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The Agricultural Resource Management Survey (ARMS) conducted annually by the USDA's Economic Research Service collects data on US agriculture, ranging from production practices to the financial condition of farm and ranch enterprises and the farm household. The purpose of this article is to consider what could make ARMS useful from a farmer's point of view.

A Delphi method is used to gather input from a panel of experts.

Results show that increasing the usability of the ARMS to agricultural producers involves expanding the content and relevance of the data collected. Specific types of data needed are identified. Also, recommendations are made concerning how the usefulness and relevance of the data could be increased by refining the sample frame. Finally, it is argued here that after making some adjustments to the ARMS sample frame to create nationally representative data, the ARMS project could serve as a hugely important basis for reporting economic performance levels for American agriculture.

This study offers insights from agricultural finance experts on how the ARMS could be improved to expand the quality and usefulness of its output for both professionals and agricultural producers.

The purpose of this research is to examine the effect of accounting for complex organizational forms on data collection with the Agricultural Resource Management Survey (ARMS).

This research reviews the literature from accounting theory along with the goals of data collection for policy analysis to draw conclusions about the applicability of accounting pronouncements.

Historically, the financial data collected in ARMS were based on financial accounting standards which were adequate for most purposes. However, this study develops the fact that many of these financial accounting standards were created to provide information for equity market transactions. The complexities of accounting for consolidations will provide valuable information, but implementing these standards will require accounting sophistication that is not prevalent in agriculture.

By drawing accounting theory together with the targeted use of data, this study offers guidelines to improve the data quality for a growing complex US agriculture.

What copulas are, their estimation, and use is illustrated using a geographical diversification example. To accomplish this, dependencies between county-level yields are calculated for non-irrigated wheat, upland cotton, and sorghum using Pearson linear correlation and Kendall's tau. The use of Kendall's tau allows the implementation of copulas to estimate the dependency between county-level yields. The paper aims to discuss these issues.

Four parametric copulas, Gaussian, Frank, Clayton, and Gumbel, are used to estimate Kendall's tau. These four estimates of Kendall's tau are compared to Pearson's linear correlation, a more typical measure of dependence. Using this information, functions are estimated to determine the relationships between dependencies and changes in geographic and climate data.

The effect on county-level crop yields based on changes of geographical and climate variables differed among the different dependency measures among the three different crops. Implementing alternative dependency measures changed the statistical significance and the signs of the coefficients in the sorghum and cotton dependence functions. Copula-based elasticities are consistently less than the linear correlation elasticities for wheat and cotton. For sorghum, however, the copula-based elasticities are generally larger. The results indicate that one should not take the issue of measuring dependence as a trivial matter.

This research not only extends the current literature on geographical diversification by taking a more detailed examination of factors impacting yield dependence, but also extends the copula literature by comparing estimation results using linear correlation and copula-based rank correlation.