The Nebraska Agricultural Outlook for 1938

Each year the federal government gathers data relating to agriculture through the various departments of the United States Department of Agriculture. These data are classified and analyzed by the Bureau of Agricultural Economics at Washington and all information which may be helpful to farmers is published. For several years it has been the policy of the Department of Rural Economics and the Agricultural Extension Service of the College of Agriculture, Lincoln, to select from the federal information facts which may be especially helpful to Nebraska farmers. These facts and other economic conditions in Nebraska are published this year as the Agricultural Outlook for Nebraska, 1938. The Outlook should be helpful in the marketing of the crops and livestock on hand. It should also be helpful in making farm plans for 1938.

Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling

Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple sources of uncertainty. Given the complexities of ecological studies, the hierarchical statistical model is an invaluable tool. This approach is not new in ecology, and there are many examples (both Bayesian and non-Bayesian) in the literature illustrating the benefits of this approach. In this article, we provide a baseline for concepts, notation, and methods, from which discussion on hierarchical statistical modeling in ecology can proceed. We have also planted some seeds for discussion and tried to show where the practical difficulties lie. Our thesis is that hierarchical statistical modeling is a powerful way of approaching ecological analysis in the presence of inevitable but quantifiable uncertainties, even if practical issues sometimes require pragmatic compromises.