Land pools and farming income for the East of England regenerative transition. Hectares through to revenue change, rebuilt from the workbook and checked against Phase 1.
7 · Inputs
Grouped the way the workbook groups them. Each value shows the cell it came from and the workbook's own wording on the source. No number is typed anywhere else in the model.
The model reads these, but none is an input a calculation turns on. Regional Defra figures are here so a derived number can be checked against where it came from; the size bands below the one we recruit from are read and then filtered out.
Explore
The whole model in one view. Open any part and keep going until you reach a number someone typed into the workbook. Each level shows the arithmetic, and the parts always agree with the number above them. Parts not built yet are shown greyed, so a gap reads as a gap.
1 · The funding stack
Three pools, and every hectare is in exactly one. Each income block earns on some and not others. Where it cannot, the reason is stated, because that reason is usually the additionality argument a buyer or regulator will ask about. Click any cell.
Select a cell to see how that outcome is sold, or why it cannot be.
6 · The chain
Every calculation in order. Each one names the assumptions that enter at that point, what it works out, and where its answer goes next. The arrows between assumptions and calculations are read from the running code, so this cannot disagree with what the model does.
2 · Land pools
A pool is a kind of land; a cohort is a group of farms joining in a year. They are separate: cohorts scale the pools over time, they do not change them. The pools split the whole farmed area and use the same names as the funding stack, so a hectare's pool decides what it can earn. The check column must be nil.
| Cohort | Farmed area | Arable, in production | Fallow arable | Non-arable | Of which committed | Check |
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3 · Farming income
Three things change revenue between the conventional baseline and the transition farm. Phase 1 counted only the first: it fixed the crop mix and assumed the break crop earned nothing.
4 · Landscape
Revenue change per hectare, multiplied by each cohort's surviving hectares. The trough is what the financing has to cover before the landscape turns positive.
2.1 · Scaling over time
Cohorts add up as they join, then shrink each year at the dropout rate, because the model does not replace farmers who leave. The peak is the year the last cohort enters.
8 · Against Phase 1
Phase 1 is the Deloitte and Palladium work. Treat it as a benchmark, not as correct. Every difference is written down.
Base-case yield change is identical to Phase 1: wheat −10%, −5%, 0, +5%, +10% then flat; the other crops −2%, +2.25%, +6.5%, +10.75%, +15% then flat. Low and high scenarios are new.
Phase 1 ran five equal cohorts of 49,835 ha entering 2026–2030. This model runs four ramping cohorts from 2027, sized by farm counts, because recruitment builds up rather than arriving in equal slices.
Year-on-year hectares are therefore not directly comparable.
Phase 1 derived farm counts by multiplying total farms by the share of hectares on large holdings (86.65%) rather than the share of holdings that are large (33.7%), so its 1,380 / 265 split is not a farm count. This model types farm counts and derives hectares from the Defra size bands.
Phase 1 put 10% of area into fallow and assumed no revenue from it. This model grows beans and peas on 20% of the transition rotation and sells them.
More generous than Phase 1, but still negative against the conventional baseline: beans and peas earn less per hectare than the wheat they replace. That is the crop mix step in the bridge above.
Phase 1 uses 4.4 t/ha (spring oats); this model uses 6.2 t/ha (winter oats). The workbook flags the variety difference but does not settle it. Oats are 3.1% of the rotation, so the effect is small — but they are not the same crop.
Phase 1 explicitly excluded price premiums, carbon, biodiversity net gain and every alternative revenue stream. Its £130.6m gap is the gap with no commercial revenue at all. This model exists to close part of that gap with contracted premiums and nature revenue, so the two figures must not be set against each other until the exclusions are matched.