Michael S. Kaylen

Economic feasibility of producing ethanol from lignocellulosic feedstocks

A mathematical programming model is built to analyze the economic feasibility of producing ethanol from lignocellulosic feedstocks. The optimal size of an ethanol plant is determined by the trade-off between increasing transportation costs for feedstocks versus decreasing average plant costs as the plant size increases. The ethanol plant is modeled under the assumption that it utilizes recent technological advancements in dilute acid hydrolysis. Potential feedstocks include energy crops, crop residues and woody biomass. It is found that the recent technological advancements appear to make ethanol competitive with gasoline, but only if higher valued chemicals are produced as co-products with the ethanol. The low cost and chemical composition of crop residues make them attractive as a feedstock.

Thore, Sten. Economic Logistics: The Optimization of Spatial and Sectoral Resource, Production, and Distribution Systems. New York: Greenwood Publishing Group, Inc., 1991, xi + 348 pp.,

Introduction: The Task Ahead Some Elements of Saddle-Point Theory (by W. W. Cooper and Sten Thore) The Spatial Dimension The Vertical Dimension The Time Dimension Price Formulations Resource Management by Goal Focusing Rigid Prices and/or Rigid Wages Chance-Constrained Activity Analysis and Chance-Constrained Production and Distribution Systems The Production and Distribution System as an Infinite Game Index

49.95.

Agriculture operates in an uncertain environment. Yields, prices, and resource usage can change dramatically from year to year. However, most analyses of the agricultural sector, at least those using mathematical programming methods, assume decision making is based on average yields, ignoring yield variability. This study examines how explicit consideration of stochastic yield outcomes influence a sector analysis. We develop a model that can be used for stochastic sector analysis. We extend the risk framework developed by Hazell and others to incorporate discrete yield outcomes as well as consumption activities dependent upon yield outcomes. An empirical application addresses a comparison between sector analysis with and without considerations of the economic effects of yield variability in a global warming context.

Uncertain Yields in Sectoral Welfare Analysis: An Application to Global Warming

Bayesian estimation and the exclusion of variables are two basic approaches to the improvement of vector autoregression forecasting models. This study presents a method which combines and extends several techniques within the exclusion-of-variables approach. Several quarterly hog market models are estimated and out-of-sample forecasts from 1977 through 1984 are evaluated. The results suggest the proposed method compares favorably to other exclusion-of-variables techniques as well as to the more sophisticated bayesian approaches.

Vector Autoregression Forecasting Models: Recent Developments Applied to the U.S. Hog Market

A Kalman filter algorithm and 1895–1988 data were used to estimate a U.S. corn yield model which incorporates a stochastic trend term and monthly weather indices. Yield growth peaked in 1964, with the annual increase now only 1.3 bushels per year. The empirical distribution of 1989 corn yield is derived and found to be skewed. The mean yield for 1989 was close to final USDA estimates.

Trend, Weather Variables, and the Distribution of U.S. Corn Yields

Regional corn yield models incorporating stochastic trends, prices and weather variables are estimated. Hypothesis tests suggest yield variability has increased because of an increase in error variance and an increase in weather-related effects. Decomposition of the error variance shows much of its increase is due to an increased correlaion between regional yields.

Stochastic trend, weather and US corn yield variability

Abstract Teacher migration is analyzed using Missouri beginning teacher survey data. A simultaneous equations model involving qualitative and limited dependent variables is developed. Earnings equations which account for self-selectivity are estimated for migrant and non-migrant teachers. They support the conventional human capital theory of migration. Teacher migration is most strongly influenced by home ownership and the leadership style of the school administrator.

An econometric analysis of teacher mobility

Abstract A nonlinear mathematical programming model for farm-level analysis of agricultural insurance is presented. Maximization of the expected utility of profit is the assumed objective function. Model input includes specification of the insurance policy under consideration, per acre stochastic production functions, the joint probability distribution for output prices and the random variables affecting yields, the risk aversion parameter, and any desired linear constraints. The model solution includes the optimal values for the choice variables: per acre input usages and the number of acres planted to the different crops. Means and variances are calculated for yields, profit, and indemnities. The model also returns the maximum amount the farmer would be willing to pay for the insurance policy.

Farm-level analysis of agricultural insurance: A mathematical programming approach

This paper evaluates the economic and environmental tradeoffs at watershed scale by incorporating both economic and environmental risks in agricultural production. The Target MOTAD model is modified by imposing a probability-constrained objective function to capture the yield uncertainty caused by random allocation of farming systems to soil types and by introducing environmental targets to incorporate environmental risk due to random storm events. This framework is used to determine the tradeoffs frontier between watershed net return and sediment yield and nitrogen concentration in runoff in Goodwater Creek watershed, Missouri. The frontier is significantly affected by environmental risk preference.

Watershed-Scale Economic and Environmental Tradeoffs Incorporating Risks: A Target MOTAD Approach

An approach to risk modeling is developed which uses nonlinear programming and numerical integration to directly solve the expected utility maximization problem. The approach contrasts with earlier efforts in that, rather than using an empirical density function, a joint probability density function is explicitly specified. Comparisons are done showing that this approach yields more accurate solutions than the empirical density approach even when many points are sampled from the theoretical distribution.