Bayes Linear Emulation of Simulated Crop Yield

Abstract

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.