Where Should We Apply Biochar?


A collaboration with the core Nextjournal team to publish work modeling the effect of biochar on crop yields as an explorable, data driven, reproducible article.

Problem Statement

The complex nature of biochar interactions with soils and crops as well as lack of clear understanding of the mechanics of these interactions has led to reports with conflicting interpretations, even under similar conditions. In addition, the large amount of missing data in the literature including inconsistent reporting of soil and biochar properties has made the prediction of crop yield response to biochar a very challenging modeling exercise.

We addressed this challenge by developing an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling. We used probabilistic graphical models to study the relationships between soil and biochar variables and predict the probability and magnitude of crop yield response to biochar application.


Data is fed into a hybrid Bayesian Network (BN) model featuring 84 parameters. The dynamic model can propagates any new evidence through the BN and the posterior probabilities are computed. The state of the BN model is then projected onto all cultivated lands in the US based on the 2016 Cropland Data Layer using the Gridded Soil Survey Geographic soil database for the five biochar types and two application rates, i.e., 5 and 15 Mg.


An interactive map that depicts the the probability of crop yield increase following biochar application as well as the expected yield increase for every single farm around the US.

Existing Assets

Where Should We Apply Biochar? currently includes these assets:

  • Biochar data, cropland data, soil data
  • A model to estimate the response yield of biochar applications, implemented as a Bayesian Network
  • Some work complete on a Shiny app intended to help users investigate predictions of biochar's effectiveness on US cropland

These existing assets and research will form the foundation of an interactive article. Our goal is to change how people engage research. To paraphrase Bret Victor, rather than "information to be consumed," the resulting notebook will be "used as an environment to think in."

This will be accomplished using Nextjournal's existing technologies and collaborative efforts with the Nextjournal team where appropriate, e.g. specialty graphics, user experience components, or back end services.


The project should roughly take 2 months to implement and begins upon receipt of the first paid installment of the award. This includes:

  • Results based on the paper Where should we apply biochar? intended for a broad audience ranging from average farmers to policy makers.
  • An interface allowing users to investigate the model's estimates regarding the probability of yield increase following biochar application
  • Output from user settings depicted on a map. The level of granularity is to be determined, but the model is capable of estimating to the level of individual farms.

The work will primarily take place in R.


  • $1,500 will be awarded upon receiving the initial invoice from the awardee.

  • $1,500 upon receiving the second invoice from the awardee at the completion of the project.

  • The awardee is responsible for bank transfer and currency exchange fees.

  • All individuals in the project's group will be provided a Private Research plan and receive all related benefits plus unlimited cloud compute resources.


A collaboration between Nextjournal and the Biochar group:

  • Hamze Dokoohaki, Postdoc at Boston University
  • Fernando E. Miguez, Associate Professor, Department of Agronomy, Iowa State University
  • David Laird, Professor, Department of Agronomy, Iowa State University
  • Jerome Dumortier, Associate Professor, School of Public and Environmental Affairs, Indiana University - Purdue University Indianapolis


  • Awardee - the winning individual or team of Nextjournal's Scholarship for Explorable Research.

  • Article: a published, version controlled document that includes data, code, commentary, and execution environment.

  • Notebook - a published or unpublished human-readable document that combines code, commentary, and results.

  • Reproducibility - the ability to generate the same results at some point in the future given the same data and code.

  • Remix - an exact copy of a published Nextjournal article. With remixing, it is easy to take fully accredited work, reproduce its results, and experiment with variants.

  • Explorable explanations is a term that is best illustrated in Bret Victor’s essays Ten Brighter Ideas and Explorable Explanations.