Energy and protein requirements of crossbred (Holstein x Gyr) yearling bulls and assessment of techniques for measuring methane emissions and energy expenditure of cattle - Oss, Daniela Batista (2016)
The facts that justify the development of this work are: 1 – There is few information about nutrient requirements of crossbreeds Holstein x Gyr cattle, which is widely used in Brazil; 2 – Pressure to reduce livestock greenhouse gases has prompted greater interest in research to reduce enteric methane emissions from ruminants and consequently in the development and validation of techniques for measuring methane; 3 – Considering that energy expenditure is one of the most element in overall energy budget of cattle, to have measurement techniques that are suited to different production conditions it is important to studies that seek understand energy metabolism and partition of cattle. Therefore, this work was developed from three studies. The first one aimed to estimate the energy and protein requirements of crossbred (Holstein × Gyr) yearling bulls. Twenty-four 10 months old bulls (initial body weight = 184.1 ± 23.36 kg) were used in a comparative slaughter trial. Six bulls were slaughtered at the beginning of the experiment as the reference group to estimate initial empty body weight (EBW) and energy and protein contents of the remaining animals. The remaining bulls were assigned to a completely randomized design with 3 DM intake levels, with 6 replicates. The levels of DM intake were: 1.2% of BW, 1.8% of BW and ad libitum – target orts 5%. Bulls were fed a diet consisting of 59.6% corn silage and 40.4% concentrate on a DM basis. The equation adjusted for the relationship between EBW and BW was EBW= 0.861 ±0.0031 × BW. While the relationship between empty body gain (EBG) and average daily gain (ADG) may be demonstrated using the equation: EBG= 0.934 ±0.0111 × ADG. The net energy requirements for maintenance (NE m ) were 74.8 ±2.89 kcal/EBW 0.75 /d, the metabolizable energy requirements for gain (ME m ) were 120.8 kcal/EBW 0.75 /d. The efficiency of the use of metabolizable energy for maintenance (k m ) was 61.9%. The equation adjusted to estimate the net energy for gain (NE g ) was: NE g =0.049 ±0.0011 ×EBW 0.75 × EBG 0.729±0.0532 . The efficiency of the use of metabolizable energy for gain (k g ) observed was 30.8%. The metabolizable protein requirements for maintenance (MP m ) was 3.05 g/kg BW 0.75 . The equation adjusted to estimate the net protein requirements for gain was: NP g = (87.138 ±65.1378 × EBG) + (40.436 ±21.3640 × NE g ). The efficiency of the use of metabolizable protein for gain (k) observed was 53.6%. We conclude that the estimates of energy and protein requirements were not similar to those estimated from the other assessed systems, thus we recommend using the estimates herein presented to balance diets of crossbred (Holstein × Gyr) growing bulls. The second study aimed to compare short- term measurement (30 min/d for 3 d) face mask system (FM), with SF 6 tracer and respiration chamber (RC) techniques for measuring methane emissions. The same animals, treatments and diet used in the first study were used in the second and third study. Methane emissions were measured first using the SF 6 tracer technique, followed by the FM and RC techniques, respectively. Methane collection was initiated for a period of 24 h, with the procedure repeated on 5 consecutive days. Two weeks later, the FM technique was evaluated and a single 30-min CH 4 measurement was performed each day for 3 d by collecting measurements 6 h after feeding. After 4 weeks, methane emissions from bulls were estimated using indirect open- circuit respiration chambers (RC) by placing bulls in the chambers for two 24 h periods of methane measurements. The concordance correlation coefficient (CCC) for CH 4 emission (g/d) were: 0.82, 0.82 and 0.74 for comparisons of SF 6 vs RC, FM vs RC and FM vs SF 6 , respectively. Methane emission adjusted for differences in DMI did not differ among techniques, averaging 21.5 g/kg DMI (P=0.238). However, the day-to-day (21.3%) and animal-to-animal (13.4%) variation in CH 4 yield was greater for the FM technique as compared to SF 6 (18.8% and 6.8%) and RC (12.9% and 7.5%) techniques. This higher variation has a negative impact on power of an experiment using FM, and it would require more animals (replicates) to detect treatment difference with 80% of power. The third study aimed to assess the O 2 P-HR (oxygen pulse (O 2 P) and heart rate (HR)) technique with respiration chamber (RC) and comparative slaughter (CS) methods for measuring energy expenditure (EE) of cattle. The O 2 P-HR method is an alternative technique for measuring EE, that is based on long-term measurements (24 h periods) of the HR of free-range animals, and on short-term measurement of oxygen pulse (O 2 P; mL of O 2 consumed/heart beat) which is measured by attaching a face mask (FM) to the animal‘s nose shortly (20 min). For both comparison (O 2 P-HR vs RC and O 2 P-HR vs CS), the regression analysis indicated that the slopes were not different from unity and the intercepts were not different from zero (P>0.050), which is an indicative of high accuracy of the O 2 P-HR method. On the other hand, the regression estimates of r 2 were 0.52 for comparison with RC and 0.53 for comparison with CS, indicating a moderate precision of the O 2 P-HR method. The between- animal CV was higher for the O 2 P-HR method (16.6%) when compared to RC (7.7%) or CS (6.3%). The CCC were moderate, 0.70 for O 2 P-HR vs RC and 0.73 for O 2 P-HR vs CS. The O 2 P- HR method generated EE measurements that were comparable with high accuracy and moderate precision to those estimated using RC and CS. The O 2 P-HR method may presented a negative impact on power of an experiment due to its high between-animal coefficient of variation. In the same way, the FM technique for measuring methane emissions and the O 2 P-HR for measuring energy expenditure of cattle are techniques that offer their advantages as compared to the most traditional techniques used. Therefore both evaluated methods should be more investigated to determine how best to deploy the systems to meet specific objectives, also to investigate ways to minimize associated errors.