A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys

Forecasting the end-of-year crop yield is critical for agricultural decision-making and inherently difficult. Historically, a panel of commodity specialists known as the Agricultural Statistics Board convene regularly to set estimates based on expert review of a combination of survey data and administrative/auxiliary information. To make this process less subjective and more repeatable, we develop a Bayesian hierarchical model that produces superior yield forecasts/estimates, while quantifying different sources of uncertainty. The proposed hierarchical model naturally combines information from multiple monthly surveys measured on different temporal supports, including a field measurement survey and two farmer interview surveys. The dependence between the monthly updated surveys and the serial dependence of the annual yield are incorporated at different levels of the hierarchy. The effectiveness of our approach is demonstrated through an application from the US Department of Agriculture. Empirical results indicate that the hierarchical model produces superior forecasts to both the panel of experts and the composite estimator developed by Keller and Olkin (Technical Report, National Agricultural Statistics Service, 2002), while providing an accurate measure of uncertainty.     

An empirical bayes estimator of seismic events using wavelet packet bases

An empirical Bayes (EB) estimator is constructed to denoise a time series containing quarry blasts in New England. This estimator is portable and can be used more generally to denoise seismic events. The EB estimator uses adaptive wavelet packet analysis (WPA) for transformation selection via an EB approximation to the standard entropy functional. We compare this basis selection method to the discrete wavelet transform (DWT) and best basis selection via Stein’s unbiased risk estimator (SURE). The relation of sparsity of representation to quality of estimation over a range of signal-to-noiserations (SNR) for synthetic quarry blast events is demonstrated. For functions with sparse representations, we have found that, at moderate SNR, WPA significantly out performs DWT. The EB best basis performs similarly to SURE best basis, thereby lending credence to the EB method. Finally, we compare DWT and WPA. methods for denoising quarry blast events recorded in New England.