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Activity Number:
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502
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
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Sponsor:
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Survey Research Methods Section
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Abstract #319810
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View Presentation
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Title:
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A Bayesian Hierarchical Model for Combining Several Crop Yield Indications
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Author(s):
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Nathan B. Cruze*
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Companies:
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USDA/NASS
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Keywords:
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sample surveys ;
auxiliary information ;
model-based estimation ;
agricultural statistics ;
official statistics
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Abstract:
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USDA's National Agricultural Statistics Service (NASS) conducts multiple surveys over the course of a growing season. Each of these surveys reflects current growing conditions and provides a prediction of end-of-season crop yield. In particular, NASS conducts two interview-based surveys and one field measurement survey from which indications of crop yield may be obtained. It is also known that a number of weather conditions during the growing season may contribute to changes in crop yield. This talk describes a Bayesian hierarchical model that improves end-of-season crop yield predictions by combining these several disparate sources of information. The model incorporates benchmarking of state-level forecasts with regional forecasts of crop yield and gives rise to rigorous measures of uncertainty. It also permits a useful decomposition with respect to the emphasis placed on each information source. Several aspects of covariates selection and model performance are discussed.
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Authors who are presenting talks have a * after their name.
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