Every agricultural operation that receives its first ESG questionnaire hits the same wall: questions asking for data that simply doesn't exist in any file, spreadsheet, or shoebox on the farm. Soil organic carbon levels. Scope 3 emissions from purchased feed. Biodiversity baseline surveys. Methane per livestock unit.
The instinct is either to panic, to leave every unknown field blank, or to fill them with numbers that sound plausible. All three responses are wrong. There is a better way, and it starts with understanding that “missing data” isn't a single category. What's missing on your farm falls into four distinct buckets—and each one has a different, honest response.
The Four Categories of Missing Farm Data
Before you answer a single question, walk through the questionnaire and sort every gap into one of these categories. It changes how you respond to each one.
Category 1: Data That Exists but Hasn't Been Gathered
This is the largest category for most farms—and the most encouraging one. The information exists somewhere in your operation; it just hasn't been pulled together for ESG purposes.
- Fuel receipts sitting with your accountant. Your diesel consumption is recorded in invoices from your fuel supplier. It's in your farm accounts. Nobody has summed it into an annual total and converted it to CO2e—but the raw data is there.
- Fertilizer records in your spray logs. If you keep spray records (and you're legally required to in most jurisdictions), your nitrogen application data exists. It just needs extracting and totalling.
- Livestock numbers in movement records. Your herd/flock numbers are in your movement records, your assurance audits, and your vet's files. Annual averages can be calculated from these.
- Electricity use on your utility bills. Total kWh is printed on every invoice. It just needs compiling for the reporting period.
- Training records in filing cabinets. Sprayer certificates (PA1/PA2), chainsaw qualifications, first aid training—these exist as certificates somewhere on the farm.
The honest response for this category: provide the data. It takes time to collect, but the information is available. Most farms find that 50-70% of what a questionnaire asks for falls into this bucket.
Category 2: Data That Has Never Been Tracked
This is the genuinely missing data. Your farm has never measured it because nobody asked before and you had no reason to.
- Biodiversity baseline surveys. You may know you have hedgerows and a pond, but you've never formally documented habitat features or species counts.
- Soil organic carbon measurements. Standard soil tests cover pH, P, and K. Organic matter or organic carbon testing is less common and often not included in routine analyses.
- Scope 3 emissions from purchased feed. You buy compound feed or straights. You know the tonnage. But the carbon footprint of that feed's production? That's data your feed supplier would need to provide.
- Water consumption volumes. If you're on mains water, bills show volume. If you abstract from a borehole or watercourse, you may have an abstraction license but no meter.
The honest response: acknowledge the gap clearly. “We do not currently track soil organic carbon levels. We plan to include organic matter analysis in our next soil testing cycle.” This is far better than a blank field or a fabricated number.
Category 3: Data That Is Genuinely Not Applicable
ESG questionnaires are written for general agricultural operations. Many questions simply don't apply to your specific farm type.
- Crop farms with no livestock: Livestock methane questions, manure management, animal welfare policies—all genuinely not applicable.
- Pastoral operations with no arable: Pesticide application data, crop rotation details, harvest yield records—not applicable.
- Dryland farms with no irrigation: Water abstraction volumes, irrigation efficiency metrics—not applicable.
- Family operations with no employees: Workforce diversity statistics, employee grievance mechanisms, HR policies—not applicable in the traditional sense.
The honest response: mark the field “Not applicable” with a brief explanation. “Not applicable—arable operation with no livestock.” This shows you understood the question and made a deliberate assessment, not that you skipped it.
Category 4: Data You Can Estimate Using Accepted Methods
This is the category many farms overlook—and it's the one that transforms a patchy response into a credible one. You have enough information to calculate a reasonable estimate using published methods.
- Livestock methane. You know your animal numbers by type. You've never calculated methane emissions. But the IPCC publishes Tier 1 default emission factors per head by species and region. Multiply and you have a defensible estimate.
- Fertilizer N2O. You know how much nitrogen you applied. National inventory guidelines provide emission factors per kg of N applied. The calculation is straightforward.
- Diesel CO2. You know your annual diesel consumption in litres. The emission factor (2.68 kg CO2 per litre of diesel) is standard.
- Electricity CO2. You know your kWh. Your country's grid emission factor converts this to CO2e.
The honest response: provide the estimate and state your methodology. “Estimated using IPCC Tier 1 default factors. Based on an annual average of 180 dairy cows.” Buyers and auditors accept this approach because it uses internationally recognised methods.
Accepted Estimation Methods for Farm Emissions
When you don't have directly measured data, these are the estimation approaches that ESG frameworks and buyers recognise as credible.
IPCC Tier 1 factors per livestock head. The Intergovernmental Panel on Climate Change publishes default methane emission factors for every livestock type: dairy cattle, beef cattle, sheep, pigs, poultry, goats. These vary by region (Western Europe, Eastern Europe, etc.). Tier 1 is the simplest level—a single factor per animal per year. It's not precise for your specific herd, but it's the accepted starting point.
National inventory factors for fertilizer N2O. Each country publishes national greenhouse gas inventory reports that include emission factors for nitrous oxide from applied nitrogen. These account for direct emissions (N2O released from soil after fertilizer application) and indirect emissions (from leaching and volatilisation).
Regional average fuel consumption per hectare. If your fuel records are incomplete, industry benchmarks exist for diesel use per hectare by crop type and farming system. These are less accurate than your actual records but better than zero.
Farm benchmarking data. Organisations like AHDB in the UK, Teagasc in Ireland, and equivalent bodies across Europe publish average resource-use figures for different farm types. These provide reasonable estimates when farm-specific data is unavailable.
The hierarchy is clear: measured data is best, farm-specific estimates are next, regional averages are acceptable, and acknowledged gaps are better than fabricated precision.