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TUESDAY, SEPTEMBER 1, 2026   |   SPECIAL REPORT & ANALYSIS

SPECIAL REPORT  |  USDA AGRICULTURAL DATA

USDA Data Overhaul Faces Real Test: Fix Surveys, Preserve Expertise

Hybrid technology could help, but participation and staffing remain key

Analysis  ·  September 1, 2026

Today, at the Farm Progress Show in Boone, Iowa, USDA Secretary Brooke Rollins announced the USDA Data Modernization Plan to put Farmers First, reduce unnecessary burdens on producers, and improve the timeliness, accuracy, and usefulness of the agricultural data that informs decisions across American agriculture.

USDA Secretary Brooke Rollins’ new agricultural data modernization plan is significant not because the department intends to replace traditional surveys with satellites, artificial intelligence or computer models, but because it explicitly proposes a hybrid statistical system — combining those technologies with the producer-reported information that has long been the foundation of USDA crop estimates.

The difficulties confronting USDA’s National Agricultural Statistics Service (NASS) have never been simply a matter of outdated technology. Declining survey participation, delays in collecting producer information, questions surrounding acreage estimates and the loss of experienced USDA personnel all play a role.

The modernization plan acknowledges at least part of that problem directly. USDA says producer survey response rates have been declining for decades and that the trend has contributed to reduced confidence in agricultural reports.

Figure 1. Response to USDA crop surveys, then and now. Sources: USDA/NASS. 

Table 1. Reported response rates by survey. Sources: USDA/NASS. 

Rollins’ answer is not to abandon surveys. USDA instead proposes using administrative records, satellite imagery, geospatial observations and modern crop models as a stronger foundation while preserving “essential producer-reported information.”

Table 2. The four pillars of the USDA Data Modernization Plan, and how much of each is already in practice. Source: USDA, Sept. 1, 2026; assessment by Ag Policy & Markets Daily.

That is probably the correct direction. But implementation — rather than the technologies themselves — will determine whether USDA actually produces better agricultural statistics.

The survey problem may be bigger than the technology problem

One practical problem receives relatively little attention whenever USDA crop estimates are criticized: the mechanics of getting information from farmers to NASS in time to use it. Farmers frequently receive mailed surveys late. The forms can be lengthy, and producers are often trying to complete them during some of the busiest parts of the production season. Even farmers who fill them out promptly can mail them back after the effective cutoff for a particular NASS estimate.

A response that arrives after USDA has assembled the report may eventually contribute to revisions or later estimates, but it cannot improve the initial number the market trades. That is why one of the more consequential provisions in Rollins’ plan may be much less glamorous than artificial intelligence.

USDA proposes mobile-friendly surveys, shorter question sequences, simplified navigation, improved online reporting and potentially SMS-based communication. It also wants to eliminate situations in which NASS asks farmers for information they already supplied to FSA, the Risk Management Agency or another USDA office.

The department also plans to examine prefilled survey fields using previously reported and administrative information and even consider incentives for participation in particularly burdensome surveys.

Those changes could have an immediate payoff.

A substantial push toward computer, smartphone and tablet reporting could shorten the time between USDA requesting information and NASS receiving it. A producer could potentially complete a survey from the cab of a pickup or tractor rather than setting aside paperwork for later and eventually mailing it back.

More important, electronic collection could allow USDA to identify missing or inconsistent responses almost immediately rather than discovering problems after a paper form has been processed.

Figure 2. Schematic of the two reporting paths. Illustrative, not a measured timeline. Source: Ag Policy & Markets Daily, based on the USDA Data Modernization Plan.

Recent acreage misses have raised the stakes

NASS officials are well aware of the criticism and have taken steps over the years to improve agricultural estimates. Statistical systems, however, take time to change. New models need to be tested against existing methods over multiple seasons before USDA can confidently put billions of dollars of farm and commodity-market decisions behind them.

Still, planted-acreage surprises and subsequent revisions in recent years have become a particular concern among traders and farmers.

Figure 3. June Acreage estimate versus the final planted-acreage estimate, corn and soybeans. Sources: USDA/NASS Acreage (June 2024, June 2025, June 2026) and Crop Production Annual Summary; 2024 and 2025 final figures as published or as implied by NASS year-over-year percentage changes.

That is arguably where USDA should concentrate much of the modernization effort.

Acreage should be an area in which technology can provide especially meaningful improvements. Satellite imagery can increasingly determine whether a field is planted to corn, soybeans, wheat, cotton or another crop. FSA certification provides another source of acreage information, while crop-insurance records add still another administrative dataset.

USDA proposes tying those sources together through an integrated NASS-FSA-RMA system and conducting parallel testing of administrative information, geospatial observations and models against its established methodology.

Table 3. What each acreage source contributes — and what it cannot settle on its own. Source: Ag Policy & Markets Daily analysis of USDA/NASS, FSA and RMA reporting practice.

That does not mean June Acreage or subsequent acreage estimates will suddenly become perfect. Planting can stretch over weeks, prevented planting complicates classification, double cropping matters, administrative data arrive on different schedules and satellite interpretation has its own limitations.

But combining those sources should make USDA progressively less dependent upon a single survey snapshot.

Figure 4. The hybrid system USDA describes, and the judgment step that technology does not remove. Schematic. Source: Ag Policy & Markets Daily, based on the USDA Data Modernization Plan.

Private industry is increasingly challenging USDA

Another factor pushing USDA toward modernization is competition. For decades, USDA enjoyed an almost unrivaled reputation as agriculture’s statistical authority. Private firms could forecast production, but USDA possessed the enormous survey apparatus, institutional expertise and data access necessary to establish what many viewed as the benchmark.

That gap has narrowed.

Private agricultural analytics firms have made substantial strides in satellite imagery, remote sensing, machine learning, weather modeling, field-level databases and crop-condition analysis. Some can update estimates much more frequently than USDA’s traditional reporting schedule.

That creates a challenge beyond whether USDA’s September corn yield is ultimately a bushel too high or too low. USDA also has to demonstrate that its statistical system remains timely enough to be useful in a world where commercial firms can continuously update crop assessments.

Rollins has tapped agricultural businessman and former U.S. ambassador Kip Tom as special adviser for data modernization, and USDA has been conducting listening sessions with producers at fairs and farm shows. The department says the new plan itself was developed after hearing from hundreds of producers.

Continued producer consultation is explicitly built into the program. USDA intends to hold listening sessions, commodity discussions and roundtables and work more closely with producer groups, state agriculture departments, universities and Extension. That is important because the solution cannot simply be designed by statisticians and technology vendors in Washington. Farmers have to be willing to participate.

Farmers bear some responsibility, too

There is another side to the criticism of USDA statistics. One veteran user of USDA information cautions against accepting the increasingly common argument that USDA numbers are broadly unreliable:

“The ‘USDA data is flawed or wrong’ crowd is brought on by social media. Too many people think they are experts and have a voice.”

The veteran observer acknowledges that USDA data can sometimes appear flawed and that technology can improve the process, but argues that wholesale changes are unnecessary and that workforce cuts are not solely responsible. Instead, that observer points directly at survey participation. Some producers simply do not return NASS surveys. Others return incomplete information or estimates that later prove inaccurate. That creates a basic statistical problem no amount of criticism on social media can solve.

USDA’s estimates are only as good as the underlying information and methodology available to produce them.

That is another reason the hybrid approach makes sense. Satellite and administrative information can help fill gaps created by declining participation, while producer responses remain essential for information that cannot readily be observed from space or extracted from government databases.

Personnel cuts cannot be dismissed

But farmer participation is not the entire story either. USDA and NASS have lost experienced personnel, and that carries consequences that are difficult to quantify immediately. Agricultural statistics involve considerably more than inserting survey responses into a computer. Experienced statisticians recognize anomalies, understand historical relationships, identify questionable data and know when a model result needs additional examination.

That type of institutional knowledge can take years to develop.

Modern technology can make a good statistician more productive. It cannot instantly replace one.

Machine learning can flag an unusual county yield. A satellite can detect vegetation differences. Administrative records can identify reported acreage. But someone still has to determine whether conflicting signals reflect bad information, an unusual crop, delayed reporting or a genuine change in production. That makes staffing one of the biggest unanswered questions surrounding the Rollins initiative.

USDA’s plan describes substantial new responsibilities — more integrated datasets, more modeling, additional validation, new technology platforms, artificial intelligence applications and greater transparency — but implementing all of those tasks requires people with the expertise to do them.

Some of USDA’s “new” ideas aren’t actually new

There is also reason for some skepticism about how revolutionary the initiative really is. A government contact familiar with the statistical system offered a pointed assessment:

“Honestly we already do most of what’s in here, though I suspect much of it will suddenly become ‘new accomplishments.’”

That is a fair observation. NASS already uses satellite information, administrative records and modeling. USDA’s Cropland Data Layer, for example, is hardly a new concept. The modernization document itself says USDA already relies upon field observations, sample plots, satellite imagery and administrative information and describes the initiative as building upon those capabilities.

The government contact sees the real objective as producing information that is “better, faster, and more frequent” — somehow by collecting less survey data. That captures both the promise and the contradiction in the plan.

USDA wants to reduce the burden on farmers, ask fewer duplicative questions, operate with fewer traditional survey contacts and still provide more timely and accurate information. Technology may make that possible.

But USDA will need to prove it.

Transparency could be the most important reform

Perhaps the most promising part of the plan therefore has nothing to do with AI. 

USDA proposes an annual “Crop Season in Review” that will compare estimates and forecasts issued throughout the season with final estimates and explain the data sources behind them. NASS plans retrospective reports for the 2024 and 2025 crop seasons as an initial demonstration.

The department also promises to publish performance measurements, validation results and lessons learned from modernization pilots and provide additional information about methodology and data sources. That could directly address some of the skepticism surrounding USDA numbers.

Markets understand that a crop forecast can be wrong. Agriculture is inherently uncertain. What frustrates farmers and traders is receiving a major acreage or yield revision without being able to determine clearly what USDA learned that it did not know before. Publishing response rates, explaining data limitations and showing how early-season forecasts compared with final estimates could significantly improve confidence even when USDA ultimately gets a number wrong.

Analysis

The Rollins initiative is probably best viewed as an acceleration and integration of work NASS has already begun rather than a complete reinvention of USDA statistics. And that may actually be its strongest feature.

USDA does not need to throw away a statistical system that remains enormously valuable. It needs to improve how information enters that system, exploit technologies that did not exist when many survey procedures were developed and be much more transparent about the strengths and weaknesses of the resulting numbers.

Analysts say three tests will determine whether the effort succeeds.

Table 4. Three tests of the Rollins initiative, and where the first evidence should appear. Source: Ag Policy & Markets Daily.

First, does electronic reporting materially increase timely farmer participation? Moving producers from delayed paper surveys toward phones, tablets and computers could produce one of the fastest improvements.

Second, can USDA successfully combine survey responses, administrative records and geospatial information without introducing new model biases? Running the systems in parallel before changing official methodology is essential.

Third, does USDA maintain enough experienced statistical expertise to interpret all of this additional information? Technology increases the volume of data. It does not eliminate the need for knowledgeable people who understand what the data mean.

The broad criticism that USDA statistics are routinely “wrong” is overstated. Social media and instantaneous market commentary have undoubtedly amplified every miss and revision. But USDA should not dismiss the underlying message either.

When farmers and traders increasingly question planted acreage and other major estimates, the credibility of USDA’s statistical franchise is at stake.

The modernization plan provides a reasonable blueprint for defending it. The tougher job starts now: converting technology, better survey design and greater transparency into demonstrably better estimates — while rebuilding the human expertise needed to know when the technology itself gets something wrong.

AG POLICY & MARKETS DAILY   |   SPECIAL REPORT  |  USDA AGRICULTURAL DATA — TUESDAY, SEPTEMBER 1, 2026