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 ±â€…2.9 kg/d, shrunk body weight (SBW) = 578 ±â€…83.9 kg, body condition score = 5.7 ±â€…0.73, and Mcal net energy for maintenance (NEm)/kg of feed = 1.27 ±â€…0.15 Mcal/kg. Means for the lactating dataset were DMI = 14.6 ±â€…2.24 kg/d, SBW = 503 ±â€…73.4 kg, body condition score = 4.7 ±â€…0.58, and Mcal NEm/kg feed = 1.22 ±â€…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.

Effects of diet on feed intake, weight change, and gas emissions in beef cows.

The objective of this study was to examine the effects of diet energy density on ranking for dry matter intake (DMI), residual feed intake (RFI), and greenhouse gas emissions. Forty-two mature, gestating Angus cows (600 ± 69 kg body weight [BW]; body condition score [BCS] 5.3 ± 1.1) with a wide range in DMI expected progeny difference (-1.38 to 2.91) were randomly assigned to two diet sequences; forage then concentrate (FC) or concentrate then forage (CF). The forage diet consisted of long-stem native grass hay plus protein supplement (HAY; 1.96 Mcal ME/kg DM). The concentrate diet consisted of 35% chopped grass hay and 65% concentrate feeds on a dry matter basis (MIX; 2.5 Mcal ME/kg DM). The GreenFeed Emission Monitoring system was used to determine carbon dioxide (CO2), oxygen (O2), and methane (CH4) flux. Cow performance traits, ultrasound back fat and rump fat, feed DMI, and gas flux data were analyzed in a crossover design using a mixed model including diet, period, and sequence as fixed effects and pen and cow within sequence as random effects. For all measured traits excluding DMI, there was a diet × sequence interaction (P < 0.05). The correlation between MIX and HAY DMI was 0.41 (P = 0.067) and 0.47 (P = 0.03) for FC and CF sequences, respectively. There was no relationship (P > 0.66) between HAY and MIX average daily gain (ADG), regardless of sequence. Fifty-seven percent of the variation in DMI was explained by metabolic BW, ADG, and BCS for both diets during the first period. During the second period, the same three explanatory variables accounted for 38% and 37% of the variation in DMI for MIX and HAY diets, respectively. The negative relationship between BCS and DMI was more pronounced when cows consumed the MIX diet. There was no relationship between MIX and HAY RFI, regardless of sequence (P > 0.18). During the first period, correlations for CO2, CH4, and O2 with MIX DMI were 0.69, 0.81, and 0.56 (P ≤ 0.015), respectively, and 0.76, 0.74, and 0.64 (P < 0.01) with HAY DMI. During the second period, correlations for CO2, CH4, and O2 with MIX DMI were 0.62, 0.47, and 0.56 (P ≤ 0.11), respectively. However, HAY DMI during the second period was not related to gas flux (P > 0.47). Results from this experiment indicate that feed intake of two energy-diverse diets is moderately correlated while ADG while consuming the two diets is not related. Further experimentation is necessary to determine if gas flux data can be used to predict feed intake in beef cows.

The beef cow utilizes about 74% of total feed energy required to produce beef. Therefore, a more thorough understanding of feed intake, weight gain, and feed efficiency traits in the beef cow is fundamental to reducing cost and improving the environmental footprint of beef production. In this experiment, feed intake, weight gain, and greenhouse gas emissions were studied using a crossover design (two study periods) and two diets diverse in energy density and physical characteristics; hay or a hay/concentrate mixed diet. Feed intake of the hay diet was moderately, positively correlated to feed intake when cows consumed the mixed diet. However, there was no correlation in weight gain when cows consumed hay compared to weight gain when cows consumed the mixed diet. There was generally a strong correlation between feed intake and greenhouse gas emissions during the first feeding period. However, there was no correlation between greenhouse gas fluxes and feed intake when cows consumed hay after they had first received the mixed diet. Further research is necessary to determine if greenhouse gas flux data can be used as a reliable proxy for feed intake in beef cows.

Retained energy in lactating beef cows; effects on maintenance energy requirement and voluntary feed intake.

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 (MEm). Cows were individually fed daily a mixed diet (2.62 Mcal MEl/kg, 18.2% crude protein) 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 2 = 0.92). Increased residual daily gain during lactation was associated with lower lactation maintenance energy requirements (P = 0.05; R 2 = 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.

Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection.

Many studies have evaluated the effectiveness of genomic selection (GS) using cross-validation within training populations; however, few have looked at its performance for forward prediction within a breeding program. The objectives for this study were to compare the performance of naïve GS (NGS) models without covariates and multi-trait GS (MTGS) models by predicting two years of F4: 7 advanced breeding lines for three Fusarium head blight (FHB) resistance traits, deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), and severity (SEV) in soft red winter wheat and comparing predictions with phenotypic performance over two years of selection based on selection accuracy and response to selection. On average, for DON, the NGS model correctly selected 69.2% of elite genotypes, while the MTGS model correctly selected 70.1% of elite genotypes compared with 33.0% based on phenotypic selection from the advanced generation. During the 2018 breeding cycle, GS models had the greatest response to selection for DON, FDK, and SEV compared with phenotypic selection. The MTGS model performed better than NGS during the 2019 breeding cycle for all three traits, whereas NGS outperformed MTGS during the 2018 breeding cycle for all traits except for SEV. Overall, GS models were comparable, if not better than phenotypic selection for FHB resistance traits. This is particularly helpful when adverse environmental conditions prohibit accurate phenotyping. This study also shows that MTGS models can be effective for forward prediction when there are strong correlations between traits of interest and covariates in both training and validation populations.

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.

Application of the California Net Energy System to grazed forage: feed values and requirements.

The California Net Energy System (CNES) has been successfully used for many years to generate estimates of grazing animal energy requirements, supplemental needs, and energy value of grazed forage diets. Compared to pen feeding situations, validation of feed nutritive value estimates or animal performance projections are extremely difficult in grazing animals because many of the system inputs are constantly changing. A major difficulty in applying this or any energy accounting system in the field is acquiring accurate estimates of forage intake. We discuss the various equations available to estimate forage intake for grazing animals with emphasis on beef cows. Progress has been made in recent years although there remains substantial discrepancy among various equations, particularly in the upper range of forage digestibility. Validation work and further development is needed in this area. For lactating cows, our conclusion is that the adjustment of intake for milk production (0.2 kg increase in forage intake per kg of milk produced) needs to be increased to a minimum of 0.35. A particular challenge with the CNES for grazing beef cows is the dramatic interaction that can occur between genetic potential for production traits and nutrient availability. Examples from literature are provided and a case study is presented demonstrating that energy requirements are dynamic and depend on nutrients available in grazing systems. The CNES is a useful tool in grazing beef cattle management although there remains substantial opportunity and need to improve inputs and validate the system in grazing situations.