Predicting Crop Yield in the Canadian Prairies

• Activity Number: 294
• Type: Topic Contributed
• Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
• Sponsor: SSC
• Author(s): Luke Bornn*+ and Jim Zidek
• Companies: The University of British Columbia and The University of British Columbia
• Keywords: crop yield ; non-stationary ; Bayesian ; agriculture ; spatial ; dependence

Abstract

This paper describes how spatial dependence can be incorporated into statistical models for crop yield along with the dangers of ignoring it. In particular, approaches that ignore this dependence suffer in their ability to capture (and predict) the underlying phenomena. By judiciously selecting biophysically based explanatory variables and using spatially-determined prior probability distributions, a Bayesian model for crop yield is created that not only allows for increased modelling flexibility but also for improved prediction over existing least-squares methods. Prior distributions are developed to accommodate the spatial non-stationarity arising from distinct between-region differences in agricultural policy and practice. In addition, a range of possible dimension-reduction schemes are examined in the pursuit of improved prediction.