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United States Department of Agriculture

Agricultural Research Service

Title: Impact of Mean Yield Distribution Selection on Income Risk in Cropping Systems

Authors
item Halloran, John
item Griffin, Timothy

Submitted to: American Society of Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: November 7, 2005
Publication Date: November 7, 2005
Citation: Halloran, J.M., Griffin, T.S. 2005. Impact of mean yield distribution selection on income risk in cropping systems. American Society of Agronomy Abstracts. ON CD

Technical Abstract: Researchers working in cropping systems often include an examination of potential income level and income risk associated with each system. In most cases, income risk is measured by standard deviation of the income distribution, assuming that the distribution of income is normal. Since income distribution is a composite of yield and price distributions, characterization of these latter distributions also contributes to income risk dimensions. Most yield distributions are estimated using either annualized data from long-term trials or aggregated yields for a specific region, published by official governmental sources. In this research, the impact of source yield distribution selection on income risk was examined. Specifically, we characterized potato yield data from three sources: 1) average annual yields from long-term field studies; 2) individual field plot data to construct a “grand” yield distribution over the life of the long-term study; and 3) annual historical yields published by NASS. Our results show that mean yield, standard deviation and measures of skewness varied significantly between the various constructions of yield distributions. When combined with product price, the measures of income risk also depended on the source yield distribution. These findings illustrate the need for researchers to carefully assess the sources of their data when attempting to identify the economic impact with respect to income and income variability of crop trials. This is important for accurately projecting trial results to the farm level.

Last Modified: 10/23/2014