Table 4 Recommended actions to strengthen the reproducibility of data management and analysis in agricultural research

1. Identify the types and amounts of data to be recorded, including details of crop management, pre-plant soil conditions, and weather using the ICASA or similar standards as a guide for recommended detail.

2. Plan the data management and analysis workflow at the onset of the research, recognizing that workflows can evolve as research progresses.

3. Plan explicitly for data quality control, especially for handling of missing data and outliers and for secondary sources of soil and weather data.

4. Commit to protocols for naming datafiles, variables, scripts, and other digital entities, with emphasis on readability.

5. Manage data using well-documented, open structures such as comma-separated variable format, Open Document Format, or JSON.

6. Conduct analyses within a single open-source programming environment such as R or Python.

7. Ensure the transparency of any model by providing documentation that includes equations, flow charts, and/or pseudocode.

8. Test the workflow frequently, starting from the original data whenever possible.

9. Use tools to encapsulate workflows such as RStudio projects and Jupyter Notebooks.