Livestock Production Modelling
This use case explains how to model livestock enterprises, including herd or flock dynamics, production, sales and associated costs, for agriculture and primary production in Model Reef.
You will:
Represent livestock enterprises in the branch structure.
Model opening numbers, births, purchases, sales and deaths.
Link livestock numbers to production, revenue and cost drivers.
Capture seasonality, feeding, grazing and working capital effects.
Model Reef is not a livestock management system. It turns high level herd or flock assumptions into financial forecasts and cash plans.
When to use this pattern
Use this pattern when:
Livestock is a material part of farm income and risk.
You need to understand how stocking decisions affect profit and cash.
You want to test different growth, finishing or trading strategies.
You manage multiple properties or enterprises and need consolidated views.
It works well with:
Crop Yield Forecasts for mixed enterprises.
Seasonality and Commodity Pricing.
Farm Level Working Capital Planning.
Architecture overview
Step 1: Set up branches for livestock enterprises
In the branch tree, create a structure such as:
Farm Group
Farm - North
Enterprise - Beef Cattle
Enterprise - Sheep
Farm - South
Enterprise - Dairy
Central Overheads
Within each enterprise branch, you may create sub branches for age classes or systems if you want to separate breeding and trading operations, for example Breeding Herd and Backgrounding or Finishing.
Step 2: Build herd or flock number drivers
In the Data Library, create drivers for the key number flows, for example for beef:
Opening Cows.
Opening Heifers.
Opening Steers.
Calving Rate Percentage.
Mortality Rate Percentage.
Purchase Numbers per period.
Sales Numbers per period.
You can either:
Model flows at annual or seasonal level in a higher level model, or
Use more granular frequency where you have detailed plans.
Define derived drivers such as:
Births = Opening Cows × Calving Rate.
Deaths = Opening Numbers × Mortality Rate.
Closing Numbers = Opening plus Births plus Purchases minus Sales minus Deaths.
These can be represented as variables if you want them visible in dashboards, or as intermediate drivers feeding revenue and cost.
Step 3: Link numbers to production and revenue
For each enterprise, define production drivers, for example:
Average Sale Weight per Head.
Average Liveweight Gain per Day.
Dressing Percentage to carcass weight.
Milk Yield per Cow per Day.
Wool Yield per Head per Shearing.
Then create Revenue variables, such as:
Revenue - Cattle Sales - Farm North.
Revenue - Wool Sales - Farm North.
Revenue - Milk Sales - Farm South.
Formulas might look like:
Sale Weight per Head × Number Sold × Price per Kilogram.
Milk Yield per Cow × Number of Cows × Days in Period × Price per Litre.
Wool Yield per Head × Number Shorn × Price per Kilogram.
These variables should be typed as Revenue and can be segmented further by class or market if needed.
Step 4: Add feed, husbandry and labour costs
Create Opex and Staff variables for key cost categories, for example:
Opex - Purchased Feed.
Opex - Pasture Fertiliser.
Opex - Veterinary and Animal Health.
Opex - Animal Handling, Tags and Equipment.
Staff - Livestock Labour.
Opex - Transport and Selling Costs.
Link these to drivers such as:
Feed per head per day at different times of year.
Vet cost per head or per treatment.
Labour hours per head or per enterprise.
Transport cost per head per kilometre.
Costs should scale with stocking rates, production intensity and strategy choices in scenarios.
Step 5: Capture seasonality, grazing and finishing strategies
Use timing and scenario logic to reflect strategic choices, for example:
Different turn off patterns by month or quarter.
Choice of selling as store, feeder or finished stock.
Use of supplementary feeding during dry periods.
Decisions to increase or reduce herd size for seasonal conditions.
In each case, adjust:
Sales and purchase numbers and weights.
Feed and labour costs.
Expected prices, using the Seasonality and Commodity Pricing pattern.
This allows you to compare strategies such as growing the herd versus trading more animals at lighter weights.
Step 6: Integrate livestock into whole farm and portfolio views
Because livestock enterprises are branches within farms and the group:
Farm level P&L and cash views will include both livestock and other enterprises.
Group level statements will aggregate across all farms and enterprises.
You can compare enterprises on margin per hectare, per head or per unit of labour.
Dashboards can show:
Numbers, production and revenue per enterprise.
Cost structure breakdowns.
Cash and working capital impacts across seasons.
Contributions to debt service and valuation.
Step 7: Use scenarios for climate, market and strategy risk
Clone the base model into scenario models to test:
Drought or flood effects on stocking rates and production.
Changes in sale prices, driven by commodity markets.
Alternative strategies for destocking or rebuilding.
Investment in improved genetics or feeding systems.
In each scenario, adjust number flows, production drivers, prices and cost structures and then compare:
Enterprise and whole farm profitability.
Cashflow resilience and funding needs.
Impact on loan covenants and valuation metrics.
Check your work
Number flows and production estimates align with recent history or plans when calibrated.
Costs per head and per unit of production are realistic against invoices and budgets.
Seasonal patterns match expected grazing and feeding practice.
Scenario outcomes are intuitive to both operations and finance stakeholders.
Troubleshooting
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