Optimizing Management Strategy for Biochar Production at Scale

Abstract

Artisan biochar is a carbon-rich charcoal made from crop waste, commonly produced in soil pits adjacent to the fields where the waste is sourced. Biochar’s potential for carbon sequestration and soil health improvement is widely recognized, so many social enterprises have begun to replace open field burning with biochar production, funded by the sale of carbon credits. However, large-scale implementations face challenges related to cost, labor, and methodological rigor. This paper builds a model then determines an optimal strategy for a manager to inspect a set of n workers who make biochar in a cluster of close-by soil pits, through the duration of a biochar production work shift. Then, the efficacy of this strategy was tested against common strategies for inspection such as randomization and shortest-path decision making, and the results confirm this strategy’s superiority. After that, drawing on real-world data from the “Biochar for Burning” initiative in West Bengal, India, a case study is developed to answer the question: does this optimal strategy make a significant difference when the inspector wants to maximize not work quality, but biochar quality? We revise the model to factor in a dataset of known confounders as noise, run it on the project’s real- world soil-pit coordinate dataset, and determine if the strategy still offers a significant improvement. We found a modest improvement (with respect to alternative models) we expect would be greater if we weighed worker effort level as a more important factor than flame temperature, a known confounder, which in further studies we expect will be verified. Our model consistently produced more reliable results than the alternatives as well.