metabolizable energy intake, kcal ME/kg BW ; NE = expriment 1 maternal tissue energy retained, Kcal/ 0.75 0.75 0.75 kg BW ; NE = expriment 1 milk energy retained, kcal/kg BW ; NE = expriment 1 pregnancy energy retained, kcal/kg BW ME = expriment 1 energy l y ; m 0.75 0.75 required for maintenance, kcal ME/kg BW ; VDMI = expriment 2 nonlactating voluntary dry matter intake, g/kg BW . For each cell, the top number is the correlation coefficient (r), and the bottom number is the P -value. Coefficients with P ≤ 0.05 are bolded. 6 Briggs et al. P = 0.03). However, when SBW was adjusted for BCS ac- cording to NASEM (2016), there was no significant relation - ship with NE (r = –0.31, P = 0.14; data not shown). There was a moderate negative correlation (Table 4; r = –0.40, P = 0.05) between SADG and NE , suggesting that milk energy production was antagonistic to a cow’s ability to maintain BW. This is not surprising because initial daily feed allocation was based on cow BW with no adjustment for milk yield. Secondly, the length of the experimental period did not allow time for feed intake adjustments to completely offset the impact that increased milk yield had on maternal tissue BW change. Rahnefeld et al. (1990) also reported greater BW and condition loss with increased milk yield. Similarly, Mondragon et al. (1983) reported that increasing milk yield during the first and second parity contributed to negative energy balance, re- ducing cow BW and condition at the time of calving in the subsequent parity. However, when energy change associated with maternal tissue was adjusted for BW and BCS (NE ), there was no relationship between estimated maternal tissue energy change and milk energy produced (NE ; r = –0.26, P = 0.22). While the correlation of mean BCS during late lactation to NE was not significant (r = –0.31; P = 0.14), the correla- Figure 1. There was a negative relationship of net energy partitioned tion between BCS recorded during experiment 2 and NE was to milk (NE ) and maternal tissue energy change (NE ) to metabolizable l t negative (r = –0.40; P = 0.05; data not shown). Together, these energy used for maintenance (ME ) when beef cows were limit fed a results suggest that increasing yield of milk energy was associ- 0.75 mixed concentrate/forage diet; ME , kcal/kg BW = 182.4 (6.7) – 1.572 ated with greater late-lactation BW loss. (0.11) * NE – 1.321 (0.12) * NEl (R = 0.92; all variables in the model P < Even though MEI adjustments were modest, there was a 0.001). Cows with lower maintenance requirements had more energy to allocate to milk and maternal tissue energy. moderate positive correlation between MEI and NE (Table 4; r = 0.42, P = 0.04). However, there was no relationship be- tween MEI and NE . These results suggest that additional feed energy was primarily partitioned to milk production and that For the past few decades, residual feed intake (RFI) has milk energy yield is highly sensitive to feed energy availability been used as a genetic selection tool to improve feed effi - in agreement with Jenkins and Ferrell (1994) and Lalman et ciency as it is relatively independent from mature body size, al. (2000). unlike other phenotypic measures of feed efficiency such as In this experiment, ME was computed using four pre- the gain-to-feed ratio (Basarab et al., 2011; Castro Bulle et dictor variables: MEI, NE , NE , and NE . There was no ev- al., 2007). Basarab, et al. (2011), reported a tendency for t l y idence of multicollinearity among the four variables with a positive correlation between RFI and maintenance energy tolerance ≥0.74 and variance inflation factor ≤1.34. Stepwise requirements (0.421; P = 0.10), suggesting that low RFI an- model selection revealed that most of the variation in ME imals have more net energy available for production. In our was accounted for by NE (partial R = 0.517) and NE (par- study, RFI was not calculated for experiment 1 because cows’ t l 2 2 tial R = 0.407) with minimal influence of MEI (partial R = daily feed allowance was controlled. Rather, a multiple re- 0.069) and NE (partial R = 0.006). The relationship of ME gression equation using MEI, BCS, and kg milk yield was y m to NE + NE is shown in Figure 1. As expected, when mainte- used to predict SADG (R = 0.56) and subsequently to cal- l t nance requirement was adjusted for BW, feed energy supplied, culate RADG. milk energy yield, and energy required for pregnancy, cows Residual average daily gain was negatively correlated that lost more BW had greater estimated maintenance re- to MEm (r = –0.41; P = 0.049; data not shown) and is quirement. However, increased milk energy production was characterized by the following equation: associated with lower ME . Under these conditions, even 0.75 though cows producing greater milk energy lost more BW, the MEm, kcal ME kg BW = 118.0 ± 2.9 − 46.4 ± 22.3 × RADG, kg estimated energy contained in the modest BW loss was not sufficient to offset the retained energy gained with increased Several studies report higher partial efficiency of ME milk energy yield. These results oppose previously reported use for growth in low-RFI cattle (Nkrumah et al., 2006; studies which have suggested that as milk yield potential Cantalapiedra-Hijar et al., 2018). In dairy cattle, low-RFI increases, there is an associated increase in maintenance cows showed a greater efficiency of converting feed energy requirements (Ferrell and Jenkins, 1984; Jenkins and Ferrell, to net energy as well as required less net energy for mainte- 1994; Montaño-Bermudez et al., 1990). Reynolds and Tyrrell nance than high-RFI cows with similar BW (Vandehaar et al., (2000) suggested that cattle’s maintenance requirement may 2016). Together, these studies and the results of the current not be directly related to production levels. As mentioned pre- experiment suggest that cattle with high RADG or low RFI viously, we assumed constant k to compute ME . It is pos- may have lower maintenance requirements and (or) increased m m sible that some or all the improved efficiency associated with efficiency of ME use. decreased weight loss and increased milk yield is the result The relationship of late-lactation SADG to non-lactating of improved efficiency of ME utilization ( k ; Freetly et al., VDMI is shown in Figure 2. The linear coefficient indicates 2019). that each 1 kg SADG BW loss is associated with 49.4 g/kg Retained energy and feed intake in beef cows 7 0.75 0.75 BW increase in VDMI during the dry period (experiment day. After day 19, feed intake stabilized at 88.25 g/kg BW 2). The influence of previous plane of nutrition on VDMI in per day. Calves were first exposed to the creep area and pro - beef cows is not well documented. Fox et al. (1988) estimated vided ad libitum access to TMR beginning on day –18. The DMI increased by 2.7% for each one percent decrease in body only previous exposure to concentrate feed would have been fat composition for growing cattle within the range of 21.3% the opportunity to compete for a portion of the cows’ concen- to 31.5% body fat (Fox et al., 1988). Using this relationship, trate supplement (fed on the ground in the pasture) prior to the BCNRM uses initial BCS to predict body fat composition the initiation of the experiment in early January. Creep feed (NASEM, 2016) and subsequently, to adjust feed intake for consumption data was not recorded during the adaptation previous plane of nutrition. Holder (2022) reported that DMI period (days –18 to 0). Therefore, assuming acclimation to increased by 1.5% for each 1% reduction in body fat com- the creep area, feeders, and feed was occurring during the ad- position when beef cows consumed low-quality hay and by aptation period, feed intake scaled to BW increased through 2.4% when beef cows consumed a concentrate/forage mixed day 19, for a total of 37 days, prior to stabilizing. diet. Assuming each unit of BCS change is associated with Increasing feed intake by nursing calves is expected during 3.8% change in body fat and that each unit BCS is associ- late lactation because dams’ daily milk yield declines during ated with 0.0714 × SBW change (NASEM, 2016), the linear this time while calf BW increases (Wood, 1967; Boggs et al., regression coefficient for VDMI equates to 8.3% increase in 1980; NASEM, 2016). Tedeschi et al. (2009) found that al- VDMI for each 1% loss in body fat composition. This esti- falfa hay intake in calves receiving different amounts of mate is substantially greater than that reported by Fox et al. reconstituted milk replacer was influenced by milk DMI and (1988) and Holder (2022). We are not aware of other reports calf BW. In our study, mean daily milk energy yield did not relating VDMI to previous BW change. Mean estimated body differ by month. Therefore, assuming calf milk energy in- fat composition was 18.6% (± 1.8) for these cows during ex- take is equivalent to milk energy production, milk energy in- periment 1, and below the minimum in the growing cattle take scaled to BW declined while feed intake scaled to BW data set used by Fox et al. (1988; 21.3%). Additionally, this increased over time. In this experiment where calves only had relationship should be viewed with caution because the range access to milk and TMR, after day 19, calf VDMI averaged in BCS during experiment 1 was limited to only 2.1 BCS units 2.27 ± 0.17 g/kg BW. Boggs et al. (1980) also reported rapid (3.9 to 6.0). increase in forage intake from May through September in spring-born nursing beef calves. In their study, late lacta- tion (September) grazed forage VDMI was similar, averaging Calf Performance 2.2 g/kg BW. Little data are available characterizing creep feed intake of Previous studies have documented a negative relationship nursing beef calves in the drylot (Lusby et al., 1976). The between forage intake and milk intake in grazing, nursing mean, standard deviation, and range for calf BW, ADG, beef calves (Lusby et al., 1976; Boggs et al., 1980; and and VDMI for experiment 1 are shown in Table 5. A linear- Ansotegui et al., 1991) and drylot, early-weaned dairy calves plateau spline model fit the mean daily VDMI data set with a (Abdelsamei et al., 2005). In the current experiment, there breakpoint at day 19 ± 2.0 (Figure 3). Prior to the breakpoint, was no relationship between dam’s mean daily milk energy 0.75 feed intake increased at the rate of 1.58 ± 0.29 g/kg BW per production and calf VDMI (P = 0.17; data not shown) of a mixed concentrate/forage diet. It is unknown whether this discrepancy is due to the creep diet (48% concentrate feeds) and (or) confinement housing compared to forage diets and pasture housing in the studies of Lusby et al. (1976)Boggs et al. (1980), and Ansotegui et al. (1991). Potential sources of error in our data include limited (3) measurements of milk production, differences between estimates of dam milk yield and calf milk consumption, or cross-nursing between pairs while housed in dry-lot pens. APPLICATIONS Limit feeding a mixed forage/concentrate diet during late lac- tation resulted in maintenance energy requirement 83% of the Table 5. Summary statistics of calf performance and voluntary feed intake (N = 24), experiment 1 Item Mean Min Max SD BW, kg 203.8 173.7 235.8 18.9 ADG, kg 1.63 1.32 1.96 0.16 VDMI, kg/d 4.67 3.69 5.59 0.58 Figure 2. Relationship of lactation-period shrunk average daily gain (SADG) to gestation-period voluntary dry matter intake (VDMI); VDMI, g/ Calves had ad libitum access to the same TMR fed to cows (Table 1). 0.75 2 kg BW = 102.4 (4.4) – 49.4 (20.4) × SADG, kg/day (R = 0.21; SADG BW = study-average body weight; ADG = average daily gain; VDMI = coefficient P = 0.02). voluntary dry matter intake. 8 Briggs et al. 0.75 Figure 3. Observed mean daily calf voluntary dry matter intake (VDMI; circles), and linear – plateau regression model (dashed line). VDMI, g/kg BW = 0.75 2 58.3 (3.3) + 1.576 (0.286) × day; plateau (x0) = 19.03 (2.04) and VDMI, g/kg BW = 88.25 (R = 0.61). Andresen, C. E., A. W. Wiseman, A. McGee, C. Goad, A. P. Foote, R. default value recommended by NASEM (2016). Under these Reuter, and D. L. Lalman. 2020. Maintenance energy requirements conditions, maternal tissue energy change, as estimated by and forage intake of purebred vs. crossbred beef cows. Transl. BCS and BW change, explained more of the variation in ME Anim. Sci. 4:1182–1195. doi:10.1093/tas/txaa008. than did NE . Maintenance requirement estimates were lower Ansotegui, R. P., K. M. Havstad, J. D. Wallace, and D. M. Hallford. in cows with greater total energy recovery (NE + NE ) in con- t l 1991. Effects of milk intake on forage intake and perfor- trast to previous reports conducted across different breeds. mance of suckling range calves. J. Anim. Sci. 69:899–904. More work is necessary to determine if increased retained en- doi:10.2527/1991.693899x. ergy per unit of MEI is due to lower maintenance, increased AOAC. 1990. Association of Official Analytical Chemists, Official efficiency of ME utilization, or both. Finally, post-weaning Methods of Analysis., 15th ed. Arlington, VA: AOAC. VDMI was not related to late-lactation milk energy produc- Baber, J. R., J. E. Sawyer, and T. A. Wickersham. 2018. Estimation of human-edible protein conversion efficiency, net protein contribu - tion, although sensitive to lactation period BCS and BW loss. tion, and enteric methane production from beef production in the United States. Transl. Anim. Sci. 2(4):439–450. doi:10.1093/tas/ ACKNOWLEDGMENTS txy086. Basarab, J. A., M. A. Price, J. L. Aalhus, E. K. Okine, W. M. Snelling, and K. Funding for this work was provided by the Dr. Kenneth and L. Lyle. 2011. Residual feed intake and body composition in young Caroline McDonald Eng Foundation, Oklahoma Agricultural growing cattle. Can. J. Anim. Sci. 83(2):189–204. doi:10.4141/ Experiment Station, the National Institute of Food and A02-065. https://cdnsciencepub.com/doi/abs/10.4141/A02-065. Agriculture and U.S. Department of Agriculture, under award Boggs, D. L., E. F. Smith, R. R. Schalles, B. E. Brent, L. R. Corah, number 2012-02355. and R. J. Pruitt. 1980. Effects of milk and forage intake on calf performance. J. Anim. Sci. 51:550–553. doi:10.2527/ jas1980.513550x. CONFLICT OF INTEREST STATEMENT Cantalapiedra-Hijar, G., M. Abo-Ismail, G. E. Carstens, L. L. Guan, R. Hegarty, D. A. Kenny, M. McGee, G. Plastow, A. Relling, and The authors declare no conflicts of interest. I. Ortigues-Marty. 2018. Review: biological determinants of between-animal variation in feed efficiency of growing beef cattle. Animal 12:s321–s335. doi:10.1017/S1751731118001489. LITERATURE CITED Capper, J. L. 2011. The environmental impact of beef production in the United States: 1977 compared with 2007. J. Anim. Sci. 89:4249– Abdelsamei, A., D. Fox, L. Tedeschi, M. Thonney, D. Ketchen, 4261. doi:10.2527/jas.2010-3784. and J. Stouffer. 2005. The effect of milk intake on forage in- Castro Bulle, F. C. P., P. V. Paulino, A. C. Sanches, and R. D. Sainz. 2007. take and growth of nursing calves. J. Anim. Sci. 83:940–947. Growth, carcass quality, and protein and energy metabolism in beef doi:10.2527/2005.834940x. cattle with different growth potentials and residual feed intakes1,2. Agricultural and Food Research Council (AFRC). 1993. Energy and J. Anim. Sci. 85:928–936. doi:10.2527/jas.2006-373. protein requirements of ruminants; An advisory manual prepared Cooper-Prado, M. J., N. M. Long, M. P. Davis, E. C. Wright, R. D. Mad- by the AFRC Technical Committee on Responses to Nutrients.CAB den, J. W. Dilwith, C. L. Bailey, L. J. Spicer, and R. P. Wettemann. Int., Oxford, UK. Retained energy and feed intake in beef cows 9 2014. Maintenance energy requirements of beef cows and rela- NRC 200 National Research Council. 2000. Nutrient Requirements of tionship with cow and calf performance, metabolic hormones, Beef Cattle: Seventh Revised Edition: Update 2000. Washington, and functional proteins. J. Anim. Sci. 92:3300–3315. doi:10.2527/ DC: The National Academies Press. doi:10.17226/9791. jas.2013-7155. National Research Council (NRC). 1996. Nutrient requirements of Ferrell, C. L., and T. G. Jenkins. 1984. Energy utilization by mature, beef cattle, 7th Revised Edition: Update 1996. Washington, DC: nonpregnant, nonlactating cows of different types. J. Anim. Sci. The National Academies Press. doi:10.17226/9791. 58:234–243. doi:10.2527/jas1984.581234x. NRC. 2001. Nutrient Requirements of Dairy Cattle. 7th rev. ed. Ferrell, C. L., and T. G. Jenkins. 1987. Influence of biological types on Washington, DC: The National Academies Press. energy requirements. In: Proceedings of Grazing Livestock Nutri- Patle, B. R., and V. D. Mudgal. 1977. Utilization of dietary energy tion Conference; Jackson, WY; p. 1–7. for maintenance, milk production and lipogenesis by lactating Ferrell, C. L., and T. G. Jenkins. 1996. Relationships between body con- crossbred cows during their midstage of lactation. Br. J. Nutr. dition score and empty body weight, water, fat, protein, and energy 37:23–33. doi:10.1079/bjn19770004. percentages in mature beef cows of diverse breeds. J. Anim. Sci. Rahnefeld, G. W., G. M. Weiss, and H. T. Fredeen. 1990. Milk yield 74:245. and composition in beef cows and their effect on cow and calf per- Fox, D. G., C. J. Sniffen, and J. D. O’Connor. 1988. Adjusting nutrient formance in two environments. Can. J. Anim. Sci. 70(2):409–423. requirements of beef cattle for animal and environmental varia- doi:10.4141/cjas90-053. tions. J. Anim. Sci. 66:1475–1495. Reynolds, C. K., and H. F. Tyrrell. 2000. Energy metabo- Freetly, H. C. 2019. Fiftieth Anniversary of the California Net Energy lism in lactating beef heifers. J. Anim. Sci. 78:2696–2705. System Symposium: What are the energy coefficients for cows? doi:10.2527/2000.78102696x. Transl. Anim. Sci 3:969–975. doi:10.1093/tas/txz024. Rotz, C. A., S. Asem-Hiablie, S. Place, and G. Thoma. 2019. Environ- Freetly, H. C., J. A. Nienaber, and T. Brown-Brandl. 2006. Partitioning mental footprints of beef cattle production in the United States. of energy during lactation of primiparous beef cows1. J. Anim. Sci. Agric. Sys. 169:1–13. doi:10.1016/j.agsy.2018.11.005. 84:2157–2162. doi:10.2527/jas.2005-534. Solis, J. C., F. M. Byers, G. T. Schelling, C. R. Long, L. W. Greene. 1988. Garrett, W. N. 1980. Energy utilization by growing cattle as determined Maintenance requirements and energetic efficiency of cows of in 72 comparative slaughter experiments. In: Energy Metabolism. different breed types. J. Anim. Sci. 66(3):764–773. doi:10.2527/ Butterworth-Heinemann. p. 3–7. jas1988.663764x. PMID: 3378932. Holder, A. L., M. A. Gross, A. N. Moehlenpah, C. L. Goad, M. Rolf, R. Stein, D., B. Freking, and B. Whitworth. 2015. Synchronizing heats in S. Walker, J. K. Rogers, and D. L. Lalman. 2022. Effects of diet on beef cows and heifers. In: Lalman D. L., and D. Doye, editors, Beef feed intake, weight change, and gas emissions in beef cows. J. Anim. cattle manual. 7th ed. Stillwater, OK: Oklahoma State University Sci. 100:1–9. doi:10.1093/jas/skac257. Cooperative Extension; p. 289–296. Jenkins, T. G., and C. L. Ferrell. 1994. Productivity through Tedeschi, L. O., D. G. Fox. 2009. Predicting milk and forage intake weaning of nine breeds of cattle under varying feed of nursing calves. J. Anim. Sci. 87(10):3380–3391. doi:10.2527/ availabilities: I. Initial evaluation. J. Anim. Sci. 72:2787–2797. jas.2009-2014. Epub 2009 Jul 2. PMID: 19574576. doi:10.2527/1994.72112787x. Trubenbach, L. A., T. A. Wickersham, L. N. Bierschwale, J. C. Morrill, Kuehn, L. A., and R. M. Thallman. 2016. Beef Improvement Federation J. R. Baber, and J. E., Sawyer. 2019. Limit feeding as a strategy to Annual Meeting & Symposium, June 14–17, 2016, Hilton Garden increase energy efficiency in intensified cow–calf production sys - Inn, Manhattan, Kansas. tems. Trans. Anim. Sci. 3(2):796–810 doi:10.1093/tas/txz039. Lalman, D. L., J. E. Williams, B. W. Hess, M. G. Thomas, and D. H. Vandehaar, M., L. Armentano, K. Weigel, D. M. Spurlock, R. Keisler. 2000. Effect of diet energy on milk production and met- Tempelman, and R. Veerkamp. 2016. Harnessing the genetics of abolic hormones in thin, primiparous beef heifers. J. Anim. Sci. the modern dairy cow to continue improvements in feed efficiency. 78:530–538. doi:10.2527/2000.783530x. J. Dairy Sci. 99(6):4941–4954. doi:10.3168/jds.2015-10352. Laurenz, J. C., F. M. Byers, G. T. Schelling, and L. W. Greene. 1991. Van Soest, P. J., J. B. Robertson, and B. A. Lewis. 1991. Methods for Effects of season on the maintenance requirements of mature beef dietary fiber, neutral detergent fiber and nonstarch polysaccharides cows. J. Anim. Sci. 69(5):2168–2176. doi:10.2527/1991.6952168x. in relation to animal nutrition. J. Dairy Sci. 74:3583–3597. Lusby, K. S., D. F. Stephens, and R. Totusek. 1976. Effects of milk intake doi:10.3168/jds.S0022-0302(91)78551-2. by nursing calves on forage intake on range and creep intake and Wagner, J. J., K. S. Lusby, J. W. Oltjen, J. Rakestraw, R. P. Wettemann, digestibility in drylot. J. Anim. Sci. 43:1066–1071. doi:10.2527/ and L. E. Walters. 1988. Carcass composition in mature Hereford jas1976.4351066x. cows: estimation and effect on daily metabolizable energy re- Mondragon, I., J. W. Wilton, O. B. Allen, and H. Song. 1983. Stage of quirement during winter. J. Anim. Sci. 66:603–612. doi:10.2527/ lactation effects, repeatabilities and influences on weaning weights jas1988.663603x. of yield and composition of milk yield in beef cattle. Can. J. Anim. Weiss, W. P., and A. W. Tebbe. 2018. Estimating digestible energy values Sci. 63:751−761. of feeds and diets and integrating those values into net energy sys- Montaño-Bermudez, M., M. K. Nielsen, and G. H. Deutscher. 1990. tems. Trans. Anim. Sci. 3:953–961. doi:10.1093/tas/txy119. Energy requirements for maintenance of crossbred beef cattle with Wiseman, A. W., M. Redden, A. McGee, C. Spencer, R. R. Reuter, G. W. different genetic potential for milk. J. Anim. Sci. 68:2279–2288. Horn, and D. L. Lalman. 2019. Effects of timing of weaning on en- doi:10.2527/1990.6882279x. ergy utilization in primiparous beef cows and post-weaning perfor- NASEM. 2016. Nutrient Requirements of Beef Cattle: Eighth Re- mance of their progeny. J. Anim. Sci. 97:1198–1211. doi:10.1093/ vised Edition. Available from: https://www.nap.edu/catalog/19014/ jas/skz019. nutrient-requirements-of-beef-cattle-eighth-revised-edition Wood, P. 1967. Algebraic model of the lactation curve in cattle. Nature Nkrumah, J. D., E. K. Okine, G. W. Mathison, K. Schmid, C. Li, J. A. 216:164–165. doi:10.1038/216164a0. Basarab, M. A. Price, Z. Wang, S. S. Moore. 2006. Relationships Yang, C. T., C. M. Wang, Y. G. Zhao, T. B. Chen, A. Aubry, A. W. of feedlot feed efficiency, performance, and feeding behavior with Gordon, T. Yan. 2020. Updating maintenance energy requirement metabolic rate, methane production, and energy partitioning in beef for the current sheep flocks and the associated effect of nutritional cattle. J. Anim. Sci. 84(1):145–153. doi:10.2527/2006.841145x. and animal factors. Animal 14(2):295–302. ISSN 1751-7311. PMID: 16361501. doi:10.1017/S1751731119002064.
Translational Animal Science – Oxford University Press
Published: Aug 25, 2022
Keywords: efficiency; maintenance; milk yield; milk composition; residual gain
Access the full text.
Sign up today, get DeepDyve free for 14 days.