  The analysis of the output from a large scale computer simulation experiment
can pose a challenging problem in terms of size and computation. We consider
output in the form of simulated crop yields from the Environmental Policy
Integrated Climate (EPIC) model, which requires a large number of inputs such
as fertiliser levels, weather conditions, and crop rotations inducing a high
dimensional input space. In this paper, we adopt a Bayes linear approach to
efficiently emulate crop yield as a function of the simulator fertiliser
inputs. We explore emulator diagnostics and present the results from emulation
of a subset of the simulated EPIC data output.
