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  • Articles and reports: 12-001-X199100214501
    Description:

    Although farm surveys carried out by the USDA are used to estimate crop production at the state and national levels, small area estimates at the county level are more useful for local economic decision making. County estimates are also in demand by companies selling fertilizers, pesticides, crop insurance, and farm equipment. Individual states often conduct their own surveys to provide data for county estimates of farm production. Typically, these state surveys are not carried out using probability sampling methods. An additional complication is that states impose the constraint that the sum of county estimates of crop production for all counties in a state be equal to the USDA estimate for that state. Thus, standard small area estimation procedures are not directly applicable to this problem. In this paper, we consider using regression models for obtaining county estimates of wheat production in Kansas. We describe a simulation study comparing the resulting estimates to those obtained using two standard small area estimators: the synthetic and direct estimators. We also compare several strategies for scaling the initial estimates so that they agree with the USDA estimate of the state production total.

    Release date: 1991-12-16
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  • Articles and reports: 12-001-X199100214501
    Description:

    Although farm surveys carried out by the USDA are used to estimate crop production at the state and national levels, small area estimates at the county level are more useful for local economic decision making. County estimates are also in demand by companies selling fertilizers, pesticides, crop insurance, and farm equipment. Individual states often conduct their own surveys to provide data for county estimates of farm production. Typically, these state surveys are not carried out using probability sampling methods. An additional complication is that states impose the constraint that the sum of county estimates of crop production for all counties in a state be equal to the USDA estimate for that state. Thus, standard small area estimation procedures are not directly applicable to this problem. In this paper, we consider using regression models for obtaining county estimates of wheat production in Kansas. We describe a simulation study comparing the resulting estimates to those obtained using two standard small area estimators: the synthetic and direct estimators. We also compare several strategies for scaling the initial estimates so that they agree with the USDA estimate of the state production total.

    Release date: 1991-12-16
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