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Correcting Misrepresentations of the Environmental Outcomes of the RFS

A report released recently in the Proceedings of the National Academy of the Sciences (PNAS) by Tyler Lark and other authors brought to life several misrepresentations of the environmental outcomes of the Renewable Fuel Standard (RFS). American Coalition for Ethanol and Dakota Ethanol Board Member, Corn Farmer, and self-described “student” of corn production and greenhouse gas (GHG) accounting and modeling, Ron Alverson, refutes three key misrepresentations with the facts.

Misrepresentations in Lark et al. 2022                               

  • Lark et al. estimated that "the RFS increased corn prices by 30% and the prices of other crops by 20%" which, in turn, expanded U.S. corn cultivation by 2.8 million hectares (8.7%) and total cropland by 2.1 Mha (2.4%) in the years following policy enactment (2008 to 2016)".

Facts: Corn prices during the 5-year period of 2008 through 2012 averaged $5.40 per bushel and during the 4-year period of 2013 through 2016 averaged $4.35 per bushel. A 19% reduction (Chicago Board of Trade Monthly Futures Prices). The corn cultivated area increased 2.3% during these same time periods and total cropland cultivated area increased just 8 tenths of 1% (USDA databases).

  • Lark et al. estimated that 30-year emissions associated with RFS-induced conversions to cropland were 320.4 Tg CO2e, or approximately 181 metric tons of CO2 per hectare per year.

Facts: Grasslands and pasturelands in the Western Corn Belt contain about 100-120 metric tons of soil organic carbon per hectare in the top 2 feet of soil, the layer most subject to soil organic carbon (SOC) loss when converted to cropland. If 100% of the organic carbon decomposed in the 2-foot layer of soil over a hectare, the total CO2 emissions would be 400 metric tons of CO2.  Lark's claim of 181 metric tons of CO2 per hectare over 30 years implies that 45% of the SOC in the top 2 feet of soil in converted cropland would decompose. That might happen if crop production methods and crop yields were similar to the first half of the last century when converted grasslands across the Midwest lost about 50% of SOC in the top 12 inches of soils.  However, if pasturelands or grassland are converted to cropland today in the Western Corn Belt, yields are 5-10 times higher and a majority of growers use no or reduced tillage.  Modern corn production's high grain/residue yields, along with reduced tillage intensity, result in a positive crop/soil carbon balance and tens of thousands of well-managed corn fields are sequestering CO2 at the rate of .5 metric tons of CO2 per hectare per year.  It is very likely that Lark has estimated more than three times the actual SOC loss rate from conversion of pasture and grasslands to cropland if it were to happen today in the Western Corn Belt.

  • Lark et al. estimated that nitrous oxide emissions due to higher Nitrogen fertilizer use during corn production, and Land Use Change, have increased 9-grams CO2e per megajoule of corn ethanol energy production.

Facts: A 9-gram CO2e per megajoule increase in corn production nitrous oxide emissions is a 68% increase from the current 13.2-grams CO2e per megajoule for corn production. This would imply that nitrogen (N) use rates on corn must have increased by 68%. USDA fertilizer use data indicate that total N fertilizer use per bushel of corn production was .88 lbs N per bushel in 2010, .82 lbs N per bushel 2016, and .85 lbs per bushel in 2018.  These data do not indicate any significant increase in nitrogen use during corn production, or nitrous oxide emissions from use of that N fertilizer. Furthermore, the adoption of precision fertilizer application technology has become widespread and has meant that N utilization efficiency is better than ever.                             

Alverson poses the question: Why does Lark et al. use modeling to estimate biofuel GHG emissions? It is understandable to use modeling to estimate future impacts, but why use modeling when a track record of historical facts are available?  “I think we know why,” Alverson says. “Modeling outcomes can be manipulated and biased by small changes in modeling factors.”

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