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Activity Number: 502
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #319810 View Presentation
Title: A Bayesian Hierarchical Model for Combining Several Crop Yield Indications
Author(s): Nathan B. Cruze*
Companies: USDA/NASS
Keywords: sample surveys ; auxiliary information ; model-based estimation ; agricultural statistics ; official statistics

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.

Authors who are presenting talks have a * after their name.

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