Net energy value of ammoniated wheat straw

BOARD-INVITED REVIEW: Efficiency of converting digestible energy to metabolizable energy and reevaluation of the California Net Energy System maintenance requirements and equations for predicting dietary net energy values for beef cattle

For the past several decades, nutrient requirement systems for beef cattle in North America have recommended that dietary ME can be calculated as dietary DE × 0.82, but considerable published data suggest a variable relationship between DE and ME. We reviewed the literature and tabulated the results of 23 respiration calorimetry studies (87 treatment mean data points), in which measurements of fecal, urinary, and gaseous energy were determined with beef cattle (bulls, steers, and heifers) and growing dairy cattle. Mixed-model regression analyses to adjust for the effects of the citation from which the data were obtained suggested a strong linear relationship between ME and DE (Mcal/kg of DM; ME = 0.9611 × DE - 0.2999; = 0.986, root mean square error [RMSE] = 0.048, < 0.001 for intercept, slope ≠ 0). Analysis of residuals from this simple linear regression equation indicated high correlations of residuals with other dietary components, and a slight increase in precision was obtained when dietary CP, ether extract, and starch (% of DM) concentrations were included in a multiple linear regression equation (citation-adjusted = 0.992, RMSE = 0.039). Using the simple linear relationship, we reevaluated the original data used to develop the California Net Energy System (CNES) for beef cattle by recalculating ME intake and heat production and regressing the logarithm of heat production on ME intake (both per BW, kg daily). The resulting intercept and slope of the recalculated data did not differ ( ≥ 0.34) from those reported for the original analyses of the CNES data, suggesting that use of the linear equation for calculating ME concentration was consistent with NEm and NEg values as derived in the CNES. Nonetheless, because the cubic equations recommended by the NRC to calculate dietary NEm and NEg from ME were based on conversion of DE to ME using 0.82, these equations were mathematically recalculated to account for the linear relationship between DE and ME. Overall, our review and analyses suggested that there is a strong linear relationship between DE and ME, which seems to be consistent across a wide range of dietary conditions, cattle types, and levels of intake. Applying this linear relationship to predict ME concentrations agreed with the original CNES calculations for NE requirements, thereby allowing the development of new equations for predicting dietary NEm and NEg values from ME.

Effect of feeding forage characteristic of wet- or dry-season tropical C4 grass in northern Australia, on methane production, intake and rumen outflow rates in Bos indicus steers

Methane production (MP) from Bos indicus steers fed Chloris gayana hay characteristic of the ‘dry season’ (LQH), and a fresh Urochloa mosambicensis grass (PAS) or a C. gayana hay (HQH) characteristic of the ‘wet’ season was determined. A longitudinal feeding trial incorporated a 42-day covariate period (P1) in which Brahman steers (total n = 12) were fed ad libitum LQH (g/kg DM: crude protein (CP) 25; acid detergent fibre expressed exclusive of residual ash (ADFom) 487; DM digestibility (DMD) 380) followed by a 42-day treatment period where steers (n = 4 in each) were randomly assigned to PAS, HQH or remained on LQH (control). The diet composition in P2 was HQH (g/kg DM: CP 88; ADFom 376; DMD 590), PAS (g/kg DM: CP 90; ADFom 324; DMD 630) and LQH (g/kg DM: CP 31; ADFom 461; DMD 410). For each period, on Days 35–41, individual dry-matter intakes (DMI), rumen fermentation parameters and both fluid and particulate fractional rumen outflow rates were measured. On Days 41 and 42, MP was determined using open-circuit respiration chambers. There were diet effects on MP, DMI, volatile fatty acids, and ammonia-N. Both PAS- and HQH-fed steers had greater MP (g/day; P < 0.05) and DMI (P < 0.05) than did those fed LQH, but a lower MP per kilogram DMI digested. The use of predictive equations compared with measured data confirmed prior observations that MP from tropical grasses in the northern Australian rangelands may be overestimated using the current equations for greenhouse gas accounting.

Methane emissions from beef and dairy cattle: quantifying the effect of physiological stage and diet characteristics

The prediction of methane outputs from ruminant livestock data at farm, national, and global scales is a vital part of greenhouse gas calculations. The objectives of this work were to quantify the effect of physiological stage (lactating or nonlactating) on predicting methane (CH4) outputs and to illustrate the potential improvement for a beef farming system of using more specific mathematical models to predict CH4 from cattle at different physiological stages and fed different diet types. A meta-analysis was performed on 211 treatment means from 38 studies where CH4, intake, animal, and feed characteristics had been recorded. Additional information such as type of enterprise, diet type, physiological stage, CH4 measurement technique, intake restriction, and CH4 reduction treatment application from these studies were used as classificatory factors. A series of equations for different physiological stages and diet types based on DMI or GE intake explained 96% of the variation in observed CH4 outputs (P<0.001). Resulting models were validated with an independent dataset of 172 treatment means from 20 studies. To illustrate the scale of improvement on predicted CH4 outputs from the current whole-farm prediction approach (Intergovernmental Panel on Climate Change [IPCC]), equations developed in the present study (NewEqs) were compared with the IPCC equation {CH4 (g/d)=[(GEI×Ym)×1,000]/55.65}, in which GEI is GE intake and Ym is the CH4 emission factor, in calculating CH4 outputs from 4 diverse beef systems. Observed BW and BW change data from cows with calves at side grazing either hill or lowland grassland, cows and overwintering calves and finishing steers fed contrasting diets were used to predict energy requirements, intake, and CH4 outputs. Compared with using this IPCC equation, NewEqs predicted up to 26% lower CH4 on average from individual lactating grazing cows. At the herd level, differences between equation estimates from 10 to 17% were observed in total annual accumulated CH4 when applied to the 4 diverse beef production systems. Overall, despite the small number of animals used it was demonstrated that there is a biological impact of using more specific CH4 prediction equations. Based on this approach, farm and national carbon budgets will be more accurate, contributing to reduced uncertainty in assessing mitigation options at farm and national level.

Fibrolytic enzyme and ammonia application effects on the nutritive value, intake, and digestion kinetics of bermudagrass hay in beef cattle

The objectives were to compare the effect of exogenous fibrolytic enzyme (Biocellulase A20) or anhydrous ammonia (4% DM) treatment on the nutritive value, voluntary intake, and digestion kinetics of bermudagrass (Cynodon dactylon cultivar Coastal) hay harvested after 2 maturities (5- and 13-wk regrowths). Six individually housed, ruminally cannulated Brangus steers (BW 325 ± 10 kg) were used in an experiment with a 6 × 6 Latin square design with a 3 (additives) × 2 (maturities) factorial arrangement of treatments. Each period consisted of 14 d of adaptation and 7, 4, 1, 1, and 4 d for measuring in vivo digestibility, in situ degradability, no measurements, rumen liquid fermentation and passage indices, and rate of solid passage, respectively. Steers were fed hay for ad libitum intake and supplemented with sugarcane molasses and distillers grain (supplement total of 2.88 kg DM/d). Enzyme did not affect the nutritional composition of hay but ammonia treatment decreased hay NDF, hemicellulose, and ADL concentrations and increased the CP concentration particularly for the mature lignified 13-wk hay. The enzyme increased NDF and hemicellulose digestibility of the 5-wk hay but decreased those of the 13-wk hay. Ammoniation decreased intake of hay but increased digestibility of DM, OM, NDF, hemicellulose, ADF, and cellulose and increased the ruminal in situ soluble and potentially digestible fractions and the rate of DM degradation of the 13-wk hay. Also, ammoniation increased the concentrations of ruminal NH3, total VFA, acetate, and butyrate but enzyme treatment did not. Neither enzyme addition nor ammoniation affected rate of liquid and solid passage. In conclusion, ammoniation decreased the concentration of most fiber fractions, decreased the intake of hays, and increased their CP concentration, in vivo digestibility, and in situ degradability at both maturities whereas enzyme application increased fiber digestibility of the 5-wk hay but decreased it in the case of the 13-wk hay.

Carcinogenic 4(5)-methylimidazole found in beverages, sauces, and caramel colors: chemical properties, analysis, and biological activities

Since the National Toxicology Program (NTP) identified 4(5)-methylimidazole [4(5)-MI] as a cancer causing chemical in 2007 and the State of California added it to the Proposition 65 list of compounds as a carcinogen on January 7, 2011, many researchers and regulatory agencies have become focused on the presence of 4(5)-MI in foods and beverages. 4(5)-MI has been known to form in the Maillard reaction system consisting of a sugar and ammonia-a typical caramel-color preparation method for beverages. 4(5)-MI is identified in various beverages and sauces, which are colored with caramel, as well as in caramel color itself. Analysis of 4(5)-MI is extremely difficult due to its high water solubility, but the analytical method for 4(5)-MI has progressed from conventional paper chromatography, gas chromatography, and gas chromatography-mass spectrometry to the most advanced high-performance liquid chromatography-mass spectrometry. Various studies indicate that caramel colors and carbonated beverages contain 4(5)-MI in levels ranging from 0 to around 1000 ppm and from 0 to about 500 ppm, respectively. Reports of the toxicity of 4(5)-MI at relatively high levels suggest that it may cause some adverse effects on human consumers.

Prediction of methane production from dairy and beef cattle

Methane (CH4) is one of the major greenhouse gases being targeted for reduction by the Kyoto protocol. The focus of recent research in animal science has thus been to develop or improve existing CH4 prediction models to evaluate mitigation strategies to reduce overall CH4 emissions. Eighty-three beef and 89 dairy data sets were collected and used to develop statistical models of CH4 production using dietary variables. Dry matter intake (DMI), metabolizable energy intake, neutral detergent fiber, acid detergent fiber, ether extract, lignin, and forage proportion were considered in the development of models to predict CH4 emissions. Extant models relevant to the study were also evaluated. For the beef database, the equation CH4 (MJ/d) = 2.94 (+/- 1.16) + 0.059 (+/- 0.0201) x metabolizable energy intake (MJ/d) + 1.44 (+/- 0.331) x acid detergent fiber (kg/d) - 4.16 (+/- 1.93) x lignin (kg/d) resulted in the lowest root mean square prediction error (RMSPE) value (14.4%), 88% of which was random error. For the dairy database, the equation CH4 (MJ/d) = 8.56 (+/- 2.63) + 0.14 (+/- 0.056) x forage (%) resulted in the lowest RMSPE value (20.6%) and 57% of error from random sources. An equation based on DMI also performed well for the dairy database: CH4 (MJ/d) = 3.23 (+/- 1.12) + 0.81 (+/- 0.086) x DMI (kg/d), with a RMSPE of 25.6% and 91% of error from random sources. When the dairy and beef databases were combined, the equation CH4 (MJ/d) = 3.27 (+/- 0.79) + 0.74 (+/- 0.074) x DMI (kg/d) resulted in the lowest RMSPE value (28.2%) and 83% of error from random sources. Two of the 9 extant equations evaluated predicted CH4 production adequately. However, the new models based on more commonly determined values showed an improvement in predictions over extant equations.