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Policies to Increase Mitigation of Agricultural Greenhouse Gas Emissions

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  • Toman, Michael A.

    (Resources for the Future)

  • Baker, Justin
  • Beach, Robert
  • Feng, Hongli
  • McLellan, Eileen
  • Joiner, Emily

    (Resources for the Future)

Abstract

Achieving economy-wide net-zero greenhouse gas (GHG) emissions will require seizing mitigation opportunities in agriculture. In 2018, gross GHG emissions from agriculture accounted for roughly 9 percent of the US total (Figure 1). Gross emissions is total emissions before adjusting downward for carbon storage in lands and forests (sinks). These figures reflect direct emissions from agricultural processes, not counting emissions from energy consumption in agriculture. After accounting for sinks (Figure 2), net agricultural emissions were about 10 percent of net US emissions. Although this is less than many other sectors of the US economy, reducing agricultural emissions (even as output expands to meet growing global demand) contributes to net zero by reducing the need for “negative emissions”—that is, carbon capture and storage in sectors other than agriculture.Discussion of alternatives for mitigating ag-GHG emissions has been spurred not just by reauthorization of the Farm Bill but also by the passage of the 2022 Inflation Reduction Act (IRA). In Title II, Sections 21001 and 21002 of the act call for about $20 billion in new funding for several USDA conservation programs to support voluntary ag-GHG mitigation (https://www.congress.gov/bill/117th-congress/house-bill/5376).Ag-GHG emissions come from several sources (Figure 2), and some are inherently challenging to mitigate. Crop cultivation can release soil carbon to the atmosphere as carbon dioxide (CO2), depending on tillage practices, crop residue retention, and use of cover crops. Nitrous oxide (N2O) is emitted through chemical reactions in the environment from application of synthetic nitrogen fertilizers and organic fertilizers such as manure. N2O emissions are affected by agricultural land management that influences N fluxes among soils, crops, and the atmosphere (e.g., cultivation of N-fixing crops, such as soybeans). Methane (CH4) is emitted to the atmosphere through the digestive processes of farm animals and the anaerobic decomposition of manure. Livestock waste also produces N2O during the nitrification and denitrification of the organic nitrogen in livestock manure and urine.We consider three policy approaches to voluntary reduction of ag-GHGs. Each functions by rewarding farmers and ranchers who shift practices toward lower-GHG production.The first is increased government co-financing to induce lower-GHG agricultural techniques by farmers and ranchers. This approach builds on well-established USDA conservation funding mechanisms in Title 2 of the Farm Bill. The government also could offer expanded crop insurance coverage through its Risk Management Agency to cover potentially greater risk in production yields due to the use of lower-carbon techniques.The second approach is “climate-smart” agricultural commodity programs. The Partnerships for Climate-Smart Commodities announced by USDA in spring 2022 will provide $2.8 billion to finance 70 pilot projects, with additional funding expected in a second phase (https://www.usda.gov/climate-solutions/climate-smart-commodities). The program overlaps with the conservation initiatives noted above, in that projects need to incorporate approaches for reducing ag-GHG emissions. However, it also supports improved practices for measuring and verifying GHG reductions and development of new markets for lower-GHG agricultural commodities (including lower-GHG supply chains). One question is how large the latent demand for such commodities might be, especially if they raise prices.The third approach is ag-GHG emissions reduction credit mechanisms. A farmer or rancher using a lower-GHG agricultural method could earn credits based on the estimated GHG savings relative to business as usual over a specified time period, then sell the credits. The revenues from credit sales would allow farmers and ranchers to recoup the cost of more ambitious emissions mitigation. Many companies are already looking for high-quality emissions reduction credits as part of their voluntary plans for reducing their carbon footprints. Ag-GHG emission-reduction credits could be one source. They also could play a role in complying with mandatory GHG limits.Beyond voluntary or regulatory measures, technological innovation can help reduce ag-GHG emissions. The design of policies to support technological innovation is beyond the scope of this paper, but this approach has great potential for moving the sector toward net-zero. Innovation is not a simple linear process. To illustrate, livestock feed additives can reduce methane production from enteric fermentation. Most feed additives provide only limited reductions (around 10 to 15 percent) (Hristov et al. 2013). Innovative feed additives include red algae (Asparagopsis), which seems to have the potential for reducing ruminants’ emissions by 50- to 90 percent, but its long-term effectiveness and consequences for human or animal health are unknown. In addition, the species of red algae under consideration is considered invasive in many countries (Hegarty et al. 2021).

Suggested Citation

  • Toman, Michael A. & Baker, Justin & Beach, Robert & Feng, Hongli & McLellan, Eileen & Joiner, Emily, 2022. "Policies to Increase Mitigation of Agricultural Greenhouse Gas Emissions," RFF Issue Briefs 22-10, Resources for the Future.
  • Handle: RePEc:rff:ibrief:ib-22-10
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    References listed on IDEAS

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    1. Hongli Feng & Catherine L. Kling, 2005. "The Consequences of Cobenefits for the Efficient Design of Carbon Sequestration Programs," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 53(4), pages 461-476, December.
    2. Laura McCann & Roger Claassen, 2016. "Farmer Transaction Costs of Participating in Federal Conservation Programs: Magnitudes and Determinants," Land Economics, University of Wisconsin Press, vol. 92(2), pages 256-272.
    3. Barham, Bradford L. & Chavas, Jean-Paul & Fitz, Dylan & Salas, Vanessa Ríos & Schechter, Laura, 2014. "The roles of risk and ambiguity in technology adoption," Journal of Economic Behavior & Organization, Elsevier, vol. 97(C), pages 204-218.
    4. Sawadgo, Wendiam & Plastina, Alejandro, . "The Invisible Elephant: Disadoption of Conservation Practices in the United States," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 37(01).
    5. Andrea Cattaneo, 2003. "The Pursuit of Efficiency and Its Unintended Consequences: Contract Withdrawals in the Environmental Quality Incentives Program," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 25(2), pages 449-469.
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