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.
@article{arxiv.2109.10208,
title = {Bayes Linear Emulation of Simulated Crop Yield},
author = {Muhammad Mahmudul Hasan and Jonathan A. Cumming},
journal= {arXiv preprint arXiv:2109.10208},
year = {2022}
}