The Most Important Decisions in Agriculture Happen Between Forecast Updates

Every agribusiness has a forecast.

By this point in the season, most organizations have already developed a view of production, demand, grain movement, transportation needs, and market direction. Those forecasts influence procurement plans, inventory positions, logistics decisions, and risk management strategies.

The challenge is that the market rarely waits for the next forecast update.

We are seeing that play out this season. USDA’s early-season outlook continues to project a record U.S. corn crop under trendline assumptions, yet market participants are already evaluating the implications of changing weather conditions across portions of the Corn Belt.

Agriculture operates on two different timelines. The crop cycle is seasonal, but information arrives continuously.

When I talk to grain buyers, merchandisers, processors, or agribusiness leaders today, I hear some version of the same thing: Conditions are changing faster than the systems built to manage them.

During a recent conversation on DTNsights, David Fiocco, Senior Partner in McKinsey & Company’s Global Agriculture Practice, described it this way:

“The speed of change, the speed of technology adoption, it’s just much faster and it’s in a much tighter environment than it was five years ago.”

Anyone involved in grain procurement, merchandising, processing, or transportation has felt that shift.

Weather remains a major source of uncertainty, but it now sits alongside shifting trade flows, transportation constraints, changing demand patterns, and evolving supply chain dynamics.

Forecasts Are Becoming Less Durable

Forecasts remain essential. Every agribusiness needs a working view of production, demand, logistics, and market conditions. Forecasts help organizations allocate resources, establish procurement strategies, manage risk, and communicate priorities across the business.

The problem is not forecasting. The problem is assuming the original forecast will remain the most important source of information throughout the season.

A grain buyer may begin the year focused on expected production in a particular region. Several months later, grain movement, transportation availability, basis relationships, or farmer selling activity may become more important than the original production estimate.

Markets Tell Us What Happens After the Forecast

As the season progresses, many of the most important market signals come from activity in the market itself.

Production forecasts tell us how much grain may be available. Commercial decisions often depend on a different set of questions: Where is that grain moving? How quickly is it moving? Where is demand emerging? Where is competition increasing?

For grain buyers and merchandisers, those questions become increasingly important as the season progresses because they provide insight into how the market is responding to changing conditions.

This is one reason visibility into grain movement has become increasingly valuable. At DTN, we see this through Grain Discovery. Understanding where grain is moving, where competition is increasing, and how marketing behavior is changing provides context that helps explain what is happening in the market after a forecast has been published.

Those insights help organizations answer practical questions:

• Is grain moving where we expected it to move?
• Are producers behaving the way we anticipated?
• Are local supply conditions changing?
• Are transportation patterns confirming or contradicting our assumptions?

Those are not questions that can be answered by a single forecast. They require an ongoing understanding of how market conditions are evolving.

The Real Challenge Is Updating Assumptions

Later in our conversation, Fiocco made another observation that deserves attention.

“Forecasting should be 100 times better than it is today.”

That statement is less surprising than it sounds.

In fact, it helps explain one of the more interesting conclusions from McKinsey’s report on generative AI in agriculture.

When the firm estimated that generative AI could create roughly $250 billion in annual value across agriculture and the food value chain, much of that opportunity was tied to improving forecasting, pricing, logistics, inventory management, procurement, and commercial decision-making.

Every procurement decision reflects an expectation about future supply. Every inventory decision reflects an expectation about future demand. Every logistics decision reflects an expectation about future movement.

Most agribusinesses are not struggling because they lack information. Agriculture has access to more information than at any point in its history.

If anything, they are trying to make decisions while processing more information than ever before. The challenge is accessing and stitching together different information streams and knowing when new information changes the outlook enough to require a different decision.

The challenge is accessing and stitching together different information streams and knowing when new information changes the outlook enough to require a different decision.

Looking Beyond the Forecast

Agriculture operates on two different timelines. The crop cycle is seasonal but information about weather, demand, transportation, and grain movement arrives continuously.

That reality has changed what agribusinesses need from a forecast and from their decisioning-analytics dashboards.

Agribusinesses still make many of their biggest decisions around seasonal milestones. Acreage expectations, production forecasts, procurement plans, transportation strategies, and risk positions are all built around a crop cycle that has not fundamentally changed.

What has changed is the amount of information available between those milestones.

A generation ago, organizations often waited for the next report to confirm whether conditions were changing. Today, market participants can observe changes in weather, crop conditions, grain movement, transportation activity, and demand patterns as they develop.

The result is that the value of a forecast increasingly depends on what happens after it is published.

Not because forecasts matter less. But because the real-time signals that emerge between forecasts matter more.