Rebuilding America’s Cattle Herd Would Cost Less Than You Think

A model of 27.6 million beef cows points to a ten-year plan, and the most popular fix is the worst option on the table

Read the full analysis on Substack → https://brianjamescurry.substack.com/p/how-to-rebuild-the-us-cattle-herd

Cattle have never been worth more. Feeder steers have traded above $360 per hundredweight, and ground beef has sat above $7 a pound for months. By every textbook signal, ranchers should be growing their herds as fast as biology allows.

They aren’t. The United States started 2026 with 27.6 million beef cows, the fewest since 1961, and this year’s calf crop is the ninth straight annual decline.

I built a model of the herd to find out why, and then used it to test what would actually fix it. The short version: the herd can be rebuilt within a decade for roughly $1.5 billion a year in public money. But it will not happen through higher prices, and the fix most people reach for first performs worst.

Why record prices are not enough

Keeping a heifer for breeding is an investment decision. A weaned heifer calf worth around $2,000 can be sold today, or kept, bred, and turned into a calf a year starting two years from now. Every heifer kept is a heifer that does not go to slaughter. Beef supply falls before it rises, and the whole cycle takes three to four years.

When I fit the retention decision to twelve years of USDA data, three drivers stood out:

• Prices, relative to recent memory. Ranchers judge prices against what they have seen over the last several years. A price that stays high for three years stops looking high.
• Drought. You cannot keep heifers on grass you do not have. Drought cuts retention and pushes cows to slaughter.
• The cost of carrying breeding stock. Retention tracks financing costs closely. The strongest rebuild in the data happened with interest rates near zero. The weakest retention came with the highest rates since 2007.

That asymmetry is the whole story. The price signal fades over time. Drought and financing costs weigh on the decision at full strength every year.

The herd holds steady only if ranchers keep about 45 percent of their beef heifer calves. They have kept 38 to 40 percent since 2022. Record prices have not closed that five-point gap, and the model says they will not on their own.

Left alone, the herd keeps shrinking: about 25.7 million cows by 2031 and 23.9 million by 2036. In 2,000 simulated futures with random drought, interest rates, demand shocks and disease, only 3 percent end the decade with a larger herd than today.

What actually works

Once the model reproduced the last twelve years, I ran six interventions through the same fitted behavior. I priced each one and searched 192 combinations for the cheapest way to hit specific herd targets.

Three levers do the work.

More calves per cow. Extension support, heifer development, pregnancy checking and cost-share for artificial insemination add about 1.7 million cows by 2036 for roughly $1.2 billion. These are the cheapest cows available. The gains stick after funding ends, because skills, genetics and facilities do not disappear.

Cheaper money for breeding stock. A 2-point interest rate buy-down on loans for retained heifers adds about 1 to 2 million cows for under $3 billion.

Drought protection. Forage insurance, drought forage reserves, and drought assistance tied to keeping the base herd each add about 1 to 2.5 million cows. Drought is the single biggest driver of herd loss. The model also shows the herd has no natural resting point, so every cow saved from a drought sell-off is a permanent gain.

What does not work

The obvious idea is to pay ranchers for every heifer they keep. The model says it is the worst option tested.

A $300 payment per retained heifer costs $5.8 billion over the decade and adds about 0.2 million cows. That is roughly $30,000 of public money per additional cow.

The reason is simple. The payment goes to every heifer kept, and nearly all of them would have been kept anyway. Only a small slice of the money changes anyone’s decision. In the search across 192 program mixes, no least-cost combination included a per-head payment.

What it costs

Held for 2027 to 2036, the cheapest mixes cost:

• About $4.8 billion to stop the decline, or a little under $600 million a year.
• About $6.2 billion to get back to the 2023 herd of 29 million cows.
• About $12.6 billion to return to the 2019 peak of 31.7 million cows, roughly $1.5 billion a year.

A combined package of productivity support, credit, drought protection and a small payment reaches a median of about 30 million cows by 2036. In 90 percent of simulated futures, the herd ends the decade larger than it is today. Without action, that share is 3 percent.

For consumers, the payoff is large. Real retail beef ends up about $2 a pound cheaper in 2036 than it would be with no action.

The catch nobody talks about

Added cows per $1 billion of public cost (left), and the split between public cost, consumer savings and cattle-sector revenue (right).

Beef demand is inelastic. When supply rises, prices fall by more than enough to offset the extra volume. A bigger herd means less total revenue for the cattle sector.

In the model, the five-year package saves consumers about $138 billion over the decade and lowers cattle revenue by about $94 billion.

That is not a conspiracy. Every individual ranch is a price taker and would gain from expanding. But it explains two things. It explains why no one should expect a rapid rebuild to come from producers alone. And it explains why the programs that work are the ones that make expansion safer and cheaper for the individual ranch, rather than asking ranchers to take on more risk.

How much to trust this

The model is small and honest about it.

• Backtest: it reproduces the beef cow herd from 2014 to 2026 within 1.6 percent on average, without being reset to the data along the way.
• Near-term check: its 2026 and 2027 beef production are within 1 percent of USDA’s estimates.
• Weakest estimate: the interest-rate effect, which likely reflects other 2023 to 2025 pressures too, such as input costs, rancher age and risk.
• Program assumptions: costs and effect sizes for the drought and productivity programs are my assumptions, not estimates.

The ranking holds up when the key behavioral estimates are halved or doubled. Productivity and the combined package stay near the top. The per-head payment stays last.

The full analysis

This piece is the short version. The full article on Substack walks through everything behind these numbers:

• The model: the complete stock-flow model and the math behind it.
• Calibration: how rancher behavior was backed out of USDA inventory data.
• Validation: the backtest against 2014 to 2026.
• Scenarios: six no-action scenarios, including drought, screwworm and a demand pullback.
• Dynamics: why the cattle cycle runs about nine years, and why drought sell-offs never fully reverse on their own.
• Programs: every program priced and stress-tested.
• Assets: 15 charts, 10 tables, and the Python code to reproduce all of it.

Read the full analysis on Substack → https://brianjamescurry.substack.com/p/how-to-rebuild-the-us-cattle-herd

Data: USDA NASS, ERS, AMS and APHIS; BLS; Federal Reserve. Projections and program results are conditional scenarios, not forecasts or policy endorsements.

About the Author

Brian Curry is a Kansas City-based machine learning and AI strategist, marketing economist, and applied researcher. He leads Vector1, an independent research studio working on cognitive AI, causal inference, marketing economics, and knowledge engineering, with a focus on simulation-driven intelligence systems.

Over more than twenty years, Brian has led data science and analytics initiatives at enterprises ranging from $4B to $125B in revenue, including Koch Industries, Tractor Supply, Vail Resorts, Hallmark, Garmin, and AT&T. His work applies economics and causal inference to connect marketing investment to structural outcomes, operational decisions to economic value, and AI adoption to durable competitive advantage.

He is also the founder of KC AI Lab, a 1,500-member AI and ML education community in the Midwest. He lives in Kansas City and is an active part of the Kansas City community.

Research and Open-Source Projects

The modeling approach in this paper draws on PyCausalSim and Papilon.

Causal Inference and Economics

• PyCausalSim: Simulation-driven causal discovery and evaluation, using counterfactual scenarios to isolate structural drivers.
• Papilon: Complex-system simulation and optimization, with applications in media mix modeling and adaptive architectures.
• Daedalus: Causal analytics for money-flow mapping and macroeconomic tracking.
• The Autonomy Economy: A macroeconomic framework for the combined effects of rapid productivity growth and structural price deflation in an AI-automated economy.

Forecasting and Applied Modeling

• Regimecast: Time-series regime detection, structural breaks, and economic forecasting.
• CarryNet: Financial modeling for commodity carry trade analysis.
• Foodmesh-ml: Machine learning, optimization, and simulation for distributed food systems.

Cognitive AI and Semantic Architecture

• Memory-Node Encapsulation (MNE): A cognitive AI architecture and data structure for artificial episodic memory (NeoCortex-M).
• MeaningFlow: Semantic content modeling, embeddings, clustering, and coverage gap analysis.

Contact: brian@vector1.ai | GitHub @Bodhi8 | Substack

The text of this article may be shared with attribution under Creative Commons Attribution 4.0 International (CC BY 4.0). Code and models referenced here are available separately to paid subscribers.