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Background: As the COVID-19 pandemic continues to unfold, there has been a substantial increase in the demand for prehospital services. Emergency medical service (EMS) providers have encountered a myriad of challenges that have had a discernible impact on their professional performance. This study was designed to explore the challenges faced by EMS providers during the initial phase of the COVID-19 pandemic., Methods: This qualitative research was conducted using a content analysis approach at emergency medical centers affiliated with Hamadan University of Medical Sciences in Iran between April and August 2021. This study included the participation of 21 EMS personnel, which was conducted using purposive sampling and semistructured interviews, and continued until data saturation was reached. The conventional content analysis method, as outlined by Graneheim and Lundman, was applied for data analysis., Results: The analysis of the interview data resulted in the identification of 219 primary codes, which were then organized into ten distinct categories. These categories were further consolidated into three overarching themes: personal safety challenges, professional-organizational challenges, and threatened mental health., Conclusions: EMS personnel play a critical role in healthcare during disasters and pandemics, facing challenges that can have negative effects. Managing these challenges can impact mental health and professional well-being, but awareness, support, resources, and services can help mitigate adverse consequences., (© 2024. The Author(s).)

Feed utilization efficiency is an important trait in dairy production playing a significant role in reducing feed costs and lowering methane emission. One of the metrics used to measure feed efficiency in dairy cows is residual feed intake (RFI). This metric requires routine measurement of feed intake. Since there is a positive high correlation between heat production and carbon dioxide (CO 2 ) production on the one hand and heat production and efficiency on the other hand, residual carbon dioxide (RCO 2 ) might be a useful metric to improve feed efficiency. The objectives of this study were to model the trajectories of RCO 2 and RFI as well as to estimate their repeatabilities and correlations at different stages of lactation. Daily CO 2 output and feed intake were recorded from 46 primiparous Nordic Red dairy cows using two Greenfeed Emissions Monitoring™ systems from 2 to 305 days in milk (DIM). Edited data comprised 5 995 daily averages. To calculate predicted values of CO 2 and DM intake (DMI), prediction models were developed by fitting multiple regression models to observations. Subsequently, RCO 2 and RFI were calculated by subtracting predicted values of CO 2 and DMI from their corresponding actual observations. A random regression bivariate model was fitted to estimate repeatabilities and animal correlations within lactation at different DIMs between RCO 2 and RFI traits. The model fitted included fixed effects of year-month of recording, lactation month, fixed regressions as well as random regressions for the animal effect. The residual variance was considered to be heterogeneous. Repeatabilities and animal correlations of RCO 2 and RFI between selected DIM (for every 30 DIM i.e., 6, 36,…, 246 and 276) were calculated. Repeatability of RCO 2 was high at the beginning of lactation (0.72 at DIM 6) and decreased around the peak of milk production (0.27 at DIM 96) and again increased gradually toward the end of lactation. Similarly, RFI also had high repeatability at the beginning (0.86 at DIM 6); however, it decreased in mid-lactation (0.37 at DIM 156) and then increased toward the end of lactation. Animal correlations between RCO 2 and RFI were moderate to high on the same DIM and ranged from 0.37 to 0.88. Overall, we found that animals with higher CO 2 production than expected also consume more DMI than expected, but the moderate correlation between RCO 2 and RFI found in this study calls for more research to assess the potential of RCO 2 to become a new feed efficiency metric., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

The rumen represents a dynamic microbial ecosystem where fermentation metabolites and microbial concentrations change over time in response to dietary changes. The integration of microbial genomic knowledge and dynamic modelling can enhance our system-level understanding of rumen ecosystem's function. However, such an integration between dynamic models and rumen microbiota data is lacking. The objective of this work was to integrate rumen microbiota time series determined by 16S rRNA gene amplicon sequencing into a dynamic modelling framework to link microbial data to the dynamics of the volatile fatty acids (VFA) production during fermentation. For that, we used the theory of state observers to develop a model that estimates the dynamics of VFA from the data of microbial functional proxies associated with the specific production of each VFA. We determined the microbial proxies using CowPi to infer the functional potential of the rumen microbiota and extrapolate their functional modules from KEGG (Kyoto Encyclopedia of Genes and Genomes). The approach was challenged using data from an in vitro RUSITEC experiment and from an in vivo experiment with four cows. The model performance was evaluated by the coefficient of variation of the root mean square error (CRMSE). For the in vitro case study, the mean CVRMSE were 9.8% for acetate, 14% for butyrate and 14.5% for propionate. For the in vivo case study, the mean CVRMSE were 16.4% for acetate, 15.8% for butyrate and 19.8% for propionate. The mean CVRMSE for the VFA molar fractions were 3.1% for acetate, 3.8% for butyrate and 8.9% for propionate. Ours results show the promising application of state observers integrated with microbiota time series data for predicting rumen microbial metabolism., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Davoudkhani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The effects of grass silage and red clover silage on milk fatty acid (FA) composition are extensively studied, but little is known of their effects on minor lipid constituents of milk fat globule membrane. We investigated the effects of forage:concentrate (FC) ratio in grass silage-based diets and forage type (grass silage vs. red clover silage) on selected molecular species of milk phospholipids (PL) and the FA composition of PL. Ten multiparous Nordic Red cows were offered following dietary treatments: grass silage-based diets containing 70:30 (HG) or 30:70 (LG) FC ratio or a red clover silage-based diet (RC) comprising 50:50 FC ratio on a dry matter basis. The most abundant molecular species within the phosphatidylcholines was 16:0-18:1 phosphatidylcholine that was increased by 18% in HG compared with LG milk. Dietary treatments did not affect the relative proportion of 18:1-18:1+18:0-18:2 phosphatidylethanolamine that was the most prevalent species (ca. 44%-45%) in that class. We identified the d18:1-22:0 sphingomyelin as the most abundant sphingomyelin species that tended to increase in HG milk compared with LG. The FC ratio did not affect the relative proportions of saturated FA nor monounsaturated FA in PL, but the proportion of cis-9 18:1 was elevated in HG versus LG milk, whereas the proportion of 18:2n-6 was 50% higher in LG versus HG milk. The RC diet increased monounsaturated FA and 18:3n-3 levels in PL compared with grass silage-based diets and decreased the relative proportion of saturated FA. However, the RC diet did not affect the relative proportion of polyunsaturated FA in PL, although red clover silage typically increases the proportion of polyunsaturated FA in milk fat. This study provides valuable knowledge of the minor lipid components in milk on species level in relation to common feeding strategies in high-forage systems., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

For sustainable food production selection and breeding of feed efficient animals is crucial. The objective of this study was to evaluate whether multiparous dairy cows, ranked during their first lactation based on residual energy intake (REI) as efficient (low; L-REI) or inefficient (high; H-REI), differ in terms of nutrient use efficiency, methane emissions, rumen fermentation, and gut microbiota composition. Six L-REI and 6 H-REI cows were offered two diets with either a low or high proportion of concentrates (30 vs. 50% of DM) on two consecutive periods of 21 d. Gas exchanges, milk yield, feces and urine excretions were measured in open-circuit respiratory chambers. The results indicated that L-REI cows had higher methane yields (22.6 vs. 20.4 g/kg DM intake) and derived more energy (energy balance - 36.6 vs. - 16.9 MJ/d) and protein (N balance - 6.6 vs. 18.8 g/d) from the tissues to support similar milk yields compared to H-REI cows. Nutrient intake and digestibility were not affected by REI, and there were no interactions between REI and diet. Milk yield, milk production efficiency, and milk composition were not affected by REI except for milk urea concentration that was higher for L-REI cows (14.1 vs. 10.8 mg/100 ml). The rumen and fecal microbiota community structure and function were associated with both the diet and REI, but the diet effect was more pronounced. The current study identified several physiological mechanisms underlying the differences between high and low REI cows, but further studies are needed to distinguish the quantitative role of each mechanism., (© 2024. The Author(s).)

Methane (CH 4 ) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH 4 . To address this limitation, we developed novel CH 4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH 4 production (g CH 4 /animal·d, ANIM-B models) and CH 4 yield (g CH 4 /kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH 4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH 4 emissions from sheep, providing valuable insights for future research and mitigation strategies., (© 2023. The Author(s).)

The aim of this trial was to determine the effect of a garlic and citrus extract supplement (GCE) on the performance, rumen fermentation, methane emissions, and rumen microbiome of dairy cows. Fourteen multiparous Nordic Red cows in mid-lactation from the research herd of Luke (Jokioinen, Finland) were allocated to 7 blocks in a complete randomized block design based on body weight, days in milk, dry matter intake (DMI), and milk yield. Animals within each block were randomly allocated to a diet with or without GCE. The experimental period for each block of cows (one for each of the control and GCE groups) consisted of 14 d of adaptation followed by 4 d of methane measurements inside the open circuit respiration chambers, with the first day being considered as acclimatization. Data were analyzed using the GLM procedure of SAS (SAS Institute Inc.). Methane production (g/d) and methane intensity (g/kg of energy-corrected milk) were lower by 10.3 and 11.7%, respectively, and methane yield (g/kg of DMI) tended to be lower by 9.7% in cows fed GCE compared with the control. Dry matter intake, milk production, and milk composition were similar between treatments. Rumen pH and total volatile fatty acid concentrations in rumen fluid were similar, whereas GCE tended to increase molar propionate concentration and decrease the molar ratio of acetate to propionate. Supplementation with GCE resulted in greater abundance of Succinivibrionaceae, which was associated with reduced methane. The relative abundance of the strict anaerobic Methanobrevibacter genus was reduced by GCE. The change in microbial community and rumen propionate proportion may explain the decrease in enteric methane emissions. In conclusion, feeding GCE to dairy cows for 18 d modified rumen fermentation and microbiota, leading to reduced methane production and intensity without compromising DMI or milk production in dairy cows. This could be an effective strategy for enteric methane mitigation of dairy cows., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

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., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Estimating the efficiency of N utilization for milk production (MNE) of individual cows at a large scale is difficult, particularly because of the cost of measuring feed intake. Nitrogen isotopic discrimination (Δ 15 N) between the animal (milk, plasma, or tissues) and its diet has been proposed as a biomarker of the efficiency of N utilization in a range of production systems and ruminant species. The aim of this study was to assess the ability of Δ 15 N to predict the between-animal variability in MNE in dairy cows using an extensive database. For this, 20 independent experiments conducted as either changeover (n = 14) or continuous (n = 6) trials were available and comprised an initial data set of 1,300 observations. Between-animal variability was defined as the variation observed among cows sharing the same contemporary group (CG; individuals from the same experimental site, sampling period, and dietary treatment). Milk N efficiency was calculated as the ratio between mean milk N (grams of N in milk per day) and mean N intake (grams of N intake per day) obtained from each sampling period, which lasted 9.0 ± 9.9 d (mean ± SD). Samples of milk (n = 604) or plasma (n = 696) and feeds (74 dietary treatments) were analyzed for natural 15 N abundance (δ 15 N), and then the N isotopic discrimination between the animal and the dietary treatment was calculated (Δ 15 n = δ 15 N animal - δ 15 N diet ). Data were analyzed through mixed-effect regression models considering the experiment, sampling period, and dietary treatment as random effects. In addition, repeatability estimates were calculated for each experiment to test the hypothesis of improved predictions when MNE and Δ 15 N measurements errors were lower. The considerable protein mobilization in early lactation artificially increased both MNE and Δ 15 N, leading to a positive rather than negative relationship, and this limited the implementation of this biomarker in early lactating cows. When the experimental errors of Δ 15 N and MNE decreased in a particular experiment (i.e., higher repeatability values), we observed a greater ability of Δ 15 N to predict MNE at the individual level. The predominant negative and significant correlation between Δ 15 N and MNE in mid- and late lactation demonstrated that on average Δ 15 N reflects MNE variations both across dietary treatments and between animals. The root mean squared prediction error as a percentage of average observed value was 6.8%, indicating that the model only allowed differentiation between 2 cows in terms of MNE within a CG if they differed by at least 0.112 g/g of MNE (95% confidence level), and this could represent a limitation in predicting MNE at the individual level. However, the one-way ANOVA performed to test the ability of Δ 15 N to differentiate within-CG the top 25% from the lowest 25% individuals in terms of MNE was significant, indicating that it is possible to distinguish extreme animals in terms of MNE from their N isotopic signature, which could be useful to group animals for precision feeding., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Direct measurements of methane (CH 4 ) from individual animals are difficult and expensive. Predictions based on proxies for CH 4 are a viable alternative. Most prediction models are based on multiple linear regressions (MLR) and predictor variables that are not routinely available in commercial farms, such as dry matter intake (DMI) and diet composition. The use of machine learning (ML) algorithms to predict CH 4 emissions from across-country heterogeneous data sets has not been reported. The objectives were to compare performances of ML ensemble algorithm random forest (RF) and MLR models in predicting CH 4 emissions from proxies in dairy cows, and assess effects of imputing missing data points on prediction accuracy. Data on CH 4 emissions and proxies for CH 4 from 20 herds were provided by 10 countries. The integrated data set contained 43,519 records from 3,483 cows, with 18.7% missing data points imputed using k-nearest neighbor imputation. Three data sets were created, 3k (no missing records), 21k (missing DMI imputed from milk, fat, protein, body weight), and 41k (missing DMI, milk fat, and protein records imputed). These data sets were used to test scenarios (with or without DMI, imputed vs. nonimputed DMI, milk fat, and protein), and prediction models (RF vs. MLR). Model predictive ability was evaluated within and between herds through 10-fold cross-validation. Prediction accuracy was measured as correlation between observed and predicted CH 4 , root mean squared error (RMSE) and mean normalized discounted cumulative gain (NDCG). Inclusion of DMI in the model improved within and between-herd prediction accuracy to 0.77 (RMSE = 23.3%) and 0.58 (RMSE = 31.9%) in RF and to 0.50 (RMSE = 0.327) and 0.13 (RMSE = 42.71) in MLR, respectively than when DMI was not included in the predictive model. When missing DMI records were imputed, within and between-herd accuracy increased to 0.84 (RMSE = 18.5%) and 0.63 (RMSE = 29.9%), respectively. In all scenarios, RF models out-performed MLR models. Results suggest routinely measured variables from dairy farms can be used in developing globally robust prediction models for CH 4 if coupled with state-of-the-art techniques for imputation and advanced ML algorithms for predictive modeling., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

To meet the 1.5 °C target, methane (CH4) from ruminants must be reduced by 11 to 30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identified strategies to decrease product-based (PB; CH4 per unit meat or milk) and absolute (ABS) enteric CH4 emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies—namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio—decreased CH4 per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies—namely CH4 inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds—decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH4 due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5 °C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH4 emissions.

The objective of this study was to determine the effects of milk fat depression induced by supplementing conjugated linoleic acid (CLA; trans-10,cis-12 and cis-9,trans-11 CLA) or feeding a higher starch and oil-containing diet (HSO) on metabolic changes in dairy cows after calving. The main hypothesis was that the 2 strategies to decrease milk fat yield could have different effects on performance, energy balance (EB), and inflammatory status in early lactation. Thirty-three Nordic Red dairy cows were used in a randomized block design from 1 to 112 d of lactation and fed one of the following treatments: control (CON), CLA-supplemented diet, or HSO diet. Dry matter intake and milk yield were measured daily whereas milk composition was measured weekly throughout the experiment. Nutrient digestibility, EB, and plasma hormones and metabolites were measured at 3, 7, 11, and 15 wk of lactation in respiration chambers. The HSO diet led to lower intakes of dry matter, neutral detergent fiber, and gross energy compared with CON and CLA diets. The CLA diet and especially the HSO diet resulted in lower energy-corrected milk yield during the first 7 wk of lactation than those fed CON. The EB was numerically higher for HSO and CLA diets compared with CON at wk 3 and 7. Plasma glucose concentration was higher by the CLA diet at wk 3 and by the HSO diet from wk 3 to 15 compared with CON. Plasma nonesterified fatty acids were higher at wk 3 in the CON group (indicating more lipid mobilization) but decreased thereafter to similar levels with the other groups. The HSO-fed cows had higher plasma ceruloplasmin, paraoxonase, and total bilirubin concentrations in the entire experiment and showed the highest levels of reactive oxygen metabolites. These results suggest an increased inflammatory and oxidative stress state in the HSO cows and probably different regulation of the innate immune system. This study provides evidence that milk fat depression induced by feeding HSO (as well as CLA) decreased milk fat secretion and improved EB compared with CON in early lactation. The increase in plasma glucose and paraoxonase levels with the HSO diet may imply a better ability of the liver to cope with the metabolic demand after parturition. However, the negative effect of HSO on feed intake, and the indication of increased inflammatory and oxidative stress warrant further studies before the HSO feeding strategy could be supported as an alternative to improve EB in early lactation., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

We evaluated the potential of feeding high-oil rapeseed cake or natural additives as rumen modifiers on enteric methane (CH 4 ) emissions, nutrient utilization, performance, and milk fatty acid (FA) profile of dairy cows. Eight Nordic Red dairy cows averaging (mean ± SD) 81 ± 21 d in milk and 41.0 ± 1.9 kg of milk yield at the beginning of the study were randomly assigned to a replicated 4 × 4 Latin square design with 21-d periods. Treatments comprised grass silage-based diets (45:55 forage to concentrate ratio on dry matter basis) including (1) control containing 19.3% rapeseed meal (CON), (2) CON with full replacement of rapeseed meal with rapeseed cake (RSC), (3) supplementation of CON with 50 g/d of yeast hydrolysate product plus coniferous resin acid-based compound (YHR), and (4) supplementation of CON with 20 g/d of combination of garlic-citrus extract and essential oils in a pellet (GCE). Apparent total-tract digestibility was measured using total collection of feces, and CH 4 emissions were measured in respiratory chambers on 4 consecutive days. Data collected during d 17 and 21 in each period were used for ANOVA analysis using a mixed model. Treatments did not affect dry matter intake (DMI), whereas feeding RSC increased crude protein and ether extract digestibility compared with the other diets. Emissions of CH 4 per day, per kilogram of DMI, and per kilogram of energy-corrected milk, and gross energy intake were lower for RSC compared with other diets. We found no effect of YHR on daily CH 4 emissions, whereas CH 4 yield (g of CH 4 /kg of DMI or as percentage of gross energy intake) decreased with GCE compared with CON. Treatments did not influence energy balance. Further, RSC reduced the proportion of N intake excreted in feces, and YHR improved N balance compared with CON diet. Feeding RSC resulted in greatest yields of milk and energy-corrected milk, and feed efficiency. Relative to the CON diet, RSC decreased saturated FA by 10% in milk fat by increasing cis-monounsaturated FA but also increased the proportion of trans FA. Proportion of odd- and branched-chain FA increased with GCE and YHR compared with CON. We conclude that replacing rapeseed meal by rapeseed cake decreased CH 4 emissions, whereas YHR or GCE had no effect on CH 4 emissions in this study., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH 4 ) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N 2 O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from 'none' (Type 1) to 'some' by combining key diet parameters with emission factors (EF) (Type 2) to 'many' by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH 4 and/or N 2 O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH 4 and N 2 O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Four multiparous dairy cows were used in a 4 × 4 Latin square to examine how starch level and oil mixture impact dry matter (DM) intake and digestibility, milk yield and composition, rumen fermentation, ruminal methane (CH 4 ) emissions, and microbial diversity. Experimental treatments comprised high (HS) or low (LS) levels of starch containing 0 or 30 g of a mixture of sunflower and fish oils (2:1 w / w ) per kg diet DM (LSO and HSO, respectively). Intake of DM did not differ between cows fed LS and HS diets while oil supplementation reduced DM intake. Dietary treatments did not affect milk and energy corrected milk yields. There was a tendency to have a lower milk fat concentration due to HSO compared with other treatments. Both high starch level and oil supplementation increased digestibility of gross energy. Cows receiving HS diets had higher levels of total rumen VFA while acetate was lower than LS without any differences in rumen pH, or ruminal CH 4 emissions. Although dietary oil supplementation had no impact on rumen fermentation, decreased CH 4 emissions (g/day and g/kg milk) were observed with a concomitant increase in Anoplodinium-Diplodinium sp. and Epidinium sp. but a decrease in Christensenellaceae , Ruminococcus sp., Methanobrevibacter ruminantium and Mbb. gottschalkii clades.

Breeding cows for low CH 4 emissions requires that the trait is variable and that it can be recorded with low cost from an adequate number of individuals and with high precision, but not necessarily with high accuracy if the trait is measured with high repeatability. The CH 4 :CO 2 ratio in expired breath is a trait often used as a tracer with the production of CO 2 predicted from body weight (BW), energy-corrected milk yield, and days of pregnancy. This approach assumes that efficiency of energy utilization for maintenance and production is constant. Data (307 cow-period observations) from 2 locations using the same setup for measuring CH 4 and CO 2 in respiration chambers were compiled, and observed production of CH 4 and CO 2 was compared with the equivalent predicted production using 2 different approaches. Carbon dioxide production was predicted using a previously reported model based on metabolic BW and energy-corrected milk production and a currently developed model based on energy requirements and the relationship between observed CO 2 and heat production (models 1 and 2, respectively). Animals used were categorized (low, medium, and high efficiency) according to (1) residual feed intake and (2) residual milk production. Model 1 underestimated CH 4 production by 15%, whereas model 2 overestimated CH 4 by 1.4% for the whole database. Model 1 underestimated CO 2 production by 2.8 and 0.9 kg/d for low- and high-efficiency cows, respectively, whereas model 2 underestimated CO 2 production by 0.9 kg/d for low-efficient animals but overestimated it by 1.2 kg/d for high-efficiency cows. Efficient cows produce less heat, and consequently CO 2 , per unit of metabolic body weight and energy-corrected milk than inefficient cows, challenging the use of CO 2 as a tracer gas. Because of biased estimates of CO 2 production, the models overestimated CH 4 production of high-efficiency cows by, on average, 17% relative to low-efficiency cows, respectively. Selecting low CH 4 -emitting cows using a CO 2 tracer method can therefore favor inefficient cows over efficient cows., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

A meta-analysis based on an individual-cow data set was conducted to investigate between-cow variations in the components and measurements of feed efficiency (FE) and to explore the associations among these components. Data were taken from 31 chamber studies, consisting of a total of 841 cow/period observations. The experimental diets were based on grass or corn silages, fresh grass, or a mixture of fresh grass and straw, with cereal grains or by-products as energy supplements, and soybean or canola meal as protein supplements. The average forage-to-concentrate ratio across all diets on a dry matter basis was 56:44. Variance component and repeatability estimates of FE measurements and components were determined using diet, period, and cow within experiment as random effects in mixed procedures of SAS (SAS Institute Inc., Cary, NC). The between-cow coefficient of variation (CV) in gross energy intake (GE; CV = 0.10) and milk energy (E l ) output as a proportion of GE (E l /GE; CV = 0.084) were the largest among all component traits. Similarly, the highest repeatability estimates (≥0.50) were observed for these 2 components. However, the between-cow CV in digestibility (DE/GE), metabolizability [metabolizable energy (ME)/GE], methane yield (CH 4 E/GE), proportional urinary energy output (UE/GE), and heat production (HP/GE), as well as the efficiency of ME use for lactation (k l ), were rather small. The least repeatable component of FE was UE/GE. For FE measurements, the between-cow CV in residual energy-corrected milk (RECM) was larger than for residual feed intake (RFI), suggesting a greater possibility for genetic gain in RECM than in RFI. A high DE/GE was associated with increased CH 4 E/GE (r = 0.24), HP/GE (r = 0.12), ME/GE (r = 0. 91), energy balance as a proportion of GE (EB/GE; r = 0.35), and k l (r = 0.10). However, no correlation between DE/GE and GE intake or UE/GE was observed. Increased proportional milk energy adjusted to zero energy balance (E l(0) /GE) was associated with increases in DE/GE, ME/GE, EB/GE, and k l but decreases in UE/GE, CH 4 E/GE, and HP/GE, with no effect on GE intake. In conclusion, several mechanisms are involved in the observed differences in FE among dairy cows, and reducing CH 4 E yield (CH 4 E/GE) may inadvertently result in reduced GE digestibility. However, the selection of dairy cows with improved energy utilization efficiencies offers an effective approach to lower enteric CH 4 emissions., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective of the present study was to investigate factors related to variation in feed efficiency (FE) among cows. Data included 841 cow/period observations from 31 energy metabolism studies assembled across 3 research stations. The cows were categorized into low-, medium-, and high-FE groups according to residual feed intake (RFI), residual energy-corrected milk (RECM), and feed conversion efficiency (FCE). Mixed model regression was conducted to identify differences among the efficiency groups in animal and energy metabolism traits. Partial regression coefficients of both RFI and RECM agreed with published energy requirements more closely than cofficients derived from production experiments. Within RFI groups, efficient (Low-RFI) cows ate less, had a higher digestibility, produced less methane (CH 4 ) and heat, and had a higher efficiency of metabolizable energy (ME) utilization for milk production. High-RECM (most efficient) cows produced 6.0 kg/d more of energy-corrected milk (ECM) than their Low-RECM (least efficient) contemporaries at the same feed intake. They had a higher digestibility, produced less CH 4 and heat, and had a higher efficiency of ME utilization for milk production. The contributions of improved digestibility, reduced CH 4 , and reduced urinary energy losses to increased ME intake at the same feed intake were 84, 12, and 4%, respectively. For both RFI and RECM analysis, increased metabolizability contributed to approximately 35% improved FE, with the remaining 65% attributed to the greater efficiency of utilization of ME. The analysis within RECM groups suggested that the difference in ME utilization was mainly due to the higher maintenance requirement of Low-RECM cows compared with Medium- and High-RECM cows, whereas the difference between Medium- and High-RECM cows resulted mainly from the higher efficiency of ME utilization for milk production in High-RECM cows. The main difference within FCE (ECM/DMI) categories was a greater (8.2 kg/d) ECM yield at the expense of mobilization in High-FCE cows compared with Low-FCE cows. Methane intensity (CH 4 /ECM) was lower for efficient cows than for inefficient cows. The results indicated that RFI and RECM are different traits. We concluded that there is considerable variation in FE among cows that is not related to dilution of maintenance requirement or nutrient partitioning. Improving FE is a sustainable approach to reduce CH 4 production per unit of product, and at the same time improve the economics of milk production., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Objective: To identify and compare manual and load-distributing band (LDB) cardiopulmonary resuscitation (CPR)-related injuries., Methods: Retrospective observational cohort study. Adult, nontraumatic deaths with a postmortem computed tomography scan (PMCT) performed were classified into two groups: deceased after LDB CPR or after manual CPR. PMCT scans were reviewed for thoracoabdominal injuries such as fractures, pneumothorax and hemorrhage. The injuries between groups were compared., Results: LDB CPR (n = 43) showed increased incidences of posterior rib fractures (53 vs 18%, P = 0.006), pneumothorax (23 vs 4%, P = 0.04) and more pericardial fluid (median 12  vs 6 mm, P = 0.002) compared with manual CPR (n = 29). Multivariable regression analysis revealed that LDB CPR was significantly associated with posterior rib fractures [odds ratio (OR) 5.37, 95% confidence interval (CI): 1.44-20.09, P = 0.01). Pneumothorax (OR 6.80, 95% CI: 0.73-62.99, P = 0.09) and the amount of pericardial fluid (OR 3.40, 95% CI: 0.20-56.32) were not significantly associated with LDB CPR. No significant difference was found for anterolateral rib fractures, sternal fractures, vertebral fractures, pleural fluid, hemothorax, hemopericardium, pneumoperitoneum, perihepatic, perisplenic and perirenal hemorrhage., Conclusion: Rib fractures, sternal fractures, hemothorax and hemopericardium are common CPR-related injuries. LDB CPR is significantly associated with more posterior rib fractures and a trend toward more pneumothoraces is observed when compared with manual CPR. This knowledge is important for caretakers in the case of ongoing CPR, as a pneumothorax may attribute to not achieving persistent return of spontaneous circulation, and to improve postresuscitation care of survivors.

The use of antibiotics in livestock production may trigger ecosystem disservices, including increased emissions of greenhouse gases. To evaluate this, we conducted two separate animal experiments, administering two widely used antibiotic compounds (benzylpenicillin and tetracycline) to dairy cows over a 4- or 5-day period locally and/or systemically. We then recorded enteric methane production, total gas production from dung decomposing under aerobic versus anaerobic conditions, prokaryotic community composition in rumen and dung, and accompanying changes in nutrient intake, rumen fermentation, and digestibility resulting from antibiotic administration. The focal antibiotics had no detectable effect on gas emissions from enteric fermentation or dung in aerobic conditions, while they decreased total gas production from anaerobic dung. Microbiome-level effects of benzylpenicillin proved markedly different from those previously recorded for tetracycline in dung, and did not differ by the mode of administration (local or systemic). Antibiotic effects on gas production differed substantially between dung maintained under aerobic versus anaerobic conditions and between compounds. These findings demonstrate compound- and context-dependent impacts of antibiotics on methane emissions and underlying processes, and highlight the need for a global synthesis of data on agricultural antibiotic use before understanding their climatic impacts., Competing Interests: The authors declare no competing interests., (© 2019 The Authors.)

Partners in Expert Working Group WG2 of the COST Action METHAGENE have used several methods for measuring methane output by individual dairy cattle under various environmental conditions. Methods included respiration chambers, the sulphur hexafluoride (SF 6 ) tracer technique, breath sampling during milking or feeding, the GreenFeed system, and the laser methane detector. The aim of the current study was to review and compare the suitability of methods for large-scale measurements of methane output by individual animals, which may be combined with other databases for genetic evaluations. Accuracy, precision and correlation between methods were assessed. Accuracy and precision are important, but data from different sources can be weighted or adjusted when combined if they are suitably correlated with the 'true' value. All methods showed high correlations with respiration chambers. Comparisons among alternative methods generally had lower correlations than comparisons with respiration chambers, despite higher numbers of animals and in most cases simultaneous repeated measures per cow per method. Lower correlations could be due to increased variability and imprecision of alternative methods, or maybe different aspects of methane emission are captured using different methods. Results confirm that there is sufficient correlation between methods for measurements from all methods to be combined for international genetic studies and provide a much-needed framework for comparing genetic correlations between methods should these become available.

We examined the effects of 2 grass silage-based diets differing in forage:concentrate (FC) ratio and those of a red clover silage-based diet on intake, milk production, ruminal fatty acid (FA) biohydrogenation, milk FA composition, and milk fat globule (MFG) size distribution. Ten multiparous Nordic Red cows received the following treatments: grass silage-based diets containing high (70:30, HG) or low (30:70, LG) FC ratio or a red clover silage-based diet with an FC ratio of 50:50 (RC) on a dry matter basis. Determinations of MFG were performed from fresh milk samples without addition of EDTA so the results of fat globules >1 µm in diameter are emphasized instead of the entire globule population. Lower FC ratio in grass silage-based diets increased milk production with no effect on daily fat yield, leading to 13% lower milk fat concentration. The effect of FC ratio on MFG size was moderate. It did not affect the volume-weighted diameter in grass silage-based diets, although LG lowered the volume-surface diameter of MFG in the size class >1 µm compared with HG. Compared with HG, feeding LG moderately decreased the biohydrogenation of 18:2n-6, leading to a higher level of polyunsaturated fatty acids in milk fat. Feeding RC lowered milk fat concentration and daily milk fat yield compared with grass silage-based diets. The volume-weighted diameter of MFG in the size class >1 µm was smaller in RC milk compared with grass silage-based diets. Feeding RC increased the flow of 18:3n-3 at the omasum by 2.4-fold and decreased the apparent ruminal 18:3n-3 biohydrogenation compared with grass silage-based diets despite similar intake of 18:3n-3. It also resulted in the lowest amount of saturated FA and the highest amounts of cis-9 18:1, 18:3n-3, and polyunsaturated FA in milk. In conclusion, LG decreased milk fat content and induced minor changes in MFG size distribution compared with HG, whereas RC lowered milk fat production, altered milk FA composition to nutritionally more beneficial direction, and led to smaller MFG compared with grass silage-based diets., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

A 1000-cow study across four European countries was undertaken to understand to what extent ruminant microbiomes can be controlled by the host animal and to identify characteristics of the host rumen microbiome axis that determine productivity and methane emissions. A core rumen microbiome, phylogenetically linked and with a preserved hierarchical structure, was identified. A 39-member subset of the core formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (methane emissions, rumen and blood metabolites, and milk production efficiency). These phenotypes can be predicted from the core microbiome using machine learning algorithms. The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.

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., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Very recently, we added water to a dry texturized starter diet and found substantial improvements in calf performance during summer, leading to the hypothesis that the wet starter diet would also benefit calf performance during winter. Forty-five 3-day-old male Holstein calves (BW 43.4±3.4 kg) were blocked by initial BW and distributed randomly to one of three starter diets (1 calf per pen; 15 pens per treatment) that differed only in moisture content as 90%, 75% and 50% dry matter (DM; DM90, DM75 and DM50, respectively). The starter diet comprised 55.1% ground ingredients (soybean meal, barley and corn gluten meal), 21.9% whole corn, 10% rolled barley and 10% chopped alfalfa hay. The mean ambient temperature averaged 2.1±0.9°C during the 70-day experiment. Calves were weaned at day 50 of the study. Although starter feed intake remained unaffected by treatment, the calves receiving DM75 and DM50 consumed more starter feed (DM basis) than those receiving DM90 diet during the first 20 days of the experiment. Body weight at weaning exhibited a quadratic response with the heaviest weaning weight (76.8 kg) occurring when calves consumed DM75 diet. Adding water to the dry starter diet tended to linearly increase final BW. Average daily gain during the pre- (0.67 kg/day) and post-weaning (1.22 kg/day) periods was the greatest for calves receiving DM75 and DM50, respectively. Although feed efficiency during the pre-weaning and overall periods did not differ across the treatments, a quadratic effect was detected in the post-weaning feed efficiency, with the lowest value being observed with DM75 diet. No difference was noted on skeletal growth parameters measured on days 50 and 70. Adding water to the dry starter diet linearly increased total volatile fatty acids concentration in the rumen. No difference among treatments existed in calf behavior recorded on days 35 and 70. As moisture content of the starter diet increased, the extent of sorting for long particles (>2 mm) and against fine particles (<0.125 mm) decreased. During the 70-day winter trial, adding water to the dry texturized starter diet with 10% chopped alfalfa hay resulted in a higher feed intake during the first weeks of life, a quadratic tendency toward improved growth rate during the pre-weaning period, and possibly a more functional rumen fermentation. A wet starter diet with 75% DM in the physical form offered in this study can be recommended to improve calf performance during winter.

To investigate the metabolic changes in the adipose tissue (AT) of dairy cows under milk fat depression (MFD), 30 cows were randomly allocated to a control diet, a conjugated linoleic acid (CLA)-supplemented diet, or a high-starch diet supplemented with a mixture of sunflower and fish oil (2:1; as HSO diet) from 1 to 112 d in milk. Performance of animals, milk yield, milk composition, energy balance, and blood metabolites were measured during lactation. Quantitative PCR analyses were conducted on the AT samples collected at wk 3 and 15 of lactation. The CLA and HSO diets considerably depressed milk fat yield and milk fat content at both wk 3 and 15 in the absence of significant changes in milk protein and lactose contents. In addition, the HSO diet lowered milk yield at wk 15 and decreased dry matter intake of cows from wk 3 to 15. Compared with the control, both CLA and HSO groups showed reduced body weight loss, improved energy balance, and decreased plasma concentrations of nonesterified fatty acids and β-hydroxybutyrate at early lactation. The gene expression analyses reflected suppressed lipolysis in AT of the CLA and HSO groups compared with the control at wk 3, as suggested by the downregulation of hormone-sensitive lipase and fatty acid binding protein 4 and the upregulation of perilipin 2. In addition, the HSO diet promoted lipogenesis in AT at wk 15 through the upregulation of 1-acylglycerol-3-phosphate O-acyltransferase 2, mitochondrial glycerol-3-phosphate acyltransferase, perilipin 2, and peroxisome proliferator-activated receptor γ. The CLA diet likely regulated insulin sensitivity in AT as it upregulated the transcription of various genes involved in insulin signaling, inflammatory responses, and ceramide metabolism, including protein kinase B2, nuclear factor κ B1, toll-like receptor 4, caveolin 1, serine palmitoyltransferase long chain base subunit 1, and N-acylsphingosine amidohydrolase 1. In contrast, the HSO diet resulted in little or no change in the pathways relevant to insulin sensitivity. In conclusion, the CLA and HSO diets induced a shift in energy partitioning toward AT instead of mammary gland during lactation through the regulation of different pathways., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).)

Enteric methane (CH 4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH 4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH 4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH 4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH 4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH 4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH 4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH 4 emission conversion factors for specific regions are required to improve CH 4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH 4 yield and intensity prediction, information on milk yield and composition is required for better estimation., (© 2018 John Wiley & Sons Ltd.)

Ruminant production systems are important contributors to anthropogenic methane (CH 4 ) emissions, but there are large uncertainties in national and global livestock CH 4 inventories. Sources of uncertainty in enteric CH 4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH 4 emission factors. There is also significant uncertainty associated with enteric CH 4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF 6 ) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH 4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH 4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH 4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH 4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH 4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH 4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).)

Ruminants fulfill their energy needs for growth primarily through microbial breakdown of plant biomass in the rumen. Several biotic and abiotic factors influence the efficiency of fiber degradation, which can ultimately impact animal productivity and health. To provide more insight into mechanisms involved in the modulation of fibrolytic activity, a functional DNA microarray targeting genes encoding key enzymes involved in cellulose and hemicellulose degradation by rumen microbiota was designed. Eight carbohydrate-active enzyme (CAZyme) families (GH5, GH9, GH10, GH11, GH43, GH48, CE1, and CE6) were selected which represented 392 genes from bacteria, protozoa, and fungi. The DNA microarray, designated as FibroChip, was validated using targets of increasing complexity and demonstrated sensitivity and specificity. In addition, FibroChip was evaluated for its explorative and semi-quantitative potential. Differential expression of CAZyme genes was evidenced in the rumen bacterium Fibrobacter succinogenes S85 grown on wheat straw or cellobiose. FibroChip was used to identify the expressed CAZyme genes from the targeted families in the rumen of a cow fed a mixed diet based on grass silage. Among expressed genes, those encoding GH43, GH5, and GH10 families were the most represented. Most of the F. succinogenes genes detected by the FibroChip were also detected following RNA-seq analysis of RNA transcripts obtained from the rumen fluid sample. Use of the FibroChip also indicated that transcripts of fiber degrading enzymes derived from eukaryotes (protozoa and anaerobic fungi) represented a significant proportion of the total microbial mRNA pool. FibroChip represents a reliable and high-throughput tool that enables researchers to monitor active members of fiber degradation in the rumen.

Four lipid supplements varying in chain length or degree of unsaturation were examined for their effects on milk yield and composition, ruminal CH 4 emissions, rumen fermentation, nutrient utilization, and microbial ecology in lactating dairy cows. Five Nordic Red cows fitted with rumen cannulas were used in a 5 × 5 Latin square with five 28-d periods. Treatments comprised total mixed rations based on grass silage with a forage-to-concentrate ratio of 60:40 supplemented with no lipid (CO) or 50 g/kg of diet dry matter (DM) of myristic acid (MA), rapeseed oil (RO), safflower oil (SO), or linseed oil (LO). Feeding MA resulted in the lowest DM intake, and feeding RO reduced DM intake compared with CO. Feeding MA reduced the yields of milk, milk constituents, and energy-corrected milk. Plant oils did not influence yields of milk and milk constituents, but reduced milk protein content compared with CO. Treatments had no effect on rumen fermentation characteristics, other than an increase in ammonia-N concentration due to feeding MA, RO, and SO compared with CO. Lipid supplements reduced daily ruminal CH 4 emission; however, the response was to some extent a result of lower feed intake. Lipids modified microbial community structure without affecting total counts of bacteria, archaea, and ciliate protozoa. Dietary treatments had no effect on the apparent total tract digestibility of organic matter, fiber, and gross energy. Treatments did not affect either energy secreted in milk as a proportion of energy intake or efficiency of dietary N utilization. All lipids lowered de novo fatty acid synthesis in the mammary gland. Plant oils increased proportions of milk fat 18:0, cis 18:1, trans and monounsaturated fatty acids, and decreased saturated fatty acids compared with CO and MA. Both SO and LO increased the proportion of total polyunsaturated fatty acids, total conjugated linolenic acid, and cis-9,trans-11 conjugated linoleic acid. Feeding MA clearly increased the Δ 9 desaturation of fatty acids. Our results provide compelling evidence that plant oils supplemented to a grass silage-based diet reduce ruminal CH 4 emission and milk saturated fatty acids, and increase the proportion of unsaturated fatty acids and total conjugated linoleic acid while not interfering with digestibility, rumen fermentation, rumen microbial quantities, or milk production., (The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).)

The effects of supplementing high- or low-concentrate diets with sunflower oil (SO) on rumen fermentation, nutrient utilization, and ruminal methane (CH 4 ) emissions in lactating cows were examined. Four multiparous Nordic Red dairy cows fitted with rumen cannulae were used in a 4 × 4 Latin square with a 2 × 2 factorial arrangement of treatments and 35-d periods. Experimental treatments comprised iso-nitrogenous total mixed rations based on grass silage with forage to concentrate ratio of 65:35 or 35:65 supplemented with 0 or 50 g/kg diet DM of SO. Apparent ruminal OM and starch digestibility was greater ( P < 0.05) with high- than low-concentrate diets but was unaffected by SO. Inclusion of SO in high-concentrate diet decreased ( P ≤ 0.05) apparent total tract OM, fiber, and GE, and apparent ruminal fiber digestibility. High-concentrate diets and SO shifted ( P < 0.05) fiber digestion from rumen to the hindgut. High-concentrate diet resulted in a lower rumen pH and elevated total rumen VFA concentration compared with low-concentrate diet, whereas SO increased rumen pH and decreased rumen VFA concentration when included in high-, but not low-concentrate diet ( P < 0.05 for interaction). High-concentrate diet reduced rumen ammonia-N ( P < 0.01) and molar proportion of acetate to propionate ( P < 0.01), and decreased ( P < 0.05) ruminal CH 4 emissions when expressed as g/d or g/kg OM digested in the rumen. With both low- and high-concentrate diets, SO reduced ( P < 0.05) daily emissions of CH 4 as g/d or g/kg OM digested in the rumen, but SO reduced CH 4 emissions expressed as g/kg OM intake, OM digested in total digestive tract, energy-corrected milk or % of GE intake only with low-concentrate diet ( P ≤ 0.05 for interaction). In conclusion, replacing grass silage with concentrates led to a reduction in daily ruminal CH 4 emissions that were accompanied by a shift in rumen fermentation toward the synthesis of propionate, and decreases in rumen pH and fiber digestion. Sunflower oil was effective in reducing daily CH 4 emissions in lactating cows which was accompanied by a noticeable lower feed intake with high- but not low-concentrate diet. Overall the effects of SO and greater proportion of concentrates in the diet on daily CH 4 emissions were additive but the additivity declined or vanished when different indices of CH 4 emission intensity were considered. Consequently, SO was more effective in reducing CH 4 emissions when low-concentrate diet was fed.

The ruminal microbiome, comprising large numbers of bacteria, ciliate protozoa, archaea and fungi, responds to diet and dietary additives in a complex way. The aim of this study was to investigate the benefits of increasing the depth of the community analysis in describing and explaining responses to dietary changes. Quantitative PCR, ssu rRNA amplicon based taxa composition, diversity and co-occurrence network analyses were applied to ruminal digesta samples obtained from four multiparous Nordic Red dairy cows fitted with rumen cannulae. The cows received diets with forage:concentrate ratio either 35:65 (diet H) or 65:35 (L), supplemented or not with sunflower oil (SO) (0 or 50 g/kg diet dry matter), supplied in a 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments and four 35-day periods. Digesta samples were collected on days 22 and 24 and combined. QPCR provided a broad picture in which a large fall in the abundance of fungi was seen with SO in the H but not the L diet. Amplicon sequencing showed higher community diversity indices in L as compared to H diets and revealed diet specific taxa abundance changes, highlighting large differences in protozoal and fungal composition. Methanobrevibacter ruminantium and Mbb. gottschalkii dominated archaeal communities, and their abundance correlated negatively with each other. Co-occurrence network analysis provided evidence that no microbial domain played a more central role in network formation, that some minor-abundance taxa were at nodes of highest centrality, and that microbial interactions were diet specific. Networks added new dimensions to our understanding of the diet effect on rumen microbial community interactions.

Attempts to lower the environmental footprint of milk production needs a sound understanding of the genetic and nutritional basis of methane (CH4) emissions from the dairy production systems. This in turn requires accurate and reliable techniques for the measurement of CH4 output from individual cows. Many of the available measurement techniques so far are either slow, expensive, labor intensive and are unsuitable for large-scale individual animal measurements. The main objectives of this study were to examine and validate a non-invasive individual cow CH4 measurement system that is based on photoacoustic IR spectroscopy (PAS) technique implemented in a portable gas analysis equipment (F10), referred to as PAS-F10 method and to estimate the magnitude of between-animal variations in CH4 output traits. Data were collected from 115 Nordic Red cows of the Minkiö experimental dairy farm, at the Natural Resources Institute Finland (Luke). Records on continuous daily measurements of CH4, milk yield, feed intake and BW measurements over 2 years period were compiled for data analysis. The daily CH4 output was calculated using carbon dioxide as a tracer method. Estimates from the non-invasive PAS-F10 technique were then tested against open-circuit indirect respiration calorimetric chamber measurements and against estimates from other widely used prediction models. Concordance analysis was used to establish agreement between the chamber and PAS-F10 methods. A linear mixed model was used for the analysis of the large continuous data. The daily CH4 output of cows was 555 l/day and ranged from 330 to 800 l/day. Dry matter intake, level of milk production, lactation stage and diurnal variation had significant effects on daily CH4 output. Estimates of the daily CH4 output from PAS-F10 technique compared relatively well with the other techniques. The concordance correlation coefficient between combined weekly CH4 output estimates of PAS-F10 and chamber was 0.84 with lower and upper confidence limits of 0.65 and 0.93, respectively. Similarly, when chamber CH4 measurements were predicted from PAS-F10 measurements, the mean of two separate weekly PAS-F10 measurements gave the lowest prediction error variance than either of the separate weekly PAS-F10 measurements alone. This suggests that every other week PAS-F10 measurements when combined would improve the estimation of CH4 output with PAS-F10 technique. The repeatability of daily CH4 output from PAS-F10 technique ranged from 0.40 to 0.46 indicating that some between-animal variation exist in CH4 output traits.

The biohydrogenation theory of milk fat depression (MFD) attributes decreases in milk fat in cows to the formation of specific fatty acids (FA) in the rumen. Trans-10, cis-12-CLA is the only biohydrogenation intermediate known to inhibit milk fat synthesis, but it is uncertain if increased ruminal synthesis is the sole explanation of MFD. Four lactating cows were used in a 4×4 Latin square with a 2×2 factorial arrangement of treatments and 35-d experimental periods to evaluate the effect of diets formulated to cause differences in ruminal lipid metabolism and milk fat synthesis on the flow of FA and dimethyl acetal at the omasum. Treatments comprised total mixed rations based on grass silage with a forage:concentrate ratio of 35:65 or 65:35 containing 0 or 50 g/kg sunflower oil (SO). Supplementing the high-concentrate diet with SO lowered milk fat synthesis from -20·2 to -31·9 % relative to other treatments. Decreases in milk fat were accompanied by alterations in ruminal biohydrogenation favouring the trans-10 pathway and an increase in the formation of specific intermediates including trans-4 to trans-10-18 : 1, trans-8, trans-10-CLA, trans-9, cis-11-CLA and trans-10, cis-15-18 : 2. Flow of trans-10, cis-12-CLA at the omasum was greater on high- than low-concentrate diets but unaffected by SO. In conclusion, ruminal trans-10, cis-12-CLA formation was not increased on a diet causing MFD suggesting that other biohydrogenation intermediates or additional mechanisms contribute to the regulation of fat synthesis in the bovine mammary gland.

Ruminants have a unique ability to derive energy from the degradation of plant polysaccharides through the activity of the rumen microbiota. Although this process is well studied in vitro , knowledge gaps remain regarding the relative contribution of the microbiota members and enzymes in vivo . The present study used RNA-sequencing to reveal both the expression of genes encoding carbohydrate-active enzymes (CAZymes) by the rumen microbiota of a lactating dairy cow and the microorganisms forming the fiber-degrading community. Functional analysis identified 12,237 CAZymes, accounting for 1% of the transcripts. The CAZyme profile was dominated by families GH94 (cellobiose-phosphorylase), GH13 (amylase), GH43 and GH10 (hemicellulases), GH9 and GH48 (cellulases), PL11 (pectinase) as well as GH2 and GH3 (oligosaccharidases). Our data support the pivotal role of the most characterized fibrolytic bacteria ( Prevotella, Ruminocccus and Fibrobacter ), and highlight a substantial, although most probably underestimated, contribution of fungi and ciliate protozoa to polysaccharide degradation. Particularly these results may motivate further exploration of the role and the functions of protozoa in the rumen. Moreover, an important part of the fibrolytic bacterial community remains to be characterized since one third of the CAZyme transcripts originated from distantly related strains. These findings are used to highlight limitations of current metatranscriptomics approaches to understand the functional rumen microbial community and opportunities to circumvent them.

Purpose: After death, a series of changes occur naturally in the human body in a fairly regular pattern. These postmortem changes are detectable on postmortem CT scans (PMCT) and may be useful in estimating the postmortem interval (PMI). The purpose of our study is to correlate the PMCT radiodensities of the cerebrospinal fluid (CSF) and vitreous humor (VH) to the PMI., Methods: Three patient groups were included: group A consisted of 5 donated cadavers, group B, 100 in-hospital deceased patients, and group C, 12 out-of-hospital forensic cadavers. Group A were scanned every hour for a maximum of 36 h postmortem, and the tympanic temperature was measured prior to each scan. Groups B and C were scanned once after death (PMI range 0.2-63.8 h). Radiodensities of the VH and CSF were measured in Hounsfield units. Correlation between density and PMI was determined using linear regression and the influence of temperature was assessed by a multivariate regression model. Results from group A were validated in groups B and C., Results: Group A showed increasing radiodensity of the CSF and VH over time (r (2) CSF, 0.65). PMI overruled the influence of temperature (r = 0.99 and p = 0.000). Groups B and C showed more diversity, with CSF and VH radiodensities below the mean regression line of Group A. The formula of this upper limit indicated the maximum PMI and was correct for >95 % of the cadavers., Conclusion: The results of group A showed a significant correlation between CSF radiodensity and PMI. The radiodensities in groups B and C were higher than in group A, therefore the maximum PMI can be estimated with the upper 95 % confidence interval of the correlation line of group A.

Microbial community analysis was carried out on ruminal digesta obtained directly via rumen fistula and buccal fluid, regurgitated digesta (bolus) and faeces of dairy cattle to assess if non-invasive samples could be used as proxies for ruminal digesta. Samples were collected from five cows receiving grass silage based diets containing no additional lipid or four different lipid supplements in a 5 x 5 Latin square design. Extracted DNA was analysed by qPCR and by sequencing 16S and 18S rRNA genes or the fungal ITS1 amplicons. Faeces contained few protozoa, and bacterial, fungal and archaeal communities were substantially different to ruminal digesta. Buccal and bolus samples gave much more similar profiles to ruminal digesta, although fewer archaea were detected in buccal and bolus samples. Bolus samples overall were most similar to ruminal samples. The differences between both buccal and bolus samples and ruminal digesta were consistent across all treatments. It can be concluded that either proxy sample type could be used as a predictor of the rumen microbial community, thereby enabling more convenient large-scale animal sampling for phenotyping and possible use in future animal breeding programs aimed at selecting cattle with a lower environmental footprint.

The present experiment aimed at increasing orange peel and sugar beet pulp protein content through solid-state fermentation by Trichoderma reesei and Trichoderma viride. In vitro digestibility and changes in the chemical composition of the fermented products were determined after seven days of fungal cultivation using gas production tests. The cultivation of T. reesei and T. viride on orange peels decreased neutral detergent soluble content (P<0.01) and increased cellulose, hemicellulose and lignin contents (P<0.01). Changes in fiber fractions were found to be more pronounced with T. viride. The cultivation of T. reesei and T. viride on sugar beet pulp increased neutral detergent soluble content (P<0.01) and decreased cellulose and hemicellulose contents (P<0.01). These changes were more pronounced with T. reesei. The cultivation of T. reesei or T. viride on orange peels or sugar beet pulp increased crude protein content (P<0.01) compared with the unfermented materials; however, the increase was more pronounced for orange peels fermented with T. viride when corrected for weight loss (P<0.05). After 24 and 48 h of incubation, significant decreases in cumulative gas production (P<0.01) were observed in fermented sugar beet pulp and orange peels compared with the unfermented materials. Fungal treatment of orange peels and sugar beet pulp reduced the digestibility of in vitro organic matter, metabolizable energy and average fermentation and gas production rates (P<0.01). The data showed that seven days of solid-state fermentation of orange peels and sugar beet pulp by T. reesei or T. viride can increase their crude protein content.

The potential of dietary supplements of 2 live yeast strains (Saccharomyces cerevisiae) or camelina oil to lower ruminal methane (CH4) and carbon dioxide (CO2) production and the associated effects on animal performance, rumen fermentation, rumen microbial populations, nutrient metabolism, and milk fatty acid (FA) composition of cows fed grass silage-based diets were examined. Four Finnish Ayrshire cows (53±7 d in milk) fitted with rumen cannula were used in a 4×4 Latin square with four 42-d periods. Cows received a basal total mixed ration (control treatment) with a 50:50 forage-to-concentrate ratio [on a dry matter (DM) basis] containing grass silage, the same basal total mixed ration supplemented with 1 of 2 live yeasts, A or B, administered directly in the rumen at 10(10) cfu/d (treatments A and B), or supplements of 60g of camelina oil/kg of diet DM that replaced concentrate ingredients in the basal total mixed ration (treatment CO). Relative to the control, treatments A and B had no effects on DM intake, rumen fermentation, ruminal gas production, or apparent total-tract nutrient digestibility. In contrast, treatment CO lowered DM intake and ruminal CH4 and CO2 production, responses associated with numerical nonsignificant decreases in total-tract organic matter digestibility, but no alterations in rumen fermentation characteristics or changes in the total numbers of rumen bacteria, methanogens, protozoa, and fungi. Compared with the control, treatment CO decreased the yields of milk, milk fat, lactose, and protein. Relative to treatment B, treatment CO improved nitrogen utilization due to a lower crude protein intake. Treatment A had no influence on milk FA composition, whereas treatment B increased cis-9 10:1 and decreased 11-cyclohexyl 11:0 and 24:0 concentrations. Treatment CO decreased milk fat 8:0 to 16:0 and total saturated FA, and increased 18:0, 18:1, 18:2, conjugated linoleic acid, 18:3n-3, and trans FA concentrations. Decreases in ruminal CH4 production to treatment CO were related, at least in part to lowered DM intake, whereas treatments had no effect on ruminal CH4 emission intensity (g/kg of digestible organic matter intake or milk yield). Results indicated that live yeasts A and B had no influence on animal performance, ruminal gas production, rumen fermentation, or nutrient utilization in cows fed grass silage-based diets. Dietary supplements of camelina oil decreased ruminal CH4 and CO2 production, but also lowered the yields of milk and milk constituents due to an adverse effect on intake., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)