Supporting files for "The importance of agricultural yield elasticity for indirect land use change: A Bayesian network analysis for robust uncertainty quantification" (submitted)

This data repository supports a paper submitted for publication which seeks to quantify the response of agricultural yields to increased commodity prices driven by biofuel policies.

The files are as follows:

1) Overview of Bayesian network modelling methods used in this study, with links to further reading.

2) Processed data sets used to develop models. Data provide a comprehensive set of information on Corn and Soybean production from 1990-2017. Data are at the county-level, and are also able to be grouped by USDA Agricultural Resource Region. Data are processed compilations of primarily sourced from the USDA, more detail is given in the meta data for each file. 2a & 2b) These two files provide the data used to calculate a three-year expected profit per acre grown of Corn and Soybean respectively.

2c & 2d) These two files provide the data that underpin the study's Bayesian Network models of agricultural extensification.

2e & 2f) These two files provide the data that underpin the study's Bayesian Network models of agricultural intensification.

2g & 2h) These two files provide the data that underpin the study's Bayesian Network models of commodity price.

3) Data pre-processing conducted to produce data sets.

4) Code and RDData objects used to create models.

4a) This gives the Rcode used to learn Bayesian network models using the processed data in 1a-1h.

4b) This gives the resulting RData files containing Bayesian network models created through the code in 4a.

4c) This gives code to run an holistic model that combines all the sub-component modes learned using 4b.