Crop Yield Forecasts
This use case explains how to forecast crop yields and the associated revenue and cost patterns for agriculture and primary production in Model Reef.
You will:
Represent farms, regions or crop types in the branch structure.
Build yield and area drivers for key crops.
Connect yields to pricing, input costs and labour.
Capture seasonality and timing of cash inflows and outflows.
Model Reef is not a farm management or agronomy system. It uses high level agronomic and commercial assumptions to generate financial forecasts, cashflow and valuation.
When to use this pattern
Use this pattern when:
Crop production is a core driver of business performance.
You need to translate agronomic assumptions into P&L and cash.
You want to test pricing, yield and weather scenarios.
You manage multiple farms, paddocks or regions and need consolidation.
It works well with:
Seasonality and Commodity Pricing
Farm Level Working Capital Planning
Multi Entity Group Model
Architecture overview
Crop yield forecasting uses four layers:
Structure
Branches per farm, region or crop enterprise.
Optional sub branches for paddocks, rotations or varieties.
Production drivers
Area planted.
Yield per hectare or per acre.
Losses and quality adjustments.
Price and revenue drivers
Commodity prices and basis adjustments.
Grade or quality pricing tiers.
Currency assumptions where relevant.
Cost and timing
Inputs, labour, storage and logistics.
Seasonal spend and receipt timing.
Working capital and financing impacts.
Set up branches for farms and crops
In the branch tree, create a structure that reflects how you manage operations, for example:
Group
Farm - North
Crop - Wheat
Crop - Canola
Farm - South
Crop - Barley
Crop - Legumes
Central Overheads
Each crop branch can hold yield, revenue and direct costs. Farm or group branches aggregate by location or portfolio.
If you prefer, you can instead structure by crop type at the top level with farms underneath. Choose the pattern that matches how you want to report.
Define area and yield drivers
In the Data Library, create drivers such as:
Area Planted - Wheat - Farm North (hectares or acres).
Yield per Hectare - Wheat - Farm North (tonnes per hectare).
Loss Percentage for weather, pests or operational losses.
Quality Adjustment Percentage for downgraded grain or off grade product.
For each crop branch, calculate expected production, for example:
Gross Production = Area × Yield per Hectare.
Net Production = Gross Production × (1 minus Loss Percentage).
Saleable Production = Net Production × (1 minus Quality Adjustment Percentage) if you want to track a quality haircut separately.
These series will drive revenue and logistics.
Connect yields to prices and revenue
Create price drivers for each crop, such as:
Base Commodity Price per Tonne.
Local Basis Adjustment.
Quality Discount or Premium factors.
Forward price for hedged proportions if you model hedging simply.
Then create Revenue variables per crop and farm, for example:
Revenue - Wheat - Farm North.
Revenue - Canola - Farm North.
Revenue - Barley - Farm South.
Formulas might be:
Net Price per Tonne = Base Price plus Basis Adjustment plus Quality Premium.
Revenue - Wheat - Farm North = Saleable Production - Wheat - Farm North × Net Price per Tonne - Wheat - Farm North.
You can also split revenue into different grades or contract types if needed, but ensure the granularity is maintainable.
Set variables as type Revenue so they feed directly into P&L and cash when timing is added.
Add input, labour and logistics costs
For each crop branch, create Opex and Staff variables for key cost categories, for example:
Opex - Seed - Wheat - Farm North.
Opex - Fertiliser - Wheat - Farm North.
Opex - Chemicals - Wheat - Farm North.
Opex - Fuel and Machinery Running.
Staff - Seasonal Labour.
Opex - Storage and Handling.
Opex - Freight to Port or Buyer.
Link these to drivers such as:
Cost per hectare.
Cost per tonne produced.
Labour hours per hectare or task.
Freight cost per tonne per kilometre.
This ensures cost scales appropriately when area, yield and production change under different scenarios.
Capture seasonality and timing
Use timing settings and schedules to reflect the agricultural calendar, for example:
Input spend concentrated in planting and growing seasons.
Harvest costs in a short window.
Revenue receipts at delivery or at later settlement dates.
Storage costs over off season periods.
Examples:
Fertiliser spend in months leading into planting.
Fuel and harvest labour costs in harvest months.
Revenue delayed by 30 to 90 days from delivery if you offer terms or use pool structures.
Set timing so that:
P&L shows revenue and cost where they occur in the production cycle.
Balance Sheet shows receivables, payables and inventory where relevant.
Cashflow and Cash Waterfall capture seasonal working capital needs.
If you want to approximate inventory, you can use Asset and Liability variables to hold grain between harvest and sale and link them to production and sales schedules.
Use scenarios for weather, yield and price risk
Clone the base model into scenario models to represent different conditions, for example:
Base season with average yields and prices.
Drought or poor season with reduced yields and additional costs.
Good season with above average yields and strong prices.
Commodity price shock scenarios while yields remain average.
In each scenario, adjust:
Area planted, yield per hectare and loss percentages.
Price drivers and basis adjustments.
Input costs and labour requirements if you change cropping mix.
Hedged proportions if you use simple forward pricing.
Compare scenarios using:
Revenue and gross margin per crop and per farm.
Cashflow volatility and funding needs.
Impact on loan covenants and valuation if the business is leveraged.
Tie crop forecasts into whole farm planning
Because crop branches sit within farm branches and a group branch:
Farm level reports show combined crop performance and costs per farm.
Group level reports show portfolio performance across all farms.
You can integrate livestock, horticulture or other enterprises using separate use cases, all within the same model.
Dashboards can highlight:
Contribution per crop, farm and region.
Exposure to specific commodities or price benchmarks.
Sensitivity of the whole business to changes in yield and price assumptions.
Check your work
Yield and price assumptions are benchmarked against historical data or independent forecasts.
Input and logistics cost assumptions align with real invoices or budgets.
Seasonal patterns in cashflow match actual experience when calibrated.
Scenario outcomes make intuitive sense to operational and finance stakeholders.
Troubleshooting
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