Agriculture and food data look deceptively simple from the outside. A kilogram of wheat, a liter of milk, a ton of tomatoes: these sound like concrete things. But when you start working with life cycle assessment data, especially when comparing or computing food impacts across datasets, you quickly discover that the numbers are not as stable as they appear.

I have spent a lot of time struggling with agricultural and food datasets in LCA. Not because the data is useless, but because it is complex, layered, and easy to misunderstand. The same product can have different impact values depending on geography, modeling choices, elementary flow mapping, precision, allocation rules, and impact assessment method versions.

Over time, I have learned that variance in LCA results is not always an error. Sometimes it is a signal that the underlying assumptions are different.

Regionalization Matters

One of the biggest sources of variation is regionalization.

Agriculture is deeply local. Crop yields, irrigation needs, fertilizer practices, electricity mixes, soil emissions, land use, climate, and supply chains vary significantly from one region to another. A tomato grown in a heated greenhouse in northern Europe is not the same environmental system as a tomato grown in open fields in southern Europe. Beef, rice, coffee, soy, milk, and wheat can all change substantially depending on where and how they are produced.

This creates a challenge when datasets contain regional, national, continental, or global-average processes. If one dataset uses a global average and another uses a country-specific process, the results may differ even if both are "correct" within their own modeling frame.

The lesson: before comparing values, check the geography. A mismatch between GLO, RoW, Europe, and a specific country can explain a lot.

Small Values Are Surprisingly Fragile

Another issue I have run into is precision, especially for very small values.

In food LCA, many flows have tiny values: trace emissions, pesticide residues, micronutrient-related flows, land transformation fractions, or small upstream contributions. These values may look irrelevant individually, but when they are rounded, truncated, converted, or aggregated, they can behave strangely.

For example, a value stored with high precision in one system may become 0.0000 in another export. A small methane or nitrous oxide flow may be rounded differently. A tiny elementary flow can become important if its characterization factor is large.

This is especially painful when computing impacts against methods like Environmental Footprint 3.1, where small mapped flows can still influence categories such as toxicity, eutrophication, climate change, or resource use.

The lesson: zeros are not always true zeros. Sometimes they are just lost precision.

Mapping Against EF 3.1 Is Harder Than It Looks

A major source of frustration has been mapping inventory data against EF 3.1.

At first glance, applying an impact method sounds mechanical: take inventory flows, match them to characterization factors, multiply, aggregate. In practice, the difficult part is often the matching.

Flow names may differ. Compartments may differ. Subcompartments may differ. CAS numbers may be missing or inconsistent. One dataset may use an older nomenclature, while EF 3.1 expects another. Some flows map cleanly; others require interpretation. Some should not be mapped at all unless the compartment and context are right.

This can create large differences between computed impact results and reference results. The issue may not be the arithmetic. It may be that "ammonia, air" was mapped correctly, while another flow with a similar name was mapped incorrectly, duplicated, ignored, or assigned to the wrong compartment.

The lesson: impact assessment is only as reliable as the flow mapping. Always audit unmatched, ambiguously matched, and multiply matched flows.

Agriculture Has Modeling Choices Everywhere

Beyond geography and mapping, agricultural LCA contains many methodological choices that can shift results:

• Allocation between co-products, such as milk and meat, oil and meal, grain and straw
• Treatment of biogenic carbon
• Land use and land use change assumptions
• Fertilizer emission models
• Manure management assumptions
• Irrigation and water scarcity regionalization
• Organic versus conventional production systems
• Yield assumptions
• Farm-gate versus retail or consumption boundaries
• Inclusion or exclusion of packaging, processing, storage, transport, cooking, and waste

These are not small details. They define the system being measured.

Two datasets may both describe "1 kg of food product," but one may stop at farm gate while another includes processing and packaging. One may allocate burdens economically, another physically. One may include land use change, another may not. The numbers can diverge before anything is technically wrong.

The lesson: the product name is not enough. You need the system boundary and modeling assumptions.

Reference Units Can Be Tricky

Food data often moves between units: kilograms of fresh product, dry matter, protein content, edible portion, cooked weight, raw weight, market weight, or economic value.

This creates subtle but serious comparability problems. A dataset for "1 kg maize grain" is not necessarily comparable to "1 kg maize at farm," "1 kg dry maize," or "1 kg maize meal." Moisture content alone can change the interpretation. For animal products, edible yield and carcass allocation can complicate things further.

The lesson: always check what the reference flow actually represents.

The Data Is Not Broken. It Is Contextual.

The biggest lesson I have learned is that agricultural LCA data should not be treated as a single universal truth. It is contextual data produced through methodological choices.

Variance does not automatically mean one dataset is wrong. It can mean the datasets are answering slightly different questions.

That said, this does not mean "anything goes." Good LCA work requires transparency, traceability, and careful interpretation. When results differ, the task is to understand why:

• Is it geography?
• Is it precision?
• Is it flow mapping?
• Is it allocation?
• Is it system boundary?
• Is it impact method version?
• Is it unit conversion?
• Is it a missing or unmatched flow?

Once you start asking those questions, the variance becomes less mysterious.

Conclusion

Working with agriculture and food datasets in LCA has taught me that visibility is essential before transformation.

Before mapping flows, applying EF 3.1 characterization factors, normalizing units, regionalizing processes, or aggregating results, it is important to first understand what is actually in the dataset. Simple first-stage data analysis can reveal many of the issues that later become difficult to debug: missing flows, unexpected zeros, different levels of precision, regional inconsistencies, unit mismatches, duplicate mappings, and unusual outliers.

In other words, the first step should not be transformation. It should be observation.

For food and agriculture data, this is especially important because variance is not always a mistake. It can come from real differences in geography, farming systems, modeling assumptions, or methodological choices. Without enough visibility into the raw data, it becomes very easy to "fix" something that was not broken, or to hide an important signal through aggregation.

A good LCA data workflow should therefore start with transparency: inspect the dataset, profile it, compare distributions, identify gaps, and understand the assumptions before applying complex transformations. Only then can mapping, computation, and interpretation be done with confidence.

The lesson is simple: before trying to make agricultural LCA data consistent, make it visible.