Global-Late-Blight-(Meta)Modelling

Introduction

This repository hosts R Scripts and the data necessary for reproducing my work with metamodels used to map the global risk of potato late blight for my PhD dissertation, Disease risk mapping with metamodels for coarse resolution predictors: global potato late blight risk now and under future climate conditions. This is done using blight units based on the SimCast model modified by Grünwald et al. (2002). Other scripts include tools to predict growing seasons using Ecocrop and estimating global late blight severity using monthly time-step weather data to generate maps of potato late blight risk.

The metamodelling approach can be found in Sparks, A. H., Forbes, G. A., Hijmans, R. J., & Garrett, K. A. (2011). A metamodeling framework for extending the application domain of process-based ecological models. Ecosphere, 2(8), art90. doi:10.1890/ES11-00128.1.

The study for which these models were developed can be found in Sparks, A. H., Forbes, G. A, Hijmans, R. J., & Garrett K. A. (2014). Climate change may have limited effect on global risk of potato late blight. Global Change Biology, doi:10.1111/gcb.12587.

While the first manuscript may be completely replicated, this repository is not a perfect replication of the second manuscipt, Sparks et al. (2014), but is a reproduction using freely available data that can be downloaded from the web as of the time of publishing (Jan 2016). It is intended for the user to be able to reproduce the analysis that I have done and undertake their own efforts with their own data.

Following H. Wickham’s style guide, the folders (in bold) and scripts are numbered in the consecutive order in which they should be run. I have already provided blight unit predictions based on the HUSWO data, so 01 - Models/01 - SimCast_Blight_Units.R is not necessary to run and is provided for information purposes here only the script is functional if the user has HUSWO data from 1990-1995. After that, all other scripts are fully usable as provided here.

The scripts can easily be run on a modest machine, it only takes a few hours to complete on an Early 2015 MacBook with the Core M 1.3 processor. A reasonably fast Internet connection will assist since there is a good deal of data to be downloaded from various sources. FAO data is used to provide information on the top producing countries, the most recent year provided is used by default.

For RStudio users, there is an RProj file included that makes things easier. For all others, I’ll assume you know what you’re doing and how to set your working directory accordingly with the file structure contained herein.

To cite this code, please use this DOI: 10.6084/m9.figshare.1066124

Happy modelling!

Directory structure

Acknowledgements

We appreciate support by the USAID through the International Potato Center (CIP), by NSF grant EF-0525712 as part of the joint NSF-NIH Ecology of Infectious Disease program, by NSF Grant DEB-0516046, by the USAID for the SANREM CRSP under terms of Cooperative Agreement Award No. EPP-A-00-04-00013-00 to the OIRED at Virginia Tech, by the CGIAR Research Programs Roots, Tubers and Bananas (RTB), and Climate Change, Agriculture and Food Security (CCAFS).