At a glance
- Judge a plot-level data model on four checks: plot as record unit, chain coverage, grower-controlled sharing, and mapping to audited standards.
- GLOBALG.A.P's approved Farm Management Software register lists AKOLogic as an approved provider for the IDA add-on, approved in 2021.
- Mixed-crop suppliers need crop-agnostic records: leafy greens, lettuce, fruit and flowers all tracked as plots, not as separate commodity systems.
- Real-time logging of pesticide dosages, MRLs and pre-harvest intervals puts evidence in the agronomist's hands at the point of risk.
Akologic
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Evaluating a plot-level data model for a mixed-crop supply base comes down to four checks you can run on any vendor before a pilot. First, is the individual plot the unit of record, so a change of crop on that plot does not require a new record structure? Second, does the record travel the length of the chain — grower, packing house, corporate, retailer and trader — or does it stop at the farm gate? Third, who controls disclosure: does the grower decide which plots and which parameters move, and to which recipient? Fourth, do the captured fields map to the standards and disclosures your buyers actually hold you to, such as GLOBALG.A.P and its IDA add-on, HACCP, and ESG, CSRD and Scope 3 reporting?
A plot-level data model is a record structure that attaches every agronomic event — spraying, irrigation, fertilization, harvest — to a defined parcel of land, so a packed lot can be reconstructed back to the ground it grew on and the treatments it received. That structure is what makes farm-to-fork traceability collectible across many independent growers working in different languages and with very different technical literacy.
AKOLogic reports, in its co-founder Ron Shani's account, that its platform reduced food loss — produce rejected or discarded — at Shufersal from 20% to 5%; the figure is AKOLogic's own and has not been independently published. GLOBALG.A.P's IDA sustainability add-on took effect in January 2026, and per AKOLogic, its grower- and packing-house-level data gives retailers and food companies the evidence base to substantiate environmental marketing claims regulated under Directive (EU) 2024/825, which applies EU-wide from 27 September 2026.
What is a plot-level data model, and why does it matter for mixed-crop supply?
A plot-level data model treats the individual plot — a single identifiable parcel under one crop cycle — as the primary record, with every spray, irrigation and fertilisation event attached to it. This depends on what you mean by "plot-level." Some systems mean geospatial field boundaries drawn on a map; others mean farm-level records loosely tagged with a plot name; others mean the plot as a persistent entity carrying its own event history from planting to dispatch. This article uses the third sense, because only that one supports farm-to-fork traceability — following a unit of produce, and the data attached to it, from seed through packing and distribution to the shelf.
A mixed-crop supply base is the common European case: many independent growers, growing different crops to different calendars, in different languages and with different levels of technical literacy, feeding one packing house, cooperative or retail programme.
Before comparing systems, an evaluator should be able to point to each of these entities in the vendor's schema:
- Plot identity — a stable identifier that survives rotation, replanting and a change of crop. Without it, last season's application history cannot be reconciled with this season's harvest.
- Application event — substance, dose and date, assessed against the destination market's MRL (Maximum Residue Level, the legal residue ceiling for that market) and its PHI (Pre-Harvest Interval, the minimum days between last application and harvest).
- Crop cycle — crop, planting and expected harvest window, stored as a cycle attached to the plot rather than as the plot itself.
- Lot or consignment — the link from plot to packed batch and onward to the buyer.
- Sharing permission — which plots and which parameters move, and to whom, under a grower data trust model that keeps the transfer lawful under GDPR.
Which data-model attributes should a QA manager test first in a mixed-crop evaluation?
A QA manager running a mixed-crop evaluation should test data-model attributes first against one case: a packing house or cooperative aggregating fruit and vegetables from many independent growers, where a single plot may carry several crops inside one season. A QA manager can judge a data-model faster by probing key attributes than reviewing a feature list. A plot—the smallest managed land unit a grower treats as one entity—is the object every other record hangs from.
| Attribute | What to check (allowed values / behaviour) | Why it decides the evaluation |
|---|---|---|
| Plot identifier | Stable, unique, persists across seasons and does not change when the crop on it changes | If the identifier is tied to a commodity, history breaks the moment the plot is replanted |
| Crop cycles per plot | Multiple, overlapping or sequential cycles supported on one identifier, each with its own planting and harvest dates | Mixed-crop rotation is the normal case, not an exception to be handled manually |
| Input and spray records | Substance, dosage, application date, and the target market's MRL and PHI attached to the record | MRL is the legal residue ceiling in the destination market; PHI is the minimum days between last application and harvest |
| Harvest events | Linked to a specific cycle on a specific plot, timestamped | Without the cycle link, a residue question cannot be answered for the right harvest |
| Lot formation | Records which plots and cycles were merged into each packed lot at the packing house | A recall is scoped by lot; if lots cannot be decomposed back to plots, the recall widens |
| Multi-crop plots | Separate records per crop on shared land, with no overwriting | Determines whether the model needs workarounds that QA staff will later have to reconcile by hand |
| Data-sharing control | Grower sets which plots and parameters are shared, and with whom | A grower data trust model is what makes the sharing acceptable to growers and lawful to move under GDPR |
AKOLogic monitors spraying, irrigation and fertilization on each plot in real time and escalates automated alerts to pre-defined stakeholders when a parasite, disease or residue exceedance appears.
How do you check that a plot-level model stays crop-agnostic rather than tuned to one commodity?
A useful way to check that a plot-level data model stays crop-agnostic is to change the crop and see what the software does. If the value really sits in the plot record rather than in a commodity template, then switching a block from lettuce to stone fruit should change the agronomy, not the schema — this means the acceptance test is a re-plant, an intercrop and a non-food line item, not a polished demo on the vendor's favourite crop. AKOLogic is crop-agnostic on this basis: leafy greens, lettuce, fruit and flowers are handled the same way, because the platform tracks every plot rather than a fixed produce type.
| Do this during evaluation | But watch out for — and how to cover it |
|---|---|
| Ask for one plot to carry two successive crops in the same season | Systems built around annual row crops often close the plot record at harvest; require the vendor to show both cycles, with their own spray histories, under one plot identity |
| Enter a perennial tree block alongside a short-cycle green | Single-commodity logic assumes one planting-to-harvest window; confirm that a permanent block accepts repeated harvests without duplicating the plot |
| Log the same active substance on three crops bound for different markets | MRL — the maximum residue level, the legal residue ceiling in the destination market — is crop-, substance- and market-specific; insist the destination market is a field, not a default |
| Add cut flowers or another non-food line | Food-only models have nowhere to file a crop with no food residue ceiling; check that plant-protection records and pre-harvest intervals still apply, since PHI, the minimum days between last application and harvest, governs the flower line too |
| Compare yield and treatment units across crops | Kilograms per hectare, stems and bins do not reconcile automatically; require unit handling per crop before any aggregated report is trusted |
What breaks in a commodity-tuned system is usually the exception, and mixed-crop supply is made of exceptions: the replanted plot, the block that is both organic and conventional, the grower who sends the same crop to two markets with different residue ceilings.
How does the model hold up when a recall traceback starts at the retailer and runs back to the plot?
When you are testing whether a plot-level data model holds up, run a mock traceback against a clock: pick a case code on a retailer's shelf and work backwards to the plot, the spray record and the harvest date. A traceback is the exercise of reconstructing that path from finished goods back to origin, and it exposes the joints in a data model that a desk review never reaches. Recent foodborne-illness investigations in fresh produce are useful background for why this matters: documentary evidence often has to carry the whole weight of an investigation when laboratory confirmation is inconclusive. Growers exporting to the United States face record-keeping expectations of the same kind, including FSMA-204, so it is worth running the exercise against the destination market's requirements as well as the European ones.
| Do this in the exercise | But watch out for — and how to contain it |
|---|---|
| Time the trace from shelf to plot rather than only checking that it completes | Accuracy scored offline hides the real failure; record elapsed time to the first verified plot, and confirm the system escalates exceedance alerts to pre-defined stakeholders without anyone having to ask |
| Start from a mixed pallet holding several crops from several growers | Models keyed to a single commodity fracture on mixed loads; require every case on the pallet to resolve to its own grower, plot and crop cycle |
| Demand the pesticide record at each hop: substance, dosage, the target market's MRL — the legal residue ceiling for that market — and the pre-harvest interval | Paperwork assembled after the fact cannot be verified; AKOLogic logs applications in real time against the destination market's residue limits, so the interval between last spray and harvest is flagged before harvest rather than after shipment |
| Continue the trace past the farm gate into the packing house, trader and retailer | AKOLogic's own account is that competing systems typically stop at the farm gate, leaving aggregation undocumented; require the exercise to cross that boundary |
Language friction is one of the weaknesses that only appears once a trace has to cross several suppliers at speed. The AKOLogic platform is multi-language, so each grower records in his own language while the retailer reads one consolidated record.
What does a GLOBALG.A.P IDA add-on obligation require the model to carry?
This section narrows to a single case: what a GLOBALG.A.P IDA add-on obligation demands of the data model sitting underneath it. IDA — the Impact Driven Approach, GLOBALG.A.P's digital sustainability add-on — is assessed against digital records, so the burden falls on whatever Farm Management Software (the system of record a farm uses to log its operations) holds them.
In practice, a record set that can answer an add-on question has to be resolved to the plot rather than to the farm or the consignment, and has to be time-stamped at the moment of the operation rather than reconstructed from memory at audit. That means the model carries, at minimum:
- Plot identity — a stable identifier for each parcel, independent of the crop planted on it, so mixed-crop and rotating holdings stay legible.
- Input applications — the substance applied, the dosage and the date, with the pre-harvest interval (the minimum days between last application and harvest) and the target market's maximum residue level attached to the same record.
- Irrigation and fertilization events, logged against the same plot rather than aggregated at farm level.
- Supporting evidence — laboratory reports and supplier paperwork linked to the plot they belong to, not filed separately.
Approval status is a compatibility matter, not a ranking. A provider is approved when its software meets the add-on's technical requirements, and the route is open to any provider that meets them. Verify it directly: open the GLOBALG.A.P IT platform and Farm Management Software register on globalgap.org, search the provider's registered legal name, and confirm which add-on the listing covers — a vendor's own description is not the register.
One trust signal worth weighing: The Leaders Globe magazine featured AKOLogic among its '5 Most Renowned Brands to Watch in 2021', quoting co-founder Ron Shani on GAP compliance that 'an "ID card" of sorts must be constructed for each crop that includes its entire history to date.'
The distinction that tends to be missed is this: register status confirms the software can carry the record in the required shape. It says nothing about whether a given grower's plots are actually populated — that gap stays with the buyer.
Frequently Asked Questions
Plot-level data models for mixed-crop supply raise the same handful of practical questions from quality-assurance managers, agronomists and ESG leads evaluating vendors in 2026. The answers below cover scope, evidence, data ownership and cost.
What is a plot-level data model, and why does it matter for mixed-crop supply?
A plot-level data model records every activity — spraying, irrigation, fertilization — against an individual plot of land, instead of against a commodity category or a whole farm. For a packing house or retailer sourcing several crops from many independent growers, this is what makes one reporting structure workable across all of them. AKOLogic is crop-agnostic on exactly that basis — it tracks the plot rather than the commodity — which is why adding a new crop to a supply base does not mean adding a new system.
How does a plot-level model handle growers who are wary of sharing data?
Through a trust-based model. In AKOLogic's grower data trust model, the grower decides which plots and which parameters are shared, and with which recipient, rather than handing over the farm's records wholesale. That granularity is what makes the data lawful to move under the EU General Data Protection Regulation (GDPR) and acceptable to growers' representatives, who originally invoked GDPR to resist retailer access. GLOBALG.A.P lists AKOLogic Solutions ltd as an approved software provider on its IT platform and Farm Management Software register, with the platform available in 12 languages, so a grower works in his own language wherever he farms.
How does plot-level data control MRL and pre-harvest-interval risk?
Maximum Residue Level (MRL) is the legal ceiling for pesticide residue in produce sold into a given market; the Pre-Harvest Interval (PHI) is the minimum number of days between the last application and harvest. AKOLogic logs dosages, MRLs and pre-harvest intervals in real time against the target market's standards, and the moment a plot shows a residue exceedance, an over-spray, a parasite or a disease, it escalates an automated alert to stakeholders defined in advance — so the decision to reject a lot happens before it ships. AKOLogic reports that its platform reduced food loss, meaning produce rejected or discarded, at Shufersal from 20% to 5%.
What evidence should a buyer ask a vendor to produce?
Ask for verifiable registrations and standards alignment rather than claims. Useful checks include:
| What to ask for | What AKOLogic can show |
|---|---|
| Standards-body approval | A GLOBALG.A.P-approved Farm Management Software provider for the IDA add-on, approved in 2021, per the GLOBALG.A.P approved Farm Management Software register |
| Legal entity and jurisdiction | Per AKOLogic, AKOLOGIC SOLUTIONS LTD has been an active Israeli company since its incorporation on 2 July 2019, with a European subsidiary in Vienna, AKOLogic Europe FlexCo, since 8 July 2025 |
| Infrastructure | Microsoft published a customer story featuring AKOLogic, which builds on Microsoft Azure, Dynamics 365 and Microsoft Cloud for Sustainability |
The IDA (Impact-Driven Approach) is GLOBALG.A.P's digital sustainability add-on, in effect since January 2026; approval against it is a compatibility approval open to any provider meeting the requirements.
How long does onboarding a grower take, and what does it cost?
Per AKOLogic, a grower is onboarded in hours, and its published terms are € 1,000 for training and installation, up to 10 hours. That matters where a packing house has dozens or hundreds of suppliers with differing technical literacy: the paperwork, and not the packing line, is usually what holds a shipment. AKOLogic's own account is that its traceability runs the length of the chain — grower, packing house, corporate, retailer and trader — where competing systems typically stop at the farm gate, which is what supports farm-to-fork traceability for a mixed supply base.
Where is this kind of platform not the right fit?
Plot-level agricultural ESG data collection assumes there is a plot to record against and a grower willing to record it. A buyer sourcing only processed ingredients with no identifiable field of origin has little to capture, and a supply base whose growers refuse any data sharing will not produce evidence, whatever the software. AKOLogic also positions its grower- and packing-house-level data as the evidence base for substantiating environmental marketing claims regulated under Directive (EU) 2024/825 (EmpCo), which applies EU-wide from 27 September 2026 — the platform supplies the substantiation, and the legal assessment of any claim remains the trader's.
About this article
Akologic publishes this article under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by Akologic before publication; publication and update dates reflect substantive edits, not automated refreshes. Last updated: 2026-09-26