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What you’ll build in this exercise:
  • Complete an optimization pass on your model and configuration
  • Add descriptions to all event types, datapoints, and model nodes
  • Create an operator documentation outline
  • Pass the full production readiness quality checklist
This is the capstone exercise. You’ll take the Mangrove Biochar project you’ve built across all modules and prepare it for real-world use. The goal isn’t to add new features — it’s to make what you’ve built robust, documented, and operable by someone other than you. Prerequisites: Complete all lessons in Module 6 (Lesson 6.1, Lesson 6.2, Lesson 6.3). Your Mangrove Biochar project should have passed all Module 5 integration tests.

Part 1: Design (30 min)

1. Review and optimize

Walk through your model and configuration with the four design principles in mind:
1

Least Complexity

Are there any redundant calculations? Can any steps be simplified without losing methodology compliance? Are there model nodes that don’t correspond to a methodology requirement?
2

Explicit Over Implicit

Are all unit conversions visible as separate nodes? Are there complex Keisan expressions that should be broken into simpler steps? Can a new team member understand each node’s purpose from its name?
3

Audit Trail

Can you trace every batch output back to source events and evidence? Are there any “orphan” calculations that don’t connect to the output?
4

DRY

Are any emission factors hardcoded as constants instead of static inputs? Are there duplicate calculations across different parts of the model?

2. Plan documentation

Outline the documentation you’ll create:

3. Define monitoring requirements


Part 2: Build in Mangrove (1.5 hrs)

Step 1: Optimization pass

  1. Open the Model Editor and review every node:
    • Remove any nodes that don’t feed into an output
    • Consolidate redundant intermediate calculations
    • Replace any hardcoded constants with static input references
    • Ensure all node names are descriptive
  2. Run the model Console with your test data and verify outputs haven’t changed after optimization.
After any optimization, re-run your Module 5 test cases to verify that outputs match. Optimization should not change results — only simplify the model structure.

Step 2: Add in-platform documentation

Add descriptions to every configurable element: Event types (in Data Inputs > Input Settings): Datapoints — for each datapoint, add:
  • Unit (e.g., “tonnes”, “kWh”, “km”)
  • Expected range (e.g., “Typically 5-20 t. Values > 50 t should be verified.”)
  • Source (e.g., “Weighbridge reading”)
Model nodes — for each calculated node, add:
  • The methodology equation it implements (e.g., “Equation 3.2 from Isometric Biochar Protocol v2.1”)
  • The unit of the output (e.g., “tCO2e”)

Step 3: Create operator documentation outline

Create a brief document (in a spreadsheet, FigJam, or text file) covering:
For field operators and data entry staff:
  1. Feedstock Receipt: Record when feedstock arrives. Required fields: wet mass, moisture %, feedstock type. Attach weighbridge slip as evidence.
  2. Lab Analysis: Record when lab results are received. Required: carbon %, H:C ratio. Attach lab certificate.
  3. Biochar Delivery: Record each delivery. Required: mass, carbon %. Attach delivery ticket and application record.
  4. Energy Consumption: Record monthly. Required: electricity (kWh), propane (gallons). Attach utility bills.
  5. Transportation: Record per delivery. Required: distance (km). Attach trip log or GPS record.

Step 4: Quality checklist

Walk through the complete production readiness checklist: Configuration:
  • All 5 event types configured with correct datapoints and value types
  • All static inputs (emission factors, constants) populated with current values
  • Model validated — all nodes produce correct outputs in Console
  • Batch type configured with per-delivery partitioning
  • Ledgers configured with correct units and allocation rules
Testing:
  • Module 5 happy path tests still pass after optimization
  • Edge cases (high moisture, high H:C, small delivery) handled correctly
  • Batch generation completes in < 5 minutes for a typical month
  • All ledger balances reconcile — no negative balances
Documentation:
  • All event type descriptions written
  • All datapoint descriptions include unit, range, and source
  • All model node descriptions reference methodology equations
  • Data collection guide outline created
  • Operations runbook outline created
Operational readiness:
  • Monitoring thresholds defined for events, batch outputs, ledger balances
  • Monthly batch generation cadence established
  • Escalation path defined (operator → accounting → implementation team)
  • Test data cleaned up (or clearly labeled as test data)

Success criteria

You have completed the Module 6 exercise and the full Accounting U course when:
  • Optimization complete — no redundant nodes, all constants moved to static inputs, all node names are descriptive
  • Results unchanged — post-optimization outputs match pre-optimization test results
  • In-platform documentation — every event type, datapoint, and model node has a clear description
  • Operator guides — data collection guide and operations runbook outlines created
  • Monitoring defined — thresholds for events, batch outputs, ledger balances, and validation errors
  • Quality checklist passed — all items checked across configuration, testing, documentation, and operations

Congratulations!

You’ve completed the full Accounting U curriculum. Your Mangrove Biochar project now has:
  • 5 event types capturing operational data from feedstock receipt through delivery
  • Multi-step production models calculating gross and net carbon removal
  • Per-delivery batch partitioning with LCA emissions allocated to each batch
  • Mass balance tracking across multiple ledgers
  • Comprehensive testing with documented test cases and expected outputs
  • Production-ready documentation and monitoring
You’re equipped to configure and deploy production accounting projects in Mangrove. For questions or support, reach out on Slack #team-implementation. Return to the Course Overview to review the full curriculum.