Invited review: Current enteric methane mitigation options.

Ruminant livestock are an important source of anthropogenic methane (CH4). Decreasing the emissions of enteric CH4 from ruminant production is strategic to limit the global temperature increase to 1.5°C by 2050. Research in the area of enteric CH4 mitigation has grown exponentially in the last 2 decades, with various strategies for enteric CH4 abatement being investigated: production intensification, dietary manipulation (including supplementation and processing of concentrates and lipids, and management of forage and pastures), rumen manipulation (supplementation of ionophores, 3-nitrooxypropanol, macroalgae, alternative electron acceptors, and phytochemicals), and selection of low-CH4-producing animals. Other enteric CH4 mitigation strategies are at earlier stages of research but rapidly developing. Herein, we discuss and analyze the current status of available enteric CH4 mitigation strategies with an emphasis on opportunities and barriers to their implementation in confined and partial grazing production systems, and in extensive and fully grazing production systems. For each enteric CH4 mitigation strategy, we discuss its effectiveness to decrease total CH4 emissions and emissions on a per animal product basis, safety issues, impacts on the emissions of other greenhouse gases, as well as other economic, regulatory, and societal aspects that are key to implementation. Most research has been conducted with confined animals, and considerably more research is needed to develop, adapt, and evaluate antimethanogenic strategies for grazing systems. In general, few options are currently available for extensive production systems without feed supplementation. Continuous research and development are needed to develop enteric CH4 mitigation strategies that are locally applicable. Information is needed to calculate carbon footprints of interventions on a regional basis to evaluate the impact of mitigation strategies on net greenhouse gas emissions. Economically affordable enteric CH4 mitigation solutions are urgently needed. Successful implementation of safe and effective antimethanogenic strategies will also require delivery mechanisms and adequate technical support for producers, as well as consumer involvement and acceptance. The most appropriate metrics should be used in quantifying the overall climate outcomes associated with mitigation of enteric CH4 emissions. A holistic approach is required, and buy-in is needed at all levels of the supply chain.

Life cycle assessment of pasture-based dairy production systems: Current and future performance.

To overcome the environmental challenges faced by the global agricultural sector while also ensuring economic viability, dairy farmers must improve the efficiency of their systems. To improve system efficiency, the performance of an average production system must be determined, as it establishes a benchmark from which the efficacy of proposed management practices and mitigation strategies can be assessed. Identified management practices and mitigation strategies can then be compiled to create ambitious but realistic targets for the sector to strive toward. Therefore the objective of this study was to calculate the environmental performance of an average spring-calving pasture-based dairy system and an ambitious target dairy system. Life cycle assessment (LCA) of 2 pasture-based dairy systems were conducted: (1) current average spring-calving pasture-based dairy system (current), and (2) a spring-calving pasture-based dairy system that has achieved key performance targets set by the most efficient dairy systems (target). An existing dairy LCA model was updated with country-specific emission factors, life cycle inventory data, and recommended methodologies. The environmental impact categories assessed were global warming potential, nonrenewable energy depletion, acidification potential, and eutrophication potential (marine and freshwater). Two functional units were used: per kilogram of fat- and protein-corrected milk (FPCM), and per hectare. To assess the effects of the model updates, the current dairy system was simulated twice, once with the previous version of the dairy LCA model, and second with the updated LCA model. The addition of country-specific emission factors, updated inventory data, and implementation of recommended methods has resulted in global warming potential and nonrenewable energy depletion being reduced by 10.4% and 10.9%, respectively. The updates had negligible effects on acidification and eutrophication potential. The inclusion of assumptions around carbon sequestration in grassland further reduced global warming potential by 16.4%. Moving from the current dairy system to the target dairy system was reported to reduce the environmental impact per kilogram of FPCM across all impact categories investigated. When expressed per hectare, transitioning toward the target dairy system reduced acidification, freshwater eutrophication, and nonrenewable energy depletion by 2.0%, 8.8%, and 13.8%, respectively. In contrast, transitioning toward the target dairy system increased global warming per hectare and, to a lesser degree, marine eutrophication potential per hectare. The increase in global warming and marine eutrophication potential per hectare was attributed to the increase in stocking rate and subsequently milk production per hectare (9,950 vs. 14,100 kg of FPCM/ha). This study demonstrates that the adoption of management practices that improve system efficiency will reduce the environmental impact per kilogram of FPCM and can contribute to the future sustainability of pasture-based dairy systems. However, improved system efficiency can be offset by the associated increase in productivity, highlighting the importance of reporting multiple environmental impact categories and functional units to prevent pollution swapping. New research and mitigation strategies will be required to improve the environmental sustainability of dairy systems beyond the target system in the future.

Invited review: A 2020 perspective on pasture-based dairy systems and products.

Grazing pasture is the basis for dairy production systems in regions with temperate climates, such as in Ireland, New Zealand, parts of Australia, the United States, and Europe. Milk and dairy products from cows on pasture-based farms predominantly consuming fresh grazed grass (typically classified as "grass-fed" milk) have been previously shown to possess a different nutrient profile, with potential nutritional benefits, compared with conventional milk derived from total mixed ration. Moreover, pasture-based production systems are considered more environmentally and animal welfare friendly by consumers. As such, there is significant potential for market capitalization on grass-fed dairy products. As competition in this space increases, the regulations of what constitutes as grass-fed vary between different regions of the world. With this in mind, there is a need for clear and independently accredited grass-fed standards, defining the grass-fed criteria for labeling of products as such, subsequently increasing the clarity and confidence for the consumer. This review outlines the numerous effects of pasture production systems on dairy product composition, nutritional profile, and sustainability, and highlights potential future methods for authentication.

Severe tail lesions in finisher pigs are associated with reduction in annual profit in farrow-to-finish pig farms.

OBJECTIVES: We investigated the financial impact of different prevalence levels of severe tail lesions (STL) during the finisher stage associated with changes in average daily gain (ADG) in farrow-to-finish pig farms. METHODS: Prevalence of STL was estimated for 31 farrow-to-finish pig farms. Regression tree analysis was used to identify a threshold for STL associated with differences in ADG. Then, a financial analysis was carried using the Teagasc Pig Production model. RESULTS: A threshold of ≥0.86% prevalence of STL was associated with a 4.8% decrease in ADG which translated into pigs requiring 7 days more to reach target slaughter weight than in farms below the threshold. Reduced ADG meant that farms with higher prevalence of STL used 3.6% more weaner and 1.4% more finisher feed per year increasing feed costs by 1.5%. This reduced mean annual farm profit by 15.1% in farms with higher prevalence of STL. CONCLUSIONS: Our results provide an indication of the financial effects of STL in intensive pig production systems. The identified threshold for the prevalence of STL could provide a tangible target for farmers to focus on in developing strategies to reduce tail lesions and allow farmers to complete a cost benefit analysis of controlling STL.

A bio-economic simulation study on the association between key performance indicators and pluck lesions in Irish farrow-to-finish pig farms.

BACKGROUND: Pluck lesions are associated with decreased performance in grower-finisher pigs, but their economic impact needs to be further investigated. This study aimed to identify the main pluck lesions and the cut-off value for their prevalence, associated with changes in average daily gain (ADG) during the wean-to-finish period, to simulate their effects on economic performance of farrow-to-finish farms. Pigs (n = 162 ± 51.9 per farm) from 56 farrow-to-finish farms were inspected at slaughter and the prevalence of enzootic pneumonia-like lesions, pleurisy, lung scars, abscesses, pericarditis, and liver milk spots was estimated. For each farm, annual performance indicators were obtained. Regression trees analysis (RTA) was used to identify pluck lesions and to estimate cut-off values for their prevalence associated with changes in ADG. Different scenarios were simulated as per RTA results and economic and risk analyses were performed using the Teagasc Pig Production Model. Risk analysis was performed by Monte Carlo sampling using the Microsoft Excel add-in @Risk with 10,000 iterations. RESULTS: Pleurisy and lung scars were the main lesions associated with changes in ADG. Three scenarios were simulated based on RTA results: a 728 sow farrow-to-finish farm with prevalence of i) pleurisy < 25% and lung scars < 8% (LPLSC; ADG = 760 g); ii) pleurisy < 25% and lung scar ≥8% (LPHSC; ADG = 725 g) and iii) pleurisy ≥25% (HP; ADG = 671 g). The economic analysis showed increased feed and dead animals for disposal costs, and lower sales in the HP and LPHSC scenarios than in the LPLSC scenario; thereby reducing gross margin and net profit. Results from the risk analysis showed lower probability of reaching any given level of profit in the HP scenario compared with the LPHSC and LPLSC scenarios. CONCLUSION: Under the conditions of this study, higher prevalence of pleurisy and lung scars were associated with decreased ADG during the grower-finisher period and with lower economic return in the simulated farms. These results highlight the economic benefits and importance of preventing and/or controlling respiratory disease.

Financial Analysis of Herd Status and Vaccination Practices for Porcine Reproductive and Respiratory Syndrome Virus, Swine Influenza Virus, and Mycoplasma hyopneumoniae in Farrow-to-Finish Pig Farms Using a Bio-Economic Simulation Model.

This study aimed (1) to quantify the effects of positive status and vaccination practices for porcine reproductive and respiratory syndrome virus (PRRSv), swine influenza virus (SIV) and Mycoplasma hyopneumoniae (MHYO) on the profitability of farrow-to-finish pig farms and (2) to examine the financial impact of vaccination status in PRRSv and SIV positive farms. Data from 56 Irish farrow-to-finish pig farms were used for this study. Production effects associated with herd status for the three pathogens were incorporated into the Teagasc Pig Production Model (TPPM), a bio-economic stochastic simulation model for farrow-to-finish pig farms. In the analysis, farms negative (-) for either PRRSv, SIV or MHYO were assumed as baseline when presenting results for farms positive (+) for each pathogen. While all MHYO(+) farms used vaccination against the pathogen, not all PRRSv(+) or SIV(+) farms vaccinated against the disease. For all scenarios, a 728-sow farrow-to-finish farm with weekly farrowing batches was simulated. Financial risk analysis was conducted by Monte Carlo simulation within the TPPM using the Microsoft Excel add-in @Risk. Mortality rates, feedstuff costs and price per kg of meat produced were included as input stochastic variables and annual net profit was set as stochastic output variable. Positive farms sold fewer pigs and produced less kg of meat than negative farms and had increased feed usage during the weaner and finisher stages. Variable costs increased in positive farms due to increased feed costs, more dead animals for disposal and healthcare costs. Annual mean profit was lower by 24% in vaccinated PRRSv(+), 14.6% in unvaccinated PRRSv(+), 36.7% in vaccinating SIV(+), 12.8% in unvaccinated SIV(+), and 41% in MHYO(+) farms. Negative farms were first order stochastically dominant over positive farms, indicating that for a given level of profit, the financial risk is lower by avoiding respiratory pathogens. Similarly, unvaccinated farms were second order stochastically dominant over vaccinating farms suggesting that farms that do not vaccinate are less affected by the disease. Results from this study provide further evidence to encourage farmers to undertake improved disease control measures and/or to implement eradication programs.

The Effect of Compositional Changes Due to Seasonal Variation on Milk Density and the Determination of Season-Based Density Conversion Factors for Use in the Dairy Industry.

The objective of this study was to determine the effect of seasonal variation on milk composition and establish an algorithm to predict density based on milk composition to enable the calculation of season-based density conversion calculations. A total of 1035 raw whole milk samples were collected from morning and evening milking of 60 spring-calving individual cows of different genetic groups, namely Jersey, Elite HF (Holstein-Friesian) and National Average HF, once every two weeks for a period of 9 months (March-November, 2018). The average mean and standard deviation for milk compositional traits were 4.72 ± 1.30% fat, 3.85 ± 0.61% protein and 4.69 ± 0.30% lactose and density was estimated at 1.0308 ± 0.002 g/cm3. The density of the milk samples was evaluated using three methods: a portable density meter, DMA 35; a standard desktop version, DMA 4500M; and an Association of Official Agricultural Chemists (AOAC) method using 100-mL glass pycnometers. Statistical analysis using a linear mixed model showed a significant difference in density of milk samples (p < 0.05) across seasonal and compositional variations adjusted for the effects of days in milk, parity, the feeding treatment, the genetic group and the measurement technique. The mean density values and standard error of mean estimated for milk samples in each season, i.e., spring, summer and autumn were 1.0304 ± 0.00008 g/cm3, 1.0314 ± 0.00005 g/cm3 and 1.0309 ± 0.00007 g/cm3, respectively.

Grazing Cow Behavior's Association with Mild and Moderate Lameness.

Accelerometer-based mobility scoring has focused on cow behaviors such as lying and walking. Accuracy levels as high as 91% have been previously reported. However, there has been limited replication of results. Here, measures previously identified as indicative of mobility, such as lying bouts and walking time, were examined. On a research farm and a commercial farm, 63 grazing cows' behavior was monitored in four trials (16, 16, 16, and 15 cows) using leg-worn accelerometers. Seventeen good mobility (score 0), 23 imperfect mobility (score 1), and 22 mildly impaired mobility (score 2) cows were monitored. Only modest associations with activity, standing, and lying events were found. Thus, behavior monitoring appears to be insufficient to discern mildly and moderately impaired mobility of grazing cows.

Description, evaluation, and validation of the Teagasc Pig Production Model1.

The Teagasc Pig Production Model (TPPM), a stochastic simulation model of a farrow-to-finish pig farm, was developed to investigate effects of changes in production systems on farm profitability. The model simulates, on a weekly basis, the annual production of a farm. Biological [e.g., herd size, number of litters/sow/year, and mortality rates (%)], physical (e.g., infrastructure), and technical (e.g., feeding practices) variables and their associated costs are included as components of the model. These inputs are used to calculate physical (e.g., feed usage and number of pigs slaughtered) and financial (e.g., annual cash flow, profit and loss account, and balance sheet) outputs. The model was validated using the Delphi method and by comparing the TPPM outputs to data recorded on 20 Irish pig farms through the Teagasc e-Profit monitor system and with complete receipts for the year 2016. Results showed that the TPPM closely simulates physical and financial performance of pig farms indicating that the TPPM can be used with confidence to study pig production systems under Irish conditions. Model applicability was demonstrated by investigating the impact of 2 changes in technical performance: 1) building of extra accommodation to increase body weight (BW) at sale by 15 kg (EXTRA ROOM) and 2) a change in feeding practices by providing finisher feed from 28 kg of BW (EARLY FINISHER) compared with over 38 kg of BW. In both scenarios, the same biological parameters were used. Mortality rates, feed ingredients costs, and price per kg of meat produced were included as stochastic variables with the input distributions derived based on historical data simulated using Monte Carlo sampling using the Microsoft Excel add-in @Risk. Annual mean net profit was €198,101 (90% confidence interval [CI]: €119,606-€275,539) for the TPPM base farm, €337,078 (90% CI: €246,320-€426,809) for the EXTRA ROOM, and €225,598 (90% CI: €146,685-€303,590) for the EARLY FINISHER. EXTRA ROOM was associated with higher costs and required higher income to cover the additional costs. The 90% CI of the EARLY FINISHER was similar to the TPPM base farm while the EXTRA ROOM scenario resulted in a wider confidence interval, suggesting that a change in feeding practices could be a better option for farmers looking to improve profit with minimum investment. Thus, the TPPM could be used to facilitate decision making in farrow-to-finish pig farms.

Associations between paratuberculosis ELISA results and test-day records of cows enrolled in the Irish Johne's Disease Control Program.

The effect of the Mycobacterium avium ssp. paratuberculosis (MAP) ELISA status on test-day milk performance of cows from Irish herds enrolled in the pilot national voluntary Johne's disease control program during 2013 to 2015 was estimated. A data set comprising 92,854 cows and 592,623 complete test-day records distributed across 1,700 herds was used in this study. The resulting ELISA outcome (negative, inconclusive, and positive) of each cow within each year of the program was used to allocate the cow into different scenarios representing the MAP status. At MAPscenario1, all cows testing ELISA nonnegative (i.e., inconclusive and positive) were assigned a MAP-positive status; at MAPscenario2 only cows testing ELISA-positive were assigned a MAP-positive status; at MAPscenario3 only cows testing ELISA nonnegative (inconclusive or positive) and gathered exclusively from herds where at least 2 further ELISA nonnegative (inconclusive or positive) cows were found were assigned a MAP-positive status; at MAPscenario4 only cows testing ELISA-positive that were gathered exclusively from herds where at least 2 further ELISA-positive cows were found were assigned a MAP-positive status. Milk outputs based on test-day records were standardized for fat and protein contents (SMY) and the effect of MAP ELISA status on the SMY was estimated by a linear mixed effects model structure. The SMY mean difference recorded at test day between cows with a MAP-positive status and those with a MAP-negative status within MAPscenario1 was estimated at -0.182 kg/test day; the mean difference was -0.297 kg/test day for MAPscenario2; for MAPscenario3 mean difference between MAP-positive status and MAP test-negative cows was -0.209 kg/test day, and for MAPscenario4, the difference was -0.326 kg/test day.

The creation and evaluation of a model predicting the probability of conception in seasonal-calving, pasture-based dairy cows.

Reproductive performance in pasture-based production systems has a fundamentally important effect on economic efficiency. The individual factors affecting the probability of submission and conception are multifaceted and have been extensively researched. The present study analyzed some of these factors in relation to service-level probability of conception in seasonal-calving pasture-based dairy cows to develop a predictive model of conception. Data relating to 2,966 services from 737 cows on 2 research farms were used for model development and data from 9 commercial dairy farms were used for model testing, comprising 4,212 services from 1,471 cows. The data spanned a 15-yr period and originated from seasonal-calving pasture-based dairy herds in Ireland. The calving season for the study herds extended from January to June, with peak calving in February and March. A base mixed-effects logistic regression model was created using a stepwise model-building strategy and incorporated parity, days in milk, interservice interval, calving difficulty, and predicted transmitting abilities for calving interval and milk production traits. To attempt to further improve the predictive capability of the model, the addition of effects that were not statistically significant was considered, resulting in a final model composed of the base model with the inclusion of BCS at service. The models' predictions were evaluated using discrimination to measure their ability to correctly classify positive and negative cases. Precision, recall, F-score, and area under the receiver operating characteristic curve (AUC) were calculated. Calibration tests measured the accuracy of the predicted probabilities. These included tests of overall goodness-of-fit, bias, and calibration error. Both models performed better than using the population average probability of conception. Neither of the models showed high levels of discrimination (base model AUC 0.61, final model AUC 0.62), possibly because of the narrow central range of conception rates in the study herds. The final model was found to reliably predict the probability of conception without bias when evaluated against the full external data set, with a mean absolute calibration error of 2.4%. The chosen model could be used to support a farmer's decision-making and in stochastic simulation of fertility in seasonal-calving pasture-based dairy cows.

Estimating the impact of somatic cell count on the value of milk utilising parameters obtained from the published literature.

The impact of mastitis on milk value per litre independent of the effect of mastitis on milk volume, was quantified for Ireland using a meta-analysis and a processing sector model. Changes in raw milk composition, cheese processing and composition associated with increased bulk milk somatic cell count (BMSCC) were incorporated into the model. Processing costs and market values were representative of current industry values. It was assumed that as BMSCC increased (i) milk fat and milk protein increased and milk lactose decreased, (ii) fat and protein recoveries decreased, (iii) cheese protein decreased and cheese moisture increased. Five BMSCC categories were examined from ⩽100 000 to >400 000 cells/ml. The analysis showed that as BMSCC increased the production quantities reduced. An increase in BMSCC from 100 000 to >400 000 cells/ml saw a reduction in net revenue of 3·2% per annum (€51·3 million) which corresponded to a reduction in the value of raw milk of €0·0096 cents/l.