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Abstract
Rainfall during the germination, growing and flowering periods is a major
determinant of wheat yield. The degree of uncertainty attached to a wheat-yield
prediction depends on whether the prediction is made before or after the rainfall in
each period has been realised. Bayesian predictive densities that reflect the different
levels of uncertainty in wheat-yield predictions made at four different points in time
are derived for five shires in Western Australia.