This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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294
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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SSC
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Abstract - #307954 |
Title:
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Bayesian Model Averaging for Improved Prediction of Soil Moisture and Crop Yield from Agro-Ecosystem Models
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Author(s):
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Reza Hosseini*+ and Gabriela Espino-Hernandez and Nathaniel Kenneth Newlands
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Companies:
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Simon Fraser University and The University of British Columbia and Agriculture and Agri-Food Canada
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Address:
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Department of Statistics and Actuarial Science, Burnaby, V5A 1S6,
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Keywords:
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Agriculture ;
Bayesian ;
Climate ;
Prediction ;
Uncertainty
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Abstract:
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Ecosystem models are applied in assessing environmental impacts, risks and resource decision-making. Reliability of a model for a given goal relies on reducing measurement, parameter and structural uncertainty. Crop yield and soil moisture are critical predictors required to assess the system resiliency (sensitivity, adaptability and vulnerability). We test whether Bayesian model averaging (BMA) improves ecosystem-scale predictions of crop productivity and soil water. Preliminary sensitivity analysis indicates that their predictions are most sensitive to climate variability at the seasonal-scale. We compare their predictive performance under historical climate variation at long-term cropping sites in Western North America. We examine the effectiveness of weighting BMA predictions to better inform model selection; for different application goals and data correlation structural assumptions
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