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Every dashboard above the field level (Estate Group, Regional Forecast, National Overview) is a rollup of the same per-field data described in the Data Model and Risk Model. No new sensors, no new events, no new rule cards. Only aggregation. This page defines the aggregation contract: what each metric means at each layer, how it is combined, and what confidence you can attach to the answer.
If a metric is not defined here for the layer you care about, it is not shown on the dashboard. We refuse to fabricate rollups from missing data.

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Layers

Higher layers never query field-level data live. They read pre-computed rollups written by a scheduled job. This keeps executive dashboards fast and predictable, and it makes the aggregation reproducible for audit.

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Rollup categories

Each metric on a higher-layer dashboard belongs to one of five categories. The category decides how it aggregates.
Never average an average. NDVI at estate level is sum(NDVI_field × area_field) / sum(area_field), never mean(NDVI_field). Mixing the two silently biases small fields upward.

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What rolls up cleanly, and what does not

Rolls up cleanly

  • Area totals
  • Area-weighted indices (NDVI, NDMI, NDRE)
  • Health distribution buckets
  • Alert counts by severity and category
  • Production forecast (yield-per-ha × area)
  • Verification counts and acceptance rate

Does not roll up

  • Individual rule card severity (a field is HIGH; the estate is not)
  • Scout task assignees (person-level, not areal)
  • Specific VRA prescriptions (field-specific)
  • Named recommendations (rule cards fire per field, not per estate)
Everything in the right column stays at field level. Higher layers see counts and rates of these items, not the items themselves. An estate dashboard shows “12 fields at HIGH blast risk”, not a merged “estate blast risk” (which would be meaningless).

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Forecast layer

Regional Forecast and National Overview add a forecast on top of the rollup. This is where satellite phenology, weather, and historical yield combine to project production forward. The forecast is not a single point. Every projection carries:
  • Central estimate - the median of the yield model output
  • Confidence band - P10 to P90 range
  • Confidence label - High, Medium, Low, based on the drivers listed below

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Confidence degradation

Confidence is highest at the field level and drops as you climb. Reasons: The Confidence chip on every regional and national card is the minimum of its input chips. One weak input drags the whole answer down. This is intentional. Executives should see the weakest link, not an optimistic average.

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Refresh, freshness, and lag

Every rollup card shows a Last updated timestamp and a Data through timestamp. They are not the same.
  • Last updated - when the aggregation job wrote this rollup
  • Data through - the newest field observation that was included
If a rollup’s Data through is more than one refresh cycle behind expected, the card renders a Stale badge. Common causes:
  1. Persistent cloud cover blocking imagery for a share of area (“cloud debt”)
  2. Fields with paused monitoring (billing, subscription, or manual pause)
  3. Rule card upgrade in progress: fields still on old rule versions are excluded until they migrate
Stale rollups are always visible; they are never hidden. Hiding them would let a stakeholder mistake old data for the current state.

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Privacy and access rules

Aggregation does not relax field-level access rules. If a viewer cannot see field A directly, field A’s contribution is:
  • Included in extensive sums and area-weighted means (the viewer sees the aggregate number)
  • Excluded from any drilldown, list, filter, or export that would reveal per-field identity
  • Excluded from any bucket smaller than the k-anonymity threshold (default k = 5). If a regional slice contains fewer than 5 fields, that slice collapses into “Other” until the count is met.
This is how a National Overview can show correct national totals while keeping any individual estate’s performance private from other estates.

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Reproducibility

Every rollup card can be explained: click any number and the platform shows:
  1. The list of child rollups that fed into it
  2. The aggregation rule that was applied (sum, area-weighted mean, etc.)
  3. The rollup job’s run timestamp and the input Data through cutoff
  4. Any children that were excluded and why (stale, paused, k-anon threshold, missing phenology)
The same explanation is embedded in every Verification bundle exported at the estate, regional, or national layer. Agencies and auditors reproduce numbers from the same inputs.