Systematic review for optimizing sample size in dairy cow methane emission studies in temperate regions: a comprehensive methodological approach.

This research introduces a systematic framework for calculating sample size in studies focusing on enteric methane (CH4, g/kg of DMI) yield reduction in dairy cows. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search across the Web of Science, Scopus, and PubMed Central databases for studies published from 2012 to 2023. The inclusion criteria were: studies reporting CH4 yield and its variability in dairy cows, employing specific experimental designs (Latin Square Design (LSD), Crossover Design, Randomized Complete Block Design (RCBD), and Repeated Measures Design) and measurement methods (Open-circuit respirometry chambers (RC), the GreenFeed system, and the sulfur hexafluoride tracer technique), conducted in Canada, the United States and Europe. A total of 150 studies, which included 177 reports, met our criteria and were included in the database. Our methodology for using the database for sample size calculations began by defining 6 CH4 yield reduction levels (5, 10, 15, 20, 30, and 50%). Utilizing an adjusted Cohen's f formula and a power analysis we calculated the sample sizes required for these reductions in balanced LSD and RCBD reports from studies involving 3 or 4 treatments. The results indicate that within-subject studies (i.e., LSD) require smaller sample sizes to detect CH4 yield reductions compared with between-subject studies (i.e., RCBD). Although experiments using RC typically require fewer individuals due to their higher accuracy, our results demonstrate that this expected advantage is not evident in reports from RCBD studies with 4 treatments. A key innovation of this research is the development of a web-based tool that simplifies the process of sample size calculation (samplesizecalculator.ucdavis.edu). Developed using Python, this tool leverages the extensive database to provide tailored sample size recommendations for specific experimental scenarios. It ensures that experiments are adequately powered to detect meaningful differences in CH4 emissions, thereby contributing to the scientific rigor of studies in this critical area of environmental and agricultural research. With its user-friendly interface and robust backend calculations, this tool represents a significant advancement in the methodology for planning and executing CH4 emission studies in dairy cows, aligning with global efforts toward sustainable agricultural practices and environmental conservation.

Comparative analysis of thermal indices for modeling cold and heat stress in US dairy systems.

Quantifying the effect of thermal stress on milk yields is essential to effectively manage present and future risks in dairy systems. Despite the existence of numerous heat indices designed to communicate stress thresholds, little information is available regarding the accuracy of different indices in estimating milk yield losses from both cold and heat stress at large spatiotemporal scales. To address this gap, we comparatively analyzed the performance of existing thermal indices in capturing US milk yield response to both cold and heat stress at the national scale. We selected 4 commonly used thermal indices: the temperature-humidity index (THI), black globe humidity index (BGHI), adjusted temperature-humidity index (THIadj), and comprehensive climate index (CCI). Using a statistical panel regression model with observational and reanalysis weather data from 1981 to 2020, we systematically compared the patterns of yield sensitivities and statistical performance of the 4 indices. We found that the US state-level milk yield variability was better explained by the THIadj and CCI, which combine the effects of temperature, humidity, wind, and solar radiation. Our analysis also reveals continuous and nonlinear responses of milk yields to a range of cold to heat stresses across all 4 indices. This implies that solely relying on fixed thresholds of these indices to model milk yield changes may be insufficient to capture cumulative thermal stress. Cold extremes reduced milk yields comparably to those affected by heat extremes on the national scale. Additionally, we found large spatial variability in milk yield sensitivities, implying further limitations to the use of fixed thresholds across locations. Moreover, we found decreased yield sensitivity to thermal stress in the most recent 2 decades, suggesting adaptive changes in management to reduce weather-related risks.

Modeling the relationship between heat stress, feed intake, and day relative to calving in nonlactating dairy cows.

Heat stress (HS) during the dry period can affect animal welfare, health, dry matter intake (DMI), and milk production in the subsequent lactation, which will negatively affect the profitability of dairy farms. In this study, the objective was to model the changes in DMI in pregnant nonlactating heat-stressed dairy cows with or without access to evaporative cooling systems. A database was built, composed of individual DMI records from 244 pregnant nonlactating dairy cows from an average -29.3 d (range: -42 to -21 d; SD: ±7.54 d) to -1 d relative to calving (DRC) and housed in environmental conditions in which temperature-humidity index (THI) ranged from 58.4 to 83.3, with or without access to evaporative cooling systems. Generalized additive mixed-effects models were used to describe the relationships of DMI with HS and DRC. Changes in DMI with the increase in THI and the progression of pregnancy in cows with or without evaporative cooling systems were estimated using differential equations. On average, cows housed in barns without evaporative cooling systems had a reduction in DMI of 1.30 kg/d and increased rectal temperature in 0.22°C in relation to those housed in barns with evaporative cooling systems. Dry matter intake decreased as THI increased, but the reduction was greater for noncooled cows as THI values increased. In addition, regardless of the THI, DMI started to decrease at -14 DRC for cooled cows, whereas for noncooled cows it already started at -30 DRC, relative to the previous days evaluated. The intensity of the reduction was lesser for cows that had access to evaporative cooling systems or were in the dry period in May to June as compared with those that were in the dry period in July to August or September to October. The models generated in this study, which include environmental variables, should lead to more accurate predictions of DMI during HS that can be used to formulate diets to meet the needs of the late pregnant cow because it is possible to predict changes in DMI as the heat load and DRC change. Such models are also expected to help dairy nutritionists to decide when and how to apply the dietary strategies available to attenuate the reductions in DMI with the intensity of HS and progression of pregnancy.

Modeling the effects of heat stress in animal performance and enteric methane emissions in lactating dairy cows.

Heat stress (HS) negatively affects dry matter intake (DMI), milk yield (MY), feed efficiency (FE), and free water intake (FWI) in dairy cows, with detrimental consequences to animal welfare, health, and profitability of dairy farms. Absolute enteric methane (CH4) emission, yield (CH4/DMI), and intensity (CH4/MY) may also be affected. Therefore, the goal of this study was to model the changes in dairy cow productivity, water intake, and absolute CH4 emissions, yield, and intensity with the progression (days of exposure) of a cyclical HS period in lactating dairy cows. Heat stress was induced by increasing the average temperature by 15°C (from 19°C in the thermoneutral period to 34°C) while keeping relative humidity constant at 20% (temperature-humidity index peaks of approximately 83) in climate-controlled chambers for up to 20 d. A database composed of individual records (n = 1,675) of DMI and MY from 82 heat-stressed lactating dairy cows housed in environmental chambers from 6 studies was used. Free water intake was also estimated based on DMI, dry matter, crude protein, sodium, and potassium content of the diets, and ambient temperature. Absolute CH4 emissions was estimated based on DMI, fatty acids, and dietary digestible neutral detergent fiber content of the diets. Generalized additive mixed-effects models were used to describe the relationships of DMI, MY, FE, and absolute CH4 emissions, yield, and intensity with HS. Dry matter intake and absolute CH4 emissions and yield reduced with the progression of HS up to 9 d, when it started to increase again up to 20 d. Milk yield and FE reduced with the progression of HS up to 20 d. Free water intake (kg/d) decreased during the exposure to HS mainly because of a reduction in DMI; however, when expressed in kg/kg of DMI it increased modestly. Methane intensity also reduced initially up to d 5 during HS exposure but then started to increase again following the DMI and MY pattern up to d 20. However, the reductions in CH4 emissions (absolute, yield, and intensity) occurred at the expense of decreases in DMI, MY, and FE, which are not desirable. This study provides quantitative predictions of the changes in animal performance (DMI, MY, FE, FWI) and CH4 emissions (absolute, yield, and intensity) with the progression of HS in lactating dairy cows. The models developed in this study could be used as a tool to help dairy nutritionists to decide when and how to adopt strategies to mitigate the negative effects of HS on animal health and performance and related environmental costs. Thus, more precise and accurate on-farm management decisions could be taken with the use of these models. However, application of the developed models outside of the ranges of temperature-humidity index and period of HS exposure included in this study is not recommended. Also, validation of predictive capacity of the models to predict CH4 emissions and FWI using data from in vivo studies where these variables are measured in heat-stressed lactating dairy cows is required before these models can be used.

Impact of nitrate and 3-nitrooxypropanol on the carbon footprints of milk from cattle produced in confined-feeding systems across regions in the United States: A life cycle analysis.

It is estimated that enteric methane (CH4) contributes about 70% of all livestock greenhouse gas (GHG) emissions. Several studies indicated that feed additives such as 3-nitrooxypropanol (3-NOP) and nitrate have great potential to reduce enteric emissions. The objective of this study was to determine the net effects of 3-NOP and nitrate on farmgate milk carbon footprint across various regions of the United States and to determine the variability of carbon footprint. A cradle-to-farmgate life cycle assessment was performed to determine regional and national carbon footprint to produce 1 kg of fat- and protein-corrected milk (FPCM). Records from 1,355 farms across 37 states included information on herd structure, milk production and composition, cattle diets, manure management, and farm energy. Enteric CH4, manure CH4, and nitrous oxide were calculated with either the widely used Intergovernmental Panel on Climate Change Tier 2 or region-specific equations available in the literature. Emissions were allocated between milk and meat using a biophysical allocation method. Impacts of nitrate and 3-NOP on baseline regional and national carbon footprint were accounted for using equations adjusted for dry matter intake and neutral detergent fiber. Uncertainty analysis of carbon footprint was performed using Monte Carlo simulations to capture variability due to inputs data. Overall, the milk carbon footprint for the baseline, nitrate, and 3-NOP scenarios were 1.14, 1.09 (4.8% reduction), and 1.01 (12% reduction) kg of CO2-equivalents (CO2-eq)/kg of FPCM across US regions. The greatest carbon footprint for the baseline scenario was in the Southeast (1.26 kg of CO2-eq/kg of FPCM) and lowest for the West region (1.02 kg of CO2-eq/kg of FPCM). Enteric CH4 reductions were 12.4 and 31.0% for the nitrate and 3-NOP scenarios, respectively. The uncertainty analysis showed that carbon footprint values ranged widely (0.88-1.52 and 0.56-1.84 kg of CO2-eq/kg of FPCM within 1 and 2 standard deviations, respectively), suggesting the importance of site-specific estimates of carbon footprint. Considering that 101 billion kilograms of milk was produced by the US dairy industry in 2020, the potential net reductions of GHG from the baseline 117 billion kilograms of CO2-eq were 5.6 and 13.9 billion kilograms of CO2-eq for the nitrate and 3-NOP scenarios, respectively.

Symposium review: Development of a funding program to support research on enteric methane mitigation from ruminants.

Enteric methane is a major source of greenhouse gas emissions from milk production systems. Two organizations based in the United States, the Foundation for Food and Agriculture Research and the Dairy Research Institute, have developed a collaborative program to align resources and fund projects to identify, develop, and validate new and existing mitigation options for enteric methane emissions from dairy and beef cattle. This collaborative program is called the Greener Cattle Initiative. The program will develop requests for proposals and award grants on projects that address challenges within, but not limited, to the following research areas: dairy and beef cattle nutrition, rumen microbiome, dairy and beef cattle genetics, sensing and data technology for enteric methane measurement and prediction, and socioeconomic analysis of enteric methane mitigation practices. The program is structured as a consortium with closed participation and a flat governance collaboration model. The Greener Cattle Initiative program will continue incorporating participants from the food and agriculture industry, commodity groups, and nonprofit organizations who share common objectives and contribute in-kind and matching funds to the program, up to a total of 10 organizations. Research findings will be communicated broadly, after a waiting period for exclusive access to program participants, to create shared knowledge on enteric methane mitigation. The Greener Cattle Initiative is expected to award up to $5 million in research grant funding in a 5-year period, which will contribute to advancing the voluntary greenhouse gas reduction goals established by both the United States and global dairy sectors.

The application of nonlinear programming on ration formulation for dairy cattle.

The objective of this study was to compare the application of iterative linear programming (iteLP), sequential quadratic programming (SQP), and mixed-integer nonlinear programming-based deterministic global optimization (MINLP_DGO) on ration formulation for dairy cattle based on Nutrient Requirements of Dairy Cattle (NRC, 2001). Least-cost diets were formulated for lactating cows, dry cows, and heifers. Nutrient requirements including energy, protein, and minerals, along with other limitations on dry matter intake, neutral detergent fiber, and fat were considered as constraints. Five hundred simulations were conducted, with each simulation randomly selecting 3 roughages and 5 concentrates from the feed table in NRC (2001) as the feed resource for each of 3 animal groups. Among the 500 simulations for lactating cows, 57, 45, and 21 simulations did not yield a feasible solution when using iteLP, SQP, and MINLP_DGO, respectively. All the simulations for dry cows and heifers were feasible when using SQP and MINLP_DGO, but 49 and 11 infeasible simulations occurred when using iteLP for dry cows and heifers, respectively. The average ration costs per animal per day of the feasible solutions obtained by iteLP, SQP, and MINLP_DGO were $4.78 (±0.71), $4.45 (±0.65), and $4.44 (±0.65) for lactating cows; $2.39 (±0.52), $1.48 (±0.26), and $1.48 (±0.26) for dry cows; and $0.98 (±0.72), $0.97 (±0.15), and $0.91 (±0.14) for heifers, respectively. The average computation time of iteLP, SQP, and MINLP_DGO were 0.59 (±1.87) s, 1.15 (±0.62) s, and 58.69 (±68.45) s for lactating cows; 0.041 (±0.070) s, 0.76 (±0.37) s, and 14.84 (±39.09) s for dry cows; and 1.60 (±2.90) s, 0.51 (±0.19) s, and 16.45 (±45.56) s for heifers, respectively. In conclusion, iteLP had limited capability of formulating least-cost diets when nonlinearity existed in the constraints. Both SQP and MINLP_DGO handled the nonlinear constraints well, with SQP being faster, whereas MINLP_DGO was able to return a feasible solution under some situations where SQP could not.

Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis.

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.

MILK Symposium review: Sustainability of dairy production and consumption in low-income countries with emphasis on productivity and environmental impact.

Sustainable milk production and consumption in low-income countries must address food security and climate change mitigation simultaneously. Socioeconomic sustainability is paramount in low-income countries, where milk production and consumption represent a vehicle to improve human nutrition and health, as well as the potential for economic opportunity and improved livelihood of subsistence farmers. These benefits can only be achieved with judicious use of animal stocks and agricultural practices that do not exhaust available natural resources, which are often shared by regional farming communities. Milk and dairy foods provide variety to the diet and make significant contributions to meeting the needs for high-quality protein, calcium, magnesium, selenium, riboflavin, vitamin B12, and pantothenic acid (vitamin B5) in at-risk populations, particularly children, pregnant women, and the elderly. Milk production in low-income countries occurs largely in smallholder mixed crop-livestock systems where animals play multiple roles and may suffer from undernutrition, leading to negligible or no milk production during several months of the year. Non-food roles of livestock include draft, fuel (manure), store of capital, and insurance against crop failure. These roles and the social standing associated with animal ownership may incentivize the maintenance of large herds that place stress on feed (land) and water resources. Under these circumstances, sustainable intensification (i.e., increasing milk production from currently available resources) represents the single most important and practical strategy to improve the sustainability of milk production and consumption in low-income countries. Improving the genetic potential of animals and the availability of quality feed, and providing balanced nutrition are the most promising strategies to improve milk production and sustainability in low-income countries. For example, the deficit for milk in Ethiopia is estimated at 4.5 billion liters/year, which can be closed, in part, with balanced animal nutrition. Milk production in low-income countries will be more sustainable if it relies on natural resources available locally and regionally to supply essential nutrients to at-risk human populations.

Greenhouse gas, water, and land footprint per unit of production of the California dairy industry over 50 years.

Food production including dairy has been associated with environmental impacts and resource use that has been steadily improving when adjusted per unit of product. The objective of this study was to conduct a cradle-to-farm gate environmental impact analysis and resource inventory of the California dairy production system to estimate the change in greenhouse gas emissions and water and land use over the 50-yr period between 1964 and 2014. Using a life cycle assessment according to international standards and the Food and Agriculture Organization of the United Nations guidelines, we analyzed contributions from dairy production in California to global environmental change. Production of 1 kg of energy- and protein-corrected milk (ECM) in California emitted 1.12 to 1.16 kg of CO2 equivalents (CO2e) in 2014 compared with 2.11 kg of CO2e in 1964, a reduction of 45.0 to 46.9% over the last 50 yr, depending on the model used. Greater reductions in enteric methane intensity (i.e., methane production per kilogram of ECM) were observed (reduction of 54.1 to 55.7%) compared with manure GHG (reduction of 8.73 to 11.9%) in 2014 compared with 1964. This was mainly because manure management in the state relies on lagoons for storage, which has a greater methane conversion factor than solid manure storage. Water use intensity was reduced by 88.1 to 89.9%, with water reductions of 88.7 to 90.5% in crop production, 55.3 to 59.2% in housing and milking, and 52.4 to 54% in free water intake. Improved crop genetics and management have contributed to large efficiencies in water utilization. Land requirements for crop production were reduced by 89.4 to 89.7% in 2014 compared with 1964. This was mainly due to dramatic increases in crop yields in the last 50 yr. The increases in milk production per cow through genetic improvements and better nutrition and animal care have contributed to reductions in greenhouse gas emissions and land and water usage when calculated per unit of production (intensity) basis.

Antimethanogenic effects of nitrate supplementation in cattle: A meta-analysis.

Supplementing a diet with nitrate is regarded as an effective and promising methane (CH4) mitigation strategy by competing with methanogens for available hydrogen through its reduction of ammonia in the rumen. Studies have shown major reductions in CH4 emissions with nitrate supplementation, but with large variation in response. The objective of this study was to quantitatively investigate the effect of dietary nitrate on enteric CH4 production and yield and evaluate the variables with high potential to explain the heterogeneity of between-study variability using meta-analytical models. A data set containing 56 treatments from 24 studies was developed to conduct a meta-analysis. Dry matter (DM) intake, nitrate dose (g/kg of DM), animal body weight, roughage proportion of diet, dietary crude protein and neutral detergent fiber content, CH4 measurement technique, and type of cattle (beef or dairy) were considered as explanatory variables. Average DM intake and CH4 production for dairy cows (16.2 ± 2.93 kg/d; 311 ± 58.8 g/d) were much higher than for beef cattle (8.1 ± 1.57 kg/d; 146 ± 50.9 g/d). Therefore, a relative mean difference was calculated and used to conduct random-effect and mixed-effect model analysis to eliminate the large variations between types of animal due to intake. The final mixed-effect model for CH4 production (g of CH4/d) had 3 explanatory variables and included nitrate dose, type of cattle, and DM intake. The final mixed-effect model for CH4 yield (g of CH4/kg of DM intake) had 2 explanatory variables and included nitrate dose and type of cattle. Nitrate effect sizes on CH4 production (dairy: -20.4 ± 1.89%; beef: -10.1 ± 1.52%) and yield (dairy: -15.5 ± 1.15%; beef: -8.95 ± 1.764%) were significantly different between the 2 types of cattle. When data from slow-release nitrate sources were removed from the analysis, there was no significant difference in type of cattle anymore for CH4 production and yield. Nitrate dose enhanced the mitigating effect of nitrate on CH4 production and yield by 0.911 ± 0.1407% and 0.728 ± 0.2034%, respectively, for every 1 g/kg of DM increase from its mean dietary inclusion (16.7 g/kg of DM). An increase of 1 kg of DM/d in DM intake from its mean dietary intake (11.1 kg of DM/d) decreased the effect of nitrate on CH4 production by 0.691 ± 0.2944%. Overall, this meta-analysis demonstrated that nitrate supplementation reduces CH4 production and yield in a dose-dependent manner, and that elevated DM intake decreases the effect of nitrate supplementation on CH4 production. Furthermore, the stronger antimethanogenic effect on CH4 production and yield in dairy cows than in beef steers could be related to use of slow-release nitrate in beef cattle.

Individual milk fatty acids are potential predictors of enteric methane emissions from dairy cows fed a wide range of diets: Approach by meta-analysis.

There is a need to quantify methane (CH4) emissions with alternative methods. For the past decade, milk fatty acids (MFA) could be used as proxies to predict CH4 emissions from dairy cows because of potential common rumen biochemical pathways. However, equations have been developed based on a narrow range of diets and with limited data. The objectives of this study were to (1) construct a set of empirical models based on individual data of CH4 emissions and MFA from a large number of lactating dairy cows fed a wide range of diets; (2) further increase the models' level of complexity (from farm to research level) with additional independent variables such as dietary chemical composition (organic matter, neutral detergent fiber, crude protein, starch, and ether extract), dairy performance (milk yield and composition), and animal characteristics (days in milk or body weight); and (3) evaluate the performance of the developed models on independent data sets including measurements from individual animals or average measurements of groups of animals. Prediction equations based only on MFA [C10:0, iso C17:0 + trans-9 C16:1,cis-11 C18:1, and trans-11,cis-15 C18:2 for CH4 production (g/d); iso C16:0, cis-11 C18:1, trans-10 C18:1, and cis-9,cis-12 C18:2 for CH4 yield (g/kg of dry matter intake, DMI); and iso C16:0, cis-15 C18:1, and trans-10 + trans-11 C18:1 for CH4 intensity (g/kg of milk)] had a root mean squared error of 65.1 g/d, 2.8 g/kg of DMI, and 2.9 g/kg of milk, respectively, whereas complex equations that additionally used DMI, dietary neutral detergent fiber, ether extract, days in milk, and body weight had a lower root mean squared error of 46.6 g/d, 2.6 g/kg of DMI, and 2.7 g/kg of milk, respectively). External evaluation with individual or mean data not used for equation development led to variable results. When evaluations were performed using individual cow data from an external data set, accurate predictions of CH4 production (g/d) were obtained using simple equations based on MFA. Better performance was observed on external evaluation with individual data for the simple equation of CH4 production (g/d, based on MFA), whereas better performance was observed on external evaluation mean data for the simple equation of CH4 yield (g/kg of DMI). The performance of evaluation of the models is dependent on the domain of validity of the evaluation data sets used (individual or mean).

Exogenous ß-mannanase supplementation improved immunological and metabolic responses in lactating dairy cows.

Exogenous enzymes have been used to improve nutrient utilization in several species of livestock, particularly swine and poultry. In addition, improved immunological and metabolic traits have been reported in nonruminants. The objective of this study was to determine the effects of ß-mannanase supplementation on milk yield and composition, and immunological and metabolic responses in lactating Holstein dairy cows. Two weeks after calving, 20 Holstein cows (10 multiparous and 10 primiparous) were blocked by parity and assigned to 1 of 2 diets for 182 d. All cows were housed in the same environment and fed the same basal diet. The basal diet of the treatment group was supplemented with ß-mannanase (CTCBio Inc., Seoul, South Korea) at 0.1% of concentrate dry matter. No differences were detected between the control and enzyme supplement groups in milk yield parameters or milk composition. Supplementation of ß-mannanase enzyme reduced blood haptoglobin levels in supplemented multiparous cows compared with controls. Furthermore, nonesterified fatty acid concentration levels tended to be lower in cows fed ß-mannanase, regardless of parity. Neither immunoglobulin G nor milk somatic cell count was affected by ß-mannanase supplementation, regardless of parity. The number of insemination services tended to be lower in cows fed diets supplemented with ß-mannanase. Results from this study suggest that supplementation of ß-mannanase exogenous enzyme could help to reduce instances of systemic inflammation and decrease fat mobilization in lactating Holstein cows. Multiparous cows are considered susceptible to acute infections and inflammation; thus, the enzyme had a greater effect in multiparous cows.

Invited review: Nitrogen in ruminant nutrition: A review of measurement techniques.

Nitrogen is a component of essential nutrients critical for the productivity of ruminants. If excreted in excess, N is also an important environmental pollutant contributing to acid deposition, eutrophication, human respiratory problems, and climate change. The complex microbial metabolic activity in the rumen and the effect on subsequent processes in the intestines and body tissues make the study of N metabolism in ruminants challenging compared with nonruminants. Therefore, using accurate and precise measurement techniques is imperative for obtaining reliable experimental results on N utilization by ruminants and evaluating the environmental impacts of N emission mitigation techniques. Changeover design experiments are as suitable as continuous ones for studying protein metabolism in ruminant animals, except when changes in body weight or carryover effects due to treatment are expected. Adaptation following a dietary change should be allowed for at least 2 (preferably 3) wk, and extended adaptation periods may be required if body pools can temporarily supply the nutrients studied. Dietary protein degradability in the rumen and intestines are feed characteristics determining the primary AA available to the host animal. They can be estimated using in situ, in vitro, or in vivo techniques with each having inherent advantages and disadvantages. Accurate, precise, and inexpensive laboratory assays for feed protein availability are still needed. Techniques used for direct determination of rumen microbial protein synthesis are laborious and expensive, and data variability can be unacceptably large; indirect approaches have not shown the level of accuracy required for widespread adoption. Techniques for studying postruminal digestion and absorption of nitrogenous compounds, urea recycling, and mammary AA metabolism are also laborious, expensive (especially the methods that use isotopes), and results can be variable, especially the methods based on measurements of digesta or blood flow. Volatile loss of N from feces and particularly urine can be substantial during collection, processing, and analysis of excreta, compromising the accuracy of measurements of total-tract N digestion and body N balance. In studying ruminant N metabolism, nutritionists should consider the longer term fate of manure N as well. Various techniques used to determine the effects of animal nutrition on total N, ammonia- or nitrous oxide-emitting potentials, as well as plant fertilizer value, of manure are available. Overall, methods to study ruminant N metabolism have been developed over 150 yr of animal nutrition research, but many of them are laborious and impractical for application on a large number of animals. The increasing environmental concerns associated with livestock production systems necessitate more accurate and reliable methods to determine manure N emissions in the context of feed composition and ruminant N metabolism.

Short communication: Antimethanogenic effects of 3-nitrooxypropanol depend on supplementation dose, dietary fiber content, and cattle type.

3-Nitrooxypropanol (NOP) is a promising methane (CH4) inhibitor. Recent studies have shown major reductions in CH4 emissions from beef and dairy cattle when using NOP but with large variation in response. The objective of this study was to quantitatively evaluate the factors that explain heterogeneity in response to NOP using meta-analytical approaches. Data from 11 experiments and 38 treatment means were used. Factors considered were cattle type (dairy or beef), measurement technique (GreenFeed technique, C-Lock Inc., Rapid City, SD; sulfur hexafluoride tracer technique; and respiration chamber technique), dry matter (DM) intake, body weight, NOP dose, roughage proportion, dietary crude protein content, and dietary neutral detergent fiber (NDF) content. The mean difference (MD) in CH4 production (g/d) and CH4 yield (g/kg of DM intake) was calculated by subtracting the mean of CH4 emission for the control group from that of the NOP-supplemented group. Forest plots of standardized MD indicated variable effect sizes of NOP across studies. Compared with beef cattle, dairy cattle had a much larger feed intake (22.3 ± 4.13 vs. 7.3 ± 0.97 kg of DM/d; mean ± standard deviation) and CH4 production (351 ± 94.1 vs. 124 ± 44.8 g/d). Therefore, in further analyses across dairy and beef cattle studies, MD was expressed as a proportion (%) of observed control mean. The final mixed-effect model for relative MD in CH4 production included cattle type, NOP dose, and NDF content. When adjusted for NOP dose and NDF content, the CH4-mitigating effect of NOP was less in beef cattle (-22.2 ± 3.33%) than in dairy cattle (-39.0 ± 5.40%). An increase of 10 mg/kg of DM in NOP dose from its mean (123 mg/kg of DM) enhanced the NOP effect on CH4 production decline by 2.56 ± 0.550%. However, a greater dietary NDF content impaired the NOP effect on CH4 production by 1.64 ± 0.330% per 10 g/kg DM increase in NDF content from its mean (331 g of NDF/kg of DM). The factors included in the final mixed-effect model for CH4 yield were -17.1 ± 4.23% (beef cattle) and -38.8 ± 5.49% (dairy cattle), -2.48 ± 0.734% per 10 mg/kg DM increase in NOP dose from its mean, and 1.52 ± 0.406% per 10 g/kg DM increase in NDF content from its mean. In conclusion, the present meta-analysis indicates that a greater NOP dose enhances the NOP effect on CH4 emission, whereas an increased dietary fiber content decreases its effect. 3-Nitrooxypropanol has stronger antimethanogenic effects in dairy cattle than in beef cattle.

Predicting milk protein responses and the requirement of metabolizable protein by lactating dairy cows.

The objective of this study was to develop a modeling framework to predict milk protein yield responses to varying metabolizable protein (MP) supplies and to determine the requirement of MP by lactating dairy cows. The logistic curve was used to model milk protein yield while accounting for a variable efficiency of MP utilization and between-study variability. Models were developed with databases from 2 recently published meta-analyses and based on either total MP supply or MP supply available for milk production. All models provided reasonable fit to data, with root mean square prediction error ranging from 18 to 20% of the average milk protein yield. The estimated horizontal asymptotes were 1.17 (posterior SD = 0.02) and 1.55 (posterior SD = 0.06) in the 2 databases, suggesting that the limiting milk protein yield, as MP supply increases, converges to 1.17 or 1.55 kg/d in the environments determined by the 2 databases. The observed efficiencies ranged from 0.75 to 0.18 when total MP supply was used as the denominator and above 1 to 0.24 when the MP supply available for milk production was used as the denominator. The predicted efficiencies were in good agreement with the data, decreasing nonlinearly with the MP supply. The MP requirement was calculated with a function constructed with the inverse of the logistic model and modified at regions of maximum marginal efficiency and minimum second derivative. This strategy assumes that the MP solution, or the MP needed to predict a given protein yield in the fitted logistic curve, determines the MP requirement for maintenance and lactation. Requirements calculated with the independent variable as total MP supply refer to the total requirement of maintenance plus lactation, whereas the requirement from models based on MP supply available for milk production are referent to the MP required only for lactation. The requirements were, on average, slightly smaller than the ones predicted by the current Northern American feeding system for dairy cows at lower protein yields and greater than currently recommended at high yields.

Predicting manure volatile solid output of lactating dairy cows.

Organic matter (OM) in livestock manure consisting of biodegradable and nonbiodegradable fractions is known as volatile solids (VS). According to the Intergovernmental Panel on Climate Change (IPCC) Tier 2 guidelines, methane produced by stored manure is determined based on VS. However, only biodegradable OM generates methane production. Therefore, estimates of biodegradable VS (dVS; dVS = VS - lignin) would yield better estimates of methane emissions from manure. The objective of the study was to develop mathematical models for estimating VS and dVS outputs of lactating dairy cows. Dry matter intake, dietary nutrient contents, milk yield and composition, body weight, and days in milk were used as potential predictor variables. Multicollinearity, model simplicity, and random study effects were taken into account during model development that used 857 VS and dVS measurements made on individual cows (kg/cow per day) from 43 metabolic trials conducted at the USDA Energy and Metabolism laboratory in Beltsville, Maryland. The new models and the IPCC Tier 2 model were evaluated with an independent data set including 209 VS and dVS measurements (kg/cow per day) from 2 metabolic trials conducted at the University of California, Davis. Organic matter intake (kg/d) and dietary crude protein and neutral detergent fiber contents (% of dry matter) were significantly associated with VS. A new model including these variables fitted best to data. When evaluated with independent data, the new model had a root mean squared prediction error as a percentage of average observed value (RMSPE) of 12.5%. Mean and slope biases were negligible at <1% of total prediction bias. When energy digestibility of the diet was assumed to be 67%, the IPCC Tier 2 model had a RMSPE of 13.7% and a notable mean bias for VS to be overpredicted by 0.4 kg/cow per day. A separate model including OM intake as well as dietary crude protein and neutral detergent fiber contents as predictor variables fitted best to dVS data and performed well on independent data (RMSPE = 12.7%). The Cornell Net Carbohydrate and Protein System model relying on fat-corrected milk yield and body weight more successfully predicted dry matter intake (DMI; RMSPE = 14.1%) than the simplified (RMSPE = 16.9%) and comprehensive (RMSPE = 23.4%) models to predict DMI in IPCC Tier 2 methodology. New models and the IPCC Tier 2 model using DMI from the Cornell Net Carbohydrate and Protein System model predicted VS (RMSPE = 17.7-19.4%) and dVS (RMSPE = 20%) well with small systematic bias (<10% of total bias). The present study offers empirical models that can accurately predict VS and dVS of dairy cows using routinely available data in dairy farms and thereby assist in efficiently determining methane emissions from stored manure.

Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models.

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.

Effect of Dairy Manure Storage Conditions on the Survival of E. coli O157:H7 and Listeria.

Dairy manure is regularly applied to crop fields as a solid or liquid to improve the soil nutrient status. However, pathogens may survive during manure storage and enter the environment during application. In this study, three storage practices were evaluated to understand the survival patterns of O157:H7 and spp. in dairy manure using a culture-based approach. To replicate common farm manure storage techniques, solid manure was stacked as piles with periodic turning or as static piles without turning, whereas liquid manure (feces, urine, and water) was stored as a slurry in small tanks to simulate lagoon conditions. The and levels in the manure samples were determined for 29 wk. Results showed that there was an initial reduction in bacteria levels in the first month; however, both and managed to survive in the solid manure piles for the full study period. In slurry samples, was not detected after 14 wk, but survived until the end of the experiment at relatively lower levels than in the solid manure piles. Ambient weather and pile size were identified as the main reasons for bacteria survival during the course of the experiment. The outcome of this study is important in terms of understanding pathogen survival in manure piles and slurries prior to their application to crop fields.

Relationships between postruminal casein infusion and milk production, and concentrations of plasma amino acids and blood urea in dairy cows: A multilevel mixed-effects meta-analysis.

The relationships between postruminal casein infusion and production variables and concentrations of plasma AA and blood urea were evaluated using multilevel mixed-effects models derived from literature data collected in dairy cows. The data set contained 147 treatment means [i.e., 66 controls (CTL) and 81 casein-infused (CAS) means]. Each CAS mean was paired with its corresponding CTL mean to create 81 mean differences (CAS minus CTL), which were analyzed as absolute and percentage-based units (i.e., percentage increase or decrease in CAS relative to CTL). The primary variable of interest was the difference in estimated metabolizable protein (MP) supply (ΔMP) between CAS and CTL. The other explanatory variables were based on levels in CTL: MP supply, MP balance, the ratio of duodenal microbial protein (MCP) to MP supply (MCPMP), the stage of lactation (early or mid/late) and the type of forage (grass/legume- or corn silage-based). The MP supply and MP balance influenced negatively the relationship between ΔMP and the response of true protein yield. Responses of milk urea, blood urea, and plasma urea cycle AA concentrations were associated positively with ΔMP, indicating that a large amount of infused AA was catabolized to urea. Responses of plasma essential AA concentrations were related positively to ΔMP. The relative effect of ΔMP was highest for responses of plasma His concentration in cows fed grass/legume-based diets and at high MCPMP ratios. This relationship suggests that positive responses of plasma His concentrations are associated with diets relying heavily on microbial protein synthesis (high MCP), low in crude protein (low estimated MP supply), or both. The relationship between ΔMP and responses of plasma group 2 AA (Ile, Leu, Lys, and Val) concentrations was approximately 2 times greater than that for group 1 AA (His, Met, and Phe+Tyr) at mean MCPMP and MP supply. This could reflect the low hepatic removal group 2 AA compared with group 1 AA in dairy cows. Collectively, these results provide novel information on how dietary and cow conditions may alter responses to protein supplementation.