Regenerative Agriculture Soil Carbon
Compare conventional and regenerative farms to estimate soil organic carbon increases and the carbon credits they can generate.
2
Regenerative Agriculture Soil Carbon
python • Step 2
Compare conventional and regenerative farms to estimate soil organic carbon increases and the carbon credits they can generate.
pandasedaregressionclimateagriculture
Learning Objectives
- →Compute soil carbon stock from lab measurements
- →Compare conventional and regenerative practices
- →Convert carbon stock to CO2-equivalent credits
- →Build a simple model of carbon gain vs years under practice
Dataset
View on data.worlddata.world filename
Lab path (browser)
Soil organic carbon ratios for U.S. agricultural lands — useful for regenerative agriculture baselines.
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/regenerative_soil_carbon.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? data.world ships soil_carbon.csv. Point read_csv at that path and everything else works unchanged.
Dataset Columns
farm_idpracticeyears_under_practicesoil_carbon_pctbulk_density_g_cm3depth_cmarea_ha- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Python
Loading editor...
*bleep* Output will appear here after running your code. Figures from matplotlib/seaborn render inline.
Expected output:
practice carbon_stock_Mg_ha credits_tco2e_ha
0/3
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