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Letter to the Editor

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 CH4emissions. This review documents existing methods and methodologies to measure and estimate CH4emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH4have 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 CH4for 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 CH4from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH4emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH4emissions from manure depends on the type of storage, animal housing, CH4concentration 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 CH4emissions 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 CH4in 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 CH4emissions. The objective of this study was to review existing methods and methodologies to quantify and estimate CH4emissions 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 CH4from individual animals, but each technique has its own inherent limitations. The measurement of CH4emissions from manure depends on the type of storage, animal housing, CH4concentration 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 CH4emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources. Top-down approaches estimate the amount of CH4in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point.The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies from country to country, and CH4emissions from animals and their manure are highly variable. Variance in quantification methodologies for CH4has also raised questions about the accuracy of current GHG emissions coefficients for national and international inventories and, perhaps more importantly, how best to mitigate CH4emissions.

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, COMET‐Farm, 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 farm nutrient budgets. Instructors have considerable opportunity to incorporate agricultural decision‐support tools into courses to support students connecting scientific concepts to real‐world application. Students can learn about both climate change mitigation and adaptation in agriculture courses.Free, online tools provide a way to estimate greenhouse gas emissions due to agriculture.Simulating farm management scenarios can foster systems‐thinking.Decision‐support tools designed for land managers offer students real‐life connections.