Forecasting Crop Yield for Agroclimate Risk Management

• Activity Number: 7
• Type: Invited
• Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Environmental and Ecological Statistics
• Author(s): Jim Zidek*+ and Luke Bornn
• Companies: University of British Columbia and University of British Columbia
• Keywords: climate change ; crop yield ; space - time modeling ; hierarchical Bayes ; nonstationary spatial fields ; Bayes linear model

Abstract

Although it is certain that world climate is changing, the degree, nature and impact of that change is not. Thus attention has increasingly turned to dynamically managing the risks of that change as it progresses over time. Declining food production is one such risk and the subject of this talk, which falls under the heading of agroclimate risk management. The talk will describe conventional regression based methods that relate soil moisture to crop yield. A Bayesian alternative that exploits spatial correlation and borrows strength will then be described. Based on data from a major grain producing region located in the Canadian Prairies, we then carry out an empirical assessment that shows the Bayesian model's superiority over its classical counterparts. Finally we will show the results of downscaling climate models to predict future yields using our model. Co-author: Luke Bornn, U British Columbia.