Enteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH₄ 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 CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ 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 CH₄ 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 CH₄ 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 CH₄ emission conversion factors for specific regions are required to improve CH₄ 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 CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation.

Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) 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 CH4 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 CH4 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 CH4 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 CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 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.

The study aimed to examine, simultaneously, the effects of changing dietary forage and crude protein (CP) contents on enteric methane (CH4) emissions and nitrogen (N) excretion from lactating dairy cows. Twelve post-peak lactating Holstein cows (157 ± 31 days postpartum; mean ± 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 CH4 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 CH4 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 CH4 emissions, and CH4 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 greater CH4 emission (g/kg of energy-corrected milk) and manure N excretion (g/kg of energy-corrected milk) intensities of lactating dairy cows. Cows receiving reduced CP diets had low manure N outputs and improved milk true protein production efficiencies, regardless of dietary forage content.

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.