70–80% of Farm Geo-Data Errors Are Auto-Fixable. Here's What the Other 20% Looks Like.

When a supplier sends a farm geo-data file ahead of EUDR compliance, something almost always needs fixing. The question is: what kind of problem is it, and can it be fixed automatically?

After processing real supplier files across multiple origins, a clear pattern emerges. Most errors fall into one of two categories: errors that a system can fix without human input, and errors that require going back to the field.

Here is what each category looks like in practice.

We help European green coffee and cocoa importers solve messy and non-compliant supplier farm geo-data so they can pass EUDR due diligence checks without delays or rejections.

Here is what each category looks like in practice.

What gets fixed automatically

The majority of errors in supplier files are formatting problems — not data collection problems. The underlying information exists. It just arrived in the wrong shape.

The most common auto-fixable errors are:

• Decimal separator mismatch. A coordinate field reads 14,889199 instead of 14.889199. Every geo-parser in existence will either reject this or silently misplace the farm. The fix is a single character substitution — automatic, instant, and applied across every affected field in the file.
• Latitude and longitude swapped. A farm in Honduras arrives with coordinates that place it somewhere in the Atlantic Ocean. The fix is a bounds check against the expected country bounding box, followed by a coordinate swap if the reversed values fall within bounds. Detectable and correctable without human input.
• Whitespace in identifiers. Farm IDs arrive as "SAM -013" instead of "SAM-013". The space breaks database imports and traceability links. Stripping and normalising whitespace is trivial.
• Non-standard tags in KML files. A KML file uses <n> instead of <name> — a typo in the export template that breaks every standard KML parser. Replaceable automatically once the pattern is identified.
• Typos in field names. A column arrives labelled Longuitud instead of Longitud. Not a data error — a labelling error. Detectable via fuzzy matching against known field name patterns.
• Missing or inconsistent coordinate system declarations. Files that omit the CRS declaration but contain coordinates clearly in WGS84 range can be normalised without ambiguity.

Together, these account for roughly 70–80% of the errors we see in practice. They are invisible to the human eye, fatal to automated pipelines, and fixable in milliseconds.

What cannot be fixed automatically

The remaining 20% are not formatting problems. They are data collection problems. No software can fix them, because the correct answer does not exist in the file.

• Missing coordinates. A farm record exists in the database with a name, an ID, and an area — but no coordinates. The field is empty or null. There is no way to infer where the farm is. Someone needs to go back to the farmer and collect the location.
• Point geometry for farms over 4 hectares. EUDR requires polygon data for farms larger than 4 hectares. A GPS point — a single lat/lon coordinate — does not satisfy this requirement. The polygon has to be mapped in the field. This is not a data error. It is a data gap. It can be flagged clearly, but not corrected remotely.
• Coordinates that pass bounds checks but are implausible. A farm in Colombia has coordinates that fall within Colombian territory — so the bounds check passes — but the location is in the middle of a city, or on a mountainside at 4,000 metres where no coffee grows. Detecting this requires cross-referencing against land use data such as the EU Observatory on Deforestation or Global Forest Watch. It cannot be resolved automatically. It requires field verification.
• Duplicate farm IDs mapping to different locations. Two records share the same farm ID but have different coordinates. Which one is correct? Without additional context from the supplier, there is no way to know. Both are flagged, both require clarification.

Why this distinction matters

The difference between a fixable error and an unfixable one changes what you do next.

For unfixable errors, the answer is: flag them with a precise description of what is wrong and what the supplier needs to provide. A clear error report is the difference between one round of supplier follow-up and three.

TraceBean handles both. Auto-fixable errors are corrected and documented. Unfixable errors are flagged with enough detail that the supplier knows exactly what to do — not just "there is a problem" but "farm SAM-013 has no coordinates — please collect GPS location and resubmit."