MANAGEMENT OF INTENSIVE FORAGE-BEEF PRODUCTION UNDER YIELD UNCERTAINTY

Forage production variability is incorporated into a decision theory framework for a beef producer in East Texas. The results suggest that the least risky, and also the most profitable, approach to intensive forage beef production is to plan for relatively poor weather conditions and low forage production. This results in a more diverse forage system and a smaller herd size than would be found optimal under the assumption of constant average forage production. These results also demonstrate that the assumption of constant average forage production may results in grossly exaggerated estimates of expected net returns.Livestock Production/Industries, Risk and Uncertainty,

OPTIMAL STOCKING OF RANGELAND FOR LIVESTOCK PRODUCTION WITHIN A DYNAMIC FRAMEWORK

A dynamic model is constructed and utilized to illustrate the interactions of several primary dynamic ecologic and economic relationships that are important in effective rangeland management. Within this context, the implications of various range management strategies are explored.Livestock Production/Industries,

The dynamics of crop yields in the U. S. Corn Belt as effected by weather and technological progress

Producers of agricultural products, policy makers, and consumers alike have a keen interest in what will happen to crop yields in the future. This study attempts to carefully analyze past trends in crop yields in five Corn Belt states, Illinois, Indiana, Iowa, Missouri, and Ohio, and how they have been affected by weather and technological progress over time;State average yields for corn grain, corn silage, soybeans, small grains, and meadow (leguminous hay) are modeled as a system of equations where yields are functions of weather, technological progress, and nitrogen application. Time series data on yields, nitrogen and corn prices, nitrogen application, and weather are collected. Time is used as a proxy variable for technological progress and the models from all five states are estimated using three stage least squares regression. The estimated models do a good job of fitting the 1951-1980 time series data and they illustrate that technological progress and weather are the most important factors that affect yields;The prospects of favorable future weather are analyzed by regressing weather variables from 1930-1980 or dummy variables for each state and for periods of abnormally favorable weather. These models are able to explain only a small portion of the total variance in weather, but they do indicate that the periods during 1942-1952 and 1961-1973 can be characterized as more cool and wet, and generally more favorable to crop yields than average. However, attempts to use this information to project future weather would be ludicrous;The prospects of future yield increases as a result of technological progress are examined by looking at some of the major factors that affect crop yields. It can be seen that technological progress is a large and complex set of interacting conditions, occurrences and activities that cannot be easily modeled, described, or projected. Attempts to project future yields must be based on various assumptions about future technological progress;Yields are projected for the year 2000, using the estimated models, under six different scenarios based on various assumptions about future technological progress, weather, and nitrogen application. In general, with the noted exception of wheat, there is little evidence found in the time series data that would indicate a leveling off of Corn Belt crop yields in the near future

Fine Particulate Air Pollution and Mortality: Response to Enstrom's Reanalysis of the American Cancer Society Cancer Prevention Study II Cohort

The first analysis of long-term exposures to air pollution and risk of mortality using the American Cancer Society Cancer Prevention Study II (ACS CPS-II) cohort was published in 1995.1 Subsequently, extensive independent reanalysis2 and multiple extended analyses3-7 were conducted. These studies have consistently demonstrated that exposure to fine particulate matter air pollution (PM2.5) is associated with increased risk of mortality, especially cardiopulmonary or cardiovascular disease mortality. A recent analysis by Enstrom, based on early data from the ACS CPS-II cohort, reports no significant relationship between PM2.5 and total mortality.8 The author asserts that the original analyses, reanalyses, and the extended analyses found positive PM2.5–mortality relationships because of selective use of CPS-II and PM2.5 data

Indirect adjustment for multiple missing variables applicable to environmental epidemiology

AbstractObjectivesDevelop statistical methods for survival models to indirectly adjust hazard ratios of environmental exposures for missing risk factors.MethodsA partitioned regression approach for linear models is applied to time to event survival analyses of cohort study data. Information on the correlation between observed and missing risk factors is obtained from ancillary data sources such as national health surveys. The relationship between the missing risk factors and survival is obtained from previously published studies. We first evaluated the methodology using simulations, by considering the Weibull survival distribution for a proportional hazards regression model with varied baseline functions, correlations between an adjusted variable and an adjustment variable as well as selected censoring rates. Then we illustrate the method in a large, representative Canadian cohort of the association between concentrations of ambient fine particulate matter and mortality from ischemic heart disease.ResultsIndirect adjustment for cigarette smoking habits and obesity increased the fine particulate matter-ischemic heart disease association by 3%–123%, depending on the number of variables considered in the adjustment model due to the negative correlation between these two risk factors and ambient air pollution concentrations in Canada. The simulations suggested that the method yielded small relative bias (<40%) for most cohort designs encountered in environmental epidemiology.ConclusionsThis method can accommodate adjustment for multiple missing risk factors simultaneously while accounting for the associations between observed and missing risk factors and between missing risk factors and health endpoints

Lung Cancer and Cardiovascular Disease Mortality Associated with Ambient Air Pollution and Cigarette Smoke: Shape of the Exposure–Response Relationships

Background: Lung cancer and cardiovascular disease (CVD) mortality risks increase with smoking, secondhand smoke (SHS), and exposure to fine particulate matter < 2.5 μm in diameter (PM2.5) from ambient air pollution. Recent research indicates that the exposure–response relationship for CVD is nonlinear, with a steep increase in risk at low exposures and flattening out at higher exposures. Comparable estimates of the exposure–response relationship for lung cancer are required for disease burden estimates and related public health policy assessments