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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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Widespread adoption of regenerative agriculture practices is an integral part of the US plan to achieve net‐zero greenhouse gas emissions by 2050. National incentives have particularly increased for the adoption of cover crops (CCs), which have presumably large carbon (C) sequestration potential. However, assessments of national CC climate benefits have not fully considered regional variability, changing C sequestration rates over time, and potential N2O trade‐offs. Using the DayCent soil biogeochemical model and current national survey data, we estimate CC climate change mitigation potential to be 39.0 ± 24.1 Mt CO2e year−1, which is 45%–65% lower than previous estimates, with large uncertainty attributed to N2O impacts. Three‐fourths of this climate change mitigation potential is concentrated in the North Central, Southern Great Plains and Lower Mississippi regions. Public investment should be focused in these regions to maximize CC climate benefits, but the national contribution of CC to emissions targets may be lower than previously anticipated. [ABSTRACT FROM AUTHOR]

Global food systems contribute to greenhouse gas emissions, and the mitigation of these emissions is a critical component of addressing the challenge of climate change. A variety of decision-support tools are available for agricultural producers to use to estimate how their management decisions affect emissions. These tools, often free and online, can be incorporated into agriculture and natural sciences courses, providing an engaging and interactive way for students to learn about climate change mitigation in online courses. Here, we focus on three tools: COMET-Planner, COMETFarm, and Cool Farm Tool. Each of these tools link agricultural management with estimated emissions but differ according to the scope of analysis and type of functionality. Our case study navigates how to best incorporate each tool into undergraduate courses - providing detailed examples focused on crop production. Teaching notes provide guidance on pairing these activities with lessons related to agricultural policy, science communication, and farmnutrient budgets. Instructors have considerable opportunity to incorporate agricultural decision-support tools into courses to support students connecting scientific concepts to real-world application. [ABSTRACT FROM AUTHOR]

The article discusses the claims made by Almaraz et al. regarding the potential of soil carbon sequestration (SCS) to meet the goals of the Paris Agreement. The authors argue that the estimates provided by Almaraz et al. are based on flawed assumptions and could undermine efforts to reduce greenhouse gas emissions. They highlight concerns about the availability of feedstock for SCS practices, the loss of carbon during composting and biochar production, and the questionable assumption of additive effects. The authors also point out the geographical and climate bias in empirical estimates of SCS potential and the need to consider nitrogen emissions and methane production in agricultural systems. While acknowledging the urgency of climate action, the authors caution against relying on SCS as a substitute for immediate emissions reductions. [Extracted from the article]