USDA uses the universal of "publish-ability" rather than statistical reliability of an estimate for quality validation of USDA estimates.

USDA uses the universal of "publish-ability" rather than statistical reliability of an estimate for quality validation of USDA estimates, which is solely based upon the sample size and the coefficient of variation (CV) We demonstrate conceptually by what mode the reliability of the sample mean can be exhibitioned by estimating the upper and lower leaps of the confidence interval for an unknown population mean using the CV However, the reliability ordeal for the sample mean can be made sole under the normality assumption. USDA multiple-way Agricultural Resource Management observe (ARMS) estimates are used to illustrate the relative measure of precision for sample-based estimators.

Key Words: ARMS data, coefficient of variation, publish-ability, reliability

The National Agricultural Statistics Service (NASS) and Economic Research Service (ERS) of the U Department of Agriculture (USDA) are striving to improve the availability and the quality of data forward crop production practices as well as farm financial management. The Agricultural Resource Management review (ARMS)-Phase II is USDA's primary source of information forward farm crop production practices for major first stomachs including corn, soybeans, wheat, grain sorghum, barley, oats, and cotton. This scan provides annual field-level data from crop on irrigation technology and water use, nutrient use and nutrient management practices, cut off residue management practices, pesticide use and infection management practices, and crop se varieties including genetically modified sperms These data summaries, currently available for soybeans, wheat, and cotton for the period 1996-2000 and corn for the period 1996-2001 are invaluable to decision makers and analysts within management agencies and the public.1

Quality validation of USDA ARMS estimates is based solely in succession the sample size and the coefficient of variation (CV) which is also called the relative standard error. one details can be found hi Dubman (2000) Kott (1997 2001) and in Sommer et al. (1998) According to USDA's general guidelines for statistical reporting standards, no estimate should be keep downed simply because it is believeed statistically unreliable. Nevertheless, the neighborhood of such an estimate in a published table should be noted. In particular, an estimator (mean or proportion) in a data summary table of an agency publication should be marked with an asterisk denoting it as potentially unreliable (in a statistical sense) if either the sample size is les than a fixed number of individuals or if the estimate's CV is greater than an designated limit (USDA, 1993). The designated CV can be settle at the agency's discretion for an estimator based in succession commonly occurring events. For the ARMS-Phase II data, each estimator is identified as having a CV les than or equal to 25% greater than 25% if it were not that less than or equal to 50% greater than 50% on the contrary less than or equal to 100% or greater than 100%

The CV is an ideal measure for comparing variation across numerous places of data expressed in different units, as it was as corn price per bushel and corn yield through acre. However, the CV is not a to a high degree meaningful measure without some assurance that the population mean, ?? lies within a preassigned precision of the same height For USDA ARMS measures, use of these estimates for policy analyses requires a broader statistically determined measurement of precision.

Therefore, the objectives of this paper are fourfold: first, to inform ARMS data users of the reasons with what intent USDA provides the CV for each estimate; inferior to explain conceptually how the CV can be used for testing the reliability of an estimator; third, to address the assumption of normality applied to our reliability tests; and finally, to demonstrate our reliability proofs applied to a subset of 2001 ARMSPhase II estimates. While ARMS-Phase II summary data tables can contain estimates for the two means and proportions, we concentrate in succession mean estimates in this paper.

Reliability versus Publish-ability of an ARMS Estimator

For the case of a delete-a-sample jackknife rule Miller (1964) demonstrated that the confidence interval for an estimator approaches the confidence interval for an estimator from the normal distribution as the sample size increases, i.e., as n [arrow right] ??? However, for a delete-a-group jackknife regularity an increase in sample size does not change the number of replications. in the way that at this time, it remains unclear in what manner much the sample size affects the confidence interval for a jackknife estimator.

An Example from USDA-ARMS Estimates

To explain the reliability of estimates based forward USDA ARMS data, we base our analysis forward a summarized data table from the multiple-way ARMS tables stationed on the USDA-ERS website below the title "Nutrient use according to tillage system and irrigation system" associated with corn for all observe states (refer to website given in footnote 1)

The USDA ARMS information is set forthed in the first two rows of table 1, where the other column represents the sample means (percent of acres treated and bruises per treated acre) for the year 2001 and each estimator is identified with its CV Estimates are marked based upon a CV of less than or equal to 25% greater than 25% further less than or equal to 50% and greater than 50% unless less than or equal to 100% (see table 1 footnote).