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.

Renewable energy consumption in Africa: Evidence from a bias corrected dynamic panel.

Our study investigates the determinants of renewable energy consumption in Sub-Sahara Africa. We explore the driving factors of renewable energy consumption in the context of carbon intensity for 32 Sub-Saharan African countries from 1990 to 2015. Using carbon emission intensity to identify group-specific heterogeneity, we recognize carbon-efficient and least carbon-efficient countries in the region. By relying on the corrected least squares dummy variable estimator (LSDVC), we provide evidence on the driving factors of renewable energy consumption in Sub-Saharan Africa. Consequently, the findings point to varying degrees of impact on renewable energy consumption in the region. For instance, we observe advancement in technology, quality of governance, economic progress, biomass consumption, and climatic conditions influence renewable energy consumption. With a common occurrence across all groups, the implications indicate environmental, socio-economic, and climatic factors playing an important role in renewable energy consumption. The study further shows that urbanization and economic globalization depress efforts towards renewable energy consumption. Apart from these common factors, other controlling variables including; GDP per capita, environmental awareness, and biomass affect each group differently. We conclude that, policy implications can be drawn from common factors towards harmonization of clean energy markets and developing a policy mix that combines environmental, economic, and social factors in attaining the Sustainable Development Goals.