Regenerative agricultural practice adoption on conventionally managed fields has gained momentum as a climate mitigation strategy, given the ability of these practices to sequester carbon or reduce greenhouse gas emissions. However, the geospatial and temporal variability of the impact of specific practices, such as cover cropping or no-till, pose challenges for scalable quantification of emissions reduction and deploying incentives to drive increased adoption. To quantify impact while accounting for variability and uncertainty at scale, Indigo Ag created a monitoring, reporting, and verification (MRV) pipeline to produce agricultural soil carbon credits produced at large scales (hundreds of thousands of hectares). The pipeline ingests field data from enrolled farmers, checks data quality, uses hybrid soil sampling and biogeochemical modeling to produce estimates of emissions reduction and uncertainty, and then applies deductions based on calculated uncertainty and leakage to quantify total project-wide carbon credits and monitor for durability of carbon. The implementation of a carbon project (CAR1459) from 2018 to 2022 on 553,743 ha of U.S. cropland utilizing the pipeline is estimated to have reduced emissions by 398,408.5 tCO2e, amounting to 296,662 tCO2e of soil carbon credits after uncertainty deductions. This paper explores the effect sizes associated with specific regenerative practice changes across the project domain. Cover cropping consistently resulted in a net positive climate impact and reduced emissions by 1.29 tCO2e per hectare per year, on average. Introduction of no-till was more common in the project, but it had a lower average emissions reduction of 0.38 tCO2e per hectare per year. Effect sizes for no-till vary spatiotemporally and are typically low in the first several years after adoption but increase in subsequent years. Agricultural carbon programs that capture and incentivize the nuance of outcomes of practices rather than the implementation of practices, can promote adoption of the right management practice to be deployed on the right field for maximum environmental benefit.

Regenerative soil management practices have been shown to increase soil organic carbon in cropland previously under conventional management, and farmers that adopt regenerative practices could be eligible to participate in carbon offset programs. Due to the high cost of soil sampling at large scales, project developers of agricultural carbon offset programs may employ a hybrid measurement and modeling approach to SOC quantification. While biogeochemical models allow for carbon crediting to occur on larger scales than soil sampling alone would allow, any model used must be unbiased and shown to adequately predict SOC changes, with known uncertainty, across the crops, practice changes, and geographies of interest. The "credit-ready" version of the DayCent ecosystem model, DayCent-CR, was evaluated for performance across 14 combinations of crops and practice categories. Model calibration and validation was performed with a Bayesian Markov chain Monte Carlo approach using k -fold cross validation and 668 SOC stock change measurements from 41 agricultural research sites. Overall model performance met the guidelines established by Climate Action Reserve's Soil Enrichment Protocol: >= 90% of model prediction intervals covered the measured value, and mean bias in all categories was less than pooled measurement uncertainty. Importantly, posterior distributions of DayCent-CR parameters and variance components enable the calculation of variance, which can then be used to calculate an uncertainty deduction that is applied to overall project credits to ensure conservatism. The calibrated model parameters are therefore valid for use in crediting programs within the domain of the validation dataset.

Sustainable aviation fuel (SAF) production from lipids is a technologically mature approach for replacing conventional fossil fuel use in the aviation sector, and there is increasing demand for such feedstocks. The oilseed Brassica carinata (known as Ethiopian mustard or simply carinata) is a promising SAF feedstock that can be grown as a supplemental cash crop over the winter fallow season of various annual crop rotations in the Southeast US, avoiding land use changes and potentially achieving some of the soil carbon sequestration and ecosystem service benefits of winter cover crops. However, carinata may require more intensive management than traditional cover crops, potentially leading to additional soil greenhouse gas (GHG) emissions through increased carbon losses from soil tillage and nitrous oxide (N 2 O) emissions from nitrogen fertilizer application. In this work, the 2017 version of the process-based DayCent ecosystem model was used to establish initial expectations for the total regional SAF production potential and associated soil GHG emissions when carinata is integrated as a winter crop into the existing crop rotations across its current suitability range in southern Alabama, southern Georgia, and northern Florida. Using data from academic and industry carinata field trials in the region, DayCent was calibrated to reproduce carinata yield, nitrogen response, harvest index, and biomass carbon-to-nitrogen ratio. The resulting model was then used to simulate the integration of carinata every third winter across all 2.1 Mha of actively cultivated cropland in the study area. The model predicted regional average yields of 2.9–3.0 Mg carinata seed per hectare depending on crop management assumptions. That results in the production of more than two million Mg of carinata seed annually across the study area, enough to supply approximately one billion liters of SAF. Conventional management of carinata led to only modest increases in soil carbon storage that were largely offset by additional N 2 O emissions. Climate-smart management via adopting no-till carinata establishment or using poultry litter as a nitrogen source resulted in a substantial net soil GHG sink (0.23–0.31 Mg CO 2 e ha −1 y −1 , or 0.24–0.32 Mg CO 2 e per Mg of seed produced) at the farms where carinata is cultivated.

In developing countries, it is critical that novel and swift strategies are devised to help direct and prioritize potential greenhouse gas (GHG) mitigation activities. The Carbon Benefit Project (CBP) analysis tool is a modular, web-based system that allows a consistent comparison of various projects by providing a standardized GHG benefits protocol. In this study, we used the CBP tool to estimate the GHG mitigation potential of the agriculture, forestry, and other land uses (AFOLU) sector and prioritize components for their GHG benefits in three districts of Wolaita Zone, southern Ethiopia. The study area is 90,731 ha of which about 2% was covered by forest, 7% by grassland, 78% by annual crops, 12% by home garden and 1% by settlements. The livestock population in the study area was 512,622 heads. Using the CBP's Detailed Assessment, we estimated mitigation potential in the AFOLU consisting of different managements strategies for a period between 2016 and 2030 in the smallholder agricultural landscape. The results showed an overall GHG benefit of 1,725,052 (±5%) Mg CO2e from the projected scenario in the study area. The GHG benefit was in the order of biomass C (683,757 Mg CO2e) > soil C (619,210 Mg CO2e) > livestock (408,981 Mg CO2e) illustrating the greater mitigation potential of trees in different systems. The soil C plus biomass C was high in agroforestry systems, and this component had the highest priority for GHG mitigation. This was followed by high enteric methane emission reduction in the livestock category. The GHG emission from manure increased by 71,633 Mg CO2e in the project because manure was not managed. The surprisingly low GHG benefit of the forest was primarily because of its low land cover (i.e., about 2%) in the agroecosystem. Despite the low GHG benefit in the cropland from best management practices, the improved soil quality in it can affect GHG benefits from other land uses by contributing to their conservation through food security. Thus, a comprehensive project may be a viable strategy in a mitigation effort at the agroecosystem level because of the interactions amongst the components. The CBP analysis tool is useful in prioritizing mitigation activities and may be an option to quantify GHG benefits if studies collate Teir 2 factors in data scarce areas.

R angelands have garnered attention for their potential to store carbon (C) and have been included in France's 4 per 1,000 initiative ([Minasny et al. 2017][1]), methods for maintaining or increasing C in grassland soils ([American Carbon Registry 2013][2]; [Verified Carbon Standard 2017][3]), and

Greenhouse gas (GHG) emissions from arid irrigated agricultural soil in California have been predicted to represent 8% of the state's total GHG emissions. Although specialty crops compose the majority of the state's crops in both economic value and land area, the portion of GHG emissions contributed by them is still highly uncertain. Current and emerging soil management practices affect the mitigation of those emissions. Herein, we review the scientific literature on the impact of soil management practices in California specialty crop systems on GHG nitrous oxide emissions. As such studies from most major specialty crop systems in California are limited, we focus on two annual and two perennial crops with the most data from the state: tomato, lettuce, wine grapes and almond. Nitrous oxide emission factors were developed and compared to Intergovernmental Panel on Climate Change (IPCC) emission factors, and state-wide emissions for these four crops were calculated for specific soil management practices. Dependent on crop systems and specific management practices, the emission factors developed in this study were either higher, lower or comparable to IPCC emission factors. Uncertainties caused by low gas sampling frequency in these studies were identified and discussed. These uncertainties can be remediated by robust and standardized estimates of nitrous oxide emissions from changes in soil management practices in California specialty crop systems. Promising practices to reduce nitrous oxide emissions and meet crop production goals, pertinent gaps in knowledge on this topic and limitations of this approach are discussed.

During the last decade, China's agro-food production has increased rapidly and been accompanied by the challenge of increasing greenhouse gas (GHG) emissions and other environmental pollutants from fertilizers, pesticides, and intensive energy use. Understanding the energy use and environmental impacts of crop production will help identify environmentally damaging hotspots of agro-production, allowing environmental impacts to be assessed and crop management strategies optimized. Conventional farming has been widely employed in wolfberry ( Lycium barbarum ) cultivation in China, which is an important cash tree crop not only for the rural economy but also from an ecological standpoint. Energy use and global warming potential (GWP) were investigated in a wolfberry production system in the Yellow River irrigated Jingtai region of Gansu. In total, 52 household farms were randomly selected to conduct the investigation using questionnaires. Total energy input and output were 321,800.73 and 166,888.80 MJ ha −1 , respectively, in the production system. The highest share of energy inputs was found to be electricity consumption for lifting irrigation water, accounting for 68.52%, followed by chemical fertilizer application (11.37%). Energy use efficiency was 0.52 when considering both fruit and pruned wood. Nonrenewable energy use (88.52%) was far larger than the renewable energy input. The share of GWP of different inputs were 64.52% electricity, 27.72% nitrogen (N) fertilizer, 5.07% phosphate, 2.32% diesel, and 0.37% potassium, respectively. The highest share was related to electricity consumption for irrigation, followed by N fertilizer use. Total GWP in the wolfberry planting system was 26,018.64 kg CO 2 eq ha −1 and the share of CO 2 , N 2 O, and CH 4 were 99.47%, 0.48%, and negligible respectively with CO 2 being dominant. Pathways for reducing energy use and GHG emission mitigation include: conversion to low carbon farming to establish a sustainable and cleaner production system with options of raising water use efficiency by adopting a seasonal gradient water pricing system and advanced irrigation techniques; reducing synthetic fertilizer use; and policy support: smallholder farmland transfer (concentration) for scale production, credit (small- and low-interest credit) and tax breaks.

Achieving more sustainable production of food, fiber and energy and reducing environmental burdens from agricultural systems is a global challenge. Meeting this challenge will create new opportunities for producers to provide a broader range of ecosystem services, including reducing greenhouse gas emissions (and sequestering more carbon) in their production systems. To meet these new objectives, land managers will need new decision tools and performance metrics. The COMET-Farm system was designed to fill this need by incorporating state-of-the-art greenhouse gas quantification methods into a web-based tool that can be used by farmers, ranchers, land managers and others. The system is capable of doing a full greenhouse gas assessment for CO2, CH4, and N2O, from all major on-farm emission sources (and CO2 removal into biomass and soil sinks), including land management of annual and perennial crops, pasture, range and agroforestry systems, as well as emissions from livestock and on-farm energy use. The system uses a fully spatial mapping and menu-driven graphical user interface (GUI) to facilitate data entry and evaluation of user-defined conservation practices. In this paper we provide an overview of the system and a description of the user interface and integrated databases in the system. We follow this with a brief description of the models and data requirements for the major emission source categories in the system. We illustrate the application of the system using examples of emission reductions from adoption of different conservation management practices and discuss how the tool can help meet needs for different policy-and market-driven greenhouse gas reduction efforts.

Conservation planners must assess a range of environmental, agronomic and economic impacts of implementing conservation practices on farms. While environmental impacts such as soil erosion control, improved soil quality, reduced nonpoint source pollution and a number of other sitespecific benefits are currently considered, conservation practices may also have significant climate benefits, through carbon sequestration and/or reduction of greenhouse gas (GHG) emissions. If conservation planners wish to incorporate greenhouse gas impacts in their planning process, they will need access to quick, easy-to-use tools to assess greenhouse gas impacts of conservation practices on farms. COMET-Planner (www.comet-planner.com) was developed to provide generalized estimates of GHG impacts of adoption of USDA National Resources Conservation Service (NRCS) conservation practice standards in a simple, web-based platform. Conservation scenarios were modeled in COMETFarm, a whole farm and ranch carbon and greenhouse gas accounting system based on USDA entityscale quantification methods, across a range of agricultural management, climate and soil types within Major Land Resource Areas (MLRA). Mean carbon sequestration and emission changes (CO2, N2O and CH4) associated with USDA-NRCS conservation practice adoption were estimated by MLRA. Results are provided to users via the web interface and a detailed methods report.

Although dedicated energy crops will probably be an important feedstock for future cellulosic bioenergy production, it is unknown how they can best be integrated into existing agricultural systems. Here we use the DayCent ecosystem model to simulate various scenarios for growing switchgrass in the heterogeneous landscape that surrounds a commercial-scale cellulosic ethanol biorefinery in southwestern Kansas, and quantify the associated fuel production costs and lifecycle greenhouse gas (GHG) emissions. We show that the GHG footprint of ethanol production can be reduced by up to 22 g of CO 2 equivalent per megajoule (CO 2 e MJ –1 ) through careful optimization of the soils cultivated and corresponding fertilizer application rates (the US Renewable Fuel Standard requires a 56 gCO 2 e MJ −1 lifecycle emissions reduction for ‘cellulosic’ biofuels compared with conventional gasoline). This improved climate performance is realizable at modest additional costs, less than the current value of low-carbon fuel incentives. We also demonstrate that existing subsidized switchgrass plantings within this landscape probably achieve suboptimal GHG mitigation, as would landscape designs that strictly minimize the biomass collection radius or target certain marginal lands.

Agroforestry as a land management practice presents a method for partially offsetting greenhouse gas emissions from agricultural land. Of all agroforestry practices in the United States, windbreaks in particular are used throughout the United States providing a useful starting point for deriving a modelling system which could quantify the amount of carbon sequestered on U.S. agricultural land and provide for broad usability. We present our first approximation to this end by presenting a model that estimates current and future stocks within multiple carbon pools of windbreak systems such as live trees, the O horizon, downed woody debris and standing dead trees. In this article, we describe each modelled process driving carbon fluxes within carbon pools including novel windbreak tree growth and mortality models. Our model is generalized by region and species group allowing us to run scenarios for any common tree species in any location within the contiguous United States. Integrated into the agricultural greenhouse gas accounting tool, COMET-Farm™, the windbreak component gives landowners and land managers power to view agroforestry systems in the same context as agricultural operations and provides an alternative to intensive biomass inventories.

Renewable fuel standards in the US and elsewhere mandate the production of large quantities of cellulosic biofuels with low greenhouse gas (GHG) footprints, a requirement which will likely entail extensive cultivation of dedicated bioenergy feedstock crops on marginal agricultural lands. Performance data for such systems is sparse, and non-linear interactions between the feedstock species, agronomic management intensity, and underlying soil and land characteristics complicate the development of sustainable landscape design strategies for low-impact commercial-scale feedstock production. Process-based ecosystem models are valuable for extrapolating field trial results and making predictions of productivity and associated environmental impacts that integrate the effects of spatially variable environmental factors across diverse production landscapes. However, there are few examples of ecosystem model parameterization against field trials on both prime and marginal lands or of conducting landscape-scale analyses at sufficient resolution to capture interactions between soil type, land use, and management intensity. In this work we used a data-diverse, multi-criteria approach to parameterize and validate the DayCent biogeochemistry model for upland and lowland switchgrass using data on yields, soil carbon changes, and soil nitrous oxide emissions from US field trials spanning a range of climates, soil types, and management conditions. We then conducted a high-resolution case study analysis of a real-world cellulosic biofuel landscape in Kansas in order to estimate feedstock production potential and associated direct biogenic GHG emissions footprint. Our results suggest that switchgrass yields and emissions balance can vary greatly across a landscape large enough to supply a biorefinery in response to variations in soil type and land-use history, but that within a given land base both of these performance factors can be widely modulated by changing management intensity. This in turn implies a large sustainable cellulosic biofuel landscape design space within which a system can be optimized to meet economic or environmental objectives.

The largest global source of anthropogenic CO2 emissions comes from the burning of fossil fuel and approximately 30% of total net emissions come from land use and land use change. Forestation and reforestation are regarded worldwide as effective options of sequestering carbon to mitigate climate change with relatively low costs compared with industrial greenhouse gas (GHG) emission reduction efforts. Cash trees with a steady augmentation in size are recognized as a multiple-beneficial solution to climate change in China. The reporting of C changes and GHG emissions for sustainable land management (SLM) practices such as afforestation is required for a variety of reasons, such as devising land management options and making policy. The Carbon Benefit Project (CBP) Simple Assessment Tool was employed to estimate changes in soil organic carbon (SOC) stocks and GHG emissions for wolfberry (Lycium barbarum L.) planting on secondary salinized land over a 10 year period (2004-2014) in the Jingtai oasis in Gansu with salinized barren land as baseline scenario. Results show that wolfberry plantation, an intensively managed ecosystem, served as a carbon sink with a large potential for climate change mitigation, a restorative practice for saline land and income stream generator for farmers in soil salinized regions in Gansu province. However, an increase in wolfberry production, driven by economic demands, would bring environmental pressures associated with the use of N fertilizer and irrigation. With an understanding of all of the components of an ecosystem and their interconnections using the Drivers-Pressures-State-Impact-Response (DPSIR) framework there comes a need for strategies to respond to them such as capacity building, judicious irrigation and institutional strengthening. Cost benefit analysis (CBA) suggests that wolfberry cultivation was economically profitable and socially beneficial and thus well-accepted locally in the context of carbon sequestration. This study has important implications for Gansu as it helps to understand the role cash trees can play in carbon emission reductions. Such information is necessary in devising management options for sustainable land management (SLM).

(1) Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, 80523 USA, (2) Natural Resource Ecology Lab, Colorado State University, Fort Collins, CO, 80523 USA, (3) USDA-Natural Resource Conservation Service, Air Quality and Atmospheric Change Team, Portland, OR, 97232 USA, (4) European Commission Joint Research Center, Institute for Environmental and Sustainability Land Resource, Ispra, Italy

The development of climate mitigation services partly depends on our ability to simulate, with confidence, agricultural production and greenhouse gas (GHG) emissions so as to understand the effectiveness of the mitigation approach on both gas emissions and food production. The Soil C-N Group of the Global Research Alliance (GRA) on GHG has initiated an international model benchmarking and inter-comparison that will assess GHG balance and soil C sequestration of arable crops and grasslands as affected by agricultural practices. The inter-comparison arises from collaborations between GRA, AgMIP and four FACCE-JPI projects to lead to the largest exercise in this domain. An initial stock take has been conducted, resulting in the selection of datasets from five grasslands and five crop sites worldwide. A total of 28 models used in 11countries for the prediction of GHG emissions in crop and grassland systems are contributing, ranging from process-oriented models to simpler models. The study has been set up with five successive steps that gradually release information to the modeling groups ranging from fully-blind application of the models to complete availability of the experimental measurements. Model simulations are compared to experimental measurements for crop yield and grassland dry-matter production, N2O emissions, soil C stocks and net CO2 exchanges. The precision and accuracy of the predictions are evaluated at each step of the inter-comparison with statistical methods, facilitating quantification of projection uncertainties. Results from the first step on N2O emissions with no prior information show variability between model predictions for any site and that model error tends to be conserved across sites. Moreover, the frequency distribution of N2O emissions already provides an understanding of model functioning in terms of N2O peak prediction. Further steps will allow for improved site-specific prediction and, as a final step, will expose the measured GHG emissions for model improvement.

Poster presentado al Workshop Experimental databases and model of N2O emissions by croplands: do we have what is needed to explore mitigation options (17-19 March 2014. Paris, INRA). Organized by the Croplands Group, Global Research Alliance on Agricultural Greenhouse Gases. 1 copia .pdf a-4 del original presentado por los autores.