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Canopy Profit Rate (CPR)

The cannabis industry's most accurate financial metric for measuring true cultivation profitability—not just revenue, but actual profit per square foot of canopy.

What is Canopy Profit Rate?

Canopy Profit Rate (CPR) measures the profit generated per square foot of flowering canopy over time. Unlike simple cost-per-pound calculations, CPR accounts for the full economic picture: facility utilization, operational efficiency, and true profitability.

CPR = (Revenue - Total Costs) ÷ Canopy Sq Ft ÷ Days

Expressed as $/sq ft/day for standardized comparison across facilities

Why CPR Matters

Traditional cannabis metrics like "cost per pound" fail to capture the complete financial picture. They ignore:

Real Example: A facility producing at $400/lb cost might seem efficient, but if they're only using 60% of their canopy, their CPR reveals massive opportunity cost. Full utilization at $500/lb often generates more total profit.

A-Bud Costing Methodology

CPR uses A-Bud Costing—a methodology that allocates costs based on the value each product tier generates, not just weight. This reflects economic reality:

Product Tier Typical Weight % Revenue Contribution Cost Allocation
A-Bud (Premium Flower) 55-65% ~75% of revenue Proportional to value
B-Bud (Smalls) 15-20% ~15% of revenue Proportional to value
Trim/Extraction Material 15-25% ~10% of revenue Proportional to value

The $564/lb Benchmark

Our optimized cost model targets $564/lb all-in production cost, achieved through:

CPR vs. Traditional Metrics

Metric What It Measures Limitation
Cost Per Pound Production cost per unit weight Ignores utilization, time, quality tiers
Yield Per Plant Grams harvested per plant Varies by strain, grow style; not financial
Revenue Per Sq Ft Top-line per canopy area Ignores costs; revenue ≠ profit
Canopy Profit Rate True profit per sq ft per day Complete financial picture

How Canna Data Science Calculates CPR

Our platform automates CPR calculation by integrating:

  1. Batch tracking: Harvest weights, quality grades, timeline data
  2. Financial integration: OpEx, CapEx, labor from QuickBooks or manual entry
  3. Facility mapping: Canopy square footage by room/zone
  4. Market data: Revenue by product tier and sales channel

The AI Data Scientist then analyzes trends, identifies optimization opportunities, and projects the financial impact of operational changes.

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