Superforecasting is a disciplined way to estimate how likely a clearly defined farm outcome is, update that estimate as evidence changes, and learn by checking the forecast against what actually happened. It can help with questions about prices, yields, weather, and policy—but a probability is not an instruction to buy, sell, insure, or wait. The right action also depends on potential costs, consequences, and your tolerance for risk.
What superforecasting means for a farm
In a 2016 Successful Farming feature, John Walter described farmer Bill Flack’s use of forecasting methods for decisions involving commodity and farmland prices, interest rates, federal legislation, regulation, and farm operations. The central change is from making a confident-sounding prediction to assigning a probability to a specific event.
Instead of asking, “What are corn prices going to do this winter?”, ask a question that can be resolved later: for example, whether a named cash or futures price will exceed a specified level by a particular date. Walter also lists questions about fertilizer prices, calving percentage, weather applicability, and grain storage capacity. Each needs its own measurable definition; “enough storage,” for instance, is a capacity threshold tied to a specified crop volume, not a simple prediction until the threshold is stated.
As Flack put it, “Don’t think in terms of yes, no, or maybe. Is something 80% or 25% likely to happen?” An 80% estimate means the forecaster still allows a 20% chance the event will not occur. It is not a guarantee.
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Turn a farm concern into a resolvable question
A forecast can only be scored fairly when its event and resolution are unambiguous. Walter gives this example: “Will the price of 10% ethanol, 87 octane gas at the Cenex on Highway 81 exceed $2.75 at any time on or before October 31?” The product, location, threshold, and deadline make it far more testable than “Will fuel prices increase significantly this year?”
For a farm forecast, specify the event, the relevant place or population, the threshold, the deadline, and the source that will determine the final value. Record these rules before making the estimate so that neither the question nor the outcome standard shifts after the result is known.
- Event: What exactly must happen?
- Scope: Which farm, market, location, crop, or population does the question cover?
- Threshold: What measurable value counts as success?
- Deadline: By what date must the event occur?
- Resolution source: Which record or data source will settle the question, and on what date will it be checked?
Build the estimate from history and current evidence
Start with the outside view: how often has a comparable event happened in relevant historical cases? For a price threshold, that might mean checking how often the price crossed that level during a defined period in recent years. For an operating target, use comparable seasons or prior farm records where possible. The comparison set matters: cases that differ sharply in geography, time period, or conditions may provide a poor base rate.
Then take the inside view by considering what is unusual about the present case. Current supply conditions, local weather, input availability, policy changes, or the farm’s own operating circumstances may justify moving the estimate away from the historical frequency. The University of Chicago Press Journals review discusses using comparable cases alongside case-specific reasoning, rather than allowing a persuasive narrative to replace the base rate (“Uncertain Causation, Regulation, and the Courts,” 2018).
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Update probabilities and keep a record
A forecast is not a one-time declaration. When relevant information changes, revise the probability and record both the new estimate and the reason for changing it. Flack’s group revisited many questions weekly and made small adjustments when evidence warranted them. A farm can use the same principle without adopting a rigid weekly schedule: update when new information is material to the question.
- Write down the exact question and resolution rule.
- Record the initial probability, the date, and the evidence considered.
- When new evidence arrives, record what changed and how it affects the estimate.
- At the deadline, use the preselected source to resolve the event and save the result.
A simple record can be a spreadsheet or notebook; the cited farm feature does not establish that special software is required. The value is in preserving the estimate and rationale as they stood at the time, rather than reconstructing them from memory afterward.
Use groups to challenge assumptions, not outsource judgment
Working with others can expose overlooked evidence and assumptions. Walter reports that the research he describes found teams performed 27% better than individuals in that research setting. That figure is not a demonstrated gain for every farm group. Groups also take time, and discussion can blur responsibility unless participants keep track of their own estimates.
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| Approach | Potential value | Trade-off |
|---|---|---|
| Individual forecasts | Fast to make and easy to attribute to one person. | Less opportunity to surface evidence or assumptions the forecaster missed. |
| Group discussion with individual estimates retained | Can bring more perspectives and useful disagreement to the question. | Requires time; consensus can hide differences unless individual probabilities remain recorded. |
The journal review recommends considering contrasting informed predictions and synthesizing them. In practice, ask participants to state their estimates and reasons, invite contrary evidence, and preserve accountable individual judgments rather than treating agreement as proof.
Score forecasts and learn from patterns
Once outcomes are resolved, compare them with the probabilities you recorded. Flack describes using a Brier score, a measure that evaluates probabilistic forecasts against outcomes. Keep a collection of forecasts: one striking success or miss says little about whether estimates are consistently well calibrated. Over time, look for patterns such as routinely assigning probabilities that are too high or too low.
Prospective records matter. In a 2024 IARPA transcript, former director Jason Matheny discusses the value of forecasts made before outcomes are known; claims about predicting past events do not by themselves demonstrate prospective skill. The transcript describes the ACE effort as a large forecasting project involving thousands of volunteers, millions of forecasts, and tracked scores (IARPA podcast transcript, November 18, 2024).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply probabilities to decisions without confusing them
A forecast estimates likelihood; it does not choose the action. A farmer deciding whether to insure against drought or hail, store grain, buy inputs, or commit to a price needs to weigh the probability against the financial consequences of each outcome, the cost of protection or delay, and the farm’s capacity to absorb a loss. A 10% annual chance of a severe event can matter greatly if the potential loss is large. As Flack observed, “If we’re trying to insure against such an event, it’s important to know whether the probability that it’ll occur in a given year is 1% or 10%.”
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Rare events deserve explicit attention because low probability does not mean low consequence. Walter’s feature raises extreme drought, crop-damaging hail, and farmland price declines as examples of hazards worth considering. The forecast can clarify the likelihood question; the farm’s risk tolerance and finances determine what response makes sense.
Put reported performance claims in context
Walter’s 2016 article reports several tournament-related figures: a 10% yearly improvement after a 60-minute tutorial, 27% better team performance than individual performance, and tournament superforecasters performing 30% better than intelligence analysts with classified information. It also quotes Philip Tetlock’s comparison of probability assignments at 400 days versus 80 days. These are figures reported by the 2016 feature about the research setting it describes, not established estimates of gains for farmers or evidence that a forecast remains reliable at 400 days. The article’s farmland-price example referred to 2017 and is historical, not current market guidance.
The feature points readers to Philip E. Tetlock and Dan Gardner’s Superforecasting: The Science and Art of Prediction, identified in the IARPA transcript bibliography as a 2015 Crown Publishers/Random House title. It is an educational resource, not a promise of a particular improvement in farm forecasting.
Quick Recap
A practical checklist
- Is the event specific, measurable, and tied to a deadline?
- Have you selected the source that will resolve it?
- Did you begin with a relevant historical base rate?
- Are case-specific adjustments tied to evidence rather than narrative alone?
- Have you stated a probability and recorded why?
- Will you update when meaningful new information appears?
- Can another person challenge your reasoning while you retain your own estimate?
- Will you score the forecast after resolution and review patterns across many forecasts?
- Have you considered consequences and costs separately from likelihood?
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