Date of Award:

8-2020

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Curtis Dyreson

Committee

Curtis Dyreson

Committee

John Edwards

Committee

Dan Watson

Abstract

The goal of this research is to learn about whole farm carbon models. A whole farm carbon model estimates the emissions of greenhouse gasses (GHGs) based on information for a farm. We analyzed two models, HOLOS whole-farm and COMET-Farm, by running the models on random inputs and building classifiers from the runs. HOLOS estimates GHG emissions for a particular year based on crop and animal agriculture input, while COMET-farm adds past and future farm management practices. Users of the models must manually enter farm data through a graphical user interface (GUI), which is a good method for a single farm, but makes it infeasible to calculate GHG emissions over hundreds to thousands of farms. So we automated the interface and generated random farm scenarios within ranges given by experts. We scraped the estimated carbon footprint from thousands of runs of the models and used several Regression algorithms to build predictive models that have high accuracy. By reverse engineering the whole-farm carbon models we were able to determine which farm management practices in each whole farm carbon model have the biggest impact on GHG emissions. This can help farmers and rural planners change farm management practices to decrease GHG emissions.

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