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

Quantifying the impact 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 spatio-temporal 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 four commonly used thermal indices: the Temperature and Humidity Index (THI), Black Globe Humidity Index (BGHI), Adjusted Temperature and Humidity Index (THIadj), and Comprehensive Climate Index (CCI). Using a statistical panel regression model with observational and reanalysis weather data from 1981-2020, we systematically compared the patterns of yield sensitivities and statistical performance of the four 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 a continuous and nonlinear responses of milk yields to a range of cold to heat stress across all four 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 impacted 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 two 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.

Systematic review and meta-analysis of the methionine utilization efficiency in piglets receiving different methionine sources.

Methionine (Met) is an essential amino acid that can be supplied in different chemical forms: DL-Met, L-Met, and OH-Met. This study aimed (i) to model and compare the utilization efficiency of Met for protein deposition (PD) from all sources and (ii) to determine the efficacy and efficiency of these three free Met sources in average daily gain (ADG) of post-weaning pigs fed at or below the Met requirement. A systematic review of the literature resulted in 1 898 papers being screened for title and abstract, with 24 papers meeting the inclusion criteria. The resulting database containing 208 treatment means was used. Prior to model development, the standardized ileal digestible (SID) Met requirements in percentage in the diet were determined using initial and final BW according to the NRC (2012). Data from piglets fed above the SID Met requirements were excluded from the database prior to statistical analysis. Linear mixed-effects regression models predicting ADG as a function of free Met source and SID methionine intake (Meti) or methionine + cysteine intake (Met + cysi) were used to evaluate the efficacy and efficiency of free Met source for weight gain. Moreover, Met retention was modeled assuming that 16% of ADG is deposited as PD, and that Met accounts for 2% of PD. Met utilization efficiency was calculated as Meti after maintenance divided by Met retained in PD. Met utilization efficiency was 77% for the basal diet, decreased (P < 0.01) as Meti increased, and was equal among the three free Met sources. The mixed-effects models showed no difference in ADG for three free Met sources evaluated (P > 0.05). However, the efficacy (ADG per unit of SID Meti) of free Met sources for weight gain differed between piglets fed L and DL-Met (P < 0.05), while there was no difference (P > 0.05) between piglets fed DL and OH-Met or OH and L-Met. On average, piglets fed L-Met gained 40.3 g/d more weight per unit of increase in SID Meti than those fed DL-Met (model 4; P = 0.05). The efficacy of free Met sources for ADG was also compared using SID Met + cysi as covariable. Piglets fed L- (+11.7 g/d; P = 0.02) or OH-Met (+11.5 g/d; P = 0.04) gained more weight per gram of SID Met + cysi compared to those fed DL-Met. In conclusion, although the efficacy of DL- and L-Met for ADG differed, the efficiency for PD of L-, DL-, and OH-Met were not different in piglets fed at or below Meti requirement.

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.

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.

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.

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.

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.

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.

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.

Genotype effects on energy and protein requirements in growing male goats.

Goat genotype may alter the net energy and protein requirements for maintenance (NEm and NPm, respectively) and weight gain (NEg and NPg).This study was designed to investigate and quantify the effect of goat type on NEm, NPm, NEg and NPg, and quantify the net requirements for energy and protein for dairy, meat and indigenous growing male goats. For that, comparative slaughter studies were gathered and a meta-analytical approach was used. Two distinct databases were organized: one composed of 233 individual records from 11 studies of meat (n = 81), dairy (n = 97) and indigenous (n = 55) growing male goats weighing from 4.50 to 51.0 kg, to depict NEm and NPm; and another database composed of 239 individual records from nine studies of meat (n = 87), dairy (n = 97) and indigenous (n = 55) growing male goats weighing from 4.30 to 51.0 kg, to depict NEg and NPg. Our findings showed that NEm of meat goats was 8.5% greater (336 ± 10.8 kJ/kg0.75 of empty BW; EBW) than dairy and indigenous goats (310 ± 8.20 kJ/kg0.75 EBW; P < 0.05). Whereas, NPm was not affected by goat type (1.92 ± 0.239 g/kg EBW; P = 0.91). The NPg was 185.1 ± 1.82 g/kg of EBW gain for goats weighing 5 kg BW and 192.5 ± 4.33 g/kg of EBW gain for goats weighing 45 kg BW, and thus did not change across goat type (P = 0.12). On the other hand, NEg increased from 7.29 ± 0.191 to 11.9 ± 0.386 MJ/kg of EBW in male dairy goats, and from 7.32 ± 0.144 to 15.7 ± 0.537 MJ/kg of EBW in meat and indigenous growing male goats weighing between 5 and 45 kg BW. When body protein was used as a predictor in the allometric equation instead of EBW seeking to account for the degree of maturity, goat type differences disappeared; however, this predictor showed a high variation among individuals. In conclusion, energy and protein requirements for gain in distinct goat types reflect on body composition differences. Future research should focus on better understanding the maturity degree and its consequences in the energy requirement of growing male goats and better depict the goat type effect on it, as well as on the efficiency of utilization.

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.

Review: Impact of protein and energy supply on the fate of amino acids from absorption to milk protein in dairy cows.

Making dairy farming more cost-effective and reducing nitrogen environmental pollution could be reached through a reduced input of dietary protein, provided productivity is not compromised. This could be achieved through balancing dairy rations for essential amino acids (EAA) rather than their aggregate, the metabolizable protein (MP). This review revisits the estimations of the major true protein secretions in dairy cows, milk protein yield (MPY), metabolic fecal protein (MFP), endogenous urinary loss and scurf and associated AA composition. The combined efficiency with which MP (EffMP) or EAA (EffAA) is used to support protein secretions is calculated as the sum of true protein secretions (MPY + MFP + scurf) divided by the net supply (adjusted to remove the endogenous urinary excretion: MPadj and AAadj). Using the proposed protein and AA secretions, EffMP and EffAA were predicted through meta-analyses (807 treatment means) and validated using an independent database (129 treatment means). The effects of MPadj or AAadj, plus digestible energy intake (DEI), days in milk (DIM) and parity (primiparous v. multiparous), were significant in all models. Models using (MPadj, MPadj × MPadj, DEI and DEI × DEI) or (MPadj/DEI and MPadj/DEI × MPadj/DEI) had similar corrected Akaike's information criterion, but the model using MPadj/DEI performed better in the validation database. A model that also included this ratio was, therefore, used to fitting equations to predict EffAA. These equations predicted well EffAA in the validation database except for Arg which had a strong slope bias. Predictions of MPY from predicted EffMP based on MPadj/DEI, MPadj/DEI × MPadj/DEI, DIM and parity yielded a better fit than direct predictions of MPY based on MPadj, MPadj × MPadj, DEI, DIM and parity. Predictions of MPY based on each EffAA yielded fairly similar results among AA. It is proposed to ponder the mean of MPY predictions obtained from each EffAA by the lowest prediction to retain the potential limitation from AA with the shortest supply. Overall, the revisited estimations of endogenous urinary excretion and MFP, revised AA composition of protein secretions and inclusion of a variable combined EffAA (based on AAadj/DEI, AAadj/DEI × Aadj/DEI, DIM and parity) offer the potential to improve predictions of MPY, identify which AA are potentially in short supply and, therefore, improve the AA balance of dairy rations.

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).

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.

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.

Multi-criteria evaluation of dairy cattle feed resources and animal characteristics for nutritive and environmental impacts.

On-farm nutrition and management interventions to reduce enteric CH4 (eCH4) emission, the most abundant greenhouse gas from cattle, may also affect volatile solids and N excretion. The objective was to jointly quantify eCH4 emissions, digestible volatile solids (dVS) excretion and N excretion from dairy cattle, based on dietary variables and animal characteristics, and to evaluate relationships between these emissions and excreta. Univariate and Bayesian multivariate mixed-effects models fitted to 520 individual North American dairy cow records indicated dry matter (DM) intake and dietary ADF and CP to be the main predictors for production of eCH4 emissions and dVS and N excreta (g/day). Yields (g/kg DM intake) of eCH4 emissions and dVS and N excreta were best predicted by dietary ADF, dietary CP, milk yield and milk fat content. Intensities (g/kg fat- and protein-corrected milk) of eCH4, dVS and N excreta were best predicted by dietary ADF, dietary CP, days in milk and BW. A K-fold cross-validation indicated that eCH4 and urinary N variables had larger root mean square prediction error (RMSPE; % of observed mean) than dVS, fecal N and total N production (on average 24.3% and 26.5% v. 16.7%, 15.5% and 16.2%, respectively), whereas intensity variables had larger RMSPE than production and yields (29.4%, 14.7% and 14.6%, respectively). Univariate and multivariate equations performed relatively similar (18.8% v. 19.3% RMSPE). Mutual correlations indicated a trade-off for eCH4 v. dVS yield. The multivariate model indicated a trade-off between eCH4 and dVS v. total N production, yield and intensity induced by dietary CP content.

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.

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.