Grain Marketing Plan: Build and Review One With AI

A grain producer or merchant can build a marketing plan by filling in an extension template (costs, price targets, share sold by date, storage, contract types), then use AI as a reviewer that checks the arithmetic, spots gaps and builds a scenario table. AI should not forecast prices; your adviser should see the result.

That division matters because the dangerous failure is not a slow plan but a confident one. A chat tool that has never seen today's bids will still write a sentence about where corn is heading. The use that earns its keep is the opposite: you supply every number, and it hunts for the sums that do not add up.

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What the extension template already gives you

University extension services publish grain marketing plan worksheets and fact sheets, and they agree on a skeleton. One fact sheet sets it out as five steps: know your cost of production, understand seasonal price patterns, know the available pricing tools, put the plan on paper, and implement it. The review work in this tutorial assumes you have, or can fill in, a plan with these parts:

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1. Production and cost. Acres, expected yield, total bushels, and cost of production per bushel, including variable and fixed costs. The same fact sheet suggests computing cost at high, average and low yields, because the per-bushel cost swings.
2. Price targets. A ladder of prices at which you will sell a fixed share, tied to your break-even.
3. Timing. The dates or windows for each sale: pre-harvest, harvest, post-harvest.
4. Tools. Which contract types you will use: cash sale, forward contract, basis contract, hedge with futures, put or call options.
5. Storage. What you will store, where, for how long, and what it costs.
6. Record. A marketing log that shows bushels sold, at what price, by which tool.

The template tells you the structure. It cannot tell you whether your numbers hang together, and that is where a second pair of eyes helps most.

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The plan to be reviewed: an illustrative corn operation

Take a grain farm with 400 acres of corn, an expected yield of 180 bushels an acre (72,000 bushels), and a cost of production of $4.60 a bushel at that yield. These figures are for illustration only. The plan reads:

On paper this looks tidy. 100% of the crop has a home, targets sit above the $4.60 break-even, and the sales are spread over the year, which matches the extension advice against all-or-nothing decisions. The review job is to find what is wrong with it anyway.

Step 1: give the reviewer a controlled brief

A review prompt works best when it forbids what you do not want. You are asking for an audit, not an opinion on the market. Paste the table and your assumptions, and state what the model may not do.

You are reviewing a grain marketing plan. You are not a market
forecaster and you do not know current prices. Use ONLY the numbers
I give you.

Check:
1. Do the shares add to 100% and do the bushels match the shares
   of 72,000 expected bushels?
2. Is every target price above the stated cost of production
   ($4.60/bu at 180 bu/acre)? Show the margin per bushel.
3. If yield falls to 126 bu/acre (a 30% shortfall), how many bushels
   are already committed by forward contract, and is that more than
   the crop produced? Show the sum.
4. What does the plan not say about storage costs, interest,
   quality loss, basis, margin calls or delivery windows?
5. List the questions I should put to my adviser.
Show all arithmetic. If a number is missing, say so rather than assuming.

[paste the plan table and assumptions]

The line "show all arithmetic" is non-negotiable. It lets you spot-check what the tool did, and a wrong sum shows itself in the working.

What a good review returns

Illustrative output, edited for length: "Shares add to 100%; bushels reconcile to 72,000. All targets exceed $4.60: margins range from $0.50 ($5.10) to $1.00 ($5.60). Short-crop test: forward contracts cover 7,200 + 14,400 = 21,600 bushels, which is 30% of expected production but 42.9% of a short crop of 50,400 bushels. You would still be able to deliver, but only 28,800 bushels would remain for everything else. Missing items: storage cost per bushel, interest on stored grain, shrink and quality, the basis assumption for the stored share, and delivery periods for the forward contracts."

That is a useful review for three reasons. It confirms the easy things quickly. It runs the stress test you may have skipped. And it lists the holes. None of this required a view on prices.

Step 2: price the storage decision properly

Storage is where plans quietly assume a gain. The 40% share in the plan is stored for sale at $5.10 in spring, but nothing says what it costs to wait. Extension worksheets list the costs to count: bin rental or charges, repairs and maintenance, insurance on grain in storage, quality loss, and the interest on the money you do not have while the grain sits. Put them in a sum the model can check.

Compare selling 28,800 bushels at harvest with storing them to May.
Inputs (all mine, do not change them):
- Harvest cash price: $4.05 (futures $4.55, basis -$0.50)
- Expected May cash price: $4.45 (futures $4.75, basis -$0.30)
- Commercial storage fee: $0.04 per bushel per month, 7 months
- Interest on the money: 7% a year on the harvest cash value, 7 months
- Shrink and handling loss: 0.5% of the harvest cash value
Calculate the gain from waiting and each cost per bushel. Show the
total and tell me what basis improvement would be needed to break even.

The sum, which you should check by hand: the price gain from waiting is $4.45 - $4.05 = $0.40 a bushel. Storage fee is $0.04 x 7 = $0.28. Interest is $4.05 x 7% x 7/12, about $0.165. Shrink is 0.5% of $4.05, about $0.02. Total cost is roughly $0.465, so storing loses about $0.065 a bushel, or about $1,900 on 28,800 bushels, on these assumptions. Change the inputs (cheaper on-farm storage, a bigger basis recovery) and the answer flips, which is exactly why the plan must show its inputs.

The lesson is not that storage is bad. It is that "stored, $5.10 target" in the table hides an assumption worth about $13,400 across the stored bushels (roughly $0.466 of cost times 28,800). The prices in the sum are deliberately weaker than the plan's $5.10 target: a review should test the plan in a soft market, not only at the prices you hope for. A plan built on extension advice should carry the carrying cost explicitly, and the reviewer should refuse to sign off a plan that does not.

Step 3: check the contract mix against the risk each tool leaves

Each pricing tool fixes some risks and leaves others open. Extension material describes a forward contract as fixing both price and basis, and a basis contract as fixing the basis while leaving the futures price open to be set later. Hedging with futures sets the futures price and leaves the basis open, and brings margin calls if prices rise. A reviewer can check that the plan's mix does not leave one risk unexamined.

One fact worth carrying into the review is the size of a futures contract. A standard corn futures contract covers 5,000 bushels, and the minimum price move of a quarter cent is $12.50 a contract. A plan that says "hedge 28,800 bushels" cannot do it exactly: five contracts cover 25,000 and six cover 30,000, so the plan needs to say which and how the remainder is handled. A model will not notice that unless you ask; it is a good question to put to your adviser.

Step 4: build the scenario table

A single set of numbers hides the risk. The scenario table shows what happens to income and commitments when yield and price move. Ask the model to produce it from your inputs, and then check three cells by hand.

Build a scenario table for this plan. Rows: yield 126, 180, 198 bu/acre.
Columns: harvest cash price $3.80, $4.50, $5.40.
For each cell show: total bushels, bushels committed by forward contract,
bushels unpriced, revenue (committed bushels at their contract prices,
unpriced bushels at that column's price), and margin over cost of
$4.60/bu at 180 bu/acre cost level (cost per acre fixed at $828).

The cost per acre trick matters: cost per acre of $828 (180 x $4.60) does not fall when yield falls, so cost per bushel rises. At 126 bu/acre it becomes $828 / 126 = $6.57 a bushel, which is above every target in the plan. A reviewer that applies the $4.60 figure to the short crop will say you are fine when you are not, which is why the prompt fixes cost per acre.

Illustrative cell check, for the middle yield with weak price: 72,000 bushels, 21,600 committed at an average of $5.30, 50,400 unpriced at $3.80. Revenue is $114,480 + $191,520 = $306,000. Cost is $331,200 (400 acres x $828). The margin is about -$25,200. In that cell the plan loses money despite hitting every target on the sales it made, because 70% of the crop was unpriced. A producer sees this sooner in the table than at the bank.

Step 5: have it argue against your plan

A pre-mortem prompt asks the model to imagine the plan failed and to explain how. It is a good use of a language model because writing plausible failure stories is what it does well, and each story is something you can test.

Assume it is next February and this plan lost money. List the six most
likely reasons, based only on the plan as written. For each, say which
cell or assumption it comes from and what I could do now to guard
against it.

Typical output: targets set without a deadline, so the sales never happened; a delivery window that assumed an empty bin in spring; no plan for a lender meeting if prices fall; a stored share with no sell-by date. These are not predictions, just prompts for discussion. Pick the two you recognise and fix them. The extension fact sheet names the same family of pitfalls: relying on price forecasts, trying to pick the top of the market, leaving out costs when computing net price, making all-or-nothing decisions, getting emotional, bias in your own outlook and failing to communicate with lenders and family.

A second crop: laddering five lots of soybeans

The same review works for a different crop and a simpler plan. Extension guidance describes dividing the crop into equal lots with targets stepped a small amount apart. Take an illustrative 30,000 bushels of expected soybeans, split into five lots of 6,000 bushels with targets of $11.50, $11.75, $12.00, $12.25 and $12.50, and a break-even of $10.40. If every lot sells at its target, the average is $12.00 and the margin over break-even is $1.60 a bushel, or $48,000 across the crop.

Now the question for the reviewer is what happens if only the first two lots reach target. If the other three sell at harvest for $10.80, the average is (12,000 bushels at $11.625 plus 18,000 at $10.80) / 30,000 = $11.13, a margin of $0.73, or $21,900. The plan still makes money, but at less than half the headline figure. A review prompt that asks "what if only two of the five targets are reached" finds that in seconds, and it changes the conversation from "will it hit $12?" to "can I live with $11.13?".

Five lots of 6,000 bushels, targets $11.50, $11.75, $12.00, $12.25,
$12.50, break-even $10.40, harvest cash price assumption $10.80.
Show the average price and total margin if 0, 1, 2, 3, 4 or all 5
targets are reached and the rest sells at harvest. Table only.

Illustrative output rows: 0 targets reached, $10.80 average, margin $0.40, $12,000; 2 targets reached, $11.13, $21,900; all 5, $12.00, $48,000. Whoever reads that table sees that the plan's value depends on hitting some targets, which is the honest message.

Deciding what to do with the open reserve

The 10% marked as an open reserve in the main plan is the part of a plan most likely to be left to feeling. Give it rules while you are calm: a price target, a date by which it sells at market whatever the price, and a trigger such as a drop below a stated level that moves part of it. Then ask the reviewer whether the reserve has a deadline and a trigger. A reserve without a date is not a strategy; it is a hope with a name. Extension writers note that one way to avoid emotion in the decision is to set the rule in advance and write it down, because prices rising make people optimistic and prices falling make them gloomy.

What not to ask AI to do

Some uses go wrong in predictable ways.

• Predict prices. It will produce a figure whether or not it has any basis. If you want market information, take it from your elevator's bids and the exchange's own pages and paste it in with the date.
• Fill in missing inputs. A model told to "assume reasonable storage costs" will invent them. Make it list what is missing.
• Replace the contract. Terms, delivery windows, grade discounts and default rules are in the buyer's document. Paste the clauses in for a plain-English summary and check it against the original.
• Replace the lender or adviser. The point of review is to reach them with a clearer plan, not to skip them.

Where the plan meets a merchant's book

A grain merchant or elevator runs the same logic from the other side. The purchases from farmers and the sales to processors form a position, and the plan is a set of limits: how much unpriced grain it will hold, how much must be hedged, and by when. AI can review a position spreadsheet as it reviews a farm plan. Give it the table of purchases, sales and hedges by month and ask where the plan breaches its own limits or where delivery windows do not match. Illustrative check, assuming every contract is flat-price: purchases of 120,000 bushels for March delivery, sales to processors of 90,000 and short futures hedges of 50,000. The open position is 120,000 - 90,000 = 30,000 bushels bought but not yet sold, which six 5,000-bushel contracts would cover. At ten contracts the book is over-hedged by 20,000 bushels, so a rise in futures brings margin calls on 20,000 bushels with no unsold grain behind them. The model should flag the gap and the contract count; basis or hedge-to-arrive contracts would change the sum, so say which kind each line is. Whether a deliberate over- or under-hedge is acceptable is a risk-management decision for the merchant's managers, not for the tool.

The adviser meeting: what to bring

After the review, put the plan, the scenario table and the model's list of questions on two pages. An adviser or lender will not want your chat transcript, so give them the numbers and the open questions. Include:

1. The plan table, with the cost of production and its yield assumption.
2. The storage sum, with every input visible.
3. The scenario table and the three cells that worry you.
4. The list of questions: margin and credit needs for hedges, which contracts allow a delivery date change, tax treatment of deferred payment, crop insurance interaction.

Tax and legal questions in particular belong with a qualified adviser, and so do decisions about margin, options and borrowing. The model is a pair of eyes on your sums, not a source of advice on any of them. Your own accounting data may live elsewhere; the tutorial on farm accounting software with AI helps you decide where costs should be recorded so the cost-of-production line is dependable.

Keeping the plan alive through the year

A written plan only helps if it changes when the facts change. The extension guidance suggests re-evaluating at least monthly and at most weekly, with extra reviews before harvest, in volatile markets, after weather events and when your situation changes. Make the routine cheap:

• Keep one spreadsheet with the plan, the marketing log (each sale with date, bushels, price and tool) and the scenario table.
• At each review, update bushels sold and the expected yield, then paste the updated sheet into the review prompt and ask only: what changed, what no longer adds up, and what is overdue.
• Record the decision you took and why, in a line. Next season, a decision log is more use than a memory.

If you keep records in a spreadsheet, the guide to using AI for farm records and paperwork covers keeping the data tidy, and choosing between ChatGPT, Claude or a farm app helps you pick the assistant for the job. For spreadsheet work in general, my tutorial on using ChatGPT with Excel shows how to ask for formulas you can check.

A short checklist before you rely on the plan

1. Shares add to 100%; bushels reconcile to expected production.
2. Every target sits above cost of production at the yield you actually expect, and the short-crop cost is computed, not assumed.
3. Committed forward bushels are safe under a 30% yield shortfall.
4. Storage cost per bushel is written down and subtracted before any stored price is compared.
5. Each tool's open risk is named, and margin or credit needs are covered.
6. The scenario table has been checked in three cells by hand.
7. The plan has review dates, a log and a person who will read it with you.

Questions to settle before you rely on a written plan

Can AI tell me when to sell my grain?

No. A chat tool does not know today's prices or tomorrow's, and a confident forecast from it is a guess. Use it to check that your plan is consistent, your arithmetic holds up, and your sales are spread sensibly. Keep the selling decisions with you and your adviser.

How much of the crop should I sell before harvest?

Extension guidance warns against selling everything or nothing and suggests selling in portions, for example 10% at a time at set targets. How much you sell early depends on your costs, yield risk, storage and lender. Show your adviser a scenario table before choosing.

What should I give AI so it can review my plan?

Give it your expected yield, acres, cost of production per bushel, price targets with dates, bushels contracted, storage costs and contract types. Leave out names, account numbers and lender details. Ask it to find gaps and arithmetic mistakes, and to explain every figure it uses.

How often should a grain marketing plan be reviewed?

One extension fact sheet suggests reviewing at least monthly and no more than weekly, with extra reviews before harvest, in volatile markets, after weather events and when your own situation changes. Put the dates in your calendar and record each decision in a log.

Further reads

• AI Chatbot for Agriculture: A Farm Supply Business's Setup and Cost — A farm supply business and its chatbot costs.
• Dairy Farm Budget: Test Milk Price Scenarios With ChatGPT — The same scenario method applied to milk prices on a dairy farm.
• Farmonaut Pricing vs FieldView Plus: What Satellite Monitoring Costs — What satellite crop monitoring costs, if yield data matters.
• Free AI for Agriculture: What a Small Farm Gets Without Paying — What a small farm gets from free AI tools.
• How to Use AI for Scenario Planning: Best, Worst and Likely Cases — Best, worst and likely cases built with AI for any business.
• Best AI App for Farmers: Herdwatch, AgriWebb and Farmonaut Compared — Farm apps compared, if records need a proper home.
• AI Tools and AI Development: The Complete 2026 Guide — the AI hub, including every tutorial in the AI-for-business series.

Sources: university extension fact sheets and worksheets on grain marketing plans (cost of production, price targets, pricing tools, storage and review), CME Group corn futures contract specifications.