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.     

Genome-wide identification of runs of homozygosity islands in the Gyr breed (Bos indicus)

Runs of homozygosity (ROH) are contiguous homozygous regions of the genome. These regions can be used to identify genes associated with traits of economic interest, as well as inbreeding levels. The aim of the present study was to analyse the length and distribution of ROH islands in Gyr cattle and to identify genes within these regions. A population of 173 animals selected for beef production and a population of 291 animals selected for dairy production were used. Differences in the number of short ROH (ROH_{1-2 Mb}) were observed between the two populations, while the number of long ROH (ROH_{>16 Mb}) was similar. ROH islands with the highest incidences (>0.50) overlapped in several segments of the genome in the two populations. The genes identified were associated with milk production, growth, reproduction, immune response and resistance traits. Our results contribute to the understanding of how selection can shape the distribution of ROH and ROH islands within the same breed when animals are selected for different purposes such as dairy or beef production.     

Natural occurrence of Beauveria bassiana on adults of the invasive mango seed weevil Sternochetus mangiferae (Coleoptera: Curculionidae) in Brazil

Journal of Plant Diseases and Protection - The mango seed weevil Sternochetus mangiferae (Fabricius) (Coleoptera: Curculionidae) was detected in Brazil in 2014, becoming a potential threat to mango...