EU sustainability criteria for biofuels: uncertainties in GHG emissions from cultivation

A tax on agricultural greenhouse gas (GHG) emissions has been suggested as a potential means of reducing the GHG emissions associated with agriculture and improving the emissions intensity of production. One of several difficulties in implementing such a tax is the potential uncertainty in emissions estimates. This paper explores this topic using Irish dairy farm production and economic data from the 2015 Teagasc National Farm Survey. On-farm agricultural emissions were estimated by applying the National Inventory Report methodologies at farm level, and the uncertainties in total emissions and emissions per unit of milk produced were demonstrated using a Monte Carlo Simulation approach. The average GHG emissions footprint per kg fat and protein corrected milk (FPCM) was 1.07 kg CO2e with a relatively small uncertainty range of ± 2.59 %. If taxed at a rate of €20 per tonne of CO2e, a typical farm would have to pay €7141 (± 2.25 %), which could have a significant impact on farm incomes, but is not strongly affected by emissions uncertainties. Therefore, although there would remain a number of difficulties in designing an agricultural emissions tax, the level of uncertainty in emissions does not appear to be a significant barrier in this example.

Modelling uncertainty in greenhouse gas emissions from UK agriculture at the farm level

Evaluation of a feeding strategy to reduce greenhouse gas emissions from dairy farming: The level of analysis matters

In arid environments, water shortages due to over-allocation of river flow are often compensated by lift irrigation or pumping groundwater. In such environments, farmers using pumped irrigation can deploy on-farm energy-efficient and water-saving technologies; however, pumping water requiring extra energy is associated with carbon emissions. This study explores how to increase crop production using pumped irrigation with minimal energy and carbon emissions. The purpose of this research is twofold: first, to examine on-farm energy consumption and carbon emissions in gravity and groundwater irrigation systems; and second, to explore system-level alternatives of power generation and water management for food production based on the results from the farm-level analysis. This study employs a novel system-level approach for addressing water, energy, and carbon tradeoffs under pumped irrigation using groundwater. These tradeoffs are assessed at farm and system levels. On-farm level estimates showed that farm-level interventions were insufficient to produce mutual gains. According to the results of the system-level evaluation, system-level interventions for water and energy conservation, the use of renewable energy to pump water for irrigation, and river basin scale cooperation are all required to maintain crop production while reducing energy consumption and carbon emissions.

A scientific carbon accounting system can help enterprises reduce carbon emissions. This study took an enterprise in the Yangtze River basin as a case study. The accounting classification of carbon emissions in the life cycle of lime production was assessed, and the composition of the sources of carbon emission was analyzed, covering mining explosives, fuel (diesel, coal), electricity and high-temperature limestone decomposition. Using the IPCC emission factor method, a carbon life cycle emission accounting model for lime production was established. We determined that the carbon dioxide equivalent from producing one ton of quicklime ranged from 1096.68 kg CO2 equiv. to 1176.96 kg CO2 equiv. from 2019 to 2021 in the studied case. The decomposition of limestone at a high temperature was the largest carbon emission source, accounting for 64% of the total carbon emission. Coal combustion was the second major source of carbon emissions, accounting for 31% of total carbon emissions. Based upon the main sources of carbon emission for lime production, carbon emission reduction should focus on CO2 capture technology and fuel optimization. Based on the error transfer method, we calculated that the overall uncertainty of the life cycle carbon emissions of quicklime from 2019 to 2021 are 2.13%, 2.07% and 2.09%, respectively. Using our analysis of carbon emissions, the carbon emission factor of producing one unit of quicklime in the lime enterprise in the Yangtze River basin was determined. Furthermore, this research into carbon emission reduction for lime production can provide a point of reference for the promotion of carbon neutrality in the same industry.

A reduction in the overall carbon intensity (CI) of a crop-based biofuel can be achieved by cutting down the CI of the biofuel’s feedstock, which in turn correlates significantly to agricultural management practices. Proposals are being made to incentivize low-carbon biofuel feedstocks under U.S. fuel regulatory programs to promote sustainable farming practices by individual farms. For such an incentive scheme to function properly, robust data collection and verification are needed at the farm level. This study presents our collaboration with U.S. private sector companies to collect and verify the corn production data necessary for feedstock-specific CI calculation at the farm level, through a carefully designed questionnaire, to demonstrate the practicality and feasibility of data collection at scale. We surveyed 71 farms that produced 0.2 million metric tons of corn grain in 2018 in a Midwestern U.S. state to obtain information on key parameters affecting corn ethanol feedstock CI, such as grain yields, fertilizer/chemical application rates, and agronomic practices. Feedstock-specific CI was calculated in the unit of grams (g) CO2 equivalent (CO2e) of greenhouse gases per kilogram (kg) of corn produced. Results showed large CI variations—from 119 to 407 g CO2e kg−1 of corn—due to the farm-level inventory, while the production-weighted average CI for all surveyed farms was 210 g CO2e kg−1, comparable to the national average CI of 204 g CO2e kg−1. The nitrogen fertilizer type applied and rate were identified as key factors contributing most to CI variations at the farm level. The estimated N2O emissions from fertilizer and biomass nitrogen inputs to soil accounted for 51% of the overall farm-level CI and therefore need to be better monitored at farm level with high resolution. We concluded that this feedstock-specific, farm-level CI evaluation has the potential to be used to incentivize low-carbon feedstock for biofuel production.

Overcoming the risk of inaction from emissions uncertainty in smallholder agriculture

Dairy farming is a major source of greenhouse gas (GHG) emissions in agriculture. There are numerous scientific studies analysing GHG flows and testing GHG reduction methods in dairy farming, yet very few scientific papers cover all the relevant GHG flows. GHG flows that are difficult to quantify, such as C sequestration in soils, the effects of land-use change (LUC) or the energy input used to produce capital equipment, are not always considered.This paper describes the development and application of a model for energy and GHG accounting in dairy farming. This new model enables all relevant nutrient, energy and GHG flows to be modelled at farm level. This then forms the basis for system analysis and derivation of GHG mitigation strategies. The model was used on 18 organic and 18 con-ventional farms in Germany. Calculated CO2-eq emissions per kg of Energy Corrected Milk (ECM) were 995 g on average for organic farms (org) and 1,048 g on average for conventional farms (con). The largest contribution (55 % (org) and 43 % (con)) to total GHG emissions came from enteric methane emissions (549 g CO2-eq (kg ECM)-1 (org) and 449 g CO2-eq (kg ECM)-1 (con)). On the organic dairy farms, there was an increase in soil humus and therefore carbon storage and sequestration in soils, whereas the GHG emissions for the conventional farms included CO2 emissions from LUC due to soybean usage. The significantly higher energy input in the conventional systems resulted from the production of energy-intensive concentrates, mineral fertilisers and pesticides, and transportation (imported feed).This study shows that there are many factors that influence GHG emissions in dairy farming, and that these factors often interact with each other. An increase in productivity is one of several optimisation strategies; however, it must not be at the expense of productive lifetime or require an extremely high amount of concentrates. GHG reduction in dairy farming requires farm-specific optimisation approaches due to the heterogeneity of production systems.

Globally, agriculture is directly responsible for 14% of annual greenhouse gas(GHG) emissions and induces an additional 17% through land use change, mostlyin developing countries (Vermeulen et al 2012). Agricultural intensification andexpansion in these regions is expected to catalyze the most significant relativeincreases in agricultural GHG emissions over the next decade (Smith et al 2008,Tilman et al 2011). Farms in the developing countries of sub-Saharan Africa andAsia are predominately managed by smallholders, with 80% of land holdingssmaller than ten hectares (FAO 2012). One can therefore posit that smallholderfarming significantly impacts the GHG balance of these regions today and willcontinue to do so in the near future.However, our understanding of the effect smallholder farming has on theEarth’s climate system is remarkably limited. Data quantifying existing andreduced GHG emissions and removals of smallholder production systems areavailable for only a handful of crops, livestock, and agroecosystems (Herrero et al2008, Verchot et al 2008, Palm et al 2010). For example, fewer than fifteenstudies of nitrous oxide emissions from soils have taken place in sub-SaharanAfrica, leaving the rate of emissions virtually undocumented. Due to a scarcity ofdata on GHG sources and sinks, most developing countries currently quantifyagricultural emissions and reductions using IPCC Tier 1 emissions factors.However, current Tier 1 emissions factors are either calibrated to data primarilyderived from developed countries, where agricultural production conditions aredissimilar to that in which the majority of smallholders operate, or from data thatare sparse or of mixed quality in developing countries (IPCC 2006). For the mostpart, there are insufficient emissions data characterizing smallholder agricultureto evaluate the level of accuracy or inaccuracy of current emissions estimates.Consequentially, there is no reliable information on the agricultural GHG budgetsfor developing economies. This dearth of information constrains the capacity totransition to low-carbon agricultural development, opportunities for smallholdersto capitalize on carbon markets, and the negotiating position of developingcountries in global climate policy discourse.Concerns over the poor state of information, in terms of data availability andrepresentation, have fueled appeals for new approaches to quantifying GHGemissions and removals from smallholder agriculture, for both existing conditionsand mitigation interventions (Berry and Ryan 2013, Olander et al 2013).Considering the dependence of quantification approaches on data and the currentdata deficit for smallholder systems, it is clear that in situ measurements must bea core part of initial and future strategies to improve GHG inventories and

Opportunities and limitations of farm-level greenhouse gas accounting tools: An overview based on experience from practice.

Agriculture is widely recognized as a source of considerable greenhouse gas (GHG) emissions, with opportunities for mitigation. The limited capacity to identify and collect reliable activity data and to quantify emissions by sources and removals by sinks needs to be addressed. One proposed solution is to adapt IPCC methodologies that include estimations of both CO2 emissions and carbon sequestration in agricultural systems, which were applied to Colombia at the farm level in this study. The aim of this work was to provide an assessment of GHG balances through these IPCC methodologies to identify potential GHG mitigation in sustainable agricultural systems used in Colombia that provide acceptable GHG trade‐offs to the atmosphere. Agroforestry systems made the largest contribution to this mitigation potential because of the potential to sequester carbon in both soil and biomass, giving a negative GHG emission to the atmosphere. GHG balance analysis at the Colombian farm level indicated that conventional agriculture with pastures of Pennisetum clandestinum in rotation with potatoes (PRP) in the Andean zone of Nariño (Colombia) is a large emitter of GHG with 9.1 ton CO2eq ha−1 year−1. On the other hand, in livestock systems in the Andean zone (Antioquia), intensive silvopastoral systems with 500 Eucalyptus tereticornis trees ha−1 (SSPi) on pastures is a great neutralizer of GHG emissions, accounting for −26.6 t CO2eq ha−1 year−1. Agroforestry systems play a leading role, as crop rotation and improved pastures can represent a GHG mitigation opportunity for sustainable agricultural production at the farm level in Colombia. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.

Agricultural greenhouse gas emissions of an Indian village - Who's to blame: crops or livestock?

Radley Horton,1,a Daniel Bader,1,a Yochanan Kushnir,2 Christopher Little,3 Reginald Blake,4 and Cynthia Rosenzweig5 1Columbia University Center for Climate Systems Research, New York, NY. 2Ocean and Climate Physics Department, Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY. 3Atmospheric and Environmental Research, Lexington, MA. 4Physics Department, New York City College of Technology, CUNY, Brooklyn, NY. 5Climate Impacts Group, NASA Goddard Institute for Space Studies; Center for Climate Systems Research, Columbia University Earth Institute, New York, NY

An optimal network design was carried out to prioritise the installation or refurbishment of greenhouse gas (GHG) monitoring stations around Africa. The network was optimised to reduce the uncertainty in emissions across three of the most important GHGs: CO2, CH4, and N2O. Optimal networks were derived using incremental optimisation of the percentage uncertainty reduction achieved by a Gaussian Bayesian atmospheric inversion. The solution for CO2 was driven by seasonality in net primary productivity. The solution for N2O was driven by activity in a small number of soil flux hotspots. The optimal solution for CH4 was consistent over different seasons. All solutions for CO2 and N2O placed sites in central Africa at places such as Kisangani, Kinshasa and Bunia (Democratic Republic of Congo), Dundo and Lubango (Angola), Zoétélé (Cameroon), Am Timan (Chad), and En Nahud (Sudan). Many of these sites appeared in the CH4 solutions, but with a few sites in southern Africa as well, such as Amersfoort (South Africa). The multi-species optimal network design solutions tended to have sites more evenly spread-out, but concentrated the placement of new tall-tower stations in Africa between 10ºN and 25ºS. The uncertainty reduction achieved by the multi-species network of twelve stations reached 47.8% for CO2, 34.3% for CH4, and 32.5% for N2O. The gains in uncertainty reduction diminished as stations were added to the solution, with an expected maximum of less than 60%. A reduction in the absolute uncertainty in African GHG emissions requires these additional measurement stations, as well as additional constraint from an integrated GHG observatory and a reduction in uncertainty in the prior biogenic fluxes in tropical Africa.

<p> It has been widely reported that although IPCC methodologies appropriate for national-level accounting purposes, they lack the farm level resolution and holistic approach required for whole-farm systems analysis. The importance of evaluating greenhouse gas (GHG) emissions from crop production, animal farming and agroforestry within the whole farm setting is being realized as more important than evaluating these emissions in isolation. Thus, whole-farm systems modelling is widely used for farm-level analysis. Here we compare three whole-farm models e.g. FarmSim, Holos and IFSM to simulate the effect of management practices on GHG emissions at the whole farm level and evaluate the carbon sequestration and methane oxidation potential of afforestation as a compensation mechanism for the mitigation of farm-level GHG emissions. Ideally, we would also want information on model performance in predicting GHG emissions in future climatic scenarios. Initial results indicate that these models can accurately predict CO<sub>2</sub> emissions but the accuracy of these models for predicting methane (CH<sub>4</sub>) and nitrous oxide (N<sub>2</sub>O) emissions is quite low. We found that the most prominent drivers for GHG emissions in a whole farm setting were the enteric CH<sub>4</sub> from animal farming and N<sub>2</sub>O emissions from soil management in cropland.  Thus, the low prediction accuracy for CH<sub>4</sub> and N<sub>2</sub>O emissions in whole-farm models may introduce substantial errors into GHG inventories and lead to incorrect mitigation recommendations, which necessitates further fine-tuning of these models. Efforts are ongoing to integrate carbon sequestration and soil methane oxidation potential of farm-level afforestation in the whole farm models. There are indications that afforestation can be an effective mitigation strategy. The variation we found in farm system parameters, and the inherent uncertainties associated with emissions of CH<sub>4</sub> and N<sub>2</sub>O can have substantial implications for reported agricultural emissions requiring uncertainty or sensitivity analysis in any modelling approach. Although there is considerable variation among the quality of farm data, boundary assumptions, the emission factors used we suggest that whole-farm systems models are an appropriate tool to develop and measure GHG mitigation strategies for the European farmed landscape.</p>