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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.

To meet the 1.5 °C target, methane (CH4) from ruminants must be reduced by 11 to 30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identified strategies to decrease product-based (PB; CH4 per unit meat or milk) and absolute (ABS) enteric CH4 emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies—namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio—decreased CH4 per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies—namely CH4 inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds—decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH4 due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5 °C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH4 emissions.

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.

Enteric methane (CH4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation.

Pastures, on which ruminant livestock graze, occupy one third of the earth's surface. Removing livestock from pastures can support climate change mitigation through carbon sequestration in regrowing vegetation and recovering soils, particularly in potentially forested areas. However, this would also decrease food and fiber production, generating a tradeoff with pasture productivity and the ruminant meat production pastures support. We evaluate the magnitude and distribution of this tradeoff globally, called the "carbon opportunity intensity" of pastures, at a 5-arcminute resolution. We find that removing beef-producing cattle from high-carbon intensity pastures could sequester 34 (22 to 43) GtC i.e. 125 (80 to 158) GtCO2 into ecosystems, which is an amount greater than global fossil CO2 emissions from 2021-2023. This would lead to only a minor loss of 13 (9 to 18)% of the global total beef production on pastures, predominantly within high-and upper-middle- income countries. If areas with low-carbon intensity pastures and less efficient beef production simultaneously intensified their beef production to 47% of OECD levels, this could fully counterbalance the global loss of beef production. The carbon opportunity intensity can inform policy approaches to restore ecosystems while minimizing food losses. Future work should aim to provide higher-resolution estimates for use at local and farm scales, and to incorporate a wider set of environmental indicators of outcomes beyond carbon. [ABSTRACT FROM AUTHOR]

There is growing interest in enhancing soil carbon sequestration (SCS) as a climate mitigation strategy, including neutralizing atmospheric emissions from fossil‐fuel combustion through the development of soil carbon offset markets. Several studies have focused on refining estimates of the magnitude of potential SCS or on developing methods for soil carbon quantification in markets. We call on scientists and policy makers to resist assimilating soils into carbon offset markets due to not only fundamental flaws in the logic of these markets to reach climate neutrality but also environmental justice concerns. Here, we first highlight how carbon offset markets rely on an inappropriate substitution of inert fossil carbon with dynamic stocks of soil carbon. We then note the failure of these markets to account for intersecting anthropogenic perturbations to the carbon cycle, including the soil carbon debt and ongoing agricultural emissions. Next, we invite scientists to consider soil functions beyond productivity and profitability. Finally, we describe and support historical opposition to offset markets by environmental justice advocates. We encourage scientists to consider how their research and communications can promote diverse soil functions and just climate‐change mitigation. [ABSTRACT FROM AUTHOR]

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]

Biodiversity impacts are rarely included in systems analyses of livestock production. We piloted two approaches toward quantifying biodiversity impacts, pressure‐state‐response (PSR) and potential species loss (PSL), at a cow–calf operation in Florida for which extensive environmental data were available. Using these approaches, we compared livestock production on two vegetation types, semi‐native pasture (SNP) and improved pasture (IMP), and we found fewer deleterious effects on biodiversity associated with SNP (characterized by low stocking rates and no fertilizer) than with IMP, as evidenced by a lower PSL and greater biotic integrity under PSR. Both approaches agreed in the direction of the outcome, but we argue that, when possible, they should be applied complementarily to inform both absolute and per‐unit product biodiversity impacts of livestock production. This research demonstrates how to incorporate biodiversity into life‐cycle perspectives of livestock sustainability assessments when data availability varies, supporting the expansion of multi‐metric, holistic evaluations that are absent from most livestock system analyses. [ABSTRACT FROM AUTHOR]

In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century. [ABSTRACT FROM AUTHOR]

In the age of big data, soil data are more available than ever, but -outside of a few large soil survey resources-remain largely unusable for informing soil management and understanding Earth system processes outside of the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for global relevance. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: data discovery, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century. [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]

Agricultural land management decisions are based on numerous considerations. Belowground carbon (C) storage for both ecosystem health and greenhouse gas (GHG) management is a growing motivation. Observed heterogeneity in soil C storage in croplands may be driven by various environmental, climatic and management factors. Farm system models can indicate which practices will drive C storage, provided the practice is well parameterised and the land manager can provide necessary input data. We aimed to predict soil C impacts of temperate cover cropping using simple models suitable for broad farmer use and decision support. The dataset used was initially compiled for a meta-analysis (McClelland et al., 2021) to quantify soil C response to cover crop treatments relative to a non-cover cropped system. It contains 181 data points from 40 existing studies in temperate climates. Environmental, climatic and management indicators were regressed pairwise to predict annual soil C stock change under cover cropping relative to no cover cropping. We also included the IPCC tier 1 methodology and meta-analysis response ratios in our model comparison. The ease of reliable measurement and monitoring across the modelled indicators was also considered because the best-correlated relationships are squandered if data constraints risk decision-makers being unable to use the model. Using an extended test dataset to consider priorities for model users, several regression models outperformed the IPCC tier 1 methodology. In particular, two regression models reliably predicted negative changes in soil C, which IPCC and meta-analysis factor approaches could not. A single variable regression model based on cover crop biomass (dry matter) production was the best combination of statistical power, biological relevance and parsimony. In temperate climates, we predicted an increase in soil C stocks as long as cover crop biomass production exceeded 1.3 Mg ha−1 yr−1. Our final model can be applied with estimated user input data, and avoids the need for baseline soil C as an input; this makes it relatively accessible for farmers. Parsimonious models for soil C change under land management practices can be effective and are an opportunity to increase access to soil C management information for farmers. [Display omitted] • Belowground carbon (C) storage for both ecosystem health and greenhouse gas management is of growing interest to farmers. • Farmer decision support for soil C management must have low data cost. • We compared regression models, IPCC factors and meta-analysis response ratios for soil C impacts of temperate cover crops. • The most suitable model to predict soil C change was based on cover crop biomass. • We predicted an increase in soil C stocks with cover crop biomass over 1.3 Mg ha−1. [ABSTRACT FROM AUTHOR]

Cover crops deliver numerous ecosystem services including the capacity to sequester and store atmospheric CO 2 offering promise as a natural climate solution. Farm system models must be able to accurately represent cover crop systems and estimate the associated net greenhouse gas emissions from this practice across agricultural contexts. The objectives of this study were to: (1) parameterize new and existing cover crop species for DayCent – the biogeochemical model behind the decisions-support tool, COMET-Farm – using published data on cover crop biomass production, (2) validate differences between cover crop and control systems using published and long-term data from across the U.S. for SOC and N 2 O emissions, and (3) evaluate model performance and discuss improvements to cover crop data reporting to facilitate future testing. We used published field experiment datasets from across the U.S. to estimate cover crop biomass production, SOC, and soil N 2 O emissions. We parameterized one new cover crop species in the model – sunn hemp (Crotalaria juncea L.) – and revised the parameters for existing model cover crop species using new biomass observations. We also validated the ability of the model to estimate changes in SOC and soil N 2 O emissions relative to a no cover crop control using empirical observations. For data sets used in parameterization, DayCent reasonably captured observed trends in aboveground biomass C, N, and C:N ratios across six different cover crop species and mixtures, explaining 25–69%, 14–58%, and 29–50% of the variation, respectively, with a few exceptions. Using a smaller validation dataset for a subset of these cover crop species – cereal rye (Secale cereale L.) and vetch (Vicia spp.) – the model demonstrated less agreement between modeled and observed aboveground biomass. For SOC model validation, the observed mean SOC difference between cover crop and control treatments was 178.4 g m−2 and the mean modeled difference was 158.2 g m−2. Observed soil N 2 O emissions were highly variable impacting model performance, but DayCent did capture the general trend from field measurements for cover crops to suppress soil N 2 O emissions. Our results demonstrate that even under limited data availability, process-based models like DayCent can inform on farm-scale biogeochemical processes under cover cropping. These findings are critical as cover crops are more widely adopted in the United States for their climate and environmental benefits. However, more detailed reporting from empirical studies will improve model estimates and reduce uncertainty, particularly data pertaining to farm management, soil, climate, and location. [Display omitted] • Cover crops can build soil carbon and increase soil health. • DayCent reasonably simulated cover crop aboveground biomass production. • The model captured cover crop benefits of increasing soil organic carbon. • There was poor agreement between modeled and observed soil N 2 O emissions. • These findings will support efforts to model the climate and environmental benefits of cover cropping. [ABSTRACT FROM AUTHOR]