New Tool Available to Project Soybean Cost of Production

Why It Matters

Accurate cost‑of‑production estimates help soybean growers make informed input and marketing decisions amid volatile prices, directly impacting farm profitability. The tool’s scenario‑based approach enables producers to evaluate trade‑offs between cost minimization and yield maximization.

Key Takeaways

• Tool offers three cost scenarios for soybean farms
• Base projection breakeven price $10.90 per bushel
• Push production breakeven price $8.98 per bushel
• Uses Michigan grain price forecasts and USDA data
• Editable Excel file lets farms customize budgets

Pulse Analysis

The 2026 soybean cost‑of‑production model arrives at a time when input expenses—fertilizer, chemicals, and equipment—are climbing faster than grain prices. Midwest producers, who account for a sizable share of U.S. soybean output, often lack granular budgeting tools that reflect regional market dynamics. Michigan State University Extension’s Excel‑based calculator fills that gap by translating statewide price forecasts and USDA yield statistics into actionable financial metrics. By presenting clear breakeven points, the model equips growers with a realistic benchmark for evaluating profitability before planting.

The spreadsheet offers three distinct pathways—Base Projection, Build‑up, and Push Production—each calibrated to a different yield objective and fertility strategy. The Base version targets a modest 50‑bushel per acre yield, relying on nutrient removal rates to set fertilizer rates, while the Build‑up adds soil‑test‑driven applications for long‑term soil health. Push Production pushes the target to 75 bushels, assuming aggressive input use to maximize output. Cost inputs draw from the latest Illinois production cost report, Michigan retailer prices, and five‑year FINBIN benchmarks, ensuring the estimates mirror current market conditions.

Beyond individual farm budgeting, the tool serves as a conversation starter between producers, advisors, and grain buyers, aligning expectations around price risk and yield potential. Its editable format allows users to model alternative scenarios—such as delayed fertilizer purchases or price spikes—providing a sandbox for strategic planning. As more Midwest operations adopt the calculator, aggregate data could inform regional policy discussions on input subsidies and market interventions. Ultimately, the MSU model exemplifies how university extensions can translate academic research into practical decision‑support tools that bolster farm resilience.