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.

Quantification of methane emitted by ruminants: a review of methods.

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH4), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH4 emissions. This review documents existing methods and methodologies to measure and estimate CH4 emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH4 have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH4 for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH4 from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH4 emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.

There is a need to accurately and precisely quantify greenhouse gas (GHG) emissions, specifically methane (CH4), to ensure correct reporting of GHG inventories and, perhaps more importantly, determine how to best mitigate CH4 emissions. The objective of this study was to review existing methods and methodologies to quantify and estimate CH4 emissions from ruminants. Historically, most techniques were developed for specific purposes that may limit their widespread use on commercial farms and for inventory purposes and typically required frequent calibration and equipment maintenance. Whole animal and head respiration chambers, spot sampling techniques, and tracer gas methods can be used to measure enteric CH4 from individual animals, but each technique has its own inherent limitations. The measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the most complex variable creating many uncertainties. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources. Top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point.

A Basic Model to Predict Enteric Methane Emission from Dairy Cows and Its Application to Update Operational Models for the National Inventory in Norway.

The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Development of basic models utilized information that is available only from feeding experiments. Basic models were developed using a database with 63 treatment means from 19 studies and were evaluated against an external database (n = 36, from 10 studies) along with other extant models. In total, the basic model database included 99 treatment means from 29 studies with records for enteric CH4 production (MJ/day), dry matter intake (DMI) and dietary nutrient composition. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH4 emissions than extant models. In order to propose country-specific values for the CH4 conversion factor Ym (% of gross energy intake partitioned into CH4) and thus to be able to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Ym over a wide range of feeding situations. A simulated operational database containing CH4 production (predicted by the basic model), feed intake and composition, Ym and gross energy intake (GEI), in addition to the predictor variables energy corrected milk yield and dietary concentrate share were used to develop an operational model. Input values of Ym were updated based on the results from the basic models. The predicted Ym ranged from 6.22 to 6.72%. In conclusion, the prediction accuracy of CH4 production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH4 in Norway.

Displasia ectodérmica anhidrótica familiar: reporte de un caso / Familial anhidrotic ectodermic dysplasia: a case report

Se discute un caso de displasia ectodérmica anhidrótica en un niño recién nacido, el cual fue portador de un síndrome febril al nacimiento y que se prolongó por varios días. En el curso de su desarrollo no se encontraron focos sépticos de localización evidente, por lo que hubo la necesidad de plantear esta entidad. Se hace una breve revisión de este proceso y se puso énfasis en su diagnóstico frente a cualquier cuadro febril de origen desconocido(AU)