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  • 1. A bivariate hierarchical Bayesian model for estimating cropland cash rental rates at the county level
    Articles and reports: 12-001-X201900200002
    Description:

    The National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) is responsible for estimating average cash rental rates at the county level. A cash rental rate refers to the market value of land rented on a per acre basis for cash only. Estimates of cash rental rates are useful to farmers, economists, and policy makers. NASS collects data on cash rental rates using a Cash Rent Survey. Because realized sample sizes at the county level are often too small to support reliable direct estimators, predictors based on mixed models are investigated. We specify a bivariate model to obtain predictors of 2010 cash rental rates for non-irrigated cropland using data from the 2009 Cash Rent Survey and auxiliary variables from external sources such as the 2007 Census of Agriculture. We use Bayesian methods for inference and present results for Iowa, Kansas, and Texas. Incorporating the 2009 survey data through a bivariate model leads to predictors with smaller mean squared errors than predictors based on a univariate model.

    Release date: 2019-06-27
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  • 1. A bivariate hierarchical Bayesian model for estimating cropland cash rental rates at the county level
    Articles and reports: 12-001-X201900200002
    Description:

    The National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) is responsible for estimating average cash rental rates at the county level. A cash rental rate refers to the market value of land rented on a per acre basis for cash only. Estimates of cash rental rates are useful to farmers, economists, and policy makers. NASS collects data on cash rental rates using a Cash Rent Survey. Because realized sample sizes at the county level are often too small to support reliable direct estimators, predictors based on mixed models are investigated. We specify a bivariate model to obtain predictors of 2010 cash rental rates for non-irrigated cropland using data from the 2009 Cash Rent Survey and auxiliary variables from external sources such as the 2007 Census of Agriculture. We use Bayesian methods for inference and present results for Iowa, Kansas, and Texas. Incorporating the 2009 survey data through a bivariate model leads to predictors with smaller mean squared errors than predictors based on a univariate model.

    Release date: 2019-06-27
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