Enteric methane (CH 4) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH 4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH 4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH 4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH 4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH 4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH 4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH 4 emission conversion factors for specific regions are required to improve CH 4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH 4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

Ruminant production systems are important contributors to anthropogenic methane (CH) emissions, but there are large uncertainties in national and global livestock CH inventories. Sources of uncertainty in enteric CH emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH emission factors. There is also significant uncertainty associated with enteric CH measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.

The timing of feed intake entrains circadian rhythms regulated by internal clocks in many mammals. The objective of this study was to determine if the timing of feeding entrains daily rhythms in dairy cows. Nine Holstein cows were used in a replicated 3 × 3 Latin square design with 14-d periods. An automated system recorded the timing of feed intake over the last 7 d of each period. Treatments were feeding 1×/d at 0830 h (AM) or 2030 h (PM) and feeding 2×/d in equal amounts at 0830 and 2030 h. All treatments were fed at 110% of daily intake. Cows were milked 2×/d at 0500 and 1700 h. Milk yield and composition were not changed by treatment. Daily intake did not differ, but twice-daily feeding tended to decrease total-tract digestibility of organic matter and neutral detergent fiber (NDF). A treatment by time of day interaction was observed for feeding behavior. The amount of feed consumed in the first 2h after feeding was 70% greater for PM compared with AM feeding. A low rate of intake overnight (2400 to 0500 h; 2.2 ± 0.74% daily intake/h, mean ± SD) and a moderate rate of intake in the afternoon (1200 to 1700 h; 4.8 ± 1.1% daily intake/h) was noted for all treatments, although PM slightly reduced the rate during the afternoon period compared with AM. A treatment by time of day interaction was seen for fecal NDF and indigestible NDF (iNDF) concentration, blood urea nitrogen, plasma glucose and insulin concentrations, body temperature, and lying behavior. Specifically, insulin increased and glucose decreased more after evening feeding than after morning feeding. A cosine function within a 24-h period was used to characterize daily rhythms using a random regression. Rate of feed intake during spontaneous feeding, fecal NDF and iNDF concentration, plasma glucose, insulin, NEFA, body temperature, and lying behavior fit a cosine function within a 24-h period that was modified by treatment. In conclusion, feeding time can reset the daily rhythms of feeding and lying behavior, core body temperature, fecal NDF and iNDF concentration, and plasma blood urea nitrogen, glucose, and insulin concentration of dairy cows, but has no effect on daily DMI and milk production.

Enteric methane (CH 4 ) production attributable to beef cattle contributes to global greenhouse gas emissions. Reliably estimating this contribution requires extensive CH 4 emission data from beef cattle under different management conditions worldwide. The objectives were to: 1) predict CH 4 production (g d −1 animal −1 ), yield [g (kg dry matter intake; DMI)−1 ] and intensity [g (kg average daily gain) −1 ] using an intercontinental database (data from Europe, North America, Brazil, Australia and South Korea); 2) assess the impact of geographic region, and of higher-and lower-forage diets. Linear models were developed by incrementally adding covariates. A K-fold cross-validation indicated that a CH 4 production equation using only DMI that was fitted to all available data had a root mean square prediction error (RMSPE; % of observed mean) of 31.2%. Subsets containing data with

Abstract. The study aimed to examine, simultaneously, the effects of changing dietary forage and crude protein (CP) contents on enteric methane (CH 4 ) emissions and nitrogen (N) excretion from lactating dairy cows. Twelve post-peak lactating Holstein cows (157 AE 31 days postpartum; mean AE s.d.) were randomly assigned to four treatments from a 2 · 2 factorial arrangement of two dietary forage levels [37.4% (LF) vs 53.3% (HF) of DM] and two dietary CP levels [15.2% (LP) vs 18.5% (HP) of DM] in a 4 · 4 Latin square design with four 18-day periods. Alfalfa hay was the sole source of dietary forage. Cows were fed ad libitum and milked twice daily. During the first 14 days, cows were housed in a free-stall barn, where enteric CH 4 emissions were measured using the GreenFeed system from Days 8 to 14 in each period. Cows were then moved to metabolic cages, where faeces and urine output (kg/cow.day) were measured by total collection from Days 16 to 18 of each period. No dietary forage by CP interactions were detected for DM intake, milk production, enteric CH 4 emissions, or N excretions. There was a tendency for DM intake to increase 0.6 kg/day in cows fed LF (P = 0.06). Milk production increased 2.1 kg/day in LF compared with HF (P < 0.01). Milk fat content decreased in cows fed LF compared with HF (1.07 vs 1.17 kg/day; P < 0.01). Milk contents of true protein, lactose and solid non-fat were greater in cows fed LF (P < 0.01). No difference in DM intake, milk yield and milk contents of true protein, lactose and solid non-fat was found between cows fed HP or LP. However, milk fat content increased 0.16 kg/day in cows fed HP (P < 0.05). Enteric CH 4 emissions, and CH 4 per unit of DM intake, energy-corrected milk, total digested organic matter and neutral detergent fibre were not affected by dietary CP, but decreased by LF compared with HF (P < 0.01). Milk true protein N was not affected by dietary CP content but was higher for LF compared with HF. Dietary N partitioned to milk true protein was greater in cows fed LF compared with HF (29.4% vs 26.7%; P < 0.01), also greater in cows fed LP compared with HP (30.8% vs 25.2%; P < 0.01). Dietary N partitioned to urinary N excretion was greater in cows fed HP compared with LP (39.5% vs 29.6%; P < 0.01) but was not affected by dietary CP content. Dietary N partitioned to faeces was not affected by dietary CP but increased in cows fed LP compared with HP (34.2% vs 27.8%; P < 0.01). Total N excretion (urinary plus faecal) as proportion to N intake did not differ between HP and LP, but tended to be lower in cows fed LF compared with the HF diet (64.2% vs 67.9%; P = 0.09). Both milk urea N (P < 0.01) and blood urea N (P < 0.01) declined with decreasing dietary CP or forage contents. Based on purine derivative analysis, there was a tendency for interaction between dietary CP and forage content on microbial protein synthesis (P < 0.09). Rumen microbial protein synthesis tended to be lower for high forage and low protein treatments. Increasing dietary forage contents resulted in gre...