Rob Weston of CropGPT examines the limitations of conventional crop forecasting, analysing how fragmented, retrospective and regionally coarse datasets undermine purchasing decisions. He reports on the integration of satellite imagery, localised weather data, AI modelling and ground-truth verification, and evaluates how earlier, field-validated supply intelligence can help food manufacturers mitigate global commodity volatility, protect margins and maintain production continuity.

With approximately $1 trillion traded daily in global commodity markets, the availability of raw materials dictates business survival. For food manufacturers in particular, crop performance and volatile supply chains can derail everything from product development and pricing to procurement and production schedules. Accurately planning supply is no longer just an operational requirement, it is a critical strategic imperative.

Historically, agricultural data has been fragmented, inconsistent, and frequently backward-looking. Yet many vital decisions are still made using data that may be weeks or months out of date, which fails to reflect what is actually happening in the field. Climate change is heightening this problem. With droughts, floods, and extreme temperatures occurring more frequently, by the time their impact is officially reported, manufacturers may have already made critical commercial decisions based on obsolete information.

It is therefore important that crop forecasting begins to evolve. Periodic production estimates must be supported by more comprehensive, continuously updated intelligence that gives food manufacturers the earliest possible warning of changes that could affect their business.

The data challenge

For decades, food manufacturers have relied on major national and international organisations to provide information on crop production. These forecasts are based on a wide range of information, including national production statistics, weather data, and satellite imagery. While these are all valuable sources of information, the forecasts they produce don’t always tell the full story.

For example, there’s a tendency within the industry to use national production reports, but these rarely give a true picture, as growing conditions can vary dramatically between different regions within the same country. Take Brazil, for example, its vast growing regions cover huge geographical areas, with weather patterns, soil conditions, and crop development varying considerably between locations. As a result, information collected from one part of a region may not accurately represent conditions elsewhere.

Another classic flaw is the use of weather data from broad administrative regions rather than specific crop locations. For instance, weather stations are often located near airports and so record conditions in that space, not in the crop lands miles away, completely skewing market assumptions regarding drought, temperatures or flood recovery.

The same applies to crop disease. Reports of widespread disease across a growing region can raise concerns about potential shortages, but more detailed analysis may reveal a very different picture. Field observations have shown that while a disease may be geographically widespread, it can affect only a relatively small proportion of individual trees. Assuming that the presence of disease across a region will automatically lead to a significant fall in national production can therefore result in misleading forecasts and poor purchasing decisions.

Alongside these gaps in the data, the time it takes to compile and publish many of the reports that are used is also proving problematic for forecasting purposes. The process can be notoriously slow, meaning some major forecasts are outdated before they are even put to use.

This reporting delay is especially challenging when you consider how volatile the world’s climate has become. Drought, flooding, and extreme weather events such as El Niño can affect crop conditions within days, yet without accurate, real-time information such as soil moisture data, the true extent of the impact can be difficult to assess and may not appear in official forecasts for weeks or months.

Geopolitical issues are something else that traditional reports often fail to take into account. The recent conflict in Iran and closure of the Strait of Hormuz is a great example of this, with around 30% of global fertiliser supply coming through this region, its closure disrupted supplies, pushed up prices and raised concerns about future shortages of nutrient-dependent crops such as maize. Yet delays in reporting meant the full implications for crop production were not immediately clear, making it harder for manufacturers to anticipate potential shortages and plan accordingly.

The commercial cost

Food manufacturing businesses base many of their important decisions including stock volumes, pricing and production schedules on crop forecasts, but if the information they are based on is inaccurate the commercial consequences can be dire.

For example, overestimating crop availability or underestimating prices can quickly lead to problems. Unexpected increases in ingredient costs can quickly erode profits, particularly where manufacturers are tied into fixed price supply agreements with customers. Shortages can be equally disruptive, forcing manufacturers to adjust production schedules, reformulate products or find alternative suppliers at short notice, all of which can bring additional costs and cause delays.

Recent volatility in the cocoa market is a great example of this. Back in 2024 cocoa prices hit record levels, exceeding US$10,000 per tonne. This was largely down to concerns over poor harvests and supply shortages in West Africa and had major cost implications for chocolate and confectionery manufacturers.

Whilst better forecasting can’t prevent price volatility from occurring, having a true picture of the issues earlier can help manufacturers by giving them more time to adjust, manage costs and make better informed decisions.

A new approach to forecasting

Technology has transformed how businesses in all sectors can collect and analyse data – speeding and scaling up the process unrecognisably. But the key to more accurate crop forecasting isn’t simply about having access to more data, it’s about bringing together information from multiple independent sources, comparing the findings and checking that they reflect what is actually happening on the ground in real time.

Satellite imagery, weather data and AI modelling all provide valuable information about crop conditions, development and potential yields but none can provide a complete picture on its own. Satellite imagery, for example, can identify changes in vegetation or signs of crop stress, but it cannot always explain what is causing them or how seriously production will be affected. In fact, the industry has suffered in the past from unverified satellite models repeating and amplifying historical errors because they were trained on previous flawed assumptions.

This is why ground-truth networks are such an important part of the puzzle. By having people working directly in growing regions and checking crop conditions first hand, data can be independently verified against what is actually happening in the field.

Using mobile technology, drones with cameras and soil moisture sensors, these local teams can collect in field observations and photographs of individual crops to assess health and potentially identify other issues. This information can then be instantly uploaded to a central platform, compared with satellite and weather data and AI-generated forecasts to build a much more accurate picture of production.

The value of this approach was demonstrated during a recent typhoon in Vietnam where satellite imagery and local sentiment suggested coffee crops had been catastrophically damaged; yet independent physical ground visits revealed growing areas had zero damaged branches, completely correcting an erroneous market price spike within days.

Better forecasting doesn’t remove risk or guarantee better outcomes, but it does help to improve decision making. Food manufacturers should start by understanding which crops and commodities their business depends on and where they are grown and then monitor these specific growing regions individually. This will help to ensure they have a more accurate picture of their crop production than a national production report would provide, so that they can identify any potential issues or shortages as early as possible.

Traditional crop forecasting has been an extremely important resource for food manufacturers to date but the speed at which agricultural conditions can change means that this periodic approach to reporting is no longer reliable enough.

With manufacturers under pressure to control costs, protect margins, and maintain production, greater visibility of future supply is essential, and the only way to achieve this is by using new technologies that provide an up-to-the-minute, comprehensive outlook of crop production. Ultimately, time is everything. Food manufacturers cannot control the weather or prevent crop failures, but they can improve how quickly they recognise issues and respond to emerging risks.

About CropGPT

CropGPT fuses satellite, weather, a 2,000-strong field team, and the world’s agricultural literature into forecasts, reports and alerts. Average accuracy across their published models is 96%, with the full twenty-year record open to inspection. The one-stop shop for crop intelligence.

https://cropgpt.ai