Development of a Rational Characterization Method for Iowa Fly Ash, HR-286, Annual Progress Report 2, 1987

The following report summarizes research activities on the project for the period December 1, 1986 to November 30, 1987. Research efforts for the second year deviated slightly from those described in the project proposal. By the end of the second year of testing, it was possible to begin evaluating how power plant operating conditions influenced the chemical and physical properties of fly ash obtained from one of the monitored power plants (Ottumwa Generating Station, OGS). Hence, several of the tasks initially assigned to the third year of the project (specifically tasks D, E, and F) were initiated during the second year of the project. Manpower constraints were balanced by delaying full scale implementation of the quantitative X-ray diffraction and differential thermal analysis tasks until the beginning of the third year of the project. Such changes should have little bearing on the outcome of the overall project.

Farm-Level Analysis of Risk Management Proposals, March 2000

This paper presents a detailed report of the representative farm analysis (summarized in FAPRI Policy Working Paper #01-00). At the request of several members of the Committee on Agriculture, Nutrition, and Forestry of the U.S. Senate, we have continued to analyze the impacts of the Farmers’ Risk Management Act of 1999 (S. 1666) and the Risk Management for the 21st Century Act (S. 1580). Earlier analysis reported in FAPRI Policy Working Paper #04-99 concentrated on the aggregate net farm income and government outlay impacts. The representative farm analysis is conducted for several types of farms, including both irrigated and non-irrigated cotton farms in Tom Green County, Texas; dryland wheat farms in Morton County, North Dakota and Sumner County, Kansas; and a corn farm in Webster County, Iowa. We consider additional factors that may shed light on the differential impacts of the two plans. 1. Farm-level income impacts under alternative weather scenarios. 2. Additional indirect impacts, such as a change in ability to obtain financing. 3. Implications of within-year price shocks. Our results indicate that farmers who buy crop insurance will increase their coverage levels under S. 1580. Farmers with high yield risk find that the 65 percent coverage level maximizes expected returns, but some who feel that they obtain other benefits from higher coverage will find that the S. 1580 subsidy schedule significantly lowers the cost of obtaining the additional coverage. Farmers with lower yield risk find that the increased indemnities from additional coverage will more than offset the increase in producer premium. In addition, because S. 1580 extends its increased premium subsidy percentages to revenue insurance products, farmers will have an increased incentive to buy revenue insurance. Differences in the ancillary benefits from crop insurance under the baseline and S. 1580 would be driven by the increase in insurance participation and buy-up. Given the same levels of insurance participation and buy-up, the ancillary benefits under the two scenarios would be the same.