Projections of US farm numbers by Markov processes

The structure of agriculture for the remaining years of the twentieth century is an issue crucial to farmers and ranchers. Much interest has been focused in recent years on the structure of agriculture and specifically on the size distribution and number of farms and ranches. In recent studies, Markov chain processes have been applied to help predict the numbers and sizes of farms, and to analyze the movement of firms between size classes;Each of the previous attempts to apply the Markov process to the problem of estimating and projecting farm numbers has encountered some major difficulty in deriving a solution. Several of the model formulation and computational difficulties, such as the addition of a feasibility restriction, external constraints, cycling difficulties and computer algorithms, have been solved in the first two papers of this study;This study synthesized the results , problems and advances of previous reports to predict farm numbers by size class. Values of sales classes were chosen to represent size classes of farms, thus avoiding definitional questions of farms, ranches and nonland based farming operations;A series of USDA indexes were chosen for explanatory variables in the Markov regression problem. Approximately 30 combinations of nine different indexes were applied to the Markov model;Model results indicate that the historical decline in farm numbers will continue to a level of about two million farms by 1990. The farm number projections are closely tied to growth of remaining firms. The primary factors causing or allowing firms to grow in size include increases in the prices that farmers must pay for inputs and increased productivity of those inputs, especially their own labor;The results of this study indicate a continuing trend toward fewer, larger farms. Although much of the increase in size is caused by increases in prices received rather than real growth, there will be real growth as well

A COMPARISON OF STATE AND USDA COST AND RETURN ESTIMATES

Concern has been voiced that U.S. Department of Agriculture (UDSA) Farm Costs and Returns Surveys are used for a wide variety of policy analyses but produce questionable estimates. USDA-developed crop and livestock cost and return estimates for New Mexico and other selected states are compared to estimates developed by state universities. Major differences exist, most important of which relate to the ability of the survey respondent to answer the questions posed. Regardless of the cause of the differences, closer cooperation between the USDA and state universities clearly is needed to develop consistent estimates.Research Methods/ Statistical Methods,

Economics Of Mining Coal In Iowa

The Department of Economics at Iowa State University has three major areas of research responsibility as a part of the Iowa Coal Research Project 1) economic analysis of the feasibility of a major Iowa coal producing industry, 2) analysis of the legal dimensions of mining coal in Iowa, and 3) economic analysis of the coal transportation network in Iowa. This paper summarizes research results from the economic feasibility study in an Executive Summary format. Distribution is intended for persons on and off the Iowa State University campus who are interested in the basic results, but are not concerned specifically with the research methodology employed; consequently, this paper will not discuss in detail the mathematical model used in the feasibility analysis or the development and justification of the input data. Rather, the purpose of this discussion is to present the economic climate in which the Iowa coal industry competes and the general results of the economic analyses performed to date. More detailed analyses in the feasibility area are listed in the References section [14, 15 16]

MEASURING NET BENEFITS RESULTING FROM UNIVERSITY-INDUSTRY COLLABORATION: AN EXAMPLE FROM THE NEW MEXICO CHILE TASK FORCE

Research and Development/Tech Change/Emerging Technologies,

A Risk-Return Analysis For The Midwest Farmer-Feeder

Risk has always been an important dimension of the agricultural sector, and considerable effort has been expended to incorporate risk dimensions in decision models for the farm firm. With the dramatic fluctuations in commodity prices of the 1970\u27s, the Midwest farmer has been confronted with increasing risk, particularly if cattle feeding has been a part of his farm organization. Fluctuations in feed costs, feeder cattle and fed cattle prices have resulted in wide variations in profit per head. In addition, new technology in feeding systems and housing is available and feeders must decide if that technology is feasible and if it should be adopted

Crop cost and return estimates in New Mexico, 1993

Introduction; Cost accounting data; Budget generators; The NMSU budget generator; USDA's FEDS budget generator; Objectives; Cost and net returns; Variable costs; Fixed costs; Overhead expenses; Interest on operating capital; Interest on machinery investment; Machinery costs; Net farm income; Input data; Representative farm results; Commodity prices; Pastures; Forage-based part-time farms; Vegetable production; Program crops; Dryland farms; Commercial farms; Cost of production; Crop-by-crop results; Alfalfa; Barley; Chile; Corn; Corn silage; Cotton; Grain sorghum; Others hays; Pastures; Peanuts, pinto beans, and vegetables; Wheat; Summary and conclusions; References; Appendix A: Per-acre cost and return summaries by crop; Appendix B: Per-acre summaries of the costs and returns for each crop on the representative farms modeled for 1993; Appendix C: Whole-farm summariesResearch report containing summaries and comparisons of the cost and return estimates for crops grown throughout New Mexico during the 1993 production season

Economic evaluation of alfalfa production under less than optimum irrigation levels

Objectives; Water use by alfalfa; Decision analysis; Data and methods; Alfalfa yields; Crop cost and return estimation; River data; Per-acre costs and returns; Price sensitivity analysis; Bayesian decision model; Bibliography; Appendix: Bayesian decision analysis modelResearch report containing the results of a study to determine the production of alfalfa under less than optimum irrigation levels in preparation for future agricultural water shortages

Tax audits and appeals

Guide containing brief, general information on the rights and responsibilities of taxpayers during tax audits or audit appeals

Conservation and land clearing expenses

Guide containing information on the type of soil conservation, water conservation, and land clearing expenses that are eligible for tax deductions by farms operated for profit

Crop cost and return estimates in New Mexico, 1991

Cost accounting data; Budget generators; The NMSU budget generator; USDA's FEDS budget generator; Objectives; Cost and net returns; Variable costs; Fixed costs; Overhead expenses; Interest on operating capital; Interest on machinery investment; Machinery costs; Net farm income; Input data; Representative farm results; Commodity prices; Pastures; Forage-based part-time farms; Vegetable production; Program crops; Dryland farms; Commercial farms; Cost of production; Crop-by-crop results; Alfalfa; Barley; Chile; Corn; Corn silage; Cotton; Grain sorghum; Other hays; Pastures; Peanuts, pinto beans, and vegetables; Wheat; Summary and conclusions; References; Appendix A; Appendix B; Appendix C;Research report containing summaries and comparisons of the cost and return estimates for crops grown throughout New Mexico during the 1991 production season