The Long-Run Impact of Corn-Based Ethanol on the Grain, Oilseed, and Livestock Sectors: A Preliminary Assessment, November 2006

The ongoing growth of corn-based ethanol production raises some fundamental questions about what impact continued growth will have on U.S. and world agriculture. Estimates of the long-run potential for ethanol production can be made by calculating the corn price at which the incentive to expand ethanol production disappears. Under current ethanol tax policy, if the prices of crude oil, natural gas, and distillers grains stay at current levels, then the break-even corn price is $4.05 per bushel. A multi-commodity, multi country system of integrated commodity models is used to estimate the impacts if we ever get to $4.05 corn. At this price, corn-based ethanol production would reach 31.5 billion gallons per year, or about 20% of projected U.S. fuel consumption in 2015. Supporting this level of production would require 95.6 million acres of corn to be planted. Total corn production would be approximately 15.6 billion bushels, compared to 11.0 billion bushels today. Most of the additional corn acres come from reduced soybean acreage. Wheat markets would adjust to fulfill increased demand for feed wheat. Corn exports and production of pork and poultry would all be reduced in response to higher corn prices and increased utilization of corn by ethanol plants. These results should not be viewed as a prediction of what will eventually materialize. Rather, they indicate a logical end point to the current incentives to invest in corn-based ethanol plants.

Emerging Biofuels: Outlook of Effects on U.S. Grain, Oilseed, and Livestock Markets, 2007

Projections of U.S. ethanol production and its impacts on planted acreage, crop prices, livestock production and prices, trade, and retail food costs are presented under the assumption that current tax credits and trade policies are maintained. The projections were made using a multi-product, multi-country deterministic partial equilibrium model. The impacts of higher oil prices, a drought combined with an ethanol mandate, and removal of land from the Conservation Reserve Program (CRP) relative to baseline projections are also presented. The results indicate that expanded U.S. ethanol production will cause long-run crop prices to increase. In response to higher feed costs, livestock farmgate prices will increase enough to cover the feed cost increases. Retail meat, egg, and dairy prices will also increase. If oil prices are permanently $10-per-barrel higher than assumed in the baseline projections, U.S. ethanol will expand significantly. The magnitude of the expansion will depend on the future makeup of the U.S. automobile fleet. If sufficient demand for E-85 from flex-fuel vehicles is available, corn-based ethanol production is projected to increase to over 30 billion gallons per year with the higher oil prices. The direct effect of higher feed costs is that U.S. food prices would increase by a minimum of 1.1% over baseline levels. Results of a model of a 1988-type drought combined with a large mandate for continued ethanol production show sharply higher crop prices, a drop in livestock production, and higher food prices. Corn exports would drop significantly, and feed costs would rise. Wheat feed use would rise sharply. Taking additional land out of the CRP would lower crop prices in the short run. But because long-run corn prices are determined by ethanol prices and not by corn acreage, the long-run impacts on commodity prices and food prices of a smaller CRP are modest. Cellulosic ethanol from switchgrass and biodiesel from soybeans do not become economically viable in the Corn Belt under any of the scenarios. This is so because high energy costs that increase the prices of biodiesel and switchgrass ethanol also increase the price of cornbased ethanol. So long as producers can choose between soybeans for biodiesel, switchgrass for ethanol, and corn for ethanol, they will choose to grow corn. Cellulosic ethanol from corn stover does not enter into any scenario because of the high cost of collecting and transporting corn stover over the large distances required to supply a commercial-sized ethanol facility.

Iowa Grain Facilities Map, July 1, 2009

Iowa Grain Facilities Map

Iowa Grain Facilities Map, July 1, 2010

Iowa Grain Facilities Map

Refinement of Rapid PCC Coarse Aggregate Testing Program, HR-1071, 1999

The characterization and categorization of coarse aggregates for use in portland cement concrete (PCC) pavements is a highly refined process at the Iowa Department of Transportation. Over the past 10 to 15 years, much effort has been directed at pursuing direct testing schemes to supplement or replace existing physical testing schemes. Direct testing refers to the process of directly measuring the chemical and mineralogical properties of an aggregate and then attempting to correlate those measured properties to historical performance information (i.e., field service record). This is in contrast to indirect measurement techniques, which generally attempt to extrapolate the performance of laboratory test specimens to expected field performance. The purpose of this research project was to investigate and refine the use of direct testing methods, such as X-ray analysis techniques and thermal analysis techniques, to categorize carbonate aggregates for use in portland cement concrete. The results of this study indicated that the general testing methods that are currently used to obtain data for estimating service life tend to be very reliable and have good to excellent repeatability. Several changes in the current techniques were recommended to enhance the long-term reliability of the carbonate database. These changes can be summarized as follows: (a) Limits that are more stringent need to be set on the maximum particle size in the samples subjected to testing. This should help to improve the reliability of all three of the test methods studied during this project. (b) X-ray diffraction testing needs to be refined to incorporate the use of an internal standard. This will help to minimize the influence of sample positioning errors and it will also allow for the calculation of the concentration of the various minerals present in the samples. (c) Thermal analysis data needs to be corrected for moisture content and clay content prior to calculating the carbonate content of the sample.