diff --git a/.claude/skills/model-calibration/SKILL.md b/.claude/skills/model-calibration/SKILL.md
index e56e5754..e4830215 100644
--- a/.claude/skills/model-calibration/SKILL.md
+++ b/.claude/skills/model-calibration/SKILL.md
@@ -1,6 +1,6 @@
 ---
 name: model-calibration
-description: Run, refresh, or diagnose the model's calibration pipeline (feed -> food_waste -> food_demand -> cost -> stability) that produces the per-config artefact sets under `data/curated/calibration/<source>/` (the default set is git-tracked). Covers the dependency order, the `tools/calibrate` wrapper, realistic runtime expectations, when each kind of upstream change forces a re-run, and how to diagnose the most common failure mode: a hidden supply/demand mismatch that inflates the production-stability L1 cost. Use whenever calibration is relevant -- the user touches inputs/build logic that feed the calibration solves, calibration artefacts look off, or a refresh of the artefacts is needed after a model/data change.
+description: Run, refresh, or diagnose the model's calibration pipeline (feed -> food_waste -> food_demand -> cost -> stability) that produces the per-config artefact sets under `data/curated/calibration/<source>/` (the `default` and `gbd-anchored` sets are git-tracked). Covers the dependency order, the `tools/calibrate` wrapper, realistic runtime expectations, when each kind of upstream change forces a re-run, and how to diagnose the most common failure mode: a hidden supply/demand mismatch that inflates the production-stability L1 cost. Use whenever calibration is relevant -- the user touches inputs/build logic that feed the calibration solves, calibration artefacts look off, or a refresh of the artefacts is needed after a model/data change.
 ---
 
 <!--
@@ -13,8 +13,14 @@ SPDX-License-Identifier: CC-BY-4.0
 
 The default workflow consumes five calibration artefact groups organized
 in per-config *sets* under `data/curated/calibration/<source>/`, selected
-by the `calibration.source` config key (the `default` set is
-git-tracked). Each is produced by a dedicated validation-mode solve and
+by the `calibration.source` config key. Two sets are git-tracked:
+`default` (fit against the anchoring-off baseline diet of the health-off
+default config) and `gbd-anchored` (fit against the GBD-anchored diet;
+consumed by the health-enabled configs gsa, gsa_fixed_diet, validation
+and the doc configs). `tools/calibrate` resolves the base config's
+diet.anchor_groups_to_gbd sentinel once and pins it across all five
+steps, and provenance stamps record the *resolved* anchoring. Each
+artefact group is produced by a dedicated validation-mode solve and
 absorbs a specific class of residual mismatch so that ordinary solves
 don't have to. Without these files in place, production-stability,
 costs, and food/feed accounting drift from observed 2020 reality.
@@ -168,7 +174,7 @@ sequential (each Broyden iteration depends on the previous solve).
 
 ## Output landing zones
 
-- `data/curated/calibration/<source>/*` -- one artefact set per base config, plus its `provenance.yaml` stamp; the `default` set is **git-tracked**. Commit a set together as a refresh; mixed-vintage artefacts are the most common cause of confusing downstream solves.
+- `data/curated/calibration/<source>/*` -- one artefact set per base config, plus its `provenance.yaml` stamp; the `default` and `gbd-anchored` sets are **git-tracked**. Commit a set together as a refresh; mixed-vintage artefacts are the most common cause of confusing downstream solves.
 - `processing/calibration/*` (or `processing/calibration-<source>/*` for non-default bases) -- shared upstream prep, NOT committed.
 - `results/calibration/*` -- per-iteration solve logs, NOT committed.
 - `results/calibration/calibration/deviation_penalty_trace.csv` -- per-iter Broyden trace (per-component lambda, achieved deviations, residual norm). Inspect when stability behaves oddly.
diff --git a/AGENTS.md b/AGENTS.md
index 1dba9424..fcbed709 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -175,6 +175,7 @@ See `workflow/scripts/build_model/__init__.py` for the complete reference.
 - Keep code concise: Prefer simple control flow; fail early on invalid external inputs.
 - Do your best to avoid over-engineering. If you see possibilities for simplifying, suggest improvements (but let the user approve of such drive-by refactors first).
 - Consistent style: Follow existing patterns in nearby files; don’t introduce new paradigms ad hoc.
+- Comments (code and config alike) describe the *current* state as helpfully as possible, and only where something is not self-explanatory. Never write comments that justify a change or contrast with previous behaviour ("previously X", "default flipped to Y", "used to be unconditional") — that history belongs in the commit message.
 - Reproducibility: Use the Snakemake targets below to validate changes; don’t hand‑run ad hoc pipelines unless necessary.
 - No unused imports: The linter removes them automatically; only add imports when adding code that uses them.
 - ASCII-only in code, comments, and docstrings: ruff's `RUF001`/`RUF002`/`RUF003` rules flag ambiguous Unicode look-alikes (`×`, `–`, `—`, `’`, `…`, `Σ`, `≈`, non-breaking spaces, etc.) and block pushes via the pre-push hook. Use plain ASCII substitutes: `*` for multiplication, `-` for dashes, `'` for apostrophes, `~=` for approx-equal, spell out Greek letters. Math notation in docstrings is the most common offender — write `sum over i of a_i * b_i`, not `Σ_i a_i × b_i`.
@@ -327,9 +328,11 @@ pixi run -e dev pytest -v         # verbose output
 
 Five calibrations feed the default workflow. Their outputs are organized
 in per-config artefact *sets* under `data/curated/calibration/<source>/`
-(selected by the `calibration.source` config key; the `default` set is
-git-tracked) and builds depend on them. When upstream data or build
-logic changes materially, regenerate in this order:
+(selected by the `calibration.source` config key; the `default` set --
+fit against the anchoring-off baseline diet -- and the `gbd-anchored`
+set -- consumed by the health-enabled configs -- are git-tracked) and
+builds depend on them. When upstream data or build logic changes
+materially, regenerate in this order:
 
 1. **feed** — `config/calibration/feed.yaml` → `grassland_yield.csv`,
    `fodder_conversion.csv`, `exogenous_forage.csv`,
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 14265aea..aee1b794 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -15,6 +15,28 @@ introduce breaking changes to configuration and outputs.
 
 ## [Unreleased]
 
+### Changed
+
+- The health module is now **disabled by default** (`health.enabled: false`).
+  With health off, the workflow no longer requires the manually-downloaded
+  IHME GBD data and runs end to end without it; a clear startup error is
+  raised if health (or GBD anchoring) is enabled but the data is absent.
+
+### Added
+
+- New `diet.anchor_groups_to_gbd` option that decouples GBD anchoring of the
+  baseline diet's risk-factor food groups from the health module. Defaults to
+  the sentinel `match_health` (follow `health.enabled`); set `true`/`false` to
+  control it independently. Previously anchoring was unconditional. See
+  `docs/current_diets.rst` for a quantitative description of the difference and
+  the refined-grain caveat. The baseline diet feeds calibration, so two
+  artefact sets are now committed: `default` (recalibrated against the
+  anchoring-off default diet) and `gbd-anchored` (the previous GBD-anchored
+  artefacts, consumed by the health-enabled configs via
+  `calibration.source: gbd-anchored`). Provenance stamps record the *resolved*
+  anchoring, and `tools/calibrate` pins the base config's resolved anchoring
+  across all five calibration steps.
+
 ## [0.1.0] - 2026-06-15
 
 First public release of GLADE (Global Land, Agriculture, Diet and Emissions),
diff --git a/config/calibration/cost.yaml b/config/calibration/cost.yaml
index 76f01670..df1d2529 100644
--- a/config/calibration/cost.yaml
+++ b/config/calibration/cost.yaml
@@ -128,6 +128,10 @@ validation:
 water:
   supply_scenario: "current_use"
 
+# NB: diet.anchor_groups_to_gbd is pinned to the base config's resolved value
+# by tools/calibrate (via a config overlay), so disabling health here for
+# solver performance does not change the baseline diet the artefacts are fit
+# against.
 health:
   enabled: false
 
diff --git a/config/calibration/feed.yaml b/config/calibration/feed.yaml
index 7acaefce..abcf40b6 100644
--- a/config/calibration/feed.yaml
+++ b/config/calibration/feed.yaml
@@ -83,5 +83,5 @@ emissions:
 
 solving:
   io_api: "direct"
-  solver: "gurobi"
+  solver: gurobi
   threads: 2
diff --git a/config/calibration/food_demand.yaml b/config/calibration/food_demand.yaml
index aaf60fb1..a140a4a0 100644
--- a/config/calibration/food_demand.yaml
+++ b/config/calibration/food_demand.yaml
@@ -76,5 +76,5 @@ emissions:
 
 solving:
   io_api: "direct"
-  solver: "gurobi"
+  solver: gurobi
   threads: 2
diff --git a/config/calibration/food_waste.yaml b/config/calibration/food_waste.yaml
index 45af8e39..d981bcd0 100644
--- a/config/calibration/food_waste.yaml
+++ b/config/calibration/food_waste.yaml
@@ -74,5 +74,5 @@ emissions:
 
 solving:
   io_api: "direct"
-  solver: "gurobi"
+  solver: gurobi
   threads: 2
diff --git a/config/calibration/stability.yaml b/config/calibration/stability.yaml
index 23d67ddc..12bdb0c0 100644
--- a/config/calibration/stability.yaml
+++ b/config/calibration/stability.yaml
@@ -73,7 +73,7 @@ deviation_penalty:
     generate: true   # trigger the calibrate_deviation_penalty rule
 
 solving:
-  solver: "gurobi"
+  solver: gurobi
   io_api: "direct"
   # Solves run sequentially in the iteration, so give each solve more
   # threads instead of running multiple solves in parallel.
diff --git a/config/default.yaml b/config/default.yaml
index 7d8d5fad..50b93601 100644
--- a/config/default.yaml
+++ b/config/default.yaml
@@ -839,6 +839,25 @@ weight_conversion:
 # --- section: diet ---
 diet:
   baseline_age: "All ages"
+  # Whether to anchor the per-country baseline-diet totals of the health
+  # risk-factor food groups (health.risk_factors, e.g. fruits, vegetables,
+  # red_meat, ...) to GBD dietary-exposure intake instead of the GDD/FAOSTAT
+  # estimate. Anchoring aligns the baseline diet with the same intake basis
+  # the GBD relative-risk curves are calibrated against, so it is required
+  # for attributable-burden numbers to be comparable to GBD; it is otherwise
+  # a modest, mostly distributional change to the baseline diet (see
+  # docs/current_diets.rst). The sentinel "match_health" follows health.enabled, so a
+  # health run anchors and a no-health run does not (and then needs no IHME
+  # GBD data on disk). Set explicitly to true/false to decouple the two.
+  # NOTE: refined "grain" is additionally adjusted by the whole-grain cereal
+  # residual fix whenever whole_grains is anchored -- see docs/current_diets.rst.
+  # CALIBRATION COUPLING: this flag changes the baseline diet, which the
+  # calibration artefacts under data/curated/calibration/ are fit against.
+  # Changing it (directly or via health.enabled) means the `default` artefact
+  # set no longer matches; point calibration.source at the `gbd-anchored` set
+  # (fit with anchoring on) or recalibrate. The provenance check errors on a
+  # mismatch (see docs/calibration.rst).
+  anchor_groups_to_gbd: match_health
   # Foods whose per-country intake is computed directly from FAOSTAT Food
   # Balance Sheet supply rather than disaggregated from GDD/GBD group
   # totals. For each listed food the override sets
@@ -1217,7 +1236,14 @@ commodities:
 
 # --- section: health ---
 health:
-  enabled: true  # Whether to include health costs in the objective function
+  # Whether to include the dietary health-burden module. When false, the
+  # manually-downloaded IHME GBD data is not needed (see docs/health.rst).
+  # IMPORTANT: this also drives diet.anchor_groups_to_gbd (sentinel
+  # "match_health"), so flipping it changes the baseline diet. The git-tracked
+  # calibration artefacts under data/curated/calibration/ are computed against a
+  # specific baseline diet, so after changing this (or anchoring) you must rerun
+  # `tools/calibrate` with matching options (see docs/calibration.rst).
+  enabled: false
   region_clusters: 30
   breakpoint_rel_tol: 0.05  # Max Stage 1 PWL deviation as a fraction of each RR curve's amplitude (5%)
   log_rr_points: 15
diff --git a/config/example.yaml b/config/example.yaml
index e292f23b..1c24ece7 100644
--- a/config/example.yaml
+++ b/config/example.yaml
@@ -27,6 +27,21 @@ name: "example"
 # emissions:
 #   ghg_price: 200  # USD per tonne CO2-eq
 
+# Enable the dietary health-burden module. This requires the manually-
+# downloaded IHME GBD data (see docs/health.rst); when left disabled (the
+# default) none of that data is needed. Enabling health also anchors the
+# baseline diet to GBD intake exposure (diet.anchor_groups_to_gbd defaults to
+# "match_health"); set that key explicitly to decouple the two.
+# With anchoring on, also consume the calibration artefacts fit against the
+# anchored diet (the default set is fit against the anchoring-off diet).
+# health:
+#   enabled: true
+#   value_per_yll: 50000  # USD per year of life lost (0 = evaluate post-hoc only)
+# diet:
+#   anchor_groups_to_gbd: true  # or false to keep the GDD/FAOSTAT-only diet
+# calibration:
+#   source: "gbd-anchored"
+
 # Restrict to specific countries (ISO 3166-1 alpha-3 codes)
 # countries:
 #   - NOR
diff --git a/config/gsa.yaml b/config/gsa.yaml
index fd9d279a..6f86b7f0 100644
--- a/config/gsa.yaml
+++ b/config/gsa.yaml
@@ -30,6 +30,16 @@
 # live in a companion config, gsa_l1.yaml, on the paper branch.
 
 name: "gsa"
+
+health:
+  enabled: true
+
+# With health on, the baseline diet is GBD-anchored, so consume the artefact
+# set calibrated against that diet (the `default` set is anchoring-off).
+# Regenerate with `tools/calibrate --base config/gsa.yaml`.
+calibration:
+  source: "gbd-anchored"
+
 scenarios:
   # Baseline scenario for calibrating piecewise food utility blocks. Consumer
   # values (dual variables) are taken from this enforced-baseline solve.
diff --git a/config/gsa_fixed_diet.yaml b/config/gsa_fixed_diet.yaml
index c55fec46..7c6cbcee 100644
--- a/config/gsa_fixed_diet.yaml
+++ b/config/gsa_fixed_diet.yaml
@@ -122,8 +122,18 @@ sensitivity_analysis:
 validation:
   enforce_baseline_diet: true
 
-# Keep post-hoc YLL evaluation disabled: diet is fixed, so health outputs
-# would be essentially constant across scenarios and just add solve time.
+diet:
+  # Anchor to GBD so the fixed baseline diet matches the health-enabled
+  # gsa.yaml diet (with health off, the sentinel would resolve to false).
+  anchor_groups_to_gbd: true
+
+# Anchored baseline diet -> consume the artefact set calibrated against it
+# (the `default` set is anchoring-off).
+calibration:
+  source: "gbd-anchored"
+
+# No post-hoc YLL evaluation: diet is fixed, so health outputs would be
+# essentially constant across scenarios and just add solve time.
 health:
   enabled: false
 
diff --git a/config/schemas/config.schema.yaml b/config/schemas/config.schema.yaml
index 4718542e..990ac11b 100644
--- a/config/schemas/config.schema.yaml
+++ b/config/schemas/config.schema.yaml
@@ -988,6 +988,7 @@ properties:
     type: object
     required:
       - baseline_age
+      - anchor_groups_to_gbd
       - source_basis
       - gdd_ia
     additionalProperties: false
@@ -995,6 +996,12 @@ properties:
       baseline_age:
         type: string
         description: "Age group for baseline diet (e.g., 'All ages')"
+      anchor_groups_to_gbd:
+        oneOf:
+          - type: boolean
+          - type: string
+            enum: ["match_health"]
+        description: "Anchor risk-factor food-group baseline-diet totals to GBD intake exposure. true/false, or 'match_health' to follow health.enabled."
       fbs_override_foods:
         type: array
         uniqueItems: true
diff --git a/config/tutorial/01_ghg_prices.yaml b/config/tutorial/01_ghg_prices.yaml
index 77607c96..1f6e1c27 100644
--- a/config/tutorial/01_ghg_prices.yaml
+++ b/config/tutorial/01_ghg_prices.yaml
@@ -55,8 +55,10 @@ scenarios:
 validation:
   use_actual_yields: true
 
-# This tutorial does not use health costs.
+# Health module on for the GBD-anchored baseline diet and health outputs,
+# but with zero weight in the objective (post-hoc YLL evaluation only).
 health:
+  enabled: true
   value_per_yll: 0
 
 # Compare all three scenarios in auto-generated plots.
diff --git a/config/tutorial/02_consumer_values.yaml b/config/tutorial/02_consumer_values.yaml
index 072ff4d3..5ed1f8f7 100644
--- a/config/tutorial/02_consumer_values.yaml
+++ b/config/tutorial/02_consumer_values.yaml
@@ -85,7 +85,10 @@ scenarios:
 validation:
   use_actual_yields: true
 
+# Health module on for the GBD-anchored baseline diet and health outputs,
+# but with zero weight in the objective (post-hoc YLL evaluation only).
 health:
+  enabled: true
   value_per_yll: 0
 
 plotting:
diff --git a/config/validation.yaml b/config/validation.yaml
index 0066b3db..9574d3bb 100644
--- a/config/validation.yaml
+++ b/config/validation.yaml
@@ -31,9 +31,16 @@ cost_calibration:
 food_demand_calibration:
   enabled: false
 
+# Health module on with zero objective weight: post-hoc YLL evaluation only.
 health:
+  enabled: true
   value_per_yll: 0
 
+# With health on, the baseline diet is GBD-anchored, so consume the artefact
+# set calibrated against that diet (the `default` set is anchoring-off).
+calibration:
+  source: "gbd-anchored"
+
 emissions:
   ghg_pricing_enabled: false
 
diff --git a/data/curated/calibration/default/animal_cost.csv b/data/curated/calibration/default/animal_cost.csv
index 1f45298c..b0302633 100644
--- a/data/curated/calibration/default/animal_cost.csv
+++ b/data/curated/calibration/default/animal_cost.csv
@@ -1,179 +1,179 @@
 product,country,correction_bnusd_per_mt_feed
-dairy,AFG,0.019429629668593407
-dairy,AGO,-0.14344584196805954
-dairy,ALB,-0.023533342871814966
-dairy,ARE,-0.04143645071235369
-dairy,ARG,-0.19398537278175354
-dairy,ARM,-0.011384313460439444
+dairy,AFG,0.009775234386324883
+dairy,AGO,-0.1468677781522274
+dairy,ALB,0.028023586375638843
+dairy,ARE,-0.04836616409011185
+dairy,ARG,-0.19965669512748718
+dairy,ARM,-0.021885029273107648
 dairy,ASM,-0.0
-dairy,ATG,-0.24454953521490097
-dairy,AUS,-0.05782910343259573
-dairy,AUT,0.008029387099668384
-dairy,AZE,-0.10857096128165722
-dairy,BDI,-0.19728994369506836
-dairy,BEL,-0.04548066761344671
-dairy,BEN,-0.2638223394751549
-dairy,BFA,-0.269638791680336
-dairy,BGD,-0.06376965902745724
-dairy,BGR,0.030918722972273827
-dairy,BHS,-0.13557196035981178
-dairy,BIH,0.12179218605160713
-dairy,BLR,-0.034614475443959236
-dairy,BLZ,-0.2122793346643448
-dairy,BOL,-0.2064630165696144
-dairy,BRA,-0.170407272875309
-dairy,BRB,-0.3012048602104187
-dairy,BRN,-0.06272971606813371
-dairy,BTN,-0.19480304047465324
-dairy,BWA,-0.24311847239732742
-dairy,CAF,-0.14549646526575089
-dairy,CAN,-0.08521859161555767
-dairy,CHE,-0.03879193961620331
-dairy,CHL,-0.18294814229011536
-dairy,CHN,0.14896606653928757
-dairy,CIV,-0.2627760320901871
-dairy,CMR,-0.2687765583395958
-dairy,COD,-0.13804767839610577
-dairy,COG,-0.1433083601295948
-dairy,COL,-0.2351013422012329
-dairy,COM,-0.12192871049046516
-dairy,CPV,-0.23724471777677536
-dairy,CRI,-0.05980869010090828
-dairy,CUB,-0.2101064920425415
-dairy,CYP,0.023470744490623474
-dairy,CZE,-0.0012322113616392016
-dairy,DEU,-0.000984611688181758
-dairy,DJI,-0.0307138804346323
-dairy,DNK,-0.02052507922053337
-dairy,DOM,-0.06886794418096542
-dairy,DZA,-0.04815932270139456
-dairy,ECU,-0.17810802161693573
-dairy,EGY,-0.1400747150182724
-dairy,ERI,-0.12191927060484886
-dairy,ESP,-0.07785327918827534
-dairy,EST,-0.0013701971620321274
-dairy,ETH,-0.15060389414429665
-dairy,FIN,0.07162491045892239
-dairy,FJI,-0.07856539636850357
-dairy,FRA,-0.07695738971233368
-dairy,GAB,-0.09453262016177177
-dairy,GBR,0.005195342469960451
-dairy,GEO,-0.061261977069079876
-dairy,GHA,-0.2660602629184723
-dairy,GIN,-0.13472575321793556
-dairy,GMB,-0.24835162609815598
-dairy,GNB,-0.2703486606478691
+dairy,ATG,-0.20854145288467407
+dairy,AUS,-0.07088138302788138
+dairy,AUT,-0.0029114224016666412
+dairy,AZE,-0.0643788380548358
+dairy,BDI,-0.208308644592762
+dairy,BEL,0.019830304197967052
+dairy,BEN,-0.2670818790793419
+dairy,BFA,-0.27394019067287445
+dairy,BGD,-0.07141656382009387
+dairy,BGR,0.02044672518968582
+dairy,BHS,-0.10683811269700527
+dairy,BIH,0.11757176741957664
+dairy,BLR,-0.045248162001371384
+dairy,BLZ,-0.2015547826886177
+dairy,BOL,-0.21501590311527252
+dairy,BRA,-0.22465115040540695
+dairy,BRB,-0.30794185400009155
+dairy,BRN,-0.0752631826326251
+dairy,BTN,-0.2185128517448902
+dairy,BWA,-0.24830980598926544
+dairy,CAF,-0.15660355985164642
+dairy,CAN,-0.034870133735239506
+dairy,CHE,-0.0393193899653852
+dairy,CHL,-0.1893482506275177
+dairy,CHN,0.1607140377163887
+dairy,CIV,-0.26611560583114624
+dairy,CMR,-0.2728709429502487
+dairy,COD,-0.14927423745393753
+dairy,COG,-0.1545010581612587
+dairy,COL,-0.1667589470744133
+dairy,COM,-0.13520731776952744
+dairy,CPV,-0.23950542509555817
+dairy,CRI,-0.014960226602852345
+dairy,CUB,-0.1991748958826065
+dairy,CYP,0.021383875980973244
+dairy,CZE,0.005600878037512302
+dairy,DEU,0.02896498329937458
+dairy,DJI,-0.03034788928925991
+dairy,DNK,-0.024929175153374672
+dairy,DOM,-0.03341811150312424
+dairy,DZA,-0.05347512639127672
+dairy,ECU,-0.1954895555973053
+dairy,EGY,-0.04868289828300476
+dairy,ERI,-0.16784296929836273
+dairy,ESP,-0.055624060332775116
+dairy,EST,-0.005761606618762016
+dairy,ETH,-0.16132669895887375
+dairy,FIN,0.06725708581507206
+dairy,FJI,-0.04846467077732086
+dairy,FRA,-0.07994907535612583
+dairy,GAB,-0.1042232746258378
+dairy,GBR,-0.009183213114738464
+dairy,GEO,-0.06488664634525776
+dairy,GHA,-0.26966172456741333
+dairy,GIN,-0.23668600618839264
+dairy,GMB,-0.2511493191123009
+dairy,GNB,-0.27105822414159775
 dairy,GNQ,-0.0
-dairy,GRC,-0.011651250533759594
-dairy,GRD,-0.26645299792289734
-dairy,GTM,-0.17119775712490082
+dairy,GRC,-0.04338577575981617
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 dairy,GUF,-0.0
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 dairy,USA,-0.0
-dairy,UZB,0.08897577226161957
-dairy,VEN,-0.16654188558459282
-dairy,VNM,-0.14795337244868279
-dairy,VUT,-0.09853671863675117
-dairy,YEM,-0.09186061099171638
-dairy,ZAF,-0.20334048569202423
-dairy,ZMB,-0.1397862657904625
-dairy,ZWE,-0.5003806352615356
+dairy,UZB,0.11623498424887657
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+dairy,ZAF,-0.21110977232456207
+dairy,ZMB,-0.15360873565077782
+dairy,ZWE,-0.5189576745033264
 dairy-buffalo,AFG,-0.0
 dairy-buffalo,AGO,-0.0
 dairy-buffalo,ALB,-0.0
@@ -189,8 +189,8 @@ dairy-buffalo,BDI,-0.0
 dairy-buffalo,BEL,-0.0
 dairy-buffalo,BEN,-0.0
 dairy-buffalo,BFA,-0.0
-dairy-buffalo,BGD,-0.1059691458940506
-dairy-buffalo,BGR,0.0035036588087677956
+dairy-buffalo,BGD,-0.11450070329010487
+dairy-buffalo,BGR,0.10727846994996071
 dairy-buffalo,BHS,-0.0
 dairy-buffalo,BIH,-0.0
 dairy-buffalo,BLR,-0.0
@@ -198,14 +198,14 @@ dairy-buffalo,BLZ,-0.0
 dairy-buffalo,BOL,-0.0
 dairy-buffalo,BRA,-0.0
 dairy-buffalo,BRB,-0.0
-dairy-buffalo,BRN,-0.2609296813607216
+dairy-buffalo,BRN,-0.26460978388786316
 dairy-buffalo,BTN,-0.0
 dairy-buffalo,BWA,-0.0
 dairy-buffalo,CAF,-0.0
 dairy-buffalo,CAN,-0.0
 dairy-buffalo,CHE,-0.0
 dairy-buffalo,CHL,-0.0
-dairy-buffalo,CHN,-0.15695834159851074
+dairy-buffalo,CHN,-0.16509030759334564
 dairy-buffalo,CIV,-0.0
 dairy-buffalo,CMR,-0.0
 dairy-buffalo,COD,-0.0
@@ -223,7 +223,7 @@ dairy-buffalo,DNK,-0.0
 dairy-buffalo,DOM,-0.0
 dairy-buffalo,DZA,-0.0
 dairy-buffalo,ECU,-0.0
-dairy-buffalo,EGY,-0.09786959923803806
+dairy-buffalo,EGY,0.041519501246511936
 dairy-buffalo,ERI,-0.0
 dairy-buffalo,ESP,-0.0
 dairy-buffalo,EST,-0.0
@@ -239,7 +239,7 @@ dairy-buffalo,GIN,-0.0
 dairy-buffalo,GMB,-0.0
 dairy-buffalo,GNB,-0.0
 dairy-buffalo,GNQ,-0.0
-dairy-buffalo,GRC,-0.056585246697068214
+dairy-buffalo,GRC,-0.05737338587641716
 dairy-buffalo,GRD,-0.0
 dairy-buffalo,GTM,-0.0
 dairy-buffalo,GUF,-0.0
@@ -248,14 +248,14 @@ dairy-buffalo,HND,-0.0
 dairy-buffalo,HRV,-0.0
 dairy-buffalo,HTI,-0.0
 dairy-buffalo,HUN,-0.0
-dairy-buffalo,IDN,-0.1853792667388916
-dairy-buffalo,IND,-0.13360632956027985
+dairy-buffalo,IDN,-0.19634506851434708
+dairy-buffalo,IND,-0.132305808365345
 dairy-buffalo,IRL,-0.0
-dairy-buffalo,IRN,0.06616165861487389
-dairy-buffalo,IRQ,-0.17470766603946686
+dairy-buffalo,IRN,0.11357088759541512
+dairy-buffalo,IRQ,-0.15010995417833328
 dairy-buffalo,ISL,-0.0
 dairy-buffalo,ISR,-0.0
-dairy-buffalo,ITA,0.04079308919608593
+dairy-buffalo,ITA,0.11909181997179985
 dairy-buffalo,JAM,-0.0
 dairy-buffalo,JOR,-0.0
 dairy-buffalo,JPN,-0.0
@@ -268,7 +268,7 @@ dairy-buffalo,LAO,-0.0
 dairy-buffalo,LBN,-0.0
 dairy-buffalo,LBR,-0.0
 dairy-buffalo,LBY,-0.0
-dairy-buffalo,LKA,-0.09794122353196144
+dairy-buffalo,LKA,-0.2107953429222107
 dairy-buffalo,LSO,-0.0
 dairy-buffalo,LTU,-0.0
 dairy-buffalo,LUX,-0.0
@@ -280,24 +280,24 @@ dairy-buffalo,MEX,-0.0
 dairy-buffalo,MKD,-0.0
 dairy-buffalo,MLI,-0.0
 dairy-buffalo,MLT,-0.0
-dairy-buffalo,MMR,-0.10471758525818586
+dairy-buffalo,MMR,-0.12329600751399994
 dairy-buffalo,MNE,-0.0
 dairy-buffalo,MNG,-0.0
 dairy-buffalo,MOZ,-0.0
 dairy-buffalo,MRT,-0.0
 dairy-buffalo,MUS,-0.0
 dairy-buffalo,MWI,-0.0
-dairy-buffalo,MYS,-0.13420968130230904
+dairy-buffalo,MYS,-0.14460596069693565
 dairy-buffalo,NAM,-0.0
 dairy-buffalo,NER,-0.0
 dairy-buffalo,NGA,-0.0
 dairy-buffalo,NIC,-0.0
 dairy-buffalo,NLD,-0.0
 dairy-buffalo,NOR,-0.0
-dairy-buffalo,NPL,-0.07125091785565019
+dairy-buffalo,NPL,-0.07904923567548394
 dairy-buffalo,NZL,-0.0
 dairy-buffalo,OMN,-0.0
-dairy-buffalo,PAK,0.006403522565960884
+dairy-buffalo,PAK,-0.00016395561397075653
 dairy-buffalo,PAN,-0.0
 dairy-buffalo,PER,-0.0
 dairy-buffalo,PHL,-0.0
@@ -325,7 +325,7 @@ dairy-buffalo,SVK,-0.0
 dairy-buffalo,SVN,-0.0
 dairy-buffalo,SWE,-0.0
 dairy-buffalo,SWZ,-0.0
-dairy-buffalo,SYR,-0.09794544987380505
+dairy-buffalo,SYR,-0.057606047950685024
 dairy-buffalo,TCD,-0.0
 dairy-buffalo,TGO,-0.0
 dairy-buffalo,THA,-0.0
@@ -334,7 +334,7 @@ dairy-buffalo,TKM,-0.0
 dairy-buffalo,TLS,-0.0
 dairy-buffalo,TTO,-0.0
 dairy-buffalo,TUN,-0.0
-dairy-buffalo,TUR,-0.11061198636889458
+dairy-buffalo,TUR,-0.06640202552080154
 dairy-buffalo,TWN,-0.0
 dairy-buffalo,TZA,-0.0
 dairy-buffalo,UGA,-0.0
@@ -343,884 +343,884 @@ dairy-buffalo,URY,-0.0
 dairy-buffalo,USA,-0.0
 dairy-buffalo,UZB,-0.0
 dairy-buffalo,VEN,-0.0
-dairy-buffalo,VNM,-0.24921564012765884
+dairy-buffalo,VNM,-0.2701643258333206
 dairy-buffalo,VUT,-0.0
 dairy-buffalo,YEM,-0.0
 dairy-buffalo,ZAF,-0.0
 dairy-buffalo,ZMB,-0.0
 dairy-buffalo,ZWE,-0.0
-eggs,AFG,-0.12066755443811417
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-eggs,ARE,-0.0
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-eggs,ARM,-0.008815393783152103
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 eggs,ASM,-0.0
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-eggs,AUS,-0.02652224898338318
-eggs,AUT,-0.032084934413433075
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 eggs,BEL,-0.0
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 eggs,BGR,-0.0
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 eggs,CYP,-0.0
 eggs,CZE,-0.0
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 eggs,DJI,-0.0
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 eggs,FRA,-0.0
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-meat-pig,VNM,-0.03349953889846802
-meat-pig,VUT,-0.004217192530632019
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 meat-pig,YEM,-0.0
-meat-pig,ZAF,-0.03323512151837349
-meat-pig,ZMB,-0.0882929340004921
-meat-pig,ZWE,-0.08681154251098633
-meat-sheep,AFG,-0.1415824219584465
-meat-sheep,AGO,-0.09458794072270393
-meat-sheep,ALB,-0.13496389612555504
-meat-sheep,ARE,-0.06448633130639791
-meat-sheep,ARG,-0.05471206130459905
-meat-sheep,ARM,-0.1128133274614811
+meat-pig,ZAF,-0.03685153275728226
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+meat-sheep,AFG,-0.15154287964105606
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+meat-sheep,ALB,-0.14479002356529236
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+meat-sheep,ARG,-0.0648089642636478
+meat-sheep,ARM,-0.1226394735276699
 meat-sheep,ASM,-0.0
-meat-sheep,ATG,-0.17112303897738457
-meat-sheep,AUS,-0.04704216495156288
-meat-sheep,AUT,0.0351789528504014
-meat-sheep,AZE,-0.1780359148979187
-meat-sheep,BDI,-0.14206690341234207
-meat-sheep,BEL,0.0206425953656435
-meat-sheep,BEN,-0.17743873596191406
-meat-sheep,BFA,-0.1855834200978279
-meat-sheep,BGD,-0.10950366780161858
-meat-sheep,BGR,-0.10608385875821114
-meat-sheep,BHS,-0.03526198983308859
-meat-sheep,BIH,-0.027440613135695457
-meat-sheep,BLR,-0.05302349850535393
-meat-sheep,BLZ,-0.06110692769289017
-meat-sheep,BOL,-0.11021624878048897
-meat-sheep,BRA,-0.06728906091302633
-meat-sheep,BRB,-0.3250228092074394
-meat-sheep,BRN,-0.25031017512083054
-meat-sheep,BTN,-0.3725394755601883
-meat-sheep,BWA,-0.261981800198555
-meat-sheep,CAF,-0.11787089891731739
-meat-sheep,CAN,-0.020573697984218597
-meat-sheep,CHE,0.018564202822744846
-meat-sheep,CHL,-0.07935721054673195
-meat-sheep,CHN,-0.1494913250207901
-meat-sheep,CIV,-0.19012200832366943
-meat-sheep,CMR,-0.18600650131702423
-meat-sheep,COD,-0.0776509940624237
-meat-sheep,COG,-0.09685147739946842
-meat-sheep,COL,-0.13317683339118958
-meat-sheep,COM,-0.10947168618440628
-meat-sheep,CPV,-0.16500767692923546
-meat-sheep,CRI,-0.028089342638850212
-meat-sheep,CUB,-0.02748715505003929
-meat-sheep,CYP,-0.19928379356861115
-meat-sheep,CZE,0.004102927981875837
-meat-sheep,DEU,0.008420945145189762
-meat-sheep,DJI,-0.022527581080794334
-meat-sheep,DNK,0.002983657643198967
-meat-sheep,DOM,-0.1437053382396698
-meat-sheep,DZA,-0.07039424031972885
-meat-sheep,ECU,-0.06781894620507956
-meat-sheep,EGY,-0.17451205849647522
-meat-sheep,ERI,-0.08476265147328377
-meat-sheep,ESP,-0.01670026406645775
-meat-sheep,EST,-0.040265561547130346
-meat-sheep,ETH,-0.13301679491996765
-meat-sheep,FIN,-0.022305830381810665
-meat-sheep,FJI,-0.1307859607040882
-meat-sheep,FRA,0.011597903445363045
-meat-sheep,GAB,-0.06772248446941376
-meat-sheep,GBR,0.004962324630469084
-meat-sheep,GEO,-0.1736474633216858
-meat-sheep,GHA,-0.18507475405931473
-meat-sheep,GIN,-0.06224328652024269
-meat-sheep,GMB,-0.20644782483577728
-meat-sheep,GNB,-0.17151960730552673
-meat-sheep,GNQ,-0.11510683968663216
-meat-sheep,GRC,-0.0596250481903553
-meat-sheep,GRD,-0.00873809214681387
-meat-sheep,GTM,-0.04511423967778683
+meat-sheep,ATG,-0.17582404613494873
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+meat-sheep,AUT,0.025352842174470425
+meat-sheep,AZE,-0.18077809363603592
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+meat-sheep,BFA,-0.1906580850481987
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+meat-sheep,BGR,-0.11540484055876732
+meat-sheep,BHS,-0.04535917786415666
+meat-sheep,BIH,-0.029680061619728804
+meat-sheep,BLR,-0.06269703805446625
+meat-sheep,BLZ,-0.06534399837255478
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+meat-sheep,BRB,-0.34507423639297485
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+meat-sheep,BTN,-0.3727618157863617
+meat-sheep,BWA,-0.2695857733488083
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+meat-sheep,CAN,-0.02239476703107357
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+meat-sheep,CHL,-0.08945401757955551
+meat-sheep,CHN,-0.15931588783860207
+meat-sheep,CIV,-0.1942949816584587
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+meat-sheep,CRI,-0.0
+meat-sheep,CUB,-0.029070710763335228
+meat-sheep,CYP,-0.20202597230672836
+meat-sheep,CZE,-0.01875110063701868
+meat-sheep,DEU,-0.02438401069957763
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+meat-sheep,DOM,-0.09599855542182922
+meat-sheep,DZA,-0.08022036124020815
+meat-sheep,ECU,-0.11427390947937965
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+meat-sheep,ERI,-0.12670733779668808
+meat-sheep,ESP,-0.02652636356651783
+meat-sheep,EST,-0.04300772864371538
+meat-sheep,ETH,-0.14480669423937798
+meat-sheep,FIN,-0.02504797256551683
+meat-sheep,FJI,-0.09167458675801754
+meat-sheep,FRA,0.001771796029061079
+meat-sheep,GAB,-0.0795123279094696
+meat-sheep,GBR,-0.018459963146597147
+meat-sheep,GEO,-0.176389642059803
+meat-sheep,GHA,-0.18939868360757828
+meat-sheep,GIN,-0.16496524959802628
+meat-sheep,GMB,-0.21077174693346024
+meat-sheep,GNB,-0.17304538190364838
+meat-sheep,GNQ,-0.12002839148044586
+meat-sheep,GRC,-0.09054778888821602
+meat-sheep,GRD,-0.008738094009459019
+meat-sheep,GTM,-0.055227198638021946
 meat-sheep,GUF,-0.0
-meat-sheep,GUY,-0.11854496225714684
-meat-sheep,HND,-0.014772321097552776
-meat-sheep,HRV,0.010242094285786152
-meat-sheep,HTI,-0.015342592261731625
-meat-sheep,HUN,-0.007384431781247258
-meat-sheep,IDN,-0.15005137771368027
-meat-sheep,IND,-0.24086377769708633
-meat-sheep,IRL,0.0067147146910429
-meat-sheep,IRN,-0.0930343046784401
-meat-sheep,IRQ,-0.1820307895541191
-meat-sheep,ISL,-0.005296551622450352
-meat-sheep,ISR,-0.28978967666625977
-meat-sheep,ITA,-0.06233516428619623
-meat-sheep,JAM,-0.19988063722848892
-meat-sheep,JOR,-0.24173486977815628
-meat-sheep,JPN,-0.21635738760232925
-meat-sheep,KAZ,-0.22735925018787384
-meat-sheep,KEN,-0.10261344537138939
-meat-sheep,KGZ,-0.05828170367749408
+meat-sheep,GUY,-0.12864182889461517
+meat-sheep,HND,-0.01635592244565487
+meat-sheep,HRV,0.000431479187682271
+meat-sheep,HTI,-0.015342594124376774
+meat-sheep,HUN,-0.033509708009660244
+meat-sheep,IDN,-0.16226989030838013
+meat-sheep,IND,-0.24360597133636475
+meat-sheep,IRL,-0.0031114090234041214
+meat-sheep,IRN,-0.10286111012101173
+meat-sheep,IRQ,-0.18477297574281693
+meat-sheep,ISL,-0.03962093684822321
+meat-sheep,ISR,-0.2741515189409256
+meat-sheep,ITA,-0.07215986866503954
+meat-sheep,JAM,-0.20289360731840134
+meat-sheep,JOR,-0.2444770485162735
+meat-sheep,JPN,-0.2087314873933792
+meat-sheep,KAZ,-0.23718539625406265
+meat-sheep,KEN,-0.11440338380634785
+meat-sheep,KGZ,-0.06810784823028371
 meat-sheep,KHM,-0.0
-meat-sheep,KOR,-0.0862916512414813
-meat-sheep,LAO,-0.14810381084680557
-meat-sheep,LBN,-0.20625533163547516
-meat-sheep,LBR,-0.07262798398733139
-meat-sheep,LBY,-0.06057277787476778
-meat-sheep,LKA,-0.13006744533777237
-meat-sheep,LSO,-0.17383906245231628
-meat-sheep,LTU,-0.01921112500713207
-meat-sheep,LUX,0.012192433699965477
-meat-sheep,LVA,-0.027501197531819344
-meat-sheep,MAR,-0.16744696721434593
-meat-sheep,MDA,-0.1098349429666996
-meat-sheep,MDG,-0.08715871721506119
+meat-sheep,KOR,-0.0961175225675106
+meat-sheep,LAO,-0.15792997181415558
+meat-sheep,LBN,-0.20972410589456558
+meat-sheep,LBR,-0.0828758254647255
+meat-sheep,LBY,-0.0703988978639245
+meat-sheep,LKA,-0.2151702418923378
+meat-sheep,LSO,-0.18852689862251282
+meat-sheep,LTU,-0.028456038795411587
+meat-sheep,LUX,-0.0013945326209068298
+meat-sheep,LVA,-0.036769745871424675
+meat-sheep,MAR,-0.17018911987543106
+meat-sheep,MDA,-0.11927871778607368
+meat-sheep,MDG,-0.10184663534164429
 meat-sheep,MEX,-0.0
-meat-sheep,MKD,-0.06127229332923889
-meat-sheep,MLI,-0.17546019703149796
-meat-sheep,MLT,-0.1284434087574482
-meat-sheep,MMR,-0.09516548458486795
-meat-sheep,MNE,-0.12140432000160217
+meat-sheep,MKD,-0.07066655904054642
+meat-sheep,MLI,-0.18570802360773087
+meat-sheep,MLT,-0.13234564289450645
+meat-sheep,MMR,-0.10499164834618568
+meat-sheep,MNE,-0.11431922018527985
 meat-sheep,MNG,-0.023062026128172874
-meat-sheep,MOZ,-0.14742041379213333
-meat-sheep,MRT,-0.10818710923194885
-meat-sheep,MUS,-0.09375511668622494
-meat-sheep,MWI,-0.09268413484096527
-meat-sheep,MYS,-0.15167073160409927
-meat-sheep,NAM,-0.13427582010626793
-meat-sheep,NER,-0.1826118901371956
-meat-sheep,NGA,-0.10362514480948448
-meat-sheep,NIC,-0.026700954884290695
-meat-sheep,NLD,-0.08122579753398895
-meat-sheep,NOR,-0.07699795439839363
-meat-sheep,NPL,-0.10392054729163647
-meat-sheep,NZL,-0.05121631547808647
-meat-sheep,OMN,-0.23469317704439163
-meat-sheep,PAK,-0.11415157094597816
+meat-sheep,MOZ,-0.1624782457947731
+meat-sheep,MRT,-0.11843496561050415
+meat-sheep,MUS,-0.11111308820545673
+meat-sheep,MWI,-0.10737206414341927
+meat-sheep,MYS,-0.16388923674821854
+meat-sheep,NAM,-0.14896373078227043
+meat-sheep,NER,-0.18693580478429794
+meat-sheep,NGA,-0.08178254216909409
+meat-sheep,NIC,-0.0
+meat-sheep,NLD,-0.08396797068417072
+meat-sheep,NOR,-0.07973960600793362
+meat-sheep,NPL,-0.11374669335782528
+meat-sheep,NZL,-0.06343481689691544
+meat-sheep,OMN,-0.23743535578250885
+meat-sheep,PAK,-0.12397771887481213
 meat-sheep,PAN,-0.0
-meat-sheep,PER,-0.15408897399902344
-meat-sheep,PHL,-0.1357344500720501
-meat-sheep,PNG,-0.016464272513985634
-meat-sheep,POL,-0.05056854709982872
-meat-sheep,PRI,-0.010542791336774826
-meat-sheep,PRT,0.04693014919757843
-meat-sheep,PRY,-0.10917480662465096
-meat-sheep,PSE,-0.18138625845313072
-meat-sheep,ROU,-0.10743791610002518
-meat-sheep,RUS,-0.10618934407830238
-meat-sheep,RWA,-0.09293063543736935
-meat-sheep,SAU,-0.19556070119142532
-meat-sheep,SDN,-0.09227903746068478
-meat-sheep,SEN,-0.19668742269277573
+meat-sheep,PER,-0.1582706794142723
+meat-sheep,PHL,-0.14795249700546265
+meat-sheep,PNG,-0.016160599887371063
+meat-sheep,POL,-0.06039469130337238
+meat-sheep,PRI,-0.010542795062065125
+meat-sheep,PRT,0.012491218280047178
+meat-sheep,PRY,-0.06599156092852354
+meat-sheep,PSE,-0.18422464281320572
+meat-sheep,ROU,-0.11672241240739822
+meat-sheep,RUS,-0.10893189907073975
+meat-sheep,RWA,-0.10472048446536064
+meat-sheep,SAU,-0.19889269024133682
+meat-sheep,SDN,-0.10406892746686935
+meat-sheep,SEN,-0.2031412497162819
 meat-sheep,SLB,-0.0
-meat-sheep,SLE,-0.07505753636360168
-meat-sheep,SLV,-0.13402653113007545
-meat-sheep,SOM,-0.12062868103384972
-meat-sheep,SRB,0.010296006708813366
-meat-sheep,SSD,-0.08175355661660433
-meat-sheep,STP,-0.1177850179374218
-meat-sheep,SUR,-0.14126738905906677
-meat-sheep,SVK,-0.05820089019834995
-meat-sheep,SVN,0.00936001050285995
-meat-sheep,SWE,-0.023194382898509502
-meat-sheep,SWZ,-0.20302043855190277
-meat-sheep,SYR,-0.2042120322585106
-meat-sheep,TCD,-0.09572643600404263
-meat-sheep,TGO,-0.11682617664337158
-meat-sheep,THA,-0.10748309828341007
-meat-sheep,TJK,-0.21049479395151138
-meat-sheep,TKM,-0.11320069059729576
-meat-sheep,TLS,-0.12903958559036255
-meat-sheep,TTO,0.005399463232606649
-meat-sheep,TUN,-0.16800956055521965
-meat-sheep,TUR,-0.17535444721579552
+meat-sheep,SLE,-0.08530537784099579
+meat-sheep,SLV,-0.137039452791214
+meat-sheep,SOM,-0.13241857290267944
+meat-sheep,SRB,0.0004699041019193828
+meat-sheep,SSD,-0.09354344569146633
+meat-sheep,STP,-0.122706588357687
+meat-sheep,SUR,-0.1477217674255371
+meat-sheep,SVK,-0.06757794320583344
+meat-sheep,SVN,4.106154665350914e-05
+meat-sheep,SWE,-0.025936471298336983
+meat-sheep,SWZ,-0.21770837903022766
+meat-sheep,SYR,-0.207680806517601
+meat-sheep,TCD,-0.1075163260102272
+meat-sheep,TGO,-0.12707118317484856
+meat-sheep,THA,-0.11838966980576515
+meat-sheep,TJK,-0.2132369726896286
+meat-sheep,TKM,-0.12302683666348457
+meat-sheep,TLS,-0.1467193365097046
+meat-sheep,TTO,0.005382265895605087
+meat-sheep,TUN,-0.16555969417095184
+meat-sheep,TUR,-0.17809662222862244
 meat-sheep,TWN,-0.0
-meat-sheep,TZA,-0.11188142001628876
-meat-sheep,UGA,-0.2188774049282074
-meat-sheep,UKR,-0.10373803041875362
-meat-sheep,URY,-0.09967987611889839
-meat-sheep,USA,-0.03526217024773359
-meat-sheep,UZB,-0.12495506927371025
-meat-sheep,VEN,-0.0705616818740964
-meat-sheep,VNM,-0.22738662362098694
-meat-sheep,VUT,-0.11674466356635094
-meat-sheep,YEM,-0.08344261348247528
-meat-sheep,ZAF,-0.12928186357021332
-meat-sheep,ZMB,-0.10059220530092716
-meat-sheep,ZWE,-0.4724293351173401
+meat-sheep,TZA,-0.16611717641353607
+meat-sheep,UGA,-0.22475197166204453
+meat-sheep,UKR,-0.1064801849424839
+meat-sheep,URY,-0.10977669432759285
+meat-sheep,USA,-0.04353543370962143
+meat-sheep,UZB,-0.09816614352166653
+meat-sheep,VEN,-0.16307301074266434
+meat-sheep,VNM,-0.23012882471084595
+meat-sheep,VUT,-0.12690399587154388
+meat-sheep,YEM,-0.09523249790072441
+meat-sheep,ZAF,-0.13910797983407974
+meat-sheep,ZMB,-0.11528013274073601
+meat-sheep,ZWE,-0.4924807548522949
diff --git a/data/curated/calibration/default/crop_cost.csv b/data/curated/calibration/default/crop_cost.csv
index f4a55595..d5950475 100644
--- a/data/curated/calibration/default/crop_cost.csv
+++ b/data/curated/calibration/default/crop_cost.csv
@@ -1,178 +1,178 @@
 crop,country,correction_bnusd_per_mha
-alfalfa,AFG,-0.3853379487991333
-alfalfa,AGO,-0.9439449608325958
+alfalfa,AFG,-0.38436196744441986
+alfalfa,AGO,-0.9439449906349182
 alfalfa,ALB,-0.5414051562547684
-alfalfa,ARE,-0.6755251884460449
-alfalfa,ARG,-0.4919307976961136
+alfalfa,ARE,-0.6755253076553345
+alfalfa,ARG,-0.5018861889839172
 alfalfa,ARM,-0.0
 alfalfa,ATG,-7.5
-alfalfa,AUS,0.10641112923622131
+alfalfa,AUS,0.10641167312860489
 alfalfa,AUT,-0.6891505420207977
-alfalfa,AZE,0.7043356895446777
+alfalfa,AZE,0.8190054893493652
 alfalfa,BDI,-0.5490851700305939
-alfalfa,BEL,-0.28935520350933075
-alfalfa,BEN,0.7100087404251099
+alfalfa,BEL,-0.22992710024118423
+alfalfa,BEN,0.8548685610294342
 alfalfa,BFA,-7.5
-alfalfa,BGD,-0.7335467636585236
-alfalfa,BGR,-0.09852029010653496
-alfalfa,BHS,-0.6658250987529755
-alfalfa,BIH,-0.0
-alfalfa,BLR,-0.3073023557662964
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 apple,AFG,-0.0
 apple,ALB,-0.0
 apple,ARG,-7.5
@@ -279,29 +279,29 @@ apple,ZAF,-0.0
 apple,ZMB,-0.0
 apple,ZWE,-0.0
 banana,AFG,-0.0
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 banana,ATG,-0.0
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 banana,COD,-7.5
 banana,COG,-7.5
 banana,COL,-7.5
@@ -309,212 +309,212 @@ banana,COM,-7.5
 banana,CPV,-7.5
 banana,CRI,-7.5
 banana,CUB,-7.5
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 barley,BGD,7.5
-barley,BGR,-0.08686503767967224
-barley,BIH,-0.1164703443646431
-barley,BLR,-0.04188999906182289
-barley,BOL,-0.04061543941497803
-barley,BRA,-0.31738990545272827
-barley,BTN,0.4605504870414734
+barley,BGR,0.002475777408108115
+barley,BIH,-0.050780292600393295
+barley,BLR,0.007806026376783848
+barley,BOL,-0.02714168094098568
+barley,BRA,-0.228148952126503
+barley,BTN,0.45769351720809937
 barley,BWA,-0.0
-barley,CAN,-0.3403770923614502
-barley,CHE,-0.7348431050777435
-barley,CHL,-0.31543996185064316
-barley,CHN,-0.06230674125254154
-barley,COD,-0.5211364030838013
-barley,COL,-0.38231831789016724
-barley,CYP,0.027095230296254158
-barley,CZE,-0.24299579858779907
-barley,DEU,-0.14809197932481766
-barley,DNK,-0.5621056258678436
-barley,DZA,-0.042286407202482224
-barley,ECU,-0.3292815834283829
-barley,EGY,-0.787348210811615
-barley,ERI,-0.47681447863578796
-barley,ESP,-0.22335676103830338
-barley,EST,-0.050989141687750816
-barley,ETH,-0.34048475325107574
-barley,FIN,-0.22753941267728806
-barley,FRA,-0.26257529854774475
-barley,GBR,-0.513698935508728
-barley,GEO,-0.18396054953336716
-barley,GRC,-0.18985285609960556
+barley,CAN,-0.29685112833976746
+barley,CHE,-0.6286361217498779
+barley,CHL,-0.20705274865031242
+barley,CHN,0.0014692372642457485
+barley,COD,-0.50732421875
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+barley,EST,0.014945759437978268
+barley,ETH,-0.3165479004383087
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+barley,FRA,-0.16368994116783142
+barley,GBR,-0.38809771835803986
+barley,GEO,-0.16699938476085663
+barley,GRC,-0.1256214752793312
 barley,GTM,7.5
-barley,HRV,-0.07219170778989792
-barley,HUN,0.09230595454573631
-barley,IND,-0.2715345621109009
-barley,IRL,-0.35656675696372986
-barley,IRN,-0.1401877999305725
-barley,IRQ,-0.3790460526943207
-barley,ISR,0.5695439875125885
-barley,ITA,-0.14003431797027588
-barley,JOR,-0.285134881734848
-barley,JPN,0.8728950321674347
-barley,KAZ,-0.05290255323052406
-barley,KEN,-0.6218758821487427
-barley,KGZ,-0.20366936177015305
-barley,KOR,-0.043202538043260574
-barley,LAO,-0.23601998388767242
-barley,LBN,-0.22071167081594467
-barley,LBY,-0.4319453090429306
-barley,LSO,-0.5232293605804443
-barley,LTU,-0.1269603967666626
-barley,LUX,0.11587558314204216
-barley,LVA,-0.053555882535874844
-barley,MAR,-0.07371149212121964
-barley,MDA,0.12974589318037033
-barley,MEX,-0.3348051905632019
-barley,MKD,0.10532310139387846
-barley,MLT,-1.3110552467405796
-barley,MMR,-0.18126237392425537
-barley,MNE,-0.12990673258900642
-barley,MNG,-0.07662234827876091
+barley,HRV,-0.0
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+barley,IND,-0.22015665471553802
+barley,IRL,-0.25662799552083015
+barley,IRN,-0.10199224203824997
+barley,IRQ,-0.36858342587947845
+barley,ISR,0.5868535935878754
+barley,ITA,-0.08285735175013542
+barley,JOR,-0.049257177859544754
+barley,JPN,1.1783689856529236
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+barley,KEN,-0.5803991854190826
+barley,KGZ,-0.1862303763628006
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+barley,LAO,-0.24198471009731293
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+barley,MMR,-0.1391720473766327
+barley,MNE,-0.0831986553966999
+barley,MNG,-0.0775190144777298
 barley,MOZ,7.5
-barley,MRT,-0.2244560569524765
+barley,MRT,-0.19315706193447113
 barley,MWI,7.5
-barley,NAM,-0.43623749911785126
-barley,NLD,-0.965659886598587
-barley,NOR,-0.8065711259841919
-barley,NPL,0.07264386489987373
-barley,NZL,-0.36049672961235046
-barley,OMN,-3.351691484451294
-barley,PAK,-0.36677180230617523
-barley,PER,0.16733942180871964
-barley,POL,-0.16309987008571625
-barley,PRT,-0.14364802837371826
+barley,NAM,-0.7219707071781158
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+barley,NOR,-0.786570817232132
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+barley,OMN,-2.708988666534424
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+barley,PER,0.1054832860827446
+barley,POL,-0.10994373634457588
+barley,PRT,-0.0920039676129818
 barley,PRY,7.5
-barley,PSE,-0.2500181868672371
-barley,ROU,-0.24803556501865387
-barley,RUS,-0.01167596410959959
+barley,PSE,-0.20771759748458862
+barley,ROU,-0.1847398653626442
+barley,RUS,0.022683236747980118
 barley,RWA,7.5
-barley,SAU,0.24417193233966827
-barley,SDN,-0.5208059549331665
-barley,SRB,0.018527566455304623
-barley,SVK,-0.24230915307998657
-barley,SVN,0.007554816547781229
-barley,SWE,-0.3674428313970566
-barley,SWZ,3.8268796503543854
-barley,SYR,-0.2647043466567993
-barley,THA,-0.23803533613681793
-barley,TJK,-0.21763186901807785
-barley,TKM,-0.3388209640979767
-barley,TUN,-0.04373730346560478
-barley,TUR,-0.022361659444868565
-barley,TZA,-0.4475072771310806
+barley,SAU,0.3193706274032593
+barley,SDN,-0.5082430243492126
+barley,SRB,0.06350743863731623
+barley,SVK,-0.15996039658784866
+barley,SVN,0.0963093675673008
+barley,SWE,-0.31865230202674866
+barley,SWZ,3.8763365000486374
+barley,SYR,-0.2321603149175644
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+barley,TJK,-0.20233191549777985
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+barley,TUN,-0.029169680550694466
+barley,TUR,-0.008482548408210278
+barley,TZA,-0.43014998733997345
 barley,UGA,7.5
-barley,UKR,-0.20721228420734406
-barley,URY,-0.06483060866594315
-barley,USA,-0.297669917345047
-barley,UZB,-0.18670298904180527
+barley,UKR,-0.17224381864070892
+barley,URY,0.0010968458373099566
+barley,USA,-0.21126827597618103
+barley,UZB,-0.13293258100748062
 barley,VEN,-0.0
-barley,VNM,-0.3764279633760452
-barley,YEM,-0.1480489894747734
-barley,ZAF,-0.3346172869205475
-barley,ZMB,0.44975806772708893
-barley,ZWE,3.6794625520706177
+barley,VNM,-0.3515133559703827
+barley,YEM,-0.131764717400074
+barley,ZAF,-0.28450870513916016
+barley,ZMB,0.5984707474708557
+barley,ZWE,3.755083441734314
 biomass-sorghum,AFG,-0.0
 biomass-sorghum,AGO,-0.0
 biomass-sorghum,ALB,-0.0
@@ -657,1696 +657,1696 @@ biomass-sorghum,ZMB,-0.0
 biomass-sorghum,ZWE,-0.0
 buckwheat,AFG,7.5
 buckwheat,AGO,-0.0
-buckwheat,ALB,-0.34026381373405457
+buckwheat,ALB,-0.31473226845264435
 buckwheat,ARE,-0.0
-buckwheat,ARG,-0.1132350042462349
-buckwheat,ARM,-0.14355302602052689
-buckwheat,AUS,0.061543576419353485
-buckwheat,AUT,0.40539830923080444
-buckwheat,AZE,-0.02318031946197152
-buckwheat,BDI,-0.3384832292795181
-buckwheat,BEL,4.195145934820175
+buckwheat,ARG,-0.07080786675214767
+buckwheat,ARM,-0.10982950776815414
+buckwheat,AUS,0.1682348996400833
+buckwheat,AUT,0.5571326911449432
+buckwheat,AZE,0.007860350422561169
+buckwheat,BDI,-0.31818579137325287
+buckwheat,BEL,4.308464050292969
 buckwheat,BEN,-7.5
-buckwheat,BFA,-3.9459003657102585
+buckwheat,BFA,-3.9402782320976257
 buckwheat,BGD,-0.0
-buckwheat,BGR,3.821528732776642
-buckwheat,BIH,-0.1375880241394043
-buckwheat,BLR,-1.6597093660384417
+buckwheat,BGR,3.8752453923225403
+buckwheat,BIH,-0.011582749895751476
+buckwheat,BLR,-1.402353965677321
 buckwheat,BLZ,-0.0
-buckwheat,BOL,-0.29669225215911865
-buckwheat,BRA,-0.13568757474422455
-buckwheat,BTN,-0.7398595809936523
+buckwheat,BOL,-0.28166598081588745
+buckwheat,BRA,-0.09735213965177536
+buckwheat,BTN,-0.7083434462547302
 buckwheat,BWA,-3.75
 buckwheat,CAF,-3.75
-buckwheat,CAN,0.36997202038764954
-buckwheat,CHE,0.6434931755065918
-buckwheat,CHL,0.553341805934906
-buckwheat,CHN,-0.2106873095035553
-buckwheat,CIV,-3.876148208975792
+buckwheat,CAN,0.44640663266181946
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+buckwheat,CHN,-0.15005596727132797
+buckwheat,CIV,-3.8627756983041763
 buckwheat,CMR,-0.0
 buckwheat,COD,-0.0
 buckwheat,COG,-7.5
 buckwheat,COL,-7.5
-buckwheat,CYP,-0.1381441131234169
-buckwheat,CZE,0.4483575224876404
-buckwheat,DEU,0.69321009516716
-buckwheat,DNK,0.27304769307374954
-buckwheat,DZA,-0.061452221125364304
-buckwheat,ECU,-0.11253005266189575
-buckwheat,EGY,-0.11970629915595055
-buckwheat,ERI,-0.389007031917572
-buckwheat,ESP,-0.15250995755195618
-buckwheat,EST,0.18713349103927612
-buckwheat,ETH,-0.2049001008272171
-buckwheat,FIN,0.07744266651570797
-buckwheat,FRA,0.5128408521413803
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+buckwheat,ETH,-0.16212164610624313
+buckwheat,FIN,0.17461688071489334
+buckwheat,FRA,0.629973441362381
 buckwheat,GAB,-0.0
-buckwheat,GBR,0.3049951195716858
-buckwheat,GEO,-0.6187827885150909
+buckwheat,GBR,0.4082783907651901
+buckwheat,GEO,-0.5301356017589569
 buckwheat,GHA,-0.0
-buckwheat,GIN,-0.3674273192882538
+buckwheat,GIN,-0.34839172661304474
 buckwheat,GMB,-7.5
 buckwheat,GNB,-7.5
-buckwheat,GRC,-0.35138922929763794
+buckwheat,GRC,-0.32762311398983
 buckwheat,GTM,-0.0
 buckwheat,GUF,-0.0
 buckwheat,GUY,-0.0
-buckwheat,HRV,0.3449907898902893
-buckwheat,HUN,0.4579189568758011
+buckwheat,HRV,0.46024537086486816
+buckwheat,HUN,0.6169795393943787
 buckwheat,IND,7.5
-buckwheat,IRL,0.6341009438037872
-buckwheat,IRN,0.04638579115271568
-buckwheat,IRQ,0.02791389264166355
-buckwheat,ISR,-0.06325134634971619
-buckwheat,ITA,-0.14168671146035194
+buckwheat,IRL,0.7621231973171234
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 buckwheat,JOR,7.5
-buckwheat,JPN,-0.5144569873809814
-buckwheat,KAZ,-0.005827120039612055
+buckwheat,JPN,-0.4973832219839096
+buckwheat,KAZ,0.020682262256741524
 buckwheat,KEN,-7.5
-buckwheat,KGZ,3.6352781802415848
+buckwheat,KGZ,3.6837487667798996
 buckwheat,KHM,-3.75
-buckwheat,KOR,-0.1408061608672142
-buckwheat,LAO,-0.2260388284921646
+buckwheat,KOR,-0.0806836225092411
+buckwheat,LAO,-0.1832190603017807
 buckwheat,LBN,-0.0
 buckwheat,LBR,-0.0
 buckwheat,LBY,7.5
 buckwheat,LSO,-0.0
-buckwheat,LTU,0.17998933792114258
-buckwheat,LUX,0.43682892620563507
-buckwheat,LVA,0.24078983813524246
-buckwheat,MAR,-0.32410450279712677
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 buckwheat,MDA,7.5
-buckwheat,MEX,-1.326383113861084
-buckwheat,MKD,-0.07153259962797165
-buckwheat,MLI,-0.4067258983850479
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+buckwheat,MNG,-0.05737898126244545
 buckwheat,MOZ,-0.0
 buckwheat,MRT,-3.75
 buckwheat,MWI,-0.0
 buckwheat,MYS,-0.0
 buckwheat,NAM,7.5
 buckwheat,NER,-7.5
-buckwheat,NGA,-0.38269612193107605
-buckwheat,NLD,0.26306384801864624
-buckwheat,NOR,0.09328645607456565
-buckwheat,NPL,-0.2925569415092468
-buckwheat,NZL,0.5594194531440735
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+buckwheat,NZL,0.7319852113723755
 buckwheat,OMN,-7.5
 buckwheat,PER,-7.5
-buckwheat,POL,0.08421476185321808
-buckwheat,PRT,-0.1930157095193863
+buckwheat,POL,0.20831246674060822
+buckwheat,PRT,-0.15807794034481049
 buckwheat,PRY,-0.0
-buckwheat,PSE,0.2362893745303154
-buckwheat,ROU,3.845378376543522
-buckwheat,RUS,-0.0955246165394783
+buckwheat,PSE,0.34246066212654114
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 buckwheat,RWA,-7.5
-buckwheat,SAU,0.14852427877485752
+buckwheat,SAU,0.2824164927005768
 buckwheat,SDN,-7.5
-buckwheat,SEN,-0.2730281800031662
+buckwheat,SEN,-0.2488170638680458
 buckwheat,SLE,-7.5
 buckwheat,SOM,-0.0
-buckwheat,SRB,0.33741579949855804
+buckwheat,SRB,0.474866658449173
 buckwheat,SSD,-3.75
 buckwheat,SUR,-0.0
-buckwheat,SVK,0.1375400349497795
-buckwheat,SVN,0.10204009152948856
-buckwheat,SWE,0.3126779943704605
+buckwheat,SVK,0.292789950966835
+buckwheat,SVN,0.23253999650478363
+buckwheat,SWE,0.44951771199703217
 buckwheat,SWZ,-0.0
-buckwheat,SYR,-0.07181978970766068
-buckwheat,TCD,-0.22768273949623108
+buckwheat,SYR,-0.004234156105667353
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 buckwheat,TGO,-7.5
-buckwheat,THA,-0.27319659292697906
-buckwheat,TJK,3.7623356487601995
-buckwheat,TKM,-0.0
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 buckwheat,TWN,-0.0
-buckwheat,TZA,-0.2811509370803833
+buckwheat,TZA,-0.25286514312028885
 buckwheat,UGA,-0.0
-buckwheat,UKR,-0.10474169254302979
-buckwheat,URY,-0.002912199357524514
-buckwheat,USA,0.26465490460395813
-buckwheat,UZB,3.75
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+buckwheat,USA,0.333598792552948
+buckwheat,UZB,3.7757482174783945
 buckwheat,VEN,-0.0
 buckwheat,VNM,-0.0
 buckwheat,YEM,-0.0
-buckwheat,ZAF,-0.23897287994623184
+buckwheat,ZAF,-0.17252831161022186
 buckwheat,ZMB,-0.0
 buckwheat,ZWE,-7.5
-cabbage,AFG,-5.458901405334473
-cabbage,AGO,-6.242916584014893
-cabbage,ALB,-2.694267511367798
-cabbage,ARE,-0.848116934299469
-cabbage,ARG,-3.1361855268478394
-cabbage,ARM,-0.9832352204248309
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+cabbage,ARE,-0.8437827229499817
+cabbage,ARG,-3.1464935541152954
+cabbage,ARM,-0.9978720545768738
 cabbage,ATG,-7.5
 cabbage,AUS,-7.5
-cabbage,AUT,-2.7943302392959595
-cabbage,AZE,0.47921913862228394
-cabbage,BDI,-6.664140701293945
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+cabbage,BDI,-6.662137031555176
 cabbage,BEL,-7.5
-cabbage,BEN,-5.91268253326416
-cabbage,BFA,-7.260309219360352
-cabbage,BGD,-0.028768425807356834
-cabbage,BGR,-0.5199608951807022
-cabbage,BHS,-6.192922115325928
-cabbage,BIH,-0.9258549213409424
-cabbage,BLR,3.884076476097107
+cabbage,BEN,-5.961350202560425
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+cabbage,BHS,-6.318889141082764
+cabbage,BIH,-0.9802998304367065
+cabbage,BLR,3.8620680570602417
 cabbage,BLZ,-7.5
-cabbage,BOL,-2.239059090614319
+cabbage,BOL,-2.264072895050049
 cabbage,BRA,-7.5
 cabbage,BRB,-7.5
 cabbage,BRN,-7.5
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 cassava,BRB,-7.5
 cassava,BRN,-7.5
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 cassava,BWA,-7.5
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 chickpea,BRB,-7.5
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 chickpea,SUR,-0.0
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 chickpea,SWE,-7.5
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 chickpea,TWN,-7.5
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 citrus,BDI,-0.0
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 citrus,BGR,-0.0
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 citrus,CHN,-7.5
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 citrus,COM,-0.0
 citrus,CPV,-0.0
 citrus,CRI,7.5
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-citrus,CYP,-1.2825583219528198
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 citrus,DOM,7.5
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 citrus,EGY,-0.0
 citrus,ERI,-0.0
 citrus,ESP,-0.0
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 citrus,FJI,-7.5
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 citrus,IDN,-7.5
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 citrus,ISR,-7.5
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 citrus,JAM,-7.5
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 citrus,JPN,-7.5
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 citrus,LSO,-0.0
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 citrus,MKD,-0.0
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 citrus,MMR,-0.0
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 citrus,MRT,-0.0
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 citrus,NER,-0.0
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 citrus,OMN,7.5
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 citrus,PNG,-0.0
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 citrus,SUR,-7.5
 citrus,SVN,-0.0
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 citrus,TJK,-7.5
 citrus,TKM,-0.0
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 citrus,TWN,-0.0
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 citrus,UGA,-0.0
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 citrus,UZB,-7.5
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 citrus,VUT,-0.0
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 cocoa,COM,-7.5
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 cocoa,GRD,-0.0
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 cocoa,GUF,7.5
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 cocoa,JAM,-0.0
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 cocoa,LAO,-0.0
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 cocoa,MEX,-0.0
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 cocoa,PRY,7.5
 cocoa,RWA,-0.0
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 cocoa,STP,-0.0
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 cocoa,THA,-0.0
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 cocoa,TZA,-0.0
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 coconut,BHS,-7.5
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 coconut,CAF,3.75
 coconut,CHN,-7.5
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 coconut,GRD,-7.5
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 foxtail-millet,BEN,-0.539930909872055
 foxtail-millet,BFA,-0.3755684345960617
 foxtail-millet,BGD,-7.5
@@ -2355,16 +2355,16 @@ foxtail-millet,BLR,-0.41845284402370453
 foxtail-millet,BOL,-0.0
 foxtail-millet,BRA,7.5
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 oil-palm,GUF,7.5
 oil-palm,GUY,7.5
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 oil-palm,IDN,-1.9075301885604858
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 oil-palm,MOZ,-0.0
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 oil-palm,MYS,-2.86047625541687
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 oil-palm,PNG,-2.0511077642440796
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 oil-palm,SLV,-0.0
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 oil-palm,THA,-2.507928729057312
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 oil-palm,VNM,-2.1283262968063354
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 olive,ARM,-0.0
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 olive,BDI,-7.5
 olive,BGR,-0.0
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 olive,BLZ,3.75
 olive,BRA,-0.0
 olive,BRN,-7.5
@@ -3214,27 +3214,27 @@ olive,BWA,-0.0
 olive,CHL,-0.8160418272018433
 olive,CHN,-0.0
 olive,COL,-0.0
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 olive,DNK,-0.0
 olive,DOM,-3.75
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 olive,GAB,-7.5
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 olive,GIN,3.75
 olive,GMB,-4.6906039118766785
 olive,GNB,7.5
 olive,GNQ,-0.0
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 olive,GTM,-0.0
 olive,HND,-3.75
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 olive,IRQ,-0.0
 olive,ISR,0.4749826192855835
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 olive,JOR,-0.39328569173812866
 olive,KAZ,-0.0
 olive,KGZ,-0.0
@@ -3244,11 +3244,11 @@ olive,LBN,-2.4122220277786255
 olive,LBR,-7.5
 olive,LBY,-1.1340842247009277
 olive,LSO,-0.0
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 olive,MDA,-7.5
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 olive,MKD,-0.23874251544475555
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 olive,MNE,-0.5115975439548492
 olive,MNG,-7.5
 olive,NAM,-0.0
@@ -3258,12 +3258,12 @@ olive,NZL,-0.0
 olive,PAK,-0.0
 olive,PER,-0.0
 olive,PRT,0.6496662497520447
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 olive,ROU,-0.0
 olive,RUS,-0.0
 olive,RWA,-7.5
 olive,SAU,-0.0
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 olive,SOM,-0.0
 olive,SRB,-0.0
 olive,SVN,-1.1723512411117554
@@ -3271,8 +3271,8 @@ olive,SWZ,-0.0
 olive,SYR,-0.8998320698738098
 olive,TJK,-0.0
 olive,TKM,-0.0
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 olive,TWN,-7.5
 olive,URY,-0.7440776228904724
 olive,USA,-0.0
@@ -3280,2062 +3280,2063 @@ olive,UZB,-1.1885267496109009
 olive,YEM,-7.5
 olive,ZAF,-0.0
 olive,ZMB,-7.5
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 onion,AUS,-7.5
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 onion,BRN,-7.5
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 silage-maize,PRI,-1.4522095322608948
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 silage-maize,PRY,-0.32282423973083496
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 silage-maize,RWA,-0.49209724366664886
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 silage-maize,SEN,-0.0
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 silage-maize,SOM,-0.800838828086853
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 silage-maize,SSD,-1.0131532549858093
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 silage-maize,SUR,-7.5
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 silage-maize,SWE,-0.0
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 silage-maize,TCD,-0.46798017621040344
 silage-maize,TGO,-0.17069346457719803
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 silage-maize,TKM,-0.9653987288475037
 silage-maize,TLS,-0.22068962454795837
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 silage-maize,TWN,-0.0
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 silage-maize,URY,-0.3631618022918701
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 silage-maize,YEM,-0.6849543452262878
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 silage-maize,ZMB,-0.5472856760025024
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 sorghum,AFG,7.5
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 sorghum,ALB,7.5
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+sorghum,ARG,0.005415458232164383
 sorghum,ARM,7.5
-sorghum,AUS,0.2125818282365799
-sorghum,AUT,0.19404298067092896
-sorghum,AZE,0.07747026719152927
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 sorghum,BEL,-0.0
-sorghum,BEN,-0.297161266207695
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 sorghum,BIH,-0.0
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-sorghum,BOL,-0.00177929294295609
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 sorghum,CAN,7.5
 sorghum,CHE,-0.0
-sorghum,CHN,0.19951322674751282
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-sorghum,COG,-0.15370352566242218
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 sorghum,DEU,7.5
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 sorghum,EST,-0.0
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 sorghum,FJI,-7.5
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 sorghum,GEO,7.5
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 sorghum,GUY,-0.0
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 sorghum,IDN,-7.5
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 sorghum,IRN,7.5
 sorghum,IRQ,-0.0
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 sorghum,ITA,-0.0
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 sorghum,KAZ,7.5
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 sorghum,LTU,7.5
 sorghum,LUX,-0.0
 sorghum,LVA,-0.0
-sorghum,MAR,0.47893720865249634
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 sorghum,MKD,7.5
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 sorghum,MNE,-0.0
 sorghum,MNG,7.5
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 sorghum,MYS,-0.0
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 sorghum,PAN,-3.75
 sorghum,PER,7.5
 sorghum,PNG,-0.0
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-sorghum,PRY,0.13040293008089066
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 sorghum,SUR,-0.0
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 sorghum,TUR,7.5
 sorghum,TWN,-7.5
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 sorghum,VNM,-0.0
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 soybean,ARM,7.5
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-soybean,AZE,0.34570345282554626
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 soybean,CRI,7.5
-soybean,CUB,-0.24367224425077438
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 soybean,DNK,7.5
 soybean,DZA,-0.0
-soybean,ECU,-0.4436011165380478
-soybean,EGY,-0.988853245973587
-soybean,ERI,-0.31481896340847015
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 soybean,EST,7.5
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-soybean,FRA,-0.3447503298521042
-soybean,GAB,-0.6627455055713654
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 soybean,GNQ,-0.0
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-soybean,GTM,0.034896884113550186
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+soybean,GTM,0.011613997630774975
 soybean,GUF,-0.0
 soybean,GUY,-7.5
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-soybean,HRV,-0.3314940333366394
-soybean,HUN,0.054011551197618246
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 soybean,JOR,7.5
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 soybean,LVA,7.5
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 soybean,MYS,3.75
 soybean,NAM,7.5
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 soybean,PAK,7.5
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 soybean,PNG,-0.0
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 soybean,SEN,7.5
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 soybean,SSD,7.5
 soybean,SUR,-0.0
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 soybean,TJK,7.5
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 sugarbeet,GEO,7.5
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 sugarbeet,IND,7.5
 sugarbeet,IRL,-0.0
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 sugarbeet,ISR,7.5
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 sugarbeet,JOR,7.5
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 sugarbeet,MNG,7.5
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 sugarbeet,PAK,-0.0
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 sugarbeet,PSE,7.5
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 sugarbeet,SYR,-0.0
 sugarbeet,TJK,7.5
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 sugarcane,ARE,7.5
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 sugarcane,AUS,-3.696481704711914
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 sugarcane,BRB,-0.0
 sugarcane,BRN,-0.0
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 sugarcane,CAF,-2.297977924346924
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 sugarcane,CHN,-7.5
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 sugarcane,DZA,7.5
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 sugarcane,GRD,-7.5
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 sugarcane,GUF,-0.0
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 sugarcane,ISR,7.5
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-sugarcane,ZMB,-2.706911563873291
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 sugarcane,ZWE,-7.5
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 sunflower,BGD,7.5
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 sunflower,BTN,7.5
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 sunflower,COL,7.5
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 sunflower,DNK,7.5
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 sunflower,ERI,7.5
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 sunflower,FIN,7.5
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 sunflower,GUF,-0.0
 sunflower,GUY,7.5
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 sunflower,MYS,-0.0
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 sunflower,NPL,7.5
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 sunflower,PER,7.5
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 sunflower,SOM,3.75
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 sunflower,SUR,7.5
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 sunflower,TCD,-0.0
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 sunflower,TKM,-0.0
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 sunflower,VNM,7.5
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 sweet-potato,AUT,-7.5
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 sweet-potato,BRB,-7.5
 sweet-potato,BRN,-7.5
 sweet-potato,BTN,7.5
 sweet-potato,BWA,-0.0
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 sweet-potato,CHN,-7.5
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 sweet-potato,CPV,-7.5
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 sweet-potato,ESP,-7.5
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 sweet-potato,GRC,-7.5
 sweet-potato,GRD,-7.5
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 sweet-potato,GUY,3.75
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 sweet-potato,ISR,-7.5
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 sweet-potato,JAM,-7.5
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 sweet-potato,JPN,-7.5
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 sweet-potato,LUX,-7.5
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 sweet-potato,MNG,-7.5
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 sweet-potato,NZL,-7.5
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 sweet-potato,PAN,7.5
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 sweet-potato,PRT,-7.5
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 sweet-potato,SEN,-7.5
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 sweet-potato,SSD,3.75
 sweet-potato,SUR,-7.5
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 sweet-potato,SYR,-7.5
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 tea,AGO,2.2871118783950806
 tea,ALB,-7.5
 tea,ARG,-0.0
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 tea,BRA,-0.0
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-tea,BTN,5.0422492027282715
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 tea,CAF,7.5
 tea,CHL,-0.0
 tea,CHN,-7.5
-tea,CMR,6.083775520324707
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 tea,COD,-2.9547393321990967
 tea,COG,7.5
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 tea,ECU,-7.5
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 tea,GEO,-7.5
 tea,GNQ,7.5
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-tea,GTM,-2.6567373275756836
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 tea,GUF,-0.0
 tea,GUY,7.5
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 tea,HRV,-0.0
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 tea,JPN,-7.5
 tea,KEN,-7.5
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 tea,LKA,-0.0
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 tea,NGA,7.5
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 tea,PAN,7.5
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 tea,SSD,-7.5
 tea,SUR,-0.0
 tea,SWZ,-0.0
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 tea,VEN,7.5
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-tea,ZMB,-1.6728596091270447
-tea,ZWE,1.0640445947647095
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+tea,ZWE,1.0640369653701782
 tomato,AFG,-7.5
 tomato,AGO,-7.5
 tomato,ALB,-7.5
-tomato,ARE,-5.580718994140625
+tomato,ARE,-6.009433269500732
 tomato,ARG,-7.5
-tomato,ARM,-2.055345356464386
+tomato,ARM,-2.2367351055145264
 tomato,ATG,-0.0
 tomato,AUS,-7.5
 tomato,AUT,-3.75
 tomato,AZE,-7.5
 tomato,BDI,-7.5
 tomato,BEL,-3.75
-tomato,BEN,-1.8559918999671936
+tomato,BEN,-1.9216867089271545
 tomato,BFA,-7.5
-tomato,BGD,-0.2342651803046465
+tomato,BGD,-0.35448526591062546
 tomato,BGR,-7.5
-tomato,BHS,-2.2965750694274902
-tomato,BIH,-5.43223762512207
+tomato,BHS,-2.787252426147461
+tomato,BIH,-5.555819511413574
 tomato,BLR,-7.5
-tomato,BLZ,-7.127887487411499
-tomato,BOL,-0.5417996048927307
+tomato,BLZ,-7.218199253082275
+tomato,BOL,-0.6573793888092041
 tomato,BRA,-7.5
 tomato,BRB,-7.5
 tomato,BRN,-7.5
-tomato,BTN,-1.0474565029144287
+tomato,BTN,-1.0860890746116638
 tomato,BWA,-7.5
 tomato,CAF,7.5
-tomato,CAN,-4.285194635391235
+tomato,CAN,-3.670215606689453
 tomato,CHE,-7.5
 tomato,CHL,-7.5
 tomato,CHN,-7.5
-tomato,CIV,-3.785854458808899
+tomato,CIV,-3.850853681564331
 tomato,CMR,-7.5
 tomato,COD,-7.5
 tomato,COG,-7.5
@@ -5352,18 +5353,18 @@ tomato,DNK,7.5
 tomato,DOM,-7.5
 tomato,DZA,-7.5
 tomato,ECU,-7.5
-tomato,EGY,-1.4922935366630554
+tomato,EGY,-1.5385516285896301
 tomato,ERI,-3.75
-tomato,ESP,-0.44775596261024475
+tomato,ESP,-0.8938909769058228
 tomato,EST,-0.0
-tomato,ETH,-1.2253392934799194
+tomato,ETH,-1.2529821991920471
 tomato,FIN,-7.5
-tomato,FJI,-7.354276895523071
+tomato,FJI,-7.382971286773682
 tomato,FRA,-7.5
 tomato,GAB,-7.5
 tomato,GBR,-7.5
-tomato,GEO,-1.871579110622406
-tomato,GHA,-4.883427619934082
+tomato,GEO,-1.955545723438263
+tomato,GHA,-4.864142894744873
 tomato,GIN,-3.75
 tomato,GMB,-7.5
 tomato,GNB,7.5
@@ -5377,56 +5378,56 @@ tomato,HND,-7.5
 tomato,HRV,-7.5
 tomato,HTI,-7.5
 tomato,HUN,-7.5
-tomato,IDN,-6.2065958976745605
+tomato,IDN,-6.240472793579102
 tomato,IND,-7.5
-tomato,IRN,-0.07418914884328842
-tomato,IRQ,-1.2548446655273438
+tomato,IRN,-0.2685989439487457
+tomato,IRQ,-1.428577184677124
 tomato,ISR,-7.5
 tomato,ITA,-7.5
 tomato,JAM,-7.5
-tomato,JOR,4.454972267150879
+tomato,JOR,4.613020300865173
 tomato,JPN,-7.5
 tomato,KAZ,-7.5
-tomato,KEN,-4.1068878173828125
-tomato,KGZ,-7.150000095367432
+tomato,KEN,-3.9817135334014893
+tomato,KGZ,-7.208740711212158
 tomato,KHM,-3.75
-tomato,KOR,-5.972317218780518
+tomato,KOR,-6.035221338272095
 tomato,LAO,-7.5
 tomato,LBN,-7.5
 tomato,LBR,-7.5
 tomato,LBY,-7.5
-tomato,LKA,-3.201076865196228
-tomato,LSO,-5.091629505157471
+tomato,LKA,-3.2934858798980713
+tomato,LSO,-5.526358127593994
 tomato,LTU,-7.5
 tomato,LUX,-3.75
-tomato,LVA,-7.2614219188690186
-tomato,MAR,-2.476320803165436
-tomato,MDA,1.816321313381195
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+tomato,MAR,-2.9082216024398804
+tomato,MDA,1.723491132259369
 tomato,MDG,-7.5
 tomato,MEX,-7.5
-tomato,MKD,-5.337571382522583
-tomato,MLI,-7.065004348754883
+tomato,MKD,-5.520841717720032
+tomato,MLI,-7.157349109649658
 tomato,MLT,-7.5
 tomato,MMR,-7.5
 tomato,MNE,-7.5
 tomato,MNG,-0.0
 tomato,MOZ,-7.5
 tomato,MUS,-7.5
-tomato,MWI,-5.2521538734436035
+tomato,MWI,-5.070799350738525
 tomato,MYS,-3.75
 tomato,NAM,-7.5
-tomato,NER,-0.4988171309232712
+tomato,NER,-0.6928260922431946
 tomato,NGA,-7.5
 tomato,NIC,-7.5
 tomato,NLD,-7.5
 tomato,NOR,-0.0
 tomato,NPL,-7.5
-tomato,NZL,-4.934855699539185
+tomato,NZL,-4.867916703224182
 tomato,OMN,-7.5
-tomato,PAK,-0.9787199795246124
+tomato,PAK,-1.0334705114364624
 tomato,PAN,-7.5
 tomato,PER,-0.0
-tomato,PHL,-3.0687724351882935
+tomato,PHL,-3.116124391555786
 tomato,PNG,-7.5
 tomato,POL,-7.5
 tomato,PRI,-7.5
@@ -5435,14 +5436,14 @@ tomato,PRY,-7.5
 tomato,PSE,-7.5
 tomato,ROU,-7.5
 tomato,RUS,-7.5
-tomato,RWA,-3.0804985761642456
+tomato,RWA,-3.1285563707351685
 tomato,SAU,-7.5
 tomato,SDN,-7.5
-tomato,SEN,-4.333958625793457
+tomato,SEN,-4.383784651756287
 tomato,SLE,-7.5
-tomato,SLV,-6.86299729347229
+tomato,SLV,-7.094966650009155
 tomato,SOM,-7.5
-tomato,SRB,-2.029469784349203
+tomato,SRB,-2.240092620253563
 tomato,SSD,-7.5
 tomato,SUR,-7.5
 tomato,SVK,-7.5
@@ -5451,702 +5452,702 @@ tomato,SWE,-7.5
 tomato,SWZ,-7.5
 tomato,SYR,-7.5
 tomato,TCD,-7.5
-tomato,TGO,-1.2713310718536377
-tomato,THA,-4.6846232414245605
+tomato,TGO,-1.3027095794677734
+tomato,THA,-4.745960712432861
 tomato,TJK,-7.5
 tomato,TKM,-7.5
-tomato,TLS,-2.008157193660736
+tomato,TLS,-2.0356668829917908
 tomato,TTO,-7.5
-tomato,TUN,4.581813335418701
-tomato,TUR,-4.942208766937256
+tomato,TUN,4.2335450649261475
+tomato,TUR,-5.40532922744751
 tomato,TWN,-7.5
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+tomato,TZA,-0.7187851667404175
 tomato,UGA,-7.5
-tomato,UKR,7.276695728302002
+tomato,UKR,7.2868757247924805
 tomato,URY,-7.5
 tomato,USA,-7.5
 tomato,UZB,-7.5
 tomato,VEN,-7.5
 tomato,VNM,-7.5
-tomato,YEM,-5.748529672622681
+tomato,YEM,-5.8541765213012695
 tomato,ZAF,-7.5
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-tomato,ZWE,-3.832137942314148
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 watermelon,AGO,-0.0
-watermelon,ALB,1.9602904319763184
-watermelon,ARG,3.4863574504852295
-watermelon,ATG,-4.397117853164673
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 watermelon,AUS,-7.5
-watermelon,AZE,5.608684778213501
+watermelon,AZE,5.405060291290283
 watermelon,BDI,-0.0
 watermelon,BEN,-0.0
 watermelon,BFA,-0.0
 watermelon,BGD,-0.0
 watermelon,BGR,-0.0
 watermelon,BHS,-0.0
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-watermelon,BOL,1.0202150344848633
-watermelon,BRA,3.9913861751556396
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 watermelon,BRB,-0.0
 watermelon,BTN,7.5
-watermelon,BWA,0.8041449449956417
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 watermelon,CAF,-0.0
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-watermelon,CHN,-7.348897457122803
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 watermelon,CIV,-0.0
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 watermelon,COD,-0.0
 watermelon,COG,-0.0
-watermelon,COL,2.540942907333374
+watermelon,COL,2.342010259628296
 watermelon,COM,-0.0
 watermelon,CPV,-7.5
-watermelon,CRI,0.9928724765777588
-watermelon,CUB,-2.16875422000885
-watermelon,CYP,-4.212607383728027
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 watermelon,DOM,-7.5
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-watermelon,EGY,-1.6596209704875946
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 watermelon,ERI,-0.0
 watermelon,ESP,-7.5
 watermelon,ETH,-0.0
 watermelon,FJI,-7.5
-watermelon,FRA,0.5153346061706543
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 watermelon,GAB,-0.0
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 watermelon,GHA,-0.0
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 watermelon,GMB,-0.0
 watermelon,GNB,-0.0
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 watermelon,GUF,-0.0
 watermelon,GUY,-7.5
 watermelon,HND,-7.5
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 watermelon,HTI,-0.0
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 watermelon,JAM,-7.5
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 watermelon,JPN,-7.5
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 watermelon,KHM,-0.0
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 watermelon,LBN,-7.5
 watermelon,LBR,-0.0
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 watermelon,LKA,-0.0
 watermelon,LSO,-0.0
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 watermelon,MDG,-0.0
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-watermelon,MKD,4.806168079376221
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 watermelon,MMR,-0.0
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 watermelon,NIC,-0.0
 watermelon,NPL,-0.0
 watermelon,OMN,7.5
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+white-potato,TWN,-6.581104040145874
+white-potato,TZA,-0.9602226614952087
+white-potato,UGA,-7.065031051635742
+white-potato,UKR,-1.8508853912353516
 white-potato,URY,-7.5
-white-potato,USA,0.9548938274383545
-white-potato,UZB,-3.4913913011550903
-white-potato,VEN,-3.998827576637268
-white-potato,VNM,-4.851073503494263
-white-potato,YEM,-5.896713495254517
-white-potato,ZAF,-3.806148052215576
+white-potato,USA,-6.48323917388916
+white-potato,UZB,-5.730321407318115
+white-potato,VEN,-6.108365058898926
+white-potato,VNM,-6.79145359992981
+white-potato,YEM,-7.5
+white-potato,ZAF,-7.122694730758667
 white-potato,ZMB,-7.5
 white-potato,ZWE,-7.5
 yam,AFG,7.5
-yam,AGO,-2.934256672859192
-yam,ARG,-0.9703506827354431
+yam,AGO,-2.915623903274536
+yam,ARG,-0.970349907875061
 yam,ATG,-4.489343166351318
-yam,BDI,-4.483736991882324
-yam,BEN,-1.176262080669403
-yam,BFA,-2.702925205230713
-yam,BIH,-2.3084254264831543
-yam,BLZ,-1.4796286821365356
-yam,BOL,-5.195361852645874
-yam,BRA,-3.122835874557495
+yam,BDI,-4.474895000457764
+yam,BEN,-1.1533899307250977
+yam,BFA,-2.6838340759277344
+yam,BIH,-2.2863107323646545
+yam,BLZ,-1.9457488059997559
+yam,BOL,-5.195361614227295
+yam,BRA,-3.122835397720337
 yam,BRB,-7.5
-yam,BRN,-5.205021142959595
-yam,BTN,-4.7500691413879395
-yam,BWA,-4.2526044845581055
-yam,CAF,-3.997747778892517
+yam,BRN,-5.200504541397095
+yam,BTN,-4.776039123535156
+yam,BWA,-4.242413759231567
+yam,CAF,-3.9860188961029053
 yam,CHL,-0.0
-yam,CHN,-0.5765328705310822
-yam,CIV,-0.716604471206665
-yam,CMR,-2.934337615966797
-yam,COD,-4.241106748580933
-yam,COG,-4.2376439571380615
-yam,COL,-0.891929030418396
-yam,COM,-4.329634666442871
+yam,CHN,-0.5402792096138
+yam,CIV,-0.7082089781761169
+yam,CMR,-2.8625242710113525
+yam,COD,-4.230693340301514
+yam,COG,-4.227147579193115
+yam,COL,-0.8940050601959229
+yam,COM,-4.3197290897369385
 yam,CRI,-7.5
-yam,CUB,-2.660378932952881
-yam,DOM,-2.5111435651779175
-yam,ECU,-5.37092399597168
-yam,EGY,0.9687807261943817
-yam,ERI,-5.603344678878784
-yam,ETH,-1.9619032144546509
+yam,CUB,-2.660409092903137
+yam,DOM,-2.483414888381958
+yam,ECU,-5.37074089050293
+yam,EGY,1.0253205299377441
+yam,ERI,-5.601197242736816
+yam,ETH,-1.9373416304588318
 yam,FJI,-7.5
-yam,GAB,-4.2168543338775635
-yam,GHA,-1.229315161705017
-yam,GIN,-2.2652628421783447
+yam,GAB,-4.206446409225464
+yam,GHA,-1.2013828754425049
+yam,GIN,-2.2546385526657104
 yam,GMB,-7.5
-yam,GNB,-4.19596266746521
-yam,GNQ,-4.4858574867248535
+yam,GNB,-4.185503721237183
+yam,GNQ,-4.477081298828125
 yam,GRD,-7.5
 yam,GTM,7.5
 yam,GUF,7.5
 yam,GUY,-7.5
-yam,HND,-4.26451301574707
+yam,HND,-4.2645134925842285
 yam,HRV,-0.0
 yam,HTI,-5.360596656799316
-yam,IDN,-4.654703140258789
-yam,IND,-1.019332766532898
+yam,IDN,-4.645933389663696
+yam,IND,-0.9961451292037964
 yam,IRN,-0.0
-yam,ISR,-1.0109733641147614
+yam,ISR,-0.980597972869873
 yam,JAM,-7.5
-yam,JOR,4.1430752873420715
+yam,JOR,4.4035040736198425
 yam,JPN,-7.5
-yam,KEN,-1.559640347957611
-yam,KHM,-2.5132601261138916
-yam,LAO,1.9901503324508667
-yam,LBN,-4.706673860549927
-yam,LBR,-3.7932132482528687
-yam,LKA,-3.0666112899780273
+yam,KEN,-1.539107859134674
+yam,KHM,-2.4898996353149414
+yam,LAO,2.042736828327179
+yam,LBN,-4.69429886341095
+yam,LBR,-3.78039288520813
+yam,LKA,-3.049189805984497
 yam,LSO,-0.0
-yam,MDG,-4.059040307998657
-yam,MEX,2.8864649534225464
-yam,MLI,-5.245889902114868
-yam,MMR,0.9744532704353333
+yam,MDG,-4.047037363052368
+yam,MEX,3.244262456893921
+yam,MLI,-5.216529846191406
+yam,MMR,1.0198920369148254
 yam,MNE,-0.0
-yam,MOZ,-3.8874822854995728
-yam,MRT,-6.383007049560547
-yam,MUS,-3.4970991611480713
+yam,MOZ,-3.8747650384902954
+yam,MRT,-6.381232023239136
+yam,MUS,-3.481334090232849
 yam,MWI,-0.0
-yam,MYS,-2.711567759513855
-yam,NAM,-3.0224064588546753
-yam,NER,-5.324341058731079
-yam,NGA,-2.8321731090545654
-yam,NIC,-2.385351777076721
-yam,NPL,-5.01775050163269
-yam,PAK,1.2525835037231445
-yam,PAN,-3.2153533697128296
-yam,PER,-3.757173776626587
-yam,PHL,-1.6226337552070618
-yam,PNG,-2.632683753967285
+yam,MYS,-2.689858913421631
+yam,NAM,-3.0043797492980957
+yam,NER,-5.320603370666504
+yam,NGA,-2.8136426210403442
+yam,NIC,-2.3203004598617554
+yam,NPL,-5.011655330657959
+yam,PAK,1.2802709341049194
+yam,PAN,-3.215519905090332
+yam,PER,-3.4855637550354004
+yam,PHL,-1.6139899492263794
+yam,PNG,-2.6115013360977173
 yam,PRI,-7.5
 yam,PRY,7.5
-yam,PSE,-1.5258890390396118
-yam,RWA,-0.838718980550766
+yam,PSE,-1.4987291097640991
+yam,RWA,-0.8284357190132141
 yam,SAU,-0.0
-yam,SDN,-5.744183540344238
-yam,SEN,-2.15857595205307
-yam,SLB,-1.8003202676773071
-yam,SLE,-5.3781633377075195
-yam,SLV,2.729663610458374
+yam,SDN,-5.742918968200684
+yam,SEN,-2.142036259174347
+yam,SLB,-1.774495780467987
+yam,SLE,-5.374751806259155
+yam,SLV,2.7297595739364624
 yam,SOM,-0.0
-yam,SSD,-5.167559862136841
-yam,STP,-4.706610918045044
-yam,SUR,-2.3886003494262695
-yam,SWZ,-3.974542021751404
-yam,SYR,-2.0030922889709473
-yam,TCD,-4.035828590393066
-yam,TGO,-1.110143780708313
-yam,THA,-1.8080795407295227
-yam,TLS,-5.463002443313599
+yam,SSD,-5.1632537841796875
+yam,STP,-4.699181079864502
+yam,SUR,-2.388601541519165
+yam,SWZ,-3.962120532989502
+yam,SYR,-1.978471279144287
+yam,TCD,-4.024775505065918
+yam,TGO,-1.095928430557251
+yam,THA,-1.7797744274139404
+yam,TLS,-5.459644079208374
 yam,TTO,-7.5
-yam,TWN,-2.4488754272460938
-yam,TZA,-4.566601991653442
-yam,UGA,-3.9058055877685547
+yam,TWN,-2.4045424461364746
+yam,TZA,-4.558453798294067
+yam,UGA,-3.893244743347168
 yam,URY,7.5
-yam,USA,5.490311622619629
-yam,VEN,-2.3649719953536987
-yam,VNM,-3.4608877897262573
-yam,VUT,-3.1063307523727417
-yam,ZAF,-4.370602130889893
+yam,USA,5.4903130531311035
+yam,VEN,-2.3649706840515137
+yam,VNM,-3.535680413246155
+yam,VUT,-3.088310480117798
+yam,ZAF,-4.361093759536743
 yam,ZMB,3.75
-yam,ZWE,-3.2958728075027466
+yam,ZWE,-3.2789857387542725
diff --git a/data/curated/calibration/default/deviation_penalty.yaml b/data/curated/calibration/default/deviation_penalty.yaml
index e41f5ae3..3331dd52 100644
--- a/data/curated/calibration/default/deviation_penalty.yaml
+++ b/data/curated/calibration/default/deviation_penalty.yaml
@@ -14,9 +14,9 @@ components:
 - grassland
 - feed
 l1_costs:
-  cropland: 1.564181887678137
-  grassland: 0.15990866204242277
-  feed: 0.07454894173640202
+  cropland: 1.468666316419478
+  grassland: 0.16337061661222912
+  feed: 0.07852586344897906
 iterations: 3
 converged: true
-final_residual_log_inf: 0.015471831446956581
+final_residual_log_inf: 0.01697416334606783
diff --git a/data/curated/calibration/default/exogenous_feed.csv b/data/curated/calibration/default/exogenous_feed.csv
index a4e4d2b7..539ab70d 100644
--- a/data/curated/calibration/default/exogenous_feed.csv
+++ b/data/curated/calibration/default/exogenous_feed.csv
@@ -9,71 +9,74 @@ ARM,0.0,0.0,0.921945
 ATG,0.0,0.0,0.001203
 AZE,0.0,0.0,1.66953
 BFA,0.0,0.0,2.427716
-BGD,0.0,0.0,9.27415
-BHS,0.0,0.0,0.000234
+BGD,0.0,0.0,9.275321
+BHS,0.0,0.0,0.00024
 BOL,0.0,0.0,3.061832
-BRA,0.0,0.0,4.273273
+BRA,0.0,0.0,4.273265
 BRB,0.0,0.0,0.001185
 BRN,0.0,0.0,0.098262
 BTN,0.0,0.0,0.032702
 BWA,0.0,0.0,0.803633
 CAF,0.0,0.0,3.145721
 CHL,0.0,0.0,1.174709
+CHN,22.060617,8.937976,0.0
 CMR,0.0,0.0,1.961351
 COG,0.0,0.0,0.10183
-COL,0.0,0.0,4.952689
+COL,0.0,0.0,4.952398
 COM,0.0,0.0,0.023562
 CPV,0.0,0.0,0.014816
 CRI,0.0,0.0,0.47573
-CUB,0.0,0.0,0.474244
+CUB,0.0,0.0,0.474311
 CYP,0.0,0.0,0.026106
 DJI,0.0,0.0,0.253116
-DOM,0.0,0.0,0.732628
+DOM,0.0,0.0,0.732631
 DZA,0.0,0.0,7.00381
-ECU,0.0,0.0,0.999302
+ECU,0.0,0.0,0.99931
 EGY,0.0,0.0,7.632216
 ERI,0.0,0.0,0.647034
 ETH,0.0,0.0,3.538889
 GEO,0.0,0.0,0.291165
-GNB,0.0,0.0,0.082362
+GNB,0.0,0.0,0.082521
 GRD,0.0,0.0,0.003329
-GTM,0.0,0.0,0.88227
+GTM,0.0,0.0,0.88201
 HND,0.0,0.0,0.32824
 HTI,0.0,0.0,0.567069
-IND,0.0,0.0,289.219147
+IND,0.0,0.0,289.219116
 IRL,0.0,0.0,0.334866
 ISL,0.0,0.0,0.030581
 ISR,0.0,0.0,1.114113
 JAM,0.0,0.0,0.181489
 JOR,0.0,0.0,0.90952
-JPN,1.611782,1.221458,1.321806
-KEN,0.0,0.0,8.553471
-KOR,2.720941,0.374992,2.658246
-LAO,0.0,0.056044,0.0
+JPN,1.910623,1.221458,1.323049
+KEN,0.0,0.0,8.553256
+KHM,0.0,0.135121,0.0
+KOR,2.714647,0.374992,2.658246
+LAO,0.0,0.053306,0.0
 LBN,0.0,0.0,0.255602
-LBY,0.0,0.0,0.793361
-LSO,0.0,0.0,0.09329
+LBY,0.0,0.0,0.793355
+LSO,0.0,0.0,0.093215
 MAR,0.0,0.0,5.567759
 MEX,0.0,0.0,2.591533
 MLT,0.0,0.0,0.002164
 MNE,0.0,0.0,0.002303
 MNG,0.0,0.0,15.397595
 MRT,0.0,0.0,2.105997
-MYS,0.0,0.0,0.217635
+MYS,0.0,0.0,0.217634
 NAM,0.0,0.0,0.693243
 NER,0.0,0.0,1.711398
-NIC,0.0,0.0,1.291703
+NIC,0.0,0.0,1.291705
 NLD,0.0,0.0,0.117066
 NPL,0.0,0.0,15.855976
+NZL,0.0,0.72566,0.0
 OMN,0.0,0.0,1.601173
-PAK,0.0,6.85822,166.893402
-PAN,0.0,0.0,0.335552
-PER,0.0,0.0,0.826979
+PAK,0.0,13.613902,166.893402
+PAN,0.0,0.0,0.335515
+PER,0.0,0.0,0.827014
 PRI,0.0,0.0,0.093587
 PRY,0.0,0.0,0.155431
 PSE,0.0,0.0,0.253434
 RWA,0.0,0.0,0.380831
-SAU,0.0,0.0,3.207552
+SAU,0.0,0.0,3.207553
 SDN,0.0,0.0,11.693514
 SEN,0.0,0.0,0.880735
 SOM,0.0,0.0,5.319848
@@ -82,18 +85,19 @@ STP,0.0,0.0,0.00225
 SWZ,0.0,0.0,0.169067
 SYR,0.0,0.0,1.271963
 TCD,0.0,0.0,25.86199
+THA,1.626361,0.336465,0.0
 TJK,0.0,0.0,0.247151
 TKM,0.0,0.0,0.695718
-TLS,0.002871,0.01037,0.067521
-TTO,0.0,0.0,0.006878
+TLS,0.009463,0.01037,0.067521
+TTO,0.0,0.0,0.00687
 TUN,0.0,0.0,1.242337
-TUR,0.0,0.0,1.663914
-TWN,1.325626,0.00074,0.0
-TZA,0.0,0.0,9.637824
-UGA,0.0,0.0,3.557157
+TUR,0.0,0.0,1.66396
+TWN,1.330431,0.0,0.0
+TZA,0.0,0.0,9.637837
+UGA,0.0,0.0,3.557199
 URY,0.0,0.0,2.438584
-VEN,0.0,0.0,3.763239
+VEN,0.0,0.0,3.763237
 YEM,0.0,0.0,3.183531
-ZAF,0.0,0.0,5.481274
+ZAF,0.0,0.0,5.481237
 ZMB,0.0,0.0,2.941371
 ZWE,0.0,0.0,10.748748
diff --git a/data/curated/calibration/default/exogenous_forage.csv b/data/curated/calibration/default/exogenous_forage.csv
index 302824e5..785b5cbe 100644
--- a/data/curated/calibration/default/exogenous_forage.csv
+++ b/data/curated/calibration/default/exogenous_forage.csv
@@ -50,7 +50,7 @@ DNK,0.0
 DOM,0.0
 DZA,0.0
 ECU,0.0
-EGY,20.905624
+EGY,23.06752
 ERI,0.0
 ESP,0.0
 EST,0.0
@@ -76,7 +76,7 @@ HRV,0.0
 HTI,0.0
 HUN,0.0
 IDN,0.0
-IND,147.362427
+IND,158.148087
 IRL,0.0
 IRN,0.0
 IRQ,0.0
@@ -107,7 +107,7 @@ MEX,0.0
 MKD,0.0
 MLI,0.0
 MLT,0.0
-MMR,8.618978
+MMR,8.619004
 MNE,0.0
 MNG,0.0
 MOZ,0.0
@@ -156,21 +156,21 @@ SYR,0.0
 TCD,0.0
 TGO,0.0
 THA,0.0
-TJK,3.385071
-TKM,9.849616
+TJK,3.370195
+TKM,9.84958
 TLS,0.0
 TTO,0.0
 TUN,0.0
-TUR,0.0
+TUR,5.091218
 TWN,0.0
 TZA,0.0
 UGA,0.0
 UKR,0.0
 URY,0.0
 USA,0.0
-UZB,15.069183
+UZB,15.074042
 VEN,0.0
-VNM,4.006212
+VNM,7.549819
 VUT,0.0
 YEM,0.0
 ZAF,0.0
diff --git a/data/curated/calibration/default/fodder_conversion.csv b/data/curated/calibration/default/fodder_conversion.csv
index b98b623a..08823e62 100644
--- a/data/curated/calibration/default/fodder_conversion.csv
+++ b/data/curated/calibration/default/fodder_conversion.csv
@@ -44,7 +44,7 @@ CRI,1.0
 CUB,1.0
 CYP,1.0
 CZE,1.0
-DEU,0.733978
+DEU,0.733979
 DJI,1.0
 DNK,1.0
 DOM,1.0
@@ -53,7 +53,7 @@ ECU,1.0
 EGY,1.0
 ERI,1.0
 ESP,1.0
-EST,0.4755
+EST,0.475501
 ETH,1.0
 FIN,1.0
 FJI,1.0
@@ -85,7 +85,7 @@ ISR,1.0
 ITA,1.0
 JAM,1.0
 JOR,1.0
-JPN,0.763625
+JPN,0.763731
 KAZ,1.0
 KEN,1.0
 KGZ,1.0
@@ -128,13 +128,13 @@ PAK,1.0
 PAN,1.0
 PER,1.0
 PHL,1.0
-PNG,0.98602
+PNG,0.986016
 POL,1.0
 PRI,1.0
 PRT,1.0
 PRY,1.0
 PSE,1.0
-ROU,0.753902
+ROU,0.753901
 RUS,1.0
 RWA,1.0
 SAU,1.0
diff --git a/data/curated/calibration/default/food_demand.csv b/data/curated/calibration/default/food_demand.csv
index 8d13304d..415df6fb 100644
--- a/data/curated/calibration/default/food_demand.csv
+++ b/data/curated/calibration/default/food_demand.csv
@@ -1,59 +1,59 @@
 food,multiplier,net_slack_mt,consumption_mt
-apple,0.9964445828712245,0.3677937702741474,103.44602530528209
-banana,1.0,0.0,76.84251734232384
-barley-hulled,1.0,0.0,2.4754922425192945
-buckwheat,0.5704728472172369,2.0801994113294313,4.8429986273336
-cabbage,1.0575492624412763,-12.668129742145538,220.12670892302413
-carrot,1.0261888928606584,-3.1861228942871094,121.65931989715318
-cassava,1.0,0.0,149.8683383288726
-chickpea,1.0,0.0,4.478103744486589
-citrus,1.0512843915064407,-6.78463888168335,132.29442101952736
-cocoa-powder,0.9075563452415882,0.4895184128326946,5.295316526720853
-coconut,1.0,0.0,2.927419820128307
-coconut-oil,1.0,0.0,1.0790433989332087
-coffee-green,0.8670375363851814,1.5566738274646923,11.707618715416174
-cottonseed-oil,1.0,0.0,4.167233729645886
-cowpea,1.0,0.0,2.4007039731534405
-dairy,0.9734069343412688,21.343825676478446,802.6086931978352
-dairy-buffalo,0.9953466808233736,0.6439452366903424,138.38406785523694
-dry-pea,1.0,0.0,1.9984850413002277
+apple,1.0176942965794498,-1.7921872404403985,101.28615355763031
+banana,1.0,0.0,61.92605545501283
+barley-hulled,1.0,0.0,2.363381609684666
+buckwheat,0.6021654848612251,1.8252767490432689,4.58803014717455
+cabbage,1.0029202519677038,-0.6778625786682824,232.12468861078378
+carrot,0.9980453528955658,0.24450801113562193,125.09061639870924
+cassava,1.0,0.0,141.4352919480798
+chickpea,0.851845683838795,1.524140822612253,10.287522241025044
+citrus,1.0790555938505626,-10.189429759979248,128.8894215283508
+cocoa-powder,0.9075733242820169,0.4894285178525024,5.295316682662815
+coconut,1.0,0.0,6.34032663140988
+coconut-oil,1.0,0.0,0.8418856649004738
+coffee-green,0.8670401855759174,1.5566428339807317,11.70761888261768
+cottonseed-oil,1.0,0.0,3.0515467647910555
+cowpea,1.0,0.0,7.114182244494486
+dairy,1.2246117331091,-143.29542238643626,637.9694435501006
+dairy-buffalo,1.3121931617236198,-32.77072726066217,104.96939484431641
+dry-pea,1.0,0.0,5.485299755293454
 eggs,1.0,0.0,82.1042355971731
-flour-white,1.0,0.0,255.93475320073776
-flour-wholemeal,1.0,0.0,60.569861044496065
-foxtail-millet,1.0,0.0,3.410226098225394
-gram,1.0,0.0,4.478103744486589
-groundnut,1.0,0.0,8.565938512935645
-groundnut-oil,1.0,0.0,3.8057418215357757
-maize,1.0,0.0,106.49321075204716
-mango,1.0328178922234819,-1.5925385136361,48.526532502188275
+flour-white,1.0,0.0,286.239123725798
+flour-wholemeal,1.0,0.0,54.90201137149779
+foxtail-millet,1.0,0.0,3.3274160848886822
+gram,0.851845683838795,1.524140822612253,10.287522241025044
+groundnut,1.0,0.0,18.481263347034655
+groundnut-oil,1.0,0.0,2.7442013276422585
+maize,1.0,0.0,135.35636450930542
+mango,0.8877094698642342,6.339796370230033,56.458869350468376
 meat-cattle,0.952400557178112,2.308293342590332,48.49412526166998
 meat-chicken,0.9895527158773688,0.8225271608680487,78.7311947500566
 meat-pig,1.0184832063942477,-1.3977279365062714,75.62150779971125
 meat-sheep,1.0872487686362589,-0.8913660735124722,10.21637425312656
-oat,1.0,0.0,1.6780094729440407
-olive-oil,1.087174604317402,-0.3948613061802462,4.529545149898922
-onion,0.9808794720277011,5.4889238413306884,287.0696797328419
-palm-oil,1.0,0.0,26.487057077565623
-pearl-millet,1.0,0.0,3.410226098225394
-phaseolus-bean,1.0,0.0,7.570616604105453
-pigeon-pea,1.0,0.0,0.24040035817802163
-plantain,0.9198868952820304,1.9715050811446417,24.609021059477527
-potato,0.96850232522493,8.17799163016025,259.6379475170979
-rapeseed-oil,1.0,0.0,12.744215775339399
-rendered-fat,1.0,0.0,8.848241350830904
-rice-brown,1.0,0.0,50.39190488612803
-rice-white,1.0,0.0,334.26266210823087
-rye,1.0,0.0,0.9243344825217719
-sesame-oil,1.0,0.0,1.3729037433736266
-sesame-seed,1.0,0.0,1.130570922832021
-sorghum,1.0,0.0,11.647140672110254
-soy,1.0,0.0,3.8865096387889935
-soybean-oil,1.0,0.0,32.44238214136567
-sugar,1.0,0.0,154.22733178921044
-sunflower-oil,0.9065321687063365,1.3318660576501316,14.249459297558587
-sunflower-seed,1.0,0.0,1.9189327573915307
-sweet-potato,1.0,0.0,55.2135853107211
-tea-dried,0.9839945251497785,0.10681728940289759,6.673796960258187
-tomato,0.9262395867792931,10.365316073257418,140.52681676609063
-watermelon,1.233258642637127,-12.6926908493042,54.414664793577685
-yam,0.9338066770295699,2.294876266399342,34.66930142522847
+oat,1.0,0.0,1.3459041314893057
+olive-oil,1.2668818455267778,-1.0373781486414373,3.8870315311025934
+onion,0.9942682966895774,1.6232445702771656,283.2045698048314
+palm-oil,1.0,0.0,21.015993093062207
+pearl-millet,1.0,0.0,3.3274160848886822
+phaseolus-bean,1.0,0.0,19.514856620977298
+pigeon-pea,1.0,0.0,0.6056507342029818
+plantain,0.9925006851893258,0.17104906383610796,22.808625608387956
+potato,1.0,0.0,201.368981817679
+rapeseed-oil,1.0,0.0,9.829376123423572
+rendered-fat,1.0,0.0,6.7047482385273725
+rice-brown,1.0,0.0,50.360311507614824
+rice-white,1.0,0.0,368.47423265205
+rye,1.0,0.0,1.409586055518048
+sesame-oil,1.0,0.0,0.9990107708404139
+sesame-seed,1.0,0.0,2.652062442012948
+sorghum,1.0,0.0,7.308949817877874
+soy,1.0,0.0,9.501944424790508
+soybean-oil,1.0,0.0,25.274998503079587
+sugar,1.0,0.0,122.5639517629752
+sunflower-oil,1.0,0.0,11.998033070957717
+sunflower-seed,1.0,0.0,4.278090002906765
+sweet-potato,1.0,0.0,39.67487038175386
+tea-dried,0.9839926829455669,0.10682958776851592,6.673797201944581
+tomato,1.0092259783163016,-1.1898891925811768,128.97160081969196
+watermelon,1.172003167677489,-9.848674774169922,57.25868254145462
+yam,0.9337791237755573,2.2958778842339598,34.67000159364443
diff --git a/data/curated/calibration/default/food_waste.yaml b/data/curated/calibration/default/food_waste.yaml
index 1663fddc..b86e2117 100644
--- a/data/curated/calibration/default/food_waste.yaml
+++ b/data/curated/calibration/default/food_waste.yaml
@@ -9,27 +9,27 @@
 # from a solved validation scenario. Do not hand-edit; rerun
 # `tools/calibrate food_waste` instead.
 fruits:
-  baseline_consumption_mt: 303.5781328359153
-  baseline_negative_slack_mt: 111.94602305834997
+  baseline_consumption_mt: 304.3055541798858
+  baseline_negative_slack_mt: 101.51363178525935
   baseline_positive_slack_mt: 0.0
-  waste_retention_multiplier: 0.7305908177168986
+  waste_retention_multiplier: 0.7498550209156989
 oil:
-  baseline_consumption_mt: 102.02891421989989
-  baseline_negative_slack_mt: 0.34316493559163064
-  baseline_positive_slack_mt: 1.494497413728368
-  waste_retention_multiplier: 1.0114131649297862
+  baseline_consumption_mt: 78.5514709682393
+  baseline_negative_slack_mt: 1.0906066512688994
+  baseline_positive_slack_mt: 0.0
+  waste_retention_multiplier: 0.986306150167512
 starchy_vegetable:
-  baseline_consumption_mt: 548.8485709926401
+  baseline_consumption_mt: 442.3559145834927
   baseline_negative_slack_mt: 0.0
-  baseline_positive_slack_mt: 26.776431421577627
-  waste_retention_multiplier: 1.0512887576103511
+  baseline_positive_slack_mt: 2.6019802293478733
+  waste_retention_multiplier: 1.0059169003983315
 stimulants:
   baseline_consumption_mt: 23.68795094791676
   baseline_negative_slack_mt: 0.0
-  baseline_positive_slack_mt: 2.164228206801454
-  waste_retention_multiplier: 1.100550830952086
+  baseline_positive_slack_mt: 2.164119136995396
+  waste_retention_multiplier: 1.1005452540238354
 vegetables:
-  baseline_consumption_mt: 514.297463798519
-  baseline_negative_slack_mt: 255.08508318044915
+  baseline_consumption_mt: 624.8539440262484
+  baseline_negative_slack_mt: 144.53753955755383
   baseline_positive_slack_mt: 0.0
-  waste_retention_multiplier: 0.6684548094026077
+  waste_retention_multiplier: 0.8121404478194867
diff --git a/data/curated/calibration/default/grassland_cost.csv b/data/curated/calibration/default/grassland_cost.csv
index 125aaf7b..c0082fab 100644
--- a/data/curated/calibration/default/grassland_cost.csv
+++ b/data/curated/calibration/default/grassland_cost.csv
@@ -1,175 +1,175 @@
 country,correction_bnusd_per_mha
-AFG,-0.0002783827803796157
+AFG,-0.0002518995097489096
 AGO,-0.4345769137144089
 ALB,-0.20895707607269287
 ARE,-0.0
 ARG,-0.21808408945798874
 ARM,-0.0
 ATG,-0.0
-AUS,-0.11690391600131989
+AUS,-0.11690390110015869
 AUT,1.2174659058672432e-09
-AZE,0.915382593870163
+AZE,0.9842424392700195
 BDI,-1.1082043727128621e-08
-BEL,0.08366924151778221
-BEN,3.0615766048431396
-BFA,1.4070661664009094
+BEL,0.08367127552628517
+BEN,3.3434815406799316
+BFA,1.5375214219093323
 BGD,-0.0
-BGR,-0.3083830028772354
-BHS,-0.030966363847255707
-BIH,-0.4456564486026764
-BLR,-0.019071068614721298
+BGR,-0.3087073564529419
+BHS,-0.0316774295642972
+BIH,-0.4457664042711258
+BLR,-0.019591745920479298
 BLZ,-0.7379351556301117
 BOL,-0.5958932638168335
-BRA,-0.5617609918117523
-BRB,0.865568995475769
+BRA,-0.3751921057701111
+BRB,0.9515631198883057
 BRN,7.141561985015869
-BTN,-0.2882518470287323
+BTN,-0.2860439717769623
 BWA,-0.0
-CAF,-0.46485017240047455
-CAN,-0.21885906904935837
-CHE,-0.11954976990818977
+CAF,-0.4648502320051193
+CAN,-0.21693821251392365
+CHE,-0.11954900994896889
 CHL,-0.0009876639232970774
-CHN,-1.8447241956209837e-08
-CIV,-0.5140847712755203
-CMR,4.203157901763916
+CHN,-1.4773446466165296e-08
+CIV,-0.4997792989015579
+CMR,4.572341203689575
 COD,-0.8133789598941803
 COG,-0.8903338015079498
-COL,-0.6280472874641418
-COM,7.846769900510253e-10
+COL,-0.7695494294166565
+COM,7.03753166853005e-10
 CPV,-0.0
-CRI,-0.040161214768886566
-CUB,-0.17234943062067032
-CYP,0.2899330109357834
-CZE,-0.28652919828891754
+CRI,-0.5568442642688751
+CUB,-0.1725609302520752
+CYP,0.31014789640903473
+CZE,-0.2703308165073395
 DEU,-0.3941321074962616
 DJI,-0.0
-DNK,1.817373514175415
-DOM,7.18969817015136e-09
+DNK,1.7655537724494934
+DOM,-9.206058138033768e-09
 DZA,-0.0
-ECU,-0.28694531321525574
+ECU,-0.10834560729563236
 EGY,-0.0
-ERI,-0.036436695605516434
-ESP,-0.04443208500742912
+ERI,-5.40463007681069e-09
+ESP,-0.044432083144783974
 EST,-0.25669121742248535
 ETH,-1.3543125509940523e-09
-FIN,1.6001724004745483
-FJI,-0.7943975925445557
-FRA,-3.0738755540937746e-08
+FIN,1.7167028188705444
+FJI,-0.8518642485141754
+FRA,-2.7449107875554546e-08
 GAB,-0.8158496618270874
-GBR,-0.057546284049749374
+GBR,-0.04809712804853916
 GEO,-0.2082834094762802
-GHA,3.9665030371338617e-08
-GIN,-0.29034821689128876
-GMB,1.1484697461128235
-GNB,1.0530049800872803
+GHA,-2.9578572480204457e-08
+GIN,-0.16346535086631775
+GMB,1.2533257603645325
+GNB,1.1492018103599548
 GNQ,-0.5248542279005051
-GRC,-0.1027151420712471
-GRD,0.6195181608200073
-GTM,-1.962737661287406e-08
+GRC,-0.0983348861336708
+GRD,0.6877356171607971
+GTM,1.0484134893573582e-08
 GUF,-1.0722496509552002
 GUY,-1.106971263885498
 HND,-0.6534972786903381
-HRV,-0.1872950717806816
+HRV,-0.18737270683050156
 HTI,5.54345191972061e-09
 HUN,-0.4170793741941452
 IDN,-0.5775293707847595
-IND,1.61296808719635
+IND,1.91958349943161
 IRL,-2.0363737363027212e-08
 IRN,-0.0
 IRQ,-0.0
-ISL,-0.08968127891421318
-ISR,0.6709401309490204
-ITA,0.04259900003671646
-JAM,3.508358597755432
+ISL,-0.08585232496261597
+ISR,0.7179081439971924
+ITA,0.04259064048528671
+JAM,3.8619683980941772
 JOR,-0.0
-JPN,-0.49684692919254303
+JPN,-0.4974181354045868
 KAZ,-1.7263098728292903e-09
-KEN,-0.17522157728672028
+KEN,-0.17468049377202988
 KGZ,-0.0
-KHM,-0.4328657388687134
-KOR,-1.1598136895329958e-09
+KHM,-0.4334357678890228
+KOR,-1.3437390089521273e-09
 LAO,2.91002910657312e-09
-LBN,0.4902341067790985
+LBN,0.524543821811676
 LBR,-0.7553918659687042
-LBY,-0.00020786204549949616
-LKA,-0.45856063067913055
-LSO,-0.44252099096775055
-LTU,-0.052242062985897064
-LUX,0.02335542067885399
-LVA,-0.30377297103405
+LBY,-0.0
+LKA,-0.19719894230365753
+LSO,-0.443109393119812
+LTU,-0.05371060222387314
+LUX,0.037571508437395096
+LVA,-0.3037813901901245
 MAR,-0.0
 MDA,-0.36603914201259613
 MDG,-0.6942926049232483
 MEX,-0.08276043459773064
-MKD,-0.2815301865339279
+MKD,-0.2818893939256668
 MLI,1.2537319515892875e-09
 MLT,-0.0
-MMR,4.511943263452167e-09
-MNE,-0.30944468080997467
-MNG,-0.0623512826859951
-MOZ,-0.5657868981361389
+MMR,8.1712165922454e-09
+MNE,-0.2836945503950119
+MNG,-0.06247838772833347
+MOZ,-0.5657002627849579
 MRT,-3.3330131710140876e-10
 MUS,-3.315589225394433e-08
-MWI,-0.1905755028128624
-MYS,-3.07201153404435e-09
+MWI,-0.19057554006576538
+MYS,2.3094663959000172e-08
 NAM,-0.006759356707334518
 NER,-0.0
-NGA,8.823755504039354e-09
-NIC,-0.42816048860549927
+NGA,-0.19652122259140015
+NIC,-0.5845303535461426
 NLD,0.7907610237598419
-NOR,0.5461024045944214
-NPL,-1.011426720154418e-09
+NOR,0.5854896306991577
+NPL,-0.0
 NZL,-0.0
 OMN,-0.0
 PAK,-0.0
-PAN,-0.6082254946231842
-PER,-0.6303564608097076
-PHL,-0.12024912238121033
+PAN,-0.2060026302933693
+PER,-0.629342257976532
+PHL,-0.12064796686172485
 PNG,-0.7848113179206848
 POL,0.019568821415305138
 PRI,-2.7517170764213006e-09
-PRT,-0.10262405686080456
+PRT,-0.09743807837367058
 PRY,-0.536945641040802
-PSE,0.6586481034755707
+PSE,0.7093142569065094
 ROU,-0.4245498776435852
-RUS,-1.0431203456562343e-08
+RUS,-1.064132693073816e-08
 RWA,2.7728375151525597e-08
 SAU,-0.0
-SDN,-0.07339249178767204
-SEN,0.35445454716682434
-SLB,-0.06637441366910934
+SDN,-0.07209280133247375
+SEN,0.3868510276079178
+SLB,-0.06834621354937553
 SLE,-0.41614776849746704
-SLV,3.365933418273926
+SLV,3.70309579372406
 SOM,-4.7665072999691915e-09
 SRB,-0.29573993384838104
 SSD,-0.3232929855585098
-STP,0.5821866691112518
-SUR,6.8290815353393555
+STP,0.63315150141716
+SUR,6.86613392829895
 SVK,-0.4092821776866913
-SVN,-0.23763713240623474
-SWE,1.6527102589607239
-SWZ,-1.973749874650821e-08
-SYR,0.1336965560913086
-TCD,-0.0883248895406723
+SVN,-0.23911219835281372
+SWE,1.7724632024765015
+SWZ,2.2704852220556404e-08
+SYR,0.14306625723838806
+TCD,-0.0883248969912529
 TGO,-0.3382970243692398
 THA,-2.5447631557540262e-08
-TJK,0.35738885402679443
+TJK,0.39508867263793945
 TKM,-0.0
 TLS,-0.22272039204835892
-TTO,-1.3588031588795957e-08
-TUN,0.3425496220588684
-TUR,1.0832912921905518
-TWN,-0.3161562830209732
-TZA,-0.4196213632822037
-UGA,-4.5179753271895606e-08
-UKR,-0.023232679814100266
+TTO,-1.403013705214562e-09
+TUN,0.3739815652370453
+TUR,1.1239208579063416
+TWN,-0.3147922307252884
+TZA,-0.06884757429361343
+UGA,-0.7039666175842285
+UKR,-0.02324177697300911
 URY,-0.17097113281488419
-USA,-0.4050147831439972
-UZB,0.2210150510072708
-VEN,-0.5381573736667633
-VNM,5.650000095367432
-VUT,-0.17226660251617432
+USA,-0.4052763879299164
+UZB,-0.0
+VEN,-0.26539246737957
+VNM,6.096648454666138
+VUT,-0.17226548492908478
 YEM,2.695993539525432e-09
-ZAF,-0.003347835154272616
+ZAF,-0.0033502724254503846
 ZMB,-0.3225127309560776
-ZWE,1.0077592730522156
+ZWE,1.0959289073944092
diff --git a/data/curated/calibration/default/grassland_yield.csv b/data/curated/calibration/default/grassland_yield.csv
index 4b70204e..c57034c5 100644
--- a/data/curated/calibration/default/grassland_yield.csv
+++ b/data/curated/calibration/default/grassland_yield.csv
@@ -13,12 +13,12 @@ AUS,0.236373
 AUT,0.553455
 AZE,1.0
 BDI,0.437887
-BEL,0.928962
+BEL,0.928966
 BEN,1.0
 BFA,1.0
 BGD,1.0
 BGR,0.074007
-BHS,0.670605
+BHS,0.669108
 BIH,0.014661
 BLR,0.422191
 BLZ,0.101975
@@ -29,27 +29,27 @@ BRN,1.0
 BTN,0.03586
 BWA,1.0
 CAF,0.583977
-CAN,0.051827
-CHE,0.407119
+CAN,0.051832
+CHE,0.40712
 CHL,0.686938
-CHN,0.287704
+CHN,0.287707
 CIV,0.033413
 CMR,1.0
 COD,0.022128
 COG,0.00797
-COL,0.103541
+COL,0.103539
 COM,1.0
 CPV,1.0
 CRI,0.380109
-CUB,0.277716
+CUB,0.277685
 CYP,1.0
 CZE,0.138284
 DEU,0.0
 DJI,1.0
 DNK,1.0
-DOM,0.568047
+DOM,0.567979
 DZA,0.79171
-ECU,0.363762
+ECU,0.36376
 EGY,1.0
 ERI,0.518486
 ESP,0.429751
@@ -59,46 +59,46 @@ FIN,1.0
 FJI,0.041419
 FRA,0.488102
 GAB,0.006069
-GBR,0.400758
+GBR,0.40073
 GEO,0.122798
 GHA,0.178955
-GIN,0.173006
+GIN,0.173001
 GMB,1.0
 GNB,1.0
 GNQ,0.077495
-GRC,0.075747
+GRC,0.075784
 GRD,1.0
-GTM,0.571596
+GTM,0.571476
 GUF,0.0
 GUY,0.018762
 HND,0.147424
-HRV,0.284229
+HRV,0.284196
 HTI,0.92669
 HUN,0.0
 IDN,0.144268
 IND,1.0
 IRL,0.633733
-IRN,0.769462
+IRN,0.769453
 IRQ,1.0
-ISL,0.062139
+ISL,0.062237
 ISR,1.0
-ITA,0.884304
+ITA,0.884272
 JAM,1.0
 JOR,1.0
 JPN,0.0
 KAZ,0.256835
-KEN,0.467611
+KEN,0.467676
 KGZ,0.842055
 KHM,0.398086
 KOR,1.0
 LAO,0.404176
 LBN,1.0
 LBR,0.016113
-LBY,0.894502
-LKA,0.1936
-LSO,0.048047
+LBY,0.894499
+LKA,0.193473
+LSO,0.048049
 LTU,0.341665
-LUX,0.783686
+LUX,0.783687
 LVA,0.0
 MAR,1.0
 MDA,0.0
@@ -117,21 +117,21 @@ MWI,0.76798
 MYS,0.436125
 NAM,0.291928
 NER,1.0
-NGA,0.665344
-NIC,0.295305
+NGA,0.665316
+NIC,0.295306
 NLD,1.0
 NOR,1.0
 NPL,1.0
-NZL,0.560501
+NZL,0.560406
 OMN,1.0
 PAK,1.0
-PAN,0.256704
-PER,0.083649
-PHL,0.384616
+PAN,0.256559
+PER,0.083668
+PHL,0.384506
 PNG,0.0
 POL,0.560559
 PRI,0.43587
-PRT,0.081037
+PRT,0.081039
 PRY,0.068488
 PSE,1.0
 ROU,0.0
@@ -140,7 +140,7 @@ RWA,0.794264
 SAU,1.0
 SDN,0.419271
 SEN,1.0
-SLB,0.580873
+SLB,0.580748
 SLE,0.093191
 SLV,1.0
 SOM,0.555429
@@ -155,24 +155,24 @@ SWZ,0.283263
 SYR,1.0
 TCD,0.695865
 TGO,0.206827
-THA,0.856193
+THA,0.856195
 TJK,1.0
 TKM,1.0
 TLS,0.234166
-TTO,0.74618
+TTO,0.743954
 TUN,1.0
 TUR,1.0
-TWN,0.107393
-TZA,0.406752
-UGA,0.405149
-UKR,0.166786
+TWN,0.107613
+TZA,0.406749
+UGA,0.405131
+UKR,0.166781
 URY,0.270063
-USA,0.0312
+USA,0.031197
 UZB,1.0
-VEN,0.19052
+VEN,0.190522
 VNM,1.0
-VUT,0.176533
+VUT,0.176534
 YEM,0.592561
-ZAF,0.346815
+ZAF,0.346808
 ZMB,0.203235
 ZWE,1.0
diff --git a/data/curated/calibration/default/provenance.yaml b/data/curated/calibration/default/provenance.yaml
index 5d3673f9..3a136fe4 100644
--- a/data/curated/calibration/default/provenance.yaml
+++ b/data/curated/calibration/default/provenance.yaml
@@ -4,8 +4,8 @@
 # tools/calibrate; do not edit by hand.
 # Licensing: see the annotation in REUSE.toml.
 base_config: config/default.yaml
-generated_at: '2026-07-01T20:40:06+00:00'
-git_commit: 66fc774742905270edac1d5d35dbdd4e4ab42626
+generated_at: '2026-07-01T22:29:28+00:00'
+git_commit: d3f07986e72ad291e10e5a11844c18752cddf7cc
 source: default
 structural_config:
   aggregation.irrigated_area_source: current
@@ -652,6 +652,7 @@ structural_config:
   data.usda.nutrients.fat: Total lipid (fat)
   data.usda.nutrients.protein: Protein
   data.usda.retrieve_nutrition: false
+  diet.anchor_groups_to_gbd: false
   diet.baseline_age: All ages
   diet.fbs_override_foods:
   - yam
diff --git a/data/curated/calibration/gbd-anchored/animal_cost.csv b/data/curated/calibration/gbd-anchored/animal_cost.csv
new file mode 100644
index 00000000..1f45298c
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/animal_cost.csv
@@ -0,0 +1,1226 @@
+product,country,correction_bnusd_per_mt_feed
+dairy,AFG,0.019429629668593407
+dairy,AGO,-0.14344584196805954
+dairy,ALB,-0.023533342871814966
+dairy,ARE,-0.04143645071235369
+dairy,ARG,-0.19398537278175354
+dairy,ARM,-0.011384313460439444
+dairy,ASM,-0.0
+dairy,ATG,-0.24454953521490097
+dairy,AUS,-0.05782910343259573
+dairy,AUT,0.008029387099668384
+dairy,AZE,-0.10857096128165722
+dairy,BDI,-0.19728994369506836
+dairy,BEL,-0.04548066761344671
+dairy,BEN,-0.2638223394751549
+dairy,BFA,-0.269638791680336
+dairy,BGD,-0.06376965902745724
+dairy,BGR,0.030918722972273827
+dairy,BHS,-0.13557196035981178
+dairy,BIH,0.12179218605160713
+dairy,BLR,-0.034614475443959236
+dairy,BLZ,-0.2122793346643448
+dairy,BOL,-0.2064630165696144
+dairy,BRA,-0.170407272875309
+dairy,BRB,-0.3012048602104187
+dairy,BRN,-0.06272971606813371
+dairy,BTN,-0.19480304047465324
+dairy,BWA,-0.24311847239732742
+dairy,CAF,-0.14549646526575089
+dairy,CAN,-0.08521859161555767
+dairy,CHE,-0.03879193961620331
+dairy,CHL,-0.18294814229011536
+dairy,CHN,0.14896606653928757
+dairy,CIV,-0.2627760320901871
+dairy,CMR,-0.2687765583395958
+dairy,COD,-0.13804767839610577
+dairy,COG,-0.1433083601295948
+dairy,COL,-0.2351013422012329
+dairy,COM,-0.12192871049046516
+dairy,CPV,-0.23724471777677536
+dairy,CRI,-0.05980869010090828
+dairy,CUB,-0.2101064920425415
+dairy,CYP,0.023470744490623474
+dairy,CZE,-0.0012322113616392016
+dairy,DEU,-0.000984611688181758
+dairy,DJI,-0.0307138804346323
+dairy,DNK,-0.02052507922053337
+dairy,DOM,-0.06886794418096542
+dairy,DZA,-0.04815932270139456
+dairy,ECU,-0.17810802161693573
+dairy,EGY,-0.1400747150182724
+dairy,ERI,-0.12191927060484886
+dairy,ESP,-0.07785327918827534
+dairy,EST,-0.0013701971620321274
+dairy,ETH,-0.15060389414429665
+dairy,FIN,0.07162491045892239
+dairy,FJI,-0.07856539636850357
+dairy,FRA,-0.07695738971233368
+dairy,GAB,-0.09453262016177177
+dairy,GBR,0.005195342469960451
+dairy,GEO,-0.061261977069079876
+dairy,GHA,-0.2660602629184723
+dairy,GIN,-0.13472575321793556
+dairy,GMB,-0.24835162609815598
+dairy,GNB,-0.2703486606478691
+dairy,GNQ,-0.0
+dairy,GRC,-0.011651250533759594
+dairy,GRD,-0.26645299792289734
+dairy,GTM,-0.17119775712490082
+dairy,GUF,-0.0
+dairy,GUY,-0.20975037664175034
+dairy,HND,-0.17513011395931244
+dairy,HRV,-0.012483799830079079
+dairy,HTI,-0.04040829837322235
+dairy,HUN,0.0047488147392869
+dairy,IDN,-0.07947330176830292
+dairy,IND,-0.13518212735652924
+dairy,IRL,-0.07643910497426987
+dairy,IRN,0.012022069655358791
+dairy,IRQ,-0.18654124438762665
+dairy,ISL,-0.1321047767996788
+dairy,ISR,-0.1253916546702385
+dairy,ITA,-0.01128673367202282
+dairy,JAM,-0.05617798492312431
+dairy,JOR,0.07839963957667351
+dairy,JPN,0.1331169605255127
+dairy,KAZ,0.02844948787242174
+dairy,KEN,-0.1234113797545433
+dairy,KGZ,0.17862170189619064
+dairy,KHM,-0.0774066373705864
+dairy,KOR,0.1833977848291397
+dairy,LAO,-0.06888186559081078
+dairy,LBN,0.06968764960765839
+dairy,LBR,-0.10710962116718292
+dairy,LBY,-0.05695127695798874
+dairy,LKA,-0.00494186719879508
+dairy,LSO,-0.18405035138130188
+dairy,LTU,0.026316655799746513
+dairy,LUX,-0.03983202949166298
+dairy,LVA,0.018532104790210724
+dairy,MAR,-0.20673362165689468
+dairy,MDA,-0.002309698611497879
+dairy,MDG,-0.12068253196775913
+dairy,MEX,-0.05552579369395971
+dairy,MKD,-0.010522140190005302
+dairy,MLI,-0.2076418176293373
+dairy,MLT,-0.059369830414652824
+dairy,MMR,-0.056584956124424934
+dairy,MNE,0.12012782692909241
+dairy,MNG,-0.016167042776942253
+dairy,MOZ,-0.15593316406011581
+dairy,MRT,-0.16157327219843864
+dairy,MUS,-0.09081005305051804
+dairy,MWI,-0.13882508501410484
+dairy,MYS,-0.10095001384615898
+dairy,NAM,-0.17151788622140884
+dairy,NER,-0.2742208167910576
+dairy,NGA,-0.15956056490540504
+dairy,NIC,-0.28511493653059006
+dairy,NLD,-0.1237916499376297
+dairy,NOR,0.0016769617795944214
+dairy,NPL,-0.0642337673343718
+dairy,NZL,-0.06467725802212954
+dairy,OMN,-0.18222838267683983
+dairy,PAK,0.0227879723533988
+dairy,PAN,-0.15243341401219368
+dairy,PER,-0.26029617339372635
+dairy,PHL,0.02710425853729248
+dairy,PNG,-0.08279344253242016
+dairy,POL,0.01425881078466773
+dairy,PRI,-0.19545725733041763
+dairy,PRT,-0.0534888356924057
+dairy,PRY,-0.22964193671941757
+dairy,PSE,0.016415192745625973
+dairy,ROU,0.03590121492743492
+dairy,RUS,0.01579718478024006
+dairy,RWA,-0.12964098528027534
+dairy,SAU,0.15324107557535172
+dairy,SDN,-0.07867367193102837
+dairy,SEN,-0.263763390481472
+dairy,SLB,-0.05312766134738922
+dairy,SLE,-0.12126448936760426
+dairy,SLV,-0.24916129559278488
+dairy,SOM,-0.16309911012649536
+dairy,SRB,0.02889898233115673
+dairy,SSD,-0.10765414498746395
+dairy,STP,-0.21055006980895996
+dairy,SUR,-0.2560344487428665
+dairy,SVK,0.005477115046232939
+dairy,SVN,0.0028893444687128067
+dairy,SWE,0.03155193571001291
+dairy,SWZ,-0.2434571161866188
+dairy,SYR,-0.0580794564448297
+dairy,TCD,-0.13665609061717987
+dairy,TGO,-0.18102441728115082
+dairy,THA,0.04348058137111366
+dairy,TJK,0.1546802893280983
+dairy,TKM,0.08995599672198296
+dairy,TLS,-0.1358565054833889
+dairy,TTO,-0.1465824767947197
+dairy,TUN,-0.16077736765146255
+dairy,TUR,-0.1088663712143898
+dairy,TWN,0.18445171415805817
+dairy,TZA,-0.14841287210583687
+dairy,UGA,-0.27034351229667664
+dairy,UKR,-0.023471102118492126
+dairy,URY,-0.25223128497600555
+dairy,USA,-0.0
+dairy,UZB,0.08897577226161957
+dairy,VEN,-0.16654188558459282
+dairy,VNM,-0.14795337244868279
+dairy,VUT,-0.09853671863675117
+dairy,YEM,-0.09186061099171638
+dairy,ZAF,-0.20334048569202423
+dairy,ZMB,-0.1397862657904625
+dairy,ZWE,-0.5003806352615356
+dairy-buffalo,AFG,-0.0
+dairy-buffalo,AGO,-0.0
+dairy-buffalo,ALB,-0.0
+dairy-buffalo,ARE,-0.0
+dairy-buffalo,ARG,-0.0
+dairy-buffalo,ARM,-0.0
+dairy-buffalo,ASM,-0.0
+dairy-buffalo,ATG,-0.0
+dairy-buffalo,AUS,-0.0
+dairy-buffalo,AUT,-0.0
+dairy-buffalo,AZE,-0.0
+dairy-buffalo,BDI,-0.0
+dairy-buffalo,BEL,-0.0
+dairy-buffalo,BEN,-0.0
+dairy-buffalo,BFA,-0.0
+dairy-buffalo,BGD,-0.1059691458940506
+dairy-buffalo,BGR,0.0035036588087677956
+dairy-buffalo,BHS,-0.0
+dairy-buffalo,BIH,-0.0
+dairy-buffalo,BLR,-0.0
+dairy-buffalo,BLZ,-0.0
+dairy-buffalo,BOL,-0.0
+dairy-buffalo,BRA,-0.0
+dairy-buffalo,BRB,-0.0
+dairy-buffalo,BRN,-0.2609296813607216
+dairy-buffalo,BTN,-0.0
+dairy-buffalo,BWA,-0.0
+dairy-buffalo,CAF,-0.0
+dairy-buffalo,CAN,-0.0
+dairy-buffalo,CHE,-0.0
+dairy-buffalo,CHL,-0.0
+dairy-buffalo,CHN,-0.15695834159851074
+dairy-buffalo,CIV,-0.0
+dairy-buffalo,CMR,-0.0
+dairy-buffalo,COD,-0.0
+dairy-buffalo,COG,-0.0
+dairy-buffalo,COL,-0.0
+dairy-buffalo,COM,-0.0
+dairy-buffalo,CPV,-0.0
+dairy-buffalo,CRI,-0.0
+dairy-buffalo,CUB,-0.0
+dairy-buffalo,CYP,-0.0
+dairy-buffalo,CZE,-0.0
+dairy-buffalo,DEU,-0.0
+dairy-buffalo,DJI,-0.0
+dairy-buffalo,DNK,-0.0
+dairy-buffalo,DOM,-0.0
+dairy-buffalo,DZA,-0.0
+dairy-buffalo,ECU,-0.0
+dairy-buffalo,EGY,-0.09786959923803806
+dairy-buffalo,ERI,-0.0
+dairy-buffalo,ESP,-0.0
+dairy-buffalo,EST,-0.0
+dairy-buffalo,ETH,-0.0
+dairy-buffalo,FIN,-0.0
+dairy-buffalo,FJI,-0.0
+dairy-buffalo,FRA,-0.0
+dairy-buffalo,GAB,-0.0
+dairy-buffalo,GBR,-0.0
+dairy-buffalo,GEO,-0.0
+dairy-buffalo,GHA,-0.0
+dairy-buffalo,GIN,-0.0
+dairy-buffalo,GMB,-0.0
+dairy-buffalo,GNB,-0.0
+dairy-buffalo,GNQ,-0.0
+dairy-buffalo,GRC,-0.056585246697068214
+dairy-buffalo,GRD,-0.0
+dairy-buffalo,GTM,-0.0
+dairy-buffalo,GUF,-0.0
+dairy-buffalo,GUY,-0.0
+dairy-buffalo,HND,-0.0
+dairy-buffalo,HRV,-0.0
+dairy-buffalo,HTI,-0.0
+dairy-buffalo,HUN,-0.0
+dairy-buffalo,IDN,-0.1853792667388916
+dairy-buffalo,IND,-0.13360632956027985
+dairy-buffalo,IRL,-0.0
+dairy-buffalo,IRN,0.06616165861487389
+dairy-buffalo,IRQ,-0.17470766603946686
+dairy-buffalo,ISL,-0.0
+dairy-buffalo,ISR,-0.0
+dairy-buffalo,ITA,0.04079308919608593
+dairy-buffalo,JAM,-0.0
+dairy-buffalo,JOR,-0.0
+dairy-buffalo,JPN,-0.0
+dairy-buffalo,KAZ,-0.0
+dairy-buffalo,KEN,-0.0
+dairy-buffalo,KGZ,-0.0
+dairy-buffalo,KHM,-0.0
+dairy-buffalo,KOR,-0.0
+dairy-buffalo,LAO,-0.0
+dairy-buffalo,LBN,-0.0
+dairy-buffalo,LBR,-0.0
+dairy-buffalo,LBY,-0.0
+dairy-buffalo,LKA,-0.09794122353196144
+dairy-buffalo,LSO,-0.0
+dairy-buffalo,LTU,-0.0
+dairy-buffalo,LUX,-0.0
+dairy-buffalo,LVA,-0.0
+dairy-buffalo,MAR,-0.0
+dairy-buffalo,MDA,-0.0
+dairy-buffalo,MDG,-0.0
+dairy-buffalo,MEX,-0.0
+dairy-buffalo,MKD,-0.0
+dairy-buffalo,MLI,-0.0
+dairy-buffalo,MLT,-0.0
+dairy-buffalo,MMR,-0.10471758525818586
+dairy-buffalo,MNE,-0.0
+dairy-buffalo,MNG,-0.0
+dairy-buffalo,MOZ,-0.0
+dairy-buffalo,MRT,-0.0
+dairy-buffalo,MUS,-0.0
+dairy-buffalo,MWI,-0.0
+dairy-buffalo,MYS,-0.13420968130230904
+dairy-buffalo,NAM,-0.0
+dairy-buffalo,NER,-0.0
+dairy-buffalo,NGA,-0.0
+dairy-buffalo,NIC,-0.0
+dairy-buffalo,NLD,-0.0
+dairy-buffalo,NOR,-0.0
+dairy-buffalo,NPL,-0.07125091785565019
+dairy-buffalo,NZL,-0.0
+dairy-buffalo,OMN,-0.0
+dairy-buffalo,PAK,0.006403522565960884
+dairy-buffalo,PAN,-0.0
+dairy-buffalo,PER,-0.0
+dairy-buffalo,PHL,-0.0
+dairy-buffalo,PNG,-0.0
+dairy-buffalo,POL,-0.0
+dairy-buffalo,PRI,-0.0
+dairy-buffalo,PRT,-0.0
+dairy-buffalo,PRY,-0.0
+dairy-buffalo,PSE,-0.0
+dairy-buffalo,ROU,-0.0
+dairy-buffalo,RUS,-0.0
+dairy-buffalo,RWA,-0.0
+dairy-buffalo,SAU,-0.0
+dairy-buffalo,SDN,-0.0
+dairy-buffalo,SEN,-0.0
+dairy-buffalo,SLB,-0.0
+dairy-buffalo,SLE,-0.0
+dairy-buffalo,SLV,-0.0
+dairy-buffalo,SOM,-0.0
+dairy-buffalo,SRB,-0.0
+dairy-buffalo,SSD,-0.0
+dairy-buffalo,STP,-0.0
+dairy-buffalo,SUR,-0.0
+dairy-buffalo,SVK,-0.0
+dairy-buffalo,SVN,-0.0
+dairy-buffalo,SWE,-0.0
+dairy-buffalo,SWZ,-0.0
+dairy-buffalo,SYR,-0.09794544987380505
+dairy-buffalo,TCD,-0.0
+dairy-buffalo,TGO,-0.0
+dairy-buffalo,THA,-0.0
+dairy-buffalo,TJK,-0.0
+dairy-buffalo,TKM,-0.0
+dairy-buffalo,TLS,-0.0
+dairy-buffalo,TTO,-0.0
+dairy-buffalo,TUN,-0.0
+dairy-buffalo,TUR,-0.11061198636889458
+dairy-buffalo,TWN,-0.0
+dairy-buffalo,TZA,-0.0
+dairy-buffalo,UGA,-0.0
+dairy-buffalo,UKR,-0.0
+dairy-buffalo,URY,-0.0
+dairy-buffalo,USA,-0.0
+dairy-buffalo,UZB,-0.0
+dairy-buffalo,VEN,-0.0
+dairy-buffalo,VNM,-0.24921564012765884
+dairy-buffalo,VUT,-0.0
+dairy-buffalo,YEM,-0.0
+dairy-buffalo,ZAF,-0.0
+dairy-buffalo,ZMB,-0.0
+dairy-buffalo,ZWE,-0.0
+eggs,AFG,-0.12066755443811417
+eggs,AGO,-0.08855835348367691
+eggs,ALB,-0.002841589041054249
+eggs,ARE,-0.0
+eggs,ARG,-0.0660196989774704
+eggs,ARM,-0.008815393783152103
+eggs,ASM,-0.0
+eggs,ATG,-0.06674070656299591
+eggs,AUS,-0.02652224898338318
+eggs,AUT,-0.032084934413433075
+eggs,AZE,0.0049108476378023624
+eggs,BDI,-0.16401278972625732
+eggs,BEL,-0.0
+eggs,BEN,-0.16102956235408783
+eggs,BFA,-0.1684495210647583
+eggs,BGD,-0.11597085744142532
+eggs,BGR,-0.0
+eggs,BHS,-0.05255137383937836
+eggs,BIH,-0.016046056523919106
+eggs,BLR,-0.035062436014413834
+eggs,BLZ,-0.06305880844593048
+eggs,BOL,-0.0993509367108345
+eggs,BRA,-0.07130513340234756
+eggs,BRB,-0.05571287125349045
+eggs,BRN,0.004079673904925585
+eggs,BTN,-0.10383521020412445
+eggs,BWA,-0.15209805965423584
+eggs,CAF,-0.1515016108751297
+eggs,CAN,-0.060091547667980194
+eggs,CHE,-0.02177559770643711
+eggs,CHL,-0.07769565284252167
+eggs,CHN,0.004874867852777243
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diff --git a/data/curated/calibration/gbd-anchored/crop_cost.csv b/data/curated/calibration/gbd-anchored/crop_cost.csv
new file mode 100644
index 00000000..f4a55595
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/crop_cost.csv
@@ -0,0 +1,6152 @@
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+yam,MEX,2.8864649534225464
+yam,MLI,-5.245889902114868
+yam,MMR,0.9744532704353333
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+yam,MRT,-6.383007049560547
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+yam,MWI,-0.0
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+yam,PER,-3.757173776626587
+yam,PHL,-1.6226337552070618
+yam,PNG,-2.632683753967285
+yam,PRI,-7.5
+yam,PRY,7.5
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+yam,SAU,-0.0
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+yam,SEN,-2.15857595205307
+yam,SLB,-1.8003202676773071
+yam,SLE,-5.3781633377075195
+yam,SLV,2.729663610458374
+yam,SOM,-0.0
+yam,SSD,-5.167559862136841
+yam,STP,-4.706610918045044
+yam,SUR,-2.3886003494262695
+yam,SWZ,-3.974542021751404
+yam,SYR,-2.0030922889709473
+yam,TCD,-4.035828590393066
+yam,TGO,-1.110143780708313
+yam,THA,-1.8080795407295227
+yam,TLS,-5.463002443313599
+yam,TTO,-7.5
+yam,TWN,-2.4488754272460938
+yam,TZA,-4.566601991653442
+yam,UGA,-3.9058055877685547
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+yam,VEN,-2.3649719953536987
+yam,VNM,-3.4608877897262573
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+yam,ZMB,3.75
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diff --git a/data/curated/calibration/gbd-anchored/deviation_penalty.yaml b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml
new file mode 100644
index 00000000..e41f5ae3
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/deviation_penalty.yaml
@@ -0,0 +1,22 @@
+# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek
+#
+# SPDX-License-Identifier: CC-BY-4.0
+#
+# Auto-generated by workflow/scripts/calibrate_deviation_penalty.py.
+# Consumed at solve time when deviation_penalty.<component>.l1_cost
+# is the sentinel string "calibrated".
+# Do not edit by hand -- run ``tools/calibrate stability`` to regenerate.
+target_deviation_pct: 5.0
+penalty_mode: l1
+deviation_type: absolute
+components:
+- cropland
+- grassland
+- feed
+l1_costs:
+  cropland: 1.564181887678137
+  grassland: 0.15990866204242277
+  feed: 0.07454894173640202
+iterations: 3
+converged: true
+final_residual_log_inf: 0.015471831446956581
diff --git a/data/curated/calibration/gbd-anchored/exogenous_feed.csv b/data/curated/calibration/gbd-anchored/exogenous_feed.csv
new file mode 100644
index 00000000..a4e4d2b7
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/exogenous_feed.csv
@@ -0,0 +1,99 @@
+# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek
+#
+# SPDX-License-Identifier: CC-BY-4.0
+country,monogastric_protein_mt_dm,ruminant_protein_mt_dm,ruminant_roughage_mt_dm
+AFG,0.0,0.0,6.454991
+AGO,0.0,0.0,0.935713
+ARE,0.0,0.0,1.419746
+ARM,0.0,0.0,0.921945
+ATG,0.0,0.0,0.001203
+AZE,0.0,0.0,1.66953
+BFA,0.0,0.0,2.427716
+BGD,0.0,0.0,9.27415
+BHS,0.0,0.0,0.000234
+BOL,0.0,0.0,3.061832
+BRA,0.0,0.0,4.273273
+BRB,0.0,0.0,0.001185
+BRN,0.0,0.0,0.098262
+BTN,0.0,0.0,0.032702
+BWA,0.0,0.0,0.803633
+CAF,0.0,0.0,3.145721
+CHL,0.0,0.0,1.174709
+CMR,0.0,0.0,1.961351
+COG,0.0,0.0,0.10183
+COL,0.0,0.0,4.952689
+COM,0.0,0.0,0.023562
+CPV,0.0,0.0,0.014816
+CRI,0.0,0.0,0.47573
+CUB,0.0,0.0,0.474244
+CYP,0.0,0.0,0.026106
+DJI,0.0,0.0,0.253116
+DOM,0.0,0.0,0.732628
+DZA,0.0,0.0,7.00381
+ECU,0.0,0.0,0.999302
+EGY,0.0,0.0,7.632216
+ERI,0.0,0.0,0.647034
+ETH,0.0,0.0,3.538889
+GEO,0.0,0.0,0.291165
+GNB,0.0,0.0,0.082362
+GRD,0.0,0.0,0.003329
+GTM,0.0,0.0,0.88227
+HND,0.0,0.0,0.32824
+HTI,0.0,0.0,0.567069
+IND,0.0,0.0,289.219147
+IRL,0.0,0.0,0.334866
+ISL,0.0,0.0,0.030581
+ISR,0.0,0.0,1.114113
+JAM,0.0,0.0,0.181489
+JOR,0.0,0.0,0.90952
+JPN,1.611782,1.221458,1.321806
+KEN,0.0,0.0,8.553471
+KOR,2.720941,0.374992,2.658246
+LAO,0.0,0.056044,0.0
+LBN,0.0,0.0,0.255602
+LBY,0.0,0.0,0.793361
+LSO,0.0,0.0,0.09329
+MAR,0.0,0.0,5.567759
+MEX,0.0,0.0,2.591533
+MLT,0.0,0.0,0.002164
+MNE,0.0,0.0,0.002303
+MNG,0.0,0.0,15.397595
+MRT,0.0,0.0,2.105997
+MYS,0.0,0.0,0.217635
+NAM,0.0,0.0,0.693243
+NER,0.0,0.0,1.711398
+NIC,0.0,0.0,1.291703
+NLD,0.0,0.0,0.117066
+NPL,0.0,0.0,15.855976
+OMN,0.0,0.0,1.601173
+PAK,0.0,6.85822,166.893402
+PAN,0.0,0.0,0.335552
+PER,0.0,0.0,0.826979
+PRI,0.0,0.0,0.093587
+PRY,0.0,0.0,0.155431
+PSE,0.0,0.0,0.253434
+RWA,0.0,0.0,0.380831
+SAU,0.0,0.0,3.207552
+SDN,0.0,0.0,11.693514
+SEN,0.0,0.0,0.880735
+SOM,0.0,0.0,5.319848
+SSD,0.0,0.0,3.616102
+STP,0.0,0.0,0.00225
+SWZ,0.0,0.0,0.169067
+SYR,0.0,0.0,1.271963
+TCD,0.0,0.0,25.86199
+TJK,0.0,0.0,0.247151
+TKM,0.0,0.0,0.695718
+TLS,0.002871,0.01037,0.067521
+TTO,0.0,0.0,0.006878
+TUN,0.0,0.0,1.242337
+TUR,0.0,0.0,1.663914
+TWN,1.325626,0.00074,0.0
+TZA,0.0,0.0,9.637824
+UGA,0.0,0.0,3.557157
+URY,0.0,0.0,2.438584
+VEN,0.0,0.0,3.763239
+YEM,0.0,0.0,3.183531
+ZAF,0.0,0.0,5.481274
+ZMB,0.0,0.0,2.941371
+ZWE,0.0,0.0,10.748748
diff --git a/data/curated/calibration/gbd-anchored/exogenous_forage.csv b/data/curated/calibration/gbd-anchored/exogenous_forage.csv
new file mode 100644
index 00000000..302824e5
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/exogenous_forage.csv
@@ -0,0 +1,178 @@
+# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek
+#
+# SPDX-License-Identifier: CC-BY-4.0
+country,exogenous_forage_mt_dm
+AFG,0.0
+AGO,0.0
+ALB,0.0
+ARE,1.960736
+ARG,0.0
+ARM,0.0
+ATG,0.0
+AUS,0.0
+AUT,0.0
+AZE,0.0
+BDI,0.0
+BEL,0.0
+BEN,0.0
+BFA,0.0
+BGD,17.312044
+BGR,0.0
+BHS,0.0
+BIH,0.0
+BLR,0.0
+BLZ,0.0
+BOL,0.0
+BRA,0.0
+BRB,0.0
+BRN,0.0
+BTN,0.0
+BWA,0.0
+CAF,0.0
+CAN,0.0
+CHE,0.0
+CHL,0.0
+CHN,0.0
+CIV,0.0
+CMR,0.0
+COD,0.0
+COG,0.0
+COL,0.0
+COM,0.083786
+CPV,0.0
+CRI,0.0
+CUB,0.0
+CYP,0.0
+CZE,0.0
+DEU,0.0
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+DNK,0.0
+DOM,0.0
+DZA,0.0
+ECU,0.0
+EGY,20.905624
+ERI,0.0
+ESP,0.0
+EST,0.0
+ETH,0.0
+FIN,0.0
+FJI,0.0
+FRA,0.0
+GAB,0.0
+GBR,0.0
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+GHA,0.0
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+GMB,0.0
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+GNQ,0.0
+GRC,0.0
+GRD,0.0
+GTM,0.0
+GUF,0.0
+GUY,0.0
+HND,0.0
+HRV,0.0
+HTI,0.0
+HUN,0.0
+IDN,0.0
+IND,147.362427
+IRL,0.0
+IRN,0.0
+IRQ,0.0
+ISL,0.0
+ISR,0.0
+ITA,0.0
+JAM,0.0
+JOR,0.0
+JPN,0.0
+KAZ,0.0
+KEN,0.0
+KGZ,0.0
+KHM,0.0
+KOR,5.329263
+LAO,0.0
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+NPL,2.704415
+NZL,0.0
+OMN,1.78773
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+SRB,0.0
+SSD,0.0
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+SVK,0.0
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+SWE,0.0
+SWZ,0.0
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+TCD,0.0
+TGO,0.0
+THA,0.0
+TJK,3.385071
+TKM,9.849616
+TLS,0.0
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+TWN,0.0
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+UGA,0.0
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+USA,0.0
+UZB,15.069183
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+VNM,4.006212
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diff --git a/data/curated/calibration/gbd-anchored/fodder_conversion.csv b/data/curated/calibration/gbd-anchored/fodder_conversion.csv
new file mode 100644
index 00000000..b98b623a
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/fodder_conversion.csv
@@ -0,0 +1,178 @@
+# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek
+#
+# SPDX-License-Identifier: CC-BY-4.0
+country,fodder_conversion_correction
+AFG,1.0
+AGO,1.0
+ALB,1.0
+ARE,1.0
+ARG,1.0
+ARM,1.0
+ATG,1.0
+AUS,1.0
+AUT,1.0
+AZE,1.0
+BDI,1.0
+BEL,1.0
+BEN,1.0
+BFA,1.0
+BGD,1.0
+BGR,1.0
+BHS,1.0
+BIH,1.0
+BLR,1.0
+BLZ,1.0
+BOL,1.0
+BRA,1.0
+BRB,1.0
+BRN,1.0
+BTN,1.0
+BWA,1.0
+CAF,1.0
+CAN,1.0
+CHE,1.0
+CHL,1.0
+CHN,1.0
+CIV,1.0
+CMR,1.0
+COD,1.0
+COG,1.0
+COL,1.0
+COM,1.0
+CPV,1.0
+CRI,1.0
+CUB,1.0
+CYP,1.0
+CZE,1.0
+DEU,0.733978
+DJI,1.0
+DNK,1.0
+DOM,1.0
+DZA,1.0
+ECU,1.0
+EGY,1.0
+ERI,1.0
+ESP,1.0
+EST,0.4755
+ETH,1.0
+FIN,1.0
+FJI,1.0
+FRA,1.0
+GAB,1.0
+GBR,1.0
+GEO,1.0
+GHA,1.0
+GIN,1.0
+GMB,1.0
+GNB,1.0
+GNQ,1.0
+GRC,1.0
+GRD,1.0
+GTM,1.0
+GUF,0.0
+GUY,1.0
+HND,1.0
+HRV,1.0
+HTI,1.0
+HUN,0.592926
+IDN,1.0
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+IRL,1.0
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+ISL,1.0
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+JAM,1.0
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+JPN,0.763625
+KAZ,1.0
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diff --git a/data/curated/calibration/gbd-anchored/food_demand.csv b/data/curated/calibration/gbd-anchored/food_demand.csv
new file mode 100644
index 00000000..8d13304d
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/food_demand.csv
@@ -0,0 +1,59 @@
+food,multiplier,net_slack_mt,consumption_mt
+apple,0.9964445828712245,0.3677937702741474,103.44602530528209
+banana,1.0,0.0,76.84251734232384
+barley-hulled,1.0,0.0,2.4754922425192945
+buckwheat,0.5704728472172369,2.0801994113294313,4.8429986273336
+cabbage,1.0575492624412763,-12.668129742145538,220.12670892302413
+carrot,1.0261888928606584,-3.1861228942871094,121.65931989715318
+cassava,1.0,0.0,149.8683383288726
+chickpea,1.0,0.0,4.478103744486589
+citrus,1.0512843915064407,-6.78463888168335,132.29442101952736
+cocoa-powder,0.9075563452415882,0.4895184128326946,5.295316526720853
+coconut,1.0,0.0,2.927419820128307
+coconut-oil,1.0,0.0,1.0790433989332087
+coffee-green,0.8670375363851814,1.5566738274646923,11.707618715416174
+cottonseed-oil,1.0,0.0,4.167233729645886
+cowpea,1.0,0.0,2.4007039731534405
+dairy,0.9734069343412688,21.343825676478446,802.6086931978352
+dairy-buffalo,0.9953466808233736,0.6439452366903424,138.38406785523694
+dry-pea,1.0,0.0,1.9984850413002277
+eggs,1.0,0.0,82.1042355971731
+flour-white,1.0,0.0,255.93475320073776
+flour-wholemeal,1.0,0.0,60.569861044496065
+foxtail-millet,1.0,0.0,3.410226098225394
+gram,1.0,0.0,4.478103744486589
+groundnut,1.0,0.0,8.565938512935645
+groundnut-oil,1.0,0.0,3.8057418215357757
+maize,1.0,0.0,106.49321075204716
+mango,1.0328178922234819,-1.5925385136361,48.526532502188275
+meat-cattle,0.952400557178112,2.308293342590332,48.49412526166998
+meat-chicken,0.9895527158773688,0.8225271608680487,78.7311947500566
+meat-pig,1.0184832063942477,-1.3977279365062714,75.62150779971125
+meat-sheep,1.0872487686362589,-0.8913660735124722,10.21637425312656
+oat,1.0,0.0,1.6780094729440407
+olive-oil,1.087174604317402,-0.3948613061802462,4.529545149898922
+onion,0.9808794720277011,5.4889238413306884,287.0696797328419
+palm-oil,1.0,0.0,26.487057077565623
+pearl-millet,1.0,0.0,3.410226098225394
+phaseolus-bean,1.0,0.0,7.570616604105453
+pigeon-pea,1.0,0.0,0.24040035817802163
+plantain,0.9198868952820304,1.9715050811446417,24.609021059477527
+potato,0.96850232522493,8.17799163016025,259.6379475170979
+rapeseed-oil,1.0,0.0,12.744215775339399
+rendered-fat,1.0,0.0,8.848241350830904
+rice-brown,1.0,0.0,50.39190488612803
+rice-white,1.0,0.0,334.26266210823087
+rye,1.0,0.0,0.9243344825217719
+sesame-oil,1.0,0.0,1.3729037433736266
+sesame-seed,1.0,0.0,1.130570922832021
+sorghum,1.0,0.0,11.647140672110254
+soy,1.0,0.0,3.8865096387889935
+soybean-oil,1.0,0.0,32.44238214136567
+sugar,1.0,0.0,154.22733178921044
+sunflower-oil,0.9065321687063365,1.3318660576501316,14.249459297558587
+sunflower-seed,1.0,0.0,1.9189327573915307
+sweet-potato,1.0,0.0,55.2135853107211
+tea-dried,0.9839945251497785,0.10681728940289759,6.673796960258187
+tomato,0.9262395867792931,10.365316073257418,140.52681676609063
+watermelon,1.233258642637127,-12.6926908493042,54.414664793577685
+yam,0.9338066770295699,2.294876266399342,34.66930142522847
diff --git a/data/curated/calibration/gbd-anchored/food_waste.yaml b/data/curated/calibration/gbd-anchored/food_waste.yaml
new file mode 100644
index 00000000..1663fddc
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/food_waste.yaml
@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek
+#
+# SPDX-License-Identifier: CC-BY-4.0
+#
+# Per-food-group waste-retention multipliers, applied by
+# prepare_food_loss_waste.py as:
+#   new_waste = 1 - (1 - sdg_waste) * waste_retention_multiplier
+# Generated by workflow/scripts/compute_food_waste_calibration.py
+# from a solved validation scenario. Do not hand-edit; rerun
+# `tools/calibrate food_waste` instead.
+fruits:
+  baseline_consumption_mt: 303.5781328359153
+  baseline_negative_slack_mt: 111.94602305834997
+  baseline_positive_slack_mt: 0.0
+  waste_retention_multiplier: 0.7305908177168986
+oil:
+  baseline_consumption_mt: 102.02891421989989
+  baseline_negative_slack_mt: 0.34316493559163064
+  baseline_positive_slack_mt: 1.494497413728368
+  waste_retention_multiplier: 1.0114131649297862
+starchy_vegetable:
+  baseline_consumption_mt: 548.8485709926401
+  baseline_negative_slack_mt: 0.0
+  baseline_positive_slack_mt: 26.776431421577627
+  waste_retention_multiplier: 1.0512887576103511
+stimulants:
+  baseline_consumption_mt: 23.68795094791676
+  baseline_negative_slack_mt: 0.0
+  baseline_positive_slack_mt: 2.164228206801454
+  waste_retention_multiplier: 1.100550830952086
+vegetables:
+  baseline_consumption_mt: 514.297463798519
+  baseline_negative_slack_mt: 255.08508318044915
+  baseline_positive_slack_mt: 0.0
+  waste_retention_multiplier: 0.6684548094026077
diff --git a/data/curated/calibration/gbd-anchored/grassland_cost.csv b/data/curated/calibration/gbd-anchored/grassland_cost.csv
new file mode 100644
index 00000000..125aaf7b
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/grassland_cost.csv
@@ -0,0 +1,175 @@
+country,correction_bnusd_per_mha
+AFG,-0.0002783827803796157
+AGO,-0.4345769137144089
+ALB,-0.20895707607269287
+ARE,-0.0
+ARG,-0.21808408945798874
+ARM,-0.0
+ATG,-0.0
+AUS,-0.11690391600131989
+AUT,1.2174659058672432e-09
+AZE,0.915382593870163
+BDI,-1.1082043727128621e-08
+BEL,0.08366924151778221
+BEN,3.0615766048431396
+BFA,1.4070661664009094
+BGD,-0.0
+BGR,-0.3083830028772354
+BHS,-0.030966363847255707
+BIH,-0.4456564486026764
+BLR,-0.019071068614721298
+BLZ,-0.7379351556301117
+BOL,-0.5958932638168335
+BRA,-0.5617609918117523
+BRB,0.865568995475769
+BRN,7.141561985015869
+BTN,-0.2882518470287323
+BWA,-0.0
+CAF,-0.46485017240047455
+CAN,-0.21885906904935837
+CHE,-0.11954976990818977
+CHL,-0.0009876639232970774
+CHN,-1.8447241956209837e-08
+CIV,-0.5140847712755203
+CMR,4.203157901763916
+COD,-0.8133789598941803
+COG,-0.8903338015079498
+COL,-0.6280472874641418
+COM,7.846769900510253e-10
+CPV,-0.0
+CRI,-0.040161214768886566
+CUB,-0.17234943062067032
+CYP,0.2899330109357834
+CZE,-0.28652919828891754
+DEU,-0.3941321074962616
+DJI,-0.0
+DNK,1.817373514175415
+DOM,7.18969817015136e-09
+DZA,-0.0
+ECU,-0.28694531321525574
+EGY,-0.0
+ERI,-0.036436695605516434
+ESP,-0.04443208500742912
+EST,-0.25669121742248535
+ETH,-1.3543125509940523e-09
+FIN,1.6001724004745483
+FJI,-0.7943975925445557
+FRA,-3.0738755540937746e-08
+GAB,-0.8158496618270874
+GBR,-0.057546284049749374
+GEO,-0.2082834094762802
+GHA,3.9665030371338617e-08
+GIN,-0.29034821689128876
+GMB,1.1484697461128235
+GNB,1.0530049800872803
+GNQ,-0.5248542279005051
+GRC,-0.1027151420712471
+GRD,0.6195181608200073
+GTM,-1.962737661287406e-08
+GUF,-1.0722496509552002
+GUY,-1.106971263885498
+HND,-0.6534972786903381
+HRV,-0.1872950717806816
+HTI,5.54345191972061e-09
+HUN,-0.4170793741941452
+IDN,-0.5775293707847595
+IND,1.61296808719635
+IRL,-2.0363737363027212e-08
+IRN,-0.0
+IRQ,-0.0
+ISL,-0.08968127891421318
+ISR,0.6709401309490204
+ITA,0.04259900003671646
+JAM,3.508358597755432
+JOR,-0.0
+JPN,-0.49684692919254303
+KAZ,-1.7263098728292903e-09
+KEN,-0.17522157728672028
+KGZ,-0.0
+KHM,-0.4328657388687134
+KOR,-1.1598136895329958e-09
+LAO,2.91002910657312e-09
+LBN,0.4902341067790985
+LBR,-0.7553918659687042
+LBY,-0.00020786204549949616
+LKA,-0.45856063067913055
+LSO,-0.44252099096775055
+LTU,-0.052242062985897064
+LUX,0.02335542067885399
+LVA,-0.30377297103405
+MAR,-0.0
+MDA,-0.36603914201259613
+MDG,-0.6942926049232483
+MEX,-0.08276043459773064
+MKD,-0.2815301865339279
+MLI,1.2537319515892875e-09
+MLT,-0.0
+MMR,4.511943263452167e-09
+MNE,-0.30944468080997467
+MNG,-0.0623512826859951
+MOZ,-0.5657868981361389
+MRT,-3.3330131710140876e-10
+MUS,-3.315589225394433e-08
+MWI,-0.1905755028128624
+MYS,-3.07201153404435e-09
+NAM,-0.006759356707334518
+NER,-0.0
+NGA,8.823755504039354e-09
+NIC,-0.42816048860549927
+NLD,0.7907610237598419
+NOR,0.5461024045944214
+NPL,-1.011426720154418e-09
+NZL,-0.0
+OMN,-0.0
+PAK,-0.0
+PAN,-0.6082254946231842
+PER,-0.6303564608097076
+PHL,-0.12024912238121033
+PNG,-0.7848113179206848
+POL,0.019568821415305138
+PRI,-2.7517170764213006e-09
+PRT,-0.10262405686080456
+PRY,-0.536945641040802
+PSE,0.6586481034755707
+ROU,-0.4245498776435852
+RUS,-1.0431203456562343e-08
+RWA,2.7728375151525597e-08
+SAU,-0.0
+SDN,-0.07339249178767204
+SEN,0.35445454716682434
+SLB,-0.06637441366910934
+SLE,-0.41614776849746704
+SLV,3.365933418273926
+SOM,-4.7665072999691915e-09
+SRB,-0.29573993384838104
+SSD,-0.3232929855585098
+STP,0.5821866691112518
+SUR,6.8290815353393555
+SVK,-0.4092821776866913
+SVN,-0.23763713240623474
+SWE,1.6527102589607239
+SWZ,-1.973749874650821e-08
+SYR,0.1336965560913086
+TCD,-0.0883248895406723
+TGO,-0.3382970243692398
+THA,-2.5447631557540262e-08
+TJK,0.35738885402679443
+TKM,-0.0
+TLS,-0.22272039204835892
+TTO,-1.3588031588795957e-08
+TUN,0.3425496220588684
+TUR,1.0832912921905518
+TWN,-0.3161562830209732
+TZA,-0.4196213632822037
+UGA,-4.5179753271895606e-08
+UKR,-0.023232679814100266
+URY,-0.17097113281488419
+USA,-0.4050147831439972
+UZB,0.2210150510072708
+VEN,-0.5381573736667633
+VNM,5.650000095367432
+VUT,-0.17226660251617432
+YEM,2.695993539525432e-09
+ZAF,-0.003347835154272616
+ZMB,-0.3225127309560776
+ZWE,1.0077592730522156
diff --git a/data/curated/calibration/gbd-anchored/grassland_yield.csv b/data/curated/calibration/gbd-anchored/grassland_yield.csv
new file mode 100644
index 00000000..4b70204e
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/grassland_yield.csv
@@ -0,0 +1,178 @@
+# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek
+#
+# SPDX-License-Identifier: CC-BY-4.0
+country,yield_correction
+AFG,0.395294
+AGO,0.071253
+ALB,0.144938
+ARE,1.0
+ARG,0.200993
+ARM,0.550552
+ATG,1.0
+AUS,0.236373
+AUT,0.553455
+AZE,1.0
+BDI,0.437887
+BEL,0.928962
+BEN,1.0
+BFA,1.0
+BGD,1.0
+BGR,0.074007
+BHS,0.670605
+BIH,0.014661
+BLR,0.422191
+BLZ,0.101975
+BOL,0.111315
+BRA,0.229034
+BRB,1.0
+BRN,1.0
+BTN,0.03586
+BWA,1.0
+CAF,0.583977
+CAN,0.051827
+CHE,0.407119
+CHL,0.686938
+CHN,0.287704
+CIV,0.033413
+CMR,1.0
+COD,0.022128
+COG,0.00797
+COL,0.103541
+COM,1.0
+CPV,1.0
+CRI,0.380109
+CUB,0.277716
+CYP,1.0
+CZE,0.138284
+DEU,0.0
+DJI,1.0
+DNK,1.0
+DOM,0.568047
+DZA,0.79171
+ECU,0.363762
+EGY,1.0
+ERI,0.518486
+ESP,0.429751
+EST,0.0
+ETH,0.551195
+FIN,1.0
+FJI,0.041419
+FRA,0.488102
+GAB,0.006069
+GBR,0.400758
+GEO,0.122798
+GHA,0.178955
+GIN,0.173006
+GMB,1.0
+GNB,1.0
+GNQ,0.077495
+GRC,0.075747
+GRD,1.0
+GTM,0.571596
+GUF,0.0
+GUY,0.018762
+HND,0.147424
+HRV,0.284229
+HTI,0.92669
+HUN,0.0
+IDN,0.144268
+IND,1.0
+IRL,0.633733
+IRN,0.769462
+IRQ,1.0
+ISL,0.062139
+ISR,1.0
+ITA,0.884304
+JAM,1.0
+JOR,1.0
+JPN,0.0
+KAZ,0.256835
+KEN,0.467611
+KGZ,0.842055
+KHM,0.398086
+KOR,1.0
+LAO,0.404176
+LBN,1.0
+LBR,0.016113
+LBY,0.894502
+LKA,0.1936
+LSO,0.048047
+LTU,0.341665
+LUX,0.783686
+LVA,0.0
+MAR,1.0
+MDA,0.0
+MDG,0.016111
+MEX,0.371319
+MKD,0.104967
+MLI,0.387861
+MLT,1.0
+MMR,1.0
+MNE,0.077905
+MNG,0.245052
+MOZ,0.00289
+MRT,0.660517
+MUS,0.811415
+MWI,0.76798
+MYS,0.436125
+NAM,0.291928
+NER,1.0
+NGA,0.665344
+NIC,0.295305
+NLD,1.0
+NOR,1.0
+NPL,1.0
+NZL,0.560501
+OMN,1.0
+PAK,1.0
+PAN,0.256704
+PER,0.083649
+PHL,0.384616
+PNG,0.0
+POL,0.560559
+PRI,0.43587
+PRT,0.081037
+PRY,0.068488
+PSE,1.0
+ROU,0.0
+RUS,0.15561
+RWA,0.794264
+SAU,1.0
+SDN,0.419271
+SEN,1.0
+SLB,0.580873
+SLE,0.093191
+SLV,1.0
+SOM,0.555429
+SRB,0.13671
+SSD,0.142093
+STP,1.0
+SUR,1.0
+SVK,0.0
+SVN,0.252009
+SWE,1.0
+SWZ,0.283263
+SYR,1.0
+TCD,0.695865
+TGO,0.206827
+THA,0.856193
+TJK,1.0
+TKM,1.0
+TLS,0.234166
+TTO,0.74618
+TUN,1.0
+TUR,1.0
+TWN,0.107393
+TZA,0.406752
+UGA,0.405149
+UKR,0.166786
+URY,0.270063
+USA,0.0312
+UZB,1.0
+VEN,0.19052
+VNM,1.0
+VUT,0.176533
+YEM,0.592561
+ZAF,0.346815
+ZMB,0.203235
+ZWE,1.0
diff --git a/data/curated/calibration/gbd-anchored/provenance.yaml b/data/curated/calibration/gbd-anchored/provenance.yaml
new file mode 100644
index 00000000..ecc10cbe
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/provenance.yaml
@@ -0,0 +1,854 @@
+# Provenance stamp of this calibration artefact set: the
+# structural config it was fit against. Generated by
+# workflow/scripts/write_calibration_provenance.py via
+# tools/calibrate; do not edit by hand.
+# Licensing: see the annotation in REUSE.toml.
+base_config: config/gsa.yaml
+generated_at: '2026-07-01T21:08:01+00:00'
+git_commit: d3f07986e72ad291e10e5a11844c18752cddf7cc
+source: gbd-anchored
+structural_config:
+  aggregation.irrigated_area_source: current
+  aggregation.regions.allow_cross_border: false
+  aggregation.regions.method: kmeans
+  aggregation.regions.target_count: 750
+  aggregation.resource_class_quantiles:
+  - 0.25
+  - 0.5
+  - 0.75
+  aggregation.resource_class_score: regional_crop_mix_actual_yield
+  aggregation.simplify_min_area_km: 25
+  aggregation.simplify_tolerance_km: 5
+  animal_costs.fadn.exclude_costs.SE320: Feed for pigs & poultry
+  animal_costs.fadn.exclude_costs.SE325: Feed for pigs & poultry home-grown
+  animal_costs.fadn.exclude_costs.SE375: Rent paid
+  animal_costs.fadn.grazing_cost_items.SE310: Feed for grazing livestock
+  animal_costs.fadn.grazing_cost_items.SE315: Feed for grazing livestock home-grown
+  animal_costs.fadn.high_cost_threshold_usd_per_t: 50000
+  animal_costs.fadn.livestock_specific_costs.SE330: Other livestock specific costs
+  animal_costs.fadn.shared_farm_costs.SE340: Machinery & building current costs
+  animal_costs.fadn.shared_farm_costs.SE345: Energy
+  animal_costs.fadn.shared_farm_costs.SE350: Contract work
+  animal_costs.fadn.shared_farm_costs.SE356: Other direct inputs
+  animal_costs.fadn.shared_farm_costs.SE360: Depreciation
+  animal_costs.fadn.shared_farm_costs.SE370: Wages paid
+  animal_costs.fadn.shared_farm_costs.SE380: Interest paid
+  animal_costs.fadn.shared_farm_costs.SE390: Taxes
+  animal_costs.fallback_aliases.dairy-buffalo: dairy
+  animal_costs.fallback_values_usd_per_t.meat-chicken.grazing: 0
+  animal_costs.fallback_values_usd_per_t.meat-chicken.production: 1300
+  animal_costs.fallback_values_usd_per_t.meat-sheep.grazing: 2300
+  animal_costs.fallback_values_usd_per_t.meat-sheep.production: 1200
+  animal_costs.faostat.aggregate_area_code_limit: 5000
+  animal_costs.faostat.element_codes.producing_animals:
+  - '2313'
+  - '5318'
+  - '5313'
+  animal_costs.faostat.element_codes.production:
+  - '2510'
+  - '5510'
+  animal_costs.faostat.element_codes.stocks:
+  - '2111'
+  - '5111'
+  animal_costs.usda.dressed_weight_kg_per_head.meat-cattle: 350.0
+  animal_costs.usda.dressed_weight_kg_per_head.meat-pig: 90.0
+  animal_costs.usda.exclude_items:
+  - Homegrown harvested feed
+  - Purchased feed
+  - Total, feed costs
+  - Opportunity cost of land
+  - Total, operating costs
+  - Costs listed
+  animal_costs.usda.grazing_cost_items:
+  - Grazed feed
+  animal_costs.usda.include_items:
+  - Hired labor
+  - Opportunity cost of unpaid labor
+  - Bedding and litter
+  - Custom services
+  - Fuel, lube, and electricity
+  - Repairs
+  - Interest on operating capital
+  - Marketing
+  - Veterinary and medicine
+  - Capital recovery of machinery and equipment
+  - General farm overhead
+  - Taxes and insurance
+  animal_costs.usda.request_timeout_seconds: 120
+  animal_products.co_products.rendered-fat.yield_per_retail.meat-cattle: 0.09
+  animal_products.co_products.rendered-fat.yield_per_retail.meat-pig: 0.137
+  animal_products.faostat_items.dairy:
+  - Raw milk of cattle
+  - Raw milk of goats
+  - Raw milk of sheep
+  - Raw milk of camel
+  animal_products.faostat_items.dairy-buffalo:
+  - Raw milk of buffalo
+  animal_products.faostat_items.eggs:
+  - Hen eggs in shell, fresh
+  animal_products.faostat_items.meat-cattle:
+  - Meat of cattle with the bone, fresh or chilled
+  - Meat of buffalo, fresh or chilled
+  animal_products.faostat_items.meat-chicken:
+  - Meat of chickens, fresh or chilled
+  - Meat of ducks, fresh or chilled
+  - Meat of turkeys, fresh or chilled
+  - Meat of pigeons and other birds n.e.c., fresh, chilled or frozen
+  animal_products.faostat_items.meat-pig:
+  - Meat of pig with the bone, fresh or chilled
+  animal_products.faostat_items.meat-sheep:
+  - Meat of sheep, fresh or chilled
+  - Meat of goat, fresh or chilled
+  animal_products.gleam3_system_product_map.Buffalo.Grassland:
+  - dairy-buffalo
+  - meat-cattle
+  animal_products.gleam3_system_product_map.Buffalo.Mixed:
+  - dairy-buffalo
+  - meat-cattle
+  animal_products.gleam3_system_product_map.Cattle.Feedlots:
+  - meat-cattle
+  animal_products.gleam3_system_product_map.Cattle.Grassland:
+  - dairy
+  - meat-cattle
+  animal_products.gleam3_system_product_map.Cattle.Mixed:
+  - dairy
+  - meat-cattle
+  animal_products.gleam3_system_product_map.Chicken.Backyard:
+  - eggs
+  - meat-chicken
+  animal_products.gleam3_system_product_map.Chicken.Broiler:
+  - meat-chicken
+  animal_products.gleam3_system_product_map.Chicken.Layer:
+  - eggs
+  animal_products.gleam3_system_product_map.Goats.Grassland:
+  - dairy
+  - meat-sheep
+  animal_products.gleam3_system_product_map.Goats.Mixed:
+  - dairy
+  - meat-sheep
+  animal_products.gleam3_system_product_map.Pigs.Backyard:
+  - meat-pig
+  animal_products.gleam3_system_product_map.Pigs.Industrial:
+  - meat-pig
+  animal_products.gleam3_system_product_map.Pigs.Intermediate:
+  - meat-pig
+  animal_products.gleam3_system_product_map.Sheep.Grassland:
+  - dairy
+  - meat-sheep
+  animal_products.gleam3_system_product_map.Sheep.Mixed:
+  - dairy
+  - meat-sheep
+  animal_products.include:
+  - meat-cattle
+  - meat-pig
+  - meat-chicken
+  - dairy
+  - eggs
+  - dairy-buffalo
+  - meat-sheep
+  animal_products.me_scaling_clamp_factor: 2.0
+  animal_products.net_to_metabolizable_energy_conversion.k_g: 0.43
+  animal_products.net_to_metabolizable_energy_conversion.k_l: 0.64
+  animal_products.net_to_metabolizable_energy_conversion.k_m: 0.65
+  animal_products.residue_crops:
+  - banana
+  - barley
+  - cassava
+  - chickpea
+  - cowpea
+  - dry-pea
+  - dryland-rice
+  - foxtail-millet
+  - gram
+  - groundnut
+  - maize
+  - oat
+  - pearl-millet
+  - phaseolus-bean
+  - pigeonpea
+  - rapeseed
+  - rye
+  - sorghum
+  - soybean
+  - sugarbeet
+  - sugarcane
+  - sweet-potato
+  - wetland-rice
+  - wheat
+  baseline_year: 2020
+  biomass.biogas_crop_demand: data/curated/biogas_crop_demand.csv
+  biomass.crops:
+  - maize
+  - oil-palm
+  - sugarcane
+  - biomass-sorghum
+  biomass.disposal_foods:
+  - cottonseed-oil
+  - sesame-oil
+  - sesame-seed
+  - groundnut-oil
+  - groundnut
+  - coconut-oil
+  - coconut
+  - foxtail-millet
+  - soybean-oil
+  - rapeseed-oil
+  - sunflower-oil
+  - chickpea
+  - gram
+  - rendered-fat
+  biomass.enforce_baseline_demand: true
+  biomass.enforce_fiber_demand: true
+  byproducts:
+  - beet-pulp
+  - wheat-bran
+  - wheat-germ
+  - rice-bran
+  - barley-bran
+  - oat-bran
+  - buckwheat-hulls
+  - oilseed-meal
+  - palm-kernel-meal
+  - rapeseed-meal
+  - ddgs
+  - molasses
+  - maize-ethanol
+  - maize-gluten-feed
+  - maize-gluten-meal
+  - maize-starch
+  - sugarcane-ethanol
+  - cotton-lint
+  commodities.crops.classes.bulk_dry_goods.items:
+  - wheat
+  - dryland-rice
+  - wetland-rice
+  - maize
+  - barley
+  - oat
+  - rye
+  - sorghum
+  - buckwheat
+  - foxtail-millet
+  - pearl-millet
+  - soybean
+  - dry-pea
+  - chickpea
+  - cowpea
+  - gram
+  - phaseolus-bean
+  - pigeonpea
+  - cocoa
+  - coffee
+  - tea
+  - sunflower
+  - rapeseed
+  - groundnut
+  - sesame
+  - cotton
+  commodities.crops.classes.bulk_dry_goods.marketing_cost_per_t: 30
+  commodities.crops.classes.bulk_dry_goods.trade_cost_per_t_km: 0.006
+  commodities.crops.classes.bulky_fresh.items:
+  - white-potato
+  - sweet-potato
+  - cassava
+  - yam
+  - plantain
+  - sugarbeet
+  - sugarcane
+  - oil-palm
+  - alfalfa
+  - silage-maize
+  - biomass-sorghum
+  commodities.crops.classes.bulky_fresh.marketing_cost_per_t: 60
+  commodities.crops.classes.bulky_fresh.trade_cost_per_t_km: 0.014
+  commodities.crops.classes.perishable_high_value.items:
+  - tomato
+  - carrot
+  - onion
+  - cabbage
+  - banana
+  - watermelon
+  - mango
+  - citrus
+  - coconut
+  - apple
+  - olive
+  commodities.crops.classes.perishable_high_value.marketing_cost_per_t: 200
+  commodities.crops.classes.perishable_high_value.trade_cost_per_t_km: 0.022
+  commodities.crops.non_tradable:
+  - alfalfa
+  - biomass-sorghum
+  - silage-maize
+  commodities.feeds.classes.bulky_low_quality.items:
+  - ruminant_roughage
+  - monogastric_low_quality
+  commodities.feeds.classes.bulky_low_quality.marketing_cost_per_t: 35
+  commodities.feeds.classes.bulky_low_quality.trade_cost_per_t_km: 0.016
+  commodities.feeds.classes.forage.items:
+  - ruminant_forage
+  commodities.feeds.classes.forage.marketing_cost_per_t: 25
+  commodities.feeds.classes.forage.trade_cost_per_t_km: 0.012
+  commodities.feeds.classes.grain_protein.items:
+  - ruminant_grain
+  - ruminant_protein
+  - monogastric_grain
+  - monogastric_protein
+  commodities.feeds.classes.grain_protein.marketing_cost_per_t: 30
+  commodities.feeds.classes.grain_protein.trade_cost_per_t_km: 0.006
+  commodities.feeds.non_tradable:
+  - ruminant_forage
+  - ruminant_roughage
+  commodities.foods.classes.chilled_meat.items:
+  - meat-cattle
+  - meat-pig
+  - meat-chicken
+  - meat-sheep
+  commodities.foods.classes.chilled_meat.marketing_cost_per_t: 800
+  commodities.foods.classes.chilled_meat.trade_cost_per_t_km: 0.028
+  commodities.foods.classes.dairy_and_eggs.items:
+  - dairy
+  - dairy-buffalo
+  - eggs
+  commodities.foods.classes.dairy_and_eggs.marketing_cost_per_t: 300
+  commodities.foods.classes.dairy_and_eggs.trade_cost_per_t_km: 0.024
+  commodities.foods.classes.feed_byproduct.items:
+  - barley-bran
+  - beet-pulp
+  - buckwheat-hulls
+  - oat-bran
+  - rice-bran
+  - wheat-bran
+  - wheat-germ
+  - ddgs
+  - maize-gluten-feed
+  - maize-gluten-meal
+  - oilseed-meal
+  - palm-kernel-meal
+  - rapeseed-meal
+  - molasses
+  commodities.foods.classes.feed_byproduct.marketing_cost_per_t: 30
+  commodities.foods.classes.feed_byproduct.trade_cost_per_t_km: 0.008
+  commodities.foods.classes.fresh_produce.items:
+  - apple
+  - banana
+  - citrus
+  - coconut
+  - mango
+  - plantain
+  - watermelon
+  - cabbage
+  - carrot
+  - onion
+  - tomato
+  - cassava
+  - potato
+  - sweet-potato
+  - yam
+  commodities.foods.classes.fresh_produce.marketing_cost_per_t: 200
+  commodities.foods.classes.fresh_produce.trade_cost_per_t_km: 0.022
+  commodities.foods.classes.industrial_byproduct.items:
+  - cotton-lint
+  - maize-ethanol
+  - maize-starch
+  - sugarcane-ethanol
+  - rendered-fat
+  commodities.foods.classes.industrial_byproduct.marketing_cost_per_t: 40
+  commodities.foods.classes.industrial_byproduct.trade_cost_per_t_km: 0.008
+  commodities.foods.classes.processed_dry_goods.items:
+  - flour-white
+  - flour-wholemeal
+  - rice-brown
+  - rice-white
+  - barley-hulled
+  - buckwheat
+  - foxtail-millet
+  - maize
+  - oat
+  - pearl-millet
+  - rye
+  - sorghum
+  - chickpea
+  - cowpea
+  - dry-pea
+  - gram
+  - phaseolus-bean
+  - pigeon-pea
+  - soy
+  - groundnut
+  - sesame-seed
+  - sunflower-seed
+  - sugar
+  - cocoa-powder
+  - coffee-green
+  - tea-dried
+  commodities.foods.classes.processed_dry_goods.marketing_cost_per_t: 80
+  commodities.foods.classes.processed_dry_goods.trade_cost_per_t_km: 0.012
+  commodities.foods.classes.processed_oils.items:
+  - coconut-oil
+  - cottonseed-oil
+  - groundnut-oil
+  - olive-oil
+  - palm-oil
+  - rapeseed-oil
+  - sesame-oil
+  - soybean-oil
+  - sunflower-oil
+  commodities.foods.classes.processed_oils.marketing_cost_per_t: 120
+  commodities.foods.classes.processed_oils.trade_cost_per_t_km: 0.016
+  commodities.foods.non_tradable: []
+  commodities.hubs: 20
+  costs.averaging_period.end_year: 2024
+  costs.averaging_period.start_year: 2015
+  countries:
+  - AFG
+  - AGO
+  - ALB
+  - ARE
+  - ARG
+  - ARM
+  - ASM
+  - ATG
+  - AUS
+  - AUT
+  - AZE
+  - BDI
+  - BEL
+  - BEN
+  - BFA
+  - BGD
+  - BGR
+  - BHS
+  - BIH
+  - BLR
+  - BLZ
+  - BOL
+  - BRA
+  - BRB
+  - BRN
+  - BTN
+  - BWA
+  - CAF
+  - CAN
+  - CHE
+  - CHL
+  - CHN
+  - CIV
+  - CMR
+  - COD
+  - COG
+  - COL
+  - COM
+  - CPV
+  - CRI
+  - CUB
+  - CYP
+  - CZE
+  - DEU
+  - DJI
+  - DNK
+  - DOM
+  - DZA
+  - ECU
+  - EGY
+  - ERI
+  - ESP
+  - EST
+  - ETH
+  - FIN
+  - FJI
+  - FRA
+  - GAB
+  - GBR
+  - GEO
+  - GHA
+  - GIN
+  - GMB
+  - GNB
+  - GNQ
+  - GRC
+  - GRD
+  - GTM
+  - GUF
+  - GUY
+  - HND
+  - HRV
+  - HTI
+  - HUN
+  - IDN
+  - IND
+  - IRL
+  - IRN
+  - IRQ
+  - ISL
+  - ISR
+  - ITA
+  - JAM
+  - JOR
+  - JPN
+  - KAZ
+  - KEN
+  - KGZ
+  - KHM
+  - KOR
+  - LAO
+  - LBN
+  - LBR
+  - LBY
+  - LKA
+  - LSO
+  - LTU
+  - LUX
+  - LVA
+  - MAR
+  - MDA
+  - MDG
+  - MEX
+  - MKD
+  - MLI
+  - MLT
+  - MMR
+  - MNE
+  - MNG
+  - MOZ
+  - MRT
+  - MUS
+  - MWI
+  - MYS
+  - NAM
+  - NER
+  - NGA
+  - NIC
+  - NLD
+  - NOR
+  - NPL
+  - NZL
+  - OMN
+  - PAK
+  - PAN
+  - PER
+  - PHL
+  - PNG
+  - POL
+  - PRI
+  - PRT
+  - PRY
+  - PSE
+  - ROU
+  - RUS
+  - RWA
+  - SAU
+  - SDN
+  - SEN
+  - SLB
+  - SLE
+  - SLV
+  - SOM
+  - SRB
+  - SSD
+  - STP
+  - SUR
+  - SVK
+  - SVN
+  - SWE
+  - SWZ
+  - SYR
+  - TCD
+  - TGO
+  - THA
+  - TJK
+  - TKM
+  - TLS
+  - TTO
+  - TUN
+  - TUR
+  - TWN
+  - TZA
+  - UGA
+  - UKR
+  - URY
+  - USA
+  - UZB
+  - VEN
+  - VNM
+  - VUT
+  - YEM
+  - ZAF
+  - ZMB
+  - ZWE
+  crop_costs.faostat.price_element_code: 5532
+  crop_costs.faostat.yield_element_code: 5412
+  crop_costs.non_endogenous_cost_share: 0.7
+  crop_costs.outlier_cap_quantile: 0.9
+  cropgrids.suitable_area_expansion: 1.0
+  cropgrids_crops:
+  - apple
+  crops:
+  - wheat
+  - dryland-rice
+  - wetland-rice
+  - maize
+  - barley
+  - oat
+  - rye
+  - sorghum
+  - buckwheat
+  - foxtail-millet
+  - pearl-millet
+  - soybean
+  - dry-pea
+  - chickpea
+  - cowpea
+  - gram
+  - phaseolus-bean
+  - pigeonpea
+  - white-potato
+  - sweet-potato
+  - cassava
+  - yam
+  - plantain
+  - tomato
+  - carrot
+  - onion
+  - cabbage
+  - banana
+  - watermelon
+  - mango
+  - citrus
+  - coconut
+  - apple
+  - cocoa
+  - coffee
+  - tea
+  - sunflower
+  - rapeseed
+  - groundnut
+  - sesame
+  - oil-palm
+  - olive
+  - sugarcane
+  - sugarbeet
+  - cotton
+  - alfalfa
+  - silage-maize
+  - biomass-sorghum
+  currency_base_year: 2024
+  data.faostat.fbs_food_supply_element_code: 645
+  data.faostat.fbs_other_uses_element_code: 5154
+  data.faostat.fbs_production_element_code: 5511
+  data.faostat.qcl_production_element_code: 5510
+  data.gaez.climate_model: GFDL-ESM4
+  data.gaez.climate_model_ensemble: ENSEMBLE
+  data.gaez.climate_scenario: SSP126
+  data.gaez.input_level: H
+  data.gaez.period: FP2140
+  data.gaez.suitability_var: RES05-SX1
+  data.gaez.water_requirement_var: RES05-WDC
+  data.gaez.yield_var: RES05-YCX
+  data.land_cover.version: v2_1_1
+  data.land_cover.zenodo_record: '21085632'
+  data.soilgrids.target_resolution_m: 10000
+  data.usda.nutrients.cal: Energy
+  data.usda.nutrients.carb: Carbohydrate, by difference
+  data.usda.nutrients.fat: Total lipid (fat)
+  data.usda.nutrients.protein: Protein
+  data.usda.retrieve_nutrition: false
+  diet.anchor_groups_to_gbd: true
+  diet.baseline_age: All ages
+  diet.fbs_override_foods:
+  - yam
+  - cocoa-powder
+  - coffee-green
+  - tea-dried
+  - meat-cattle
+  - meat-pig
+  - meat-sheep
+  - meat-chicken
+  - eggs
+  diet.gdd_ia.cooked_to_raw.red_meat: 1.43
+  diet.nhanes.cycle: '1720'
+  diet.nhanes.reference_year: 2018
+  diet.nhanes.url: https://www.ars.usda.gov/ARSUserFiles/80400530/pdf/fped/Table_1_FPED_MaleFemale_{cycle}.pdf
+  diet.source_basis.faostat_fbs_supply.meat-cattle: carcass
+  diet.source_basis.faostat_fbs_supply.meat-chicken: carcass
+  diet.source_basis.faostat_fbs_supply.meat-pig: carcass
+  diet.source_basis.faostat_fbs_supply.meat-sheep: carcass
+  diet.source_basis.faostat_fbs_supply.tea-dried: fresh
+  diet.source_basis.gbd.fruits: fresh
+  diet.source_basis.gbd.legumes: cooked
+  diet.source_basis.gbd.milk: fresh
+  diet.source_basis.gbd.nuts_seeds: dry
+  diet.source_basis.gbd.red_meat: cooked
+  diet.source_basis.gbd.vegetables: fresh
+  diet.source_basis.gbd.whole_grains: dry
+  emissions.ch4_to_co2_factor: 27.0
+  emissions.fertilizer.frac_gasf: 0.11
+  emissions.fertilizer.frac_gasm: 0.21
+  emissions.fertilizer.frac_leach: 0.24
+  emissions.fertilizer.indirect_ef4: 0.01
+  emissions.fertilizer.indirect_ef5: 0.011
+  emissions.fertilizer.organic_n2o_factor: 0.006
+  emissions.fertilizer.synthetic_n2o_factor: 0.01
+  emissions.n2o_to_co2_factor: 273.0
+  emissions.residues.incorporation_n2o_factor: 0.01
+  emissions.rice.methane_emission_factor_kg_per_ha: 134.47
+  emissions.rice.rainfed_wetland_rice_ch4_scaling_factor: 0.54
+  fertilizer.limit: 200000000
+  fertilizer.manure_n_to_fertilizer: 0.75
+  fertilizer.marginal_cost_usd_per_tonne: 500
+  fertilizer.n_percentile: 80
+  fertilizer.proxy_rates.plantain: banana
+  fertilizer.proxy_rates.silage-maize: maize
+  fodder_decomposition.eurostat.averaging_years: 5
+  fodder_decomposition.fdd_crops:
+  - alfalfa
+  - silage-maize
+  fodder_decomposition.suitability_blend_weight: 0.7
+  fodder_decomposition.yield_corrections.ceiling: 2.0
+  fodder_decomposition.yield_corrections.enabled: true
+  fodder_decomposition.yield_corrections.eurostat_moisture: 0.65
+  fodder_decomposition.yield_corrections.floor: 0.2
+  food_demand_calibration.max_multiplier: 2.0
+  food_demand_calibration.min_multiplier: 0.5
+  food_groups.included:
+  - whole_grains
+  - grain
+  - fruits
+  - vegetables
+  - legumes
+  - nuts_seeds
+  - starchy_vegetable
+  - oil
+  - red_meat
+  - poultry
+  - dairy
+  - eggs
+  - sugar
+  - stimulants
+  - animal_fat
+  food_loss_waste_calibration.food_groups:
+  - vegetables
+  - fruits
+  - starchy_vegetable
+  - oil
+  - stimulants
+  gleam3_feed_attribution.pathway_dispatch_shares.maize_ethanol: 0.12
+  gleam3_feed_attribution.pathway_dispatch_shares.maize_wetmill: 0.07
+  gleam3_feed_attribution.pathway_dispatch_shares.sugarcane_ethanol: 0.25
+  grazing.enabled: true
+  grazing.forage_overlap_crops:
+  - alfalfa
+  - silage-maize
+  - biomass-sorghum
+  grazing.isimip_utilization_rate: 0.6
+  health.alternative_rr.red_meat: data/curated/red_meat_rr_log_linear.csv
+  health.breakpoint_rel_tol: 0.05
+  health.causes:
+  - CHD
+  - Stroke
+  - T2DM
+  - CRC
+  health.clustering.weights.gdp: 0.5
+  health.clustering.weights.geography: 1.0
+  health.clustering.weights.population: 0.3
+  health.gbd_cause_id.CHD: 493
+  health.gbd_cause_id.CRC: 441
+  health.gbd_cause_id.Stroke: 495
+  health.gbd_cause_id.T2DM: 976
+  health.gbd_rei_id.fruits: 111
+  health.gbd_rei_id.legumes: 333
+  health.gbd_rei_id.nuts_seeds: 114
+  health.gbd_rei_id.red_meat: 116
+  health.gbd_rei_id.vegetables: 112
+  health.gbd_rei_id.whole_grains: 113
+  health.intake_age_min: 11
+  health.log_rr_points: 15
+  health.region_clusters: 30
+  health.risk_cause_map.fruits:
+  - CHD
+  - Stroke
+  - T2DM
+  health.risk_cause_map.legumes:
+  - CHD
+  health.risk_cause_map.nuts_seeds:
+  - CHD
+  health.risk_cause_map.red_meat:
+  - CHD
+  - Stroke
+  - T2DM
+  - CRC
+  health.risk_cause_map.vegetables:
+  - CHD
+  - Stroke
+  health.risk_cause_map.whole_grains:
+  - CHD
+  - Stroke
+  - T2DM
+  - CRC
+  health.risk_factors:
+  - fruits
+  - vegetables
+  - nuts_seeds
+  - legumes
+  - red_meat
+  - whole_grains
+  health.ssb_sugar_g_per_100g: 5.7
+  irrigation.irrigated_crops: all
+  land.conversion_cost_forest_usd_per_ha: 8000
+  land.conversion_cost_nonforest_usd_per_ha: 2000
+  land.discount_rate: 0.05
+  land.filtering.min_area_ha: 100
+  land.filtering.min_crop_yield_t_per_ha: 0.01
+  land.filtering.min_grassland_yield_t_per_ha: 0.05
+  land.investment_horizon: 30
+  land.land_use_cost_usd_per_ha: 0.0
+  luc.cropland_source: gaez
+  luc.forest_fraction_threshold: 0.2
+  luc.horizon_years: 30
+  luc.managed_flux_mode: zero
+  luc.savanna_pvc_threshold: 75
+  multiple_cropping.double_rice.crops:
+  - wetland-rice
+  - wetland-rice
+  multiple_cropping.double_rice.water_supplies:
+  - r
+  - i
+  multiple_cropping.maize_soybean.crops:
+  - maize
+  - soybean
+  multiple_cropping.maize_soybean.water_supplies:
+  - r
+  - i
+  multiple_cropping.rice_wheat.crops:
+  - wetland-rice
+  - wheat
+  multiple_cropping.rice_wheat.water_supplies:
+  - r
+  - i
+  non_food_crops:
+  - alfalfa
+  - silage-maize
+  - biomass-sorghum
+  optimal_taxes.enabled: false
+  residues.max_feed_fraction: 0.3
+  residues.max_feed_fraction_by_region.Asia: 0.7
+  residues.max_feed_fraction_by_region.Southern Asia: 0.85
+  residues.max_feed_fraction_by_region.Sub-Saharan Africa: 0.7
+  water.huang_reference_year: 2010
+  water.supply_scenario: current_use
+  weight_conversion.carcass_to_fresh.meat-cattle: 0.67
+  weight_conversion.carcass_to_fresh.meat-chicken: 0.6
+  weight_conversion.carcass_to_fresh.meat-pig: 0.73
+  weight_conversion.carcass_to_fresh.meat-sheep: 0.66
+  weight_conversion.cooked_to_dry.grain: 0.45
+  weight_conversion.cooked_to_dry.legumes: 0.4
+  weight_conversion.cooked_to_dry.whole_grains: 0.45
+  weight_conversion.cooked_to_fresh.poultry: 1.0
+  weight_conversion.cooked_to_fresh.red_meat: 1.43
+  weight_conversion.fresh_to_dry.tea-dried: 0.22
+  yield_calibration.crops:
+  - plantain
+  - coffee
+  - tea
+  yield_calibration.enabled: true
+  yield_calibration.multiplier_max: 3.0
+  yield_calibration.multiplier_min: 0.5
diff --git a/docs/calibration.rst b/docs/calibration.rst
index 4eeaa8c5..7753dde5 100644
--- a/docs/calibration.rst
+++ b/docs/calibration.rst
@@ -16,10 +16,30 @@ directly.
 Calibration artefacts are organised in *sets*: one directory
 ``data/curated/calibration/<source>/`` per base configuration they were
 calibrated against, selected by the ``calibration.source`` config key.
-The shipped ``default`` set is version-controlled so that ordinary
-builds don't need to re-solve anything. See
+The shipped ``default`` and ``gbd-anchored`` sets are version-controlled
+so that ordinary builds don't need to re-solve anything. See
 :ref:`calibration-provenance` for how sets are tied to configs.
 
+.. admonition:: Calibration is tied to the baseline diet (and thus to health/anchoring)
+   :class: warning
+
+   Several artefacts (notably ``food_demand.csv``, ``food_waste.yaml``
+   and ``deviation_penalty.yaml``) are fit against a *specific* baseline
+   diet. The baseline diet depends on whether the GBD risk-factor groups
+   are anchored to GBD intake, which is controlled by
+   ``diet.anchor_groups_to_gbd`` (default: follow ``health.enabled``); see
+   :ref:`current-diets-gbd-anchoring`.
+
+   Two artefact sets are therefore committed: ``default``, fit against
+   the anchoring-off baseline diet of the default config, and
+   ``gbd-anchored``, fit against the GBD-anchored diet and consumed by
+   the health-enabled configs (``validation``, ``gsa``, the doc-figure
+   configs) via ``calibration.source: gbd-anchored``. ``tools/calibrate``
+   resolves anchoring from its base config and pins it across all five
+   steps, so each set is regenerated against the right diet
+   automatically. The provenance stamps record the *resolved* anchoring,
+   so consuming a set fit against the other diet fails at startup.
+
 .. _calibration-enabled-generate-pattern:
 
 .. note::
diff --git a/docs/config/doc_figures.yaml b/docs/config/doc_figures.yaml
index 2dafeae4..98f350f4 100644
--- a/docs/config/doc_figures.yaml
+++ b/docs/config/doc_figures.yaml
@@ -84,7 +84,17 @@ water:
   supply_scenario: "current_use"
 
 health:
-  enabled: false
+  # Documentation figures include health-cluster maps and the diet-attributable
+  # burden choropleth, which need the health data-prep outputs (clusters etc.).
+  # value_per_yll: 0 keeps health out of the objective (post-hoc evaluation
+  # only). Needs the manually-downloaded IHME GBD data.
+  enabled: true
+  value_per_yll: 0
+
+# With health on, the baseline diet is GBD-anchored, so consume the artefact
+# set calibrated against that diet (the `default` set is anchoring-off).
+calibration:
+  source: "gbd-anchored"
 
 solving:
   solver: "gurobi"
diff --git a/docs/config/doc_validation.yaml b/docs/config/doc_validation.yaml
index 5f61d8b0..e8475a35 100644
--- a/docs/config/doc_validation.yaml
+++ b/docs/config/doc_validation.yaml
@@ -36,9 +36,16 @@ cost_calibration:
 food_demand_calibration:
   enabled: false
 
+# Health module on with zero objective weight: post-hoc YLL evaluation only.
 health:
+  enabled: true
   value_per_yll: 0
 
+# With health on, the baseline diet is GBD-anchored, so consume the artefact
+# set calibrated against that diet (the `default` set is anchoring-off).
+calibration:
+  source: "gbd-anchored"
+
 emissions:
   ghg_pricing_enabled: false
 
diff --git a/docs/current_diets.rst b/docs/current_diets.rst
index 3d09900e..ff540c49 100644
--- a/docs/current_diets.rst
+++ b/docs/current_diets.rst
@@ -53,7 +53,9 @@ Data Sources
     consistent per-country food and energy intake estimates.
   * **Status**: Pending publication; available upon personal request
     from Marco Springmann. Will be re-licensed under CC-BY-NC on
-    release.
+    release. The public release will then become GLADE's default
+    input; until then, a temporary mode that lets GLADE run without
+    GDD-IA is under development.
   * **Coverage**: ~185 countries, per-country mean dietary intake at
     the reference year, reported in parallel grams/day and kcal/day for
     every food category.
@@ -65,10 +67,14 @@ Data Sources
     [Brauer2024]_
   * **Coverage**: country-level mean intake (g/day) for the GBD dietary
     risk factors, adults 25+.
-  * **Role**: anchors the **risk-factor** food groups (fruits,
-    vegetables, whole_grains, legumes, nuts_seeds, red_meat) so the
-    baseline lines up with the same exposure basis the GBD relative-risk
-    functions were calibrated against.
+  * **Role**: *optionally* anchors the **risk-factor** food groups
+    (fruits, vegetables, whole_grains, legumes, nuts_seeds, red_meat) so
+    the baseline lines up with the same exposure basis the GBD
+    relative-risk functions were calibrated against. Controlled by
+    ``diet.anchor_groups_to_gbd`` (see
+    :ref:`current-diets-gbd-anchoring`). When anchoring is off, these
+    groups use the GDD-IA/FAOSTAT estimate like every other group and
+    this dataset is not needed on disk.
 
 **NHANES — What We Eat in America / FPED**
   * **Provider**: USDA ARS / CDC NHANES
@@ -117,6 +123,112 @@ Units in the merged ``dietary_intake.csv`` distinguish ``g/day (fresh
 wt)`` from ``g/day (milk equiv)`` for dairy and ``g/day (refined sugar
 eq)`` for sugar.
 
+.. _current-diets-gbd-anchoring:
+
+GBD Anchoring of Risk-Factor Groups (optional)
+----------------------------------------------
+
+For the six GBD dietary **risk-factor** groups (``fruits``,
+``vegetables``, ``whole_grains``, ``legumes``, ``nuts_seeds``,
+``red_meat``) the per-country food-group total can optionally be taken
+from GBD dietary-exposure intake instead of the GDD-IA/FAOSTAT estimate.
+The point of anchoring is **basis consistency with the health module**:
+the GBD relative-risk dose-response curves are calibrated against GBD's
+own intake exposure, so a GBD-anchored baseline makes the model's
+attributable-burden numbers directly comparable to GBD's.
+
+Controlled by ``diet.anchor_groups_to_gbd``:
+
+* ``match_health`` (default) -- follow ``health.enabled``. A health run
+  anchors; a no-health run does not.
+* ``true`` / ``false`` -- force anchoring on or off, decoupled from the
+  health module.
+
+When anchoring is on, the run needs the manually-downloaded IHME GBD
+data (see :doc:`data_sources`); when off, it needs none of it.
+
+When to turn it on
+~~~~~~~~~~~~~~~~~~~
+
+Turn anchoring **on** when you care about compatibility with GBD health
+metrics -- i.e. whenever the health module is enabled, or when comparing
+the model's diet-attributable disease burden against GBD estimates.
+Otherwise the GDD-IA/FAOSTAT baseline (anchoring off) is a perfectly
+reasonable diet and avoids the manual GBD download entirely; this is the
+default for a no-health run.
+
+How much does it change the baseline diet?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The switch is **not** cosmetic at the diet level. Across ~174 countries,
+anchoring moves the per-country total of every risk-factor group, and in
+about half of the affected country-group cells by more than 50 %
+relative. The direction is systematic (values below are the mean
+per-country shift, *anchored minus non-anchored*, g/day):
+
+.. list-table::
+   :header-rows: 1
+   :widths: 20 20 60
+
+   * - Group
+     - Mean shift (g/day)
+     - Direction
+   * - vegetables
+     - -56
+     - anchoring **lowers** vegetable intake (e.g. ~172 -> ~116 g/day mean)
+   * - red_meat
+     - -35
+     - lowers in ~98 % of countries
+   * - legumes
+     - -15
+     - lowers in ~94 % of countries
+   * - nuts_seeds
+     - -6
+     - lowers in ~95 % of countries
+   * - whole_grains
+     - -4.5 (mixed)
+     - large *relative* swings; GBD is often much higher where GDD-IA
+       reports near-zero whole grain
+   * - fruits
+     - +2.4 (mixed)
+     - roughly unchanged on average, country-specific either way
+
+In words: the GBD-anchored ("health-on") baseline has systematically
+**less** vegetables, red meat, legumes and nuts than the GDD-IA/FAOSTAT
+("health-off") baseline, and a markedly different whole-grain profile.
+
+Whether this matters for *physical* outcomes depends on the question.
+From a global agricultural and land-use perspective the effect is
+expected to be modest -- the shifts are partly redistributive within the
+diet and are further absorbed by the production-stability calibration
+(see :doc:`calibration`) -- though not strictly negligible. The reason
+to anchor is primarily **health-metric compatibility**, not a large
+expected change in land use or emissions.
+
+.. admonition:: Caveat: the option is not *exactly* "only the six risk groups"
+
+   Anchoring ``whole_grains`` to GBD's narrow whole-grain definition can
+   discard cereal energy that GDD-IA reports more broadly. To preserve
+   each country's cereal energy budget, a **cereal residual fix**
+   reassigns that deficit to refined ``grain`` (see
+   ``apply_cereal_residual_fix`` in ``estimate_baseline_diet.py``), and
+   the kcal normalisation then treats refined ``grain`` as anchored too.
+   So enabling anchoring also adjusts refined ``grain`` even though it is
+   not itself a GBD risk factor. The fix only runs when ``whole_grains``
+   is anchored, so it is inert when anchoring is off.
+
+.. admonition:: Calibration coupling
+   :class: warning
+
+   The calibration artefacts under ``data/curated/calibration/`` are fit
+   against a *specific* baseline diet. Two sets are committed: ``default``
+   (anchoring off) and ``gbd-anchored`` (anchoring on, used by the
+   health-enabled configs via ``calibration.source``). Changing
+   ``diet.anchor_groups_to_gbd`` (or ``health.enabled``, which drives it)
+   changes the baseline diet, so point ``calibration.source`` at the
+   matching set -- the provenance check errors otherwise (see
+   :doc:`calibration`).
+
 GDD-IA to Food Group Mapping
 -----------------------------
 
diff --git a/docs/data_sources.rst b/docs/data_sources.rst
index 81e22c09..f24915af 100644
--- a/docs/data_sources.rst
+++ b/docs/data_sources.rst
@@ -17,12 +17,25 @@ Manual Download Checklist
 
 Several licensed datasets cannot be fetched automatically. While their use is free for non-commercial research purposes, these have to be downloaded manually or require API key registration.
 
-**Required manual downloads:**
+**Always required:**
 
-1. Create an account with IHME and download GBD death rates as described in :ref:`ihme-gbd-mortality`.
-2. Download the IHME 2019 relative risk workbook ``IHME_GBD_2019_RELATIVE_RISKS_Y2020M10D15.XLSX`` (:ref:`ihme-relative-risks`).
+1. Obtain the **GDD-IA** intake CSVs by personal request to Marco Springmann and place them as ``data/manually_downloaded/GDD-IA-intake_grams_{year}.csv`` and ``data/manually_downloaded/GDD-IA-intake_kcals_{year}.csv`` (:ref:`gdd-ia-dietary-intake`). A public GDD-IA release is upcoming and will then be the default input; until then, a temporary mode that lets GLADE run without GDD-IA is under development.
+
+**Required only for the health module / GBD diet anchoring:**
+
+The following IHME GBD datasets are needed **only** when the health
+module is enabled (``health.enabled: true``) or when the baseline diet
+anchors to GBD (``diet.anchor_groups_to_gbd``; see
+:ref:`current-diets-gbd-anchoring`). With both off -- the default -- they
+are not required and the workflow runs without them. If they are missing
+while needed, the workflow stops at startup with an explicit message.
+
+2. Create an account with IHME and download GBD death rates as described in :ref:`ihme-gbd-mortality`.
 3. Download the IHME 2023 dietary risk exposure estimates (two archives, ``IHME_GBD_2023_RISK_EXPOSURE_DIET_1`` and ``_2``) (:ref:`ihme-diet-risk-exposure`).
-4. Obtain the **GDD-IA** intake CSVs by personal request to the Global Dietary Database team and place them as ``data/manually_downloaded/GDD-IA-intake_grams_{year}.csv`` and ``data/manually_downloaded/GDD-IA-intake_kcals_{year}.csv`` (:ref:`gdd-ia-dietary-intake`).
+
+**Optional (only to regenerate curated health inputs):**
+
+4. The IHME 2019 relative risk workbook ``IHME_GBD_2019_RELATIVE_RISKS_Y2020M10D15.XLSX`` (:ref:`ihme-relative-risks`) is only used by a standalone curation script to regenerate the git-tracked RR age-attenuation table; it is not consumed by the normal workflow.
 
 The one build-time credential is a free USDA FoodData Central key, used only to refresh the nutritional data (see :doc:`introduction`). Everything else is fetched from public downloads, the Zenodo land-cover mirror (:ref:`copernicus-land-cover`), or bundled data.
 
diff --git a/docs/health.rst b/docs/health.rst
index f5271140..e0c45f93 100644
--- a/docs/health.rst
+++ b/docs/health.rst
@@ -12,6 +12,27 @@ dietary choices. It begins with the epidemiological concepts that underpin the
 methodology, then explains the implementation strategy for embedding these
 nonlinear relationships into a linear optimisation framework.
 
+.. admonition:: The health module is off by default
+   :class: note
+
+   ``health.enabled`` defaults to ``false``. The health module relies on
+   manually-downloaded IHME GBD data (mortality rates and dietary
+   risk-exposure; see :doc:`data_sources`), which cannot be fetched
+   automatically. When health is disabled, none of that data is required
+   and the workflow builds, solves, and analyses without it -- the
+   YLL stores, health objective term, and health analysis/plots are
+   simply omitted, and a clear error is raised if the data is missing
+   while health is on.
+
+   Enabling health also turns on **GBD anchoring of the baseline diet**
+   by default (``diet.anchor_groups_to_gbd: match_health``), so that the
+   model's attributable-burden numbers share the GBD intake basis. That
+   anchoring changes the baseline diet and is an independent switch -- see
+   :ref:`current-diets-gbd-anchoring`. Because the baseline diet feeds the
+   calibration, a run must consume the artefact set matching its resolved
+   anchoring: ``calibration.source: default`` (anchoring off) or
+   ``gbd-anchored`` (anchoring on); see :doc:`calibration`.
+
 Conceptual Framework
 --------------------
 
diff --git a/docs/introduction.rst b/docs/introduction.rst
index 35f2b74a..bb97631d 100644
--- a/docs/introduction.rst
+++ b/docs/introduction.rst
@@ -116,8 +116,10 @@ manually:
   registration).
 * **GDD-IA (Global Dietary Database — Integrated Assessment)** —
   available upon personal request from Marco Springmann, pending
-  publication (will be released under CC-BY-NC). See :doc:`current_diets`
-  and the :ref:`gdd-ia-dietary-intake` entry in :doc:`data_sources`.
+  publication (will be released under CC-BY-NC); a temporary
+  GDD-IA-free mode is under development to bridge the gap until the
+  public release. See :doc:`current_diets` and the
+  :ref:`gdd-ia-dietary-intake` entry in :doc:`data_sources`.
 
 The one build-time credential is an optional, free `USDA FoodData Central
 <https://fdc.nal.usda.gov/api-key-signup>`_ key, used to refresh the nutritional
diff --git a/tests/config/test.yaml b/tests/config/test.yaml
index 79ef399f..3dbf943a 100644
--- a/tests/config/test.yaml
+++ b/tests/config/test.yaml
@@ -25,6 +25,12 @@ scenarios:
     emissions:
       ghg_pricing_enabled: true
 
+diet:
+  # Anchor the baseline diet to GBD (despite health being off) so the
+  # integration tests exercise the anchoring path; CI stages the IHME GBD
+  # inputs this needs.
+  anchor_groups_to_gbd: true
+
 health:
   enabled: false
 
diff --git a/tests/test_baseline_diet.py b/tests/test_baseline_diet.py
index bedb17fb..9bc35c68 100644
--- a/tests/test_baseline_diet.py
+++ b/tests/test_baseline_diet.py
@@ -791,25 +791,30 @@ def test_food_level_kpg_hits_target_for_heterogeneous_group(self):
             ), f"country {c} kcal={kcal:.2f} != target 600"
 
     def test_anchored_groups_are_not_rescaled(self):
-        """Foods in GBD-anchored groups (and the refined-grain residual)
-        must keep their consumption unchanged; only unanchored foods get
-        scaled to close the residual.
+        """Foods in GBD-anchored groups (and, when whole_grains is anchored,
+        the refined-grain residual) must keep their consumption unchanged;
+        only unanchored foods get scaled to close the residual.
         """
         baseline_diet = pd.DataFrame(
             {
-                "country": ["A", "A", "A"],
-                "food": ["red-meat-item", "cassava", "rice-white"],
-                "food_group": ["red_meat", "starchy_vegetable", "grain"],
-                "consumption_g_per_day": [50.0, 500.0, 100.0],
+                "country": ["A", "A", "A", "A"],
+                "food": ["red-meat-item", "cassava", "rice-white", "oat"],
+                "food_group": [
+                    "red_meat",
+                    "starchy_vegetable",
+                    "grain",
+                    "whole_grains",
+                ],
+                "consumption_g_per_day": [50.0, 500.0, 100.0, 0.0],
             }
         )
-        kpg = {"red-meat-item": 2.5, "cassava": 1.59, "rice-white": 3.65}
+        kpg = {"red-meat-item": 2.5, "cassava": 1.59, "rice-white": 3.65, "oat": 3.0}
         target_df = pd.DataFrame({"country": ["A"], "kcal_target_modelled": [1000.0]})
 
         result = apply_kcal_normalisation(
             baseline_diet,
             target_df,
-            gbd_anchored_groups={"red_meat"},
+            gbd_anchored_groups={"red_meat", "whole_grains"},
             kcal_per_g_food=kpg,
         )
 
@@ -832,6 +837,40 @@ def test_anchored_groups_are_not_rescaled(self):
         # factor = 510/795 = 0.6415. post: 500 * 0.6415 = 320.75.
         assert cassava_g == pytest.approx(320.75, rel=1e-3)
 
+    def test_grain_rescaled_when_whole_grains_not_anchored(self):
+        """Without the whole-grain anchor the cereal residual fix does not
+        run, so refined grain is an ordinary estimate and must be rescaled
+        along with every other unanchored food (uniformly, here, since no
+        group is anchored at all).
+        """
+        baseline_diet = pd.DataFrame(
+            {
+                "country": ["A", "A"],
+                "food": ["cassava", "rice-white"],
+                "food_group": ["starchy_vegetable", "grain"],
+                "consumption_g_per_day": [500.0, 100.0],
+            }
+        )
+        kpg = {"cassava": 1.59, "rice-white": 3.65}
+        # Pre-scaling kcal = 500*1.59 + 100*3.65 = 1160; target 580 -> factor 0.5.
+        target_df = pd.DataFrame({"country": ["A"], "kcal_target_modelled": [580.0]})
+
+        result = apply_kcal_normalisation(
+            baseline_diet,
+            target_df,
+            gbd_anchored_groups=set(),
+            kcal_per_g_food=kpg,
+        )
+
+        grain_g = float(
+            result.loc[result["food"] == "rice-white", "consumption_g_per_day"].iloc[0]
+        )
+        cassava_g = float(
+            result.loc[result["food"] == "cassava", "consumption_g_per_day"].iloc[0]
+        )
+        assert grain_g == pytest.approx(50.0)
+        assert cassava_g == pytest.approx(250.0)
+
     def test_missing_food_raises(self):
         """Foods without a kcal/g entry should raise; the previous code
         silently substituted zero.
diff --git a/tools/calibrate b/tools/calibrate
index d1f4cbc2..35169f03 100755
--- a/tools/calibrate
+++ b/tools/calibrate
@@ -84,6 +84,18 @@ fi
 CAL_DIR="data/curated/calibration/${SOURCE}"
 echo "[calibrate] artefact set: ${SOURCE} (${CAL_DIR})"
 
+# The artefacts are fit against the base config's baseline diet, which depends
+# on the resolved diet.anchor_groups_to_gbd (the "match_health" sentinel
+# follows health.enabled). The calibration step configs override health.enabled
+# for solver performance, which would otherwise re-resolve the sentinel per
+# step and mix baseline diets within one chain. Resolve anchoring from the
+# base config once and pin it explicitly in every step via a config overlay.
+ANCHORING=$(pixi run python "$PROVENANCE_SCRIPT" "${BASE_ARGS[@]}" --print-anchoring | tail -n1)
+ANCHOR_OVERLAY=$(mktemp -t calibrate-anchoring-XXXXXX.yaml)
+trap 'rm -f "$ANCHOR_OVERLAY"' EXIT
+printf 'diet:\n  anchor_groups_to_gbd: %s\n' "$ANCHORING" > "$ANCHOR_OVERLAY"
+echo "[calibrate] baseline-diet GBD anchoring: ${ANCHORING}"
+
 # Non-default sources get their own processing/results tree so repeated
 # calibrations against different bases don't thrash each other's builds.
 NAME_OVERRIDE=()
@@ -169,7 +181,7 @@ run_step() {
   inject_env
   local cfgfiles=()
   [[ -n "$BASE_CONFIG" ]] && cfgfiles+=("$BASE_CONFIG")
-  cfgfiles+=("$configfile")
+  cfgfiles+=("$configfile" "$ANCHOR_OVERLAY")
   echo "[calibrate] $label -- tools/smk --configfile ${cfgfiles[*]} ${NAME_OVERRIDE[*]} ${PASSTHROUGH_ARR[*]} -- ${TARGETS_ARR[*]}"
   tools/smk --configfile "${cfgfiles[@]}" "${NAME_OVERRIDE[@]}" "${PASSTHROUGH_ARR[@]}" -- "${TARGETS_ARR[@]}"
 }
@@ -181,7 +193,7 @@ check_step() {
   inject_env
   local cfgfiles=()
   [[ -n "$BASE_CONFIG" ]] && cfgfiles+=("$BASE_CONFIG")
-  cfgfiles+=("$configfile")
+  cfgfiles+=("$configfile" "$ANCHOR_OVERLAY")
   local out
   if ! out=$(tools/smk --configfile "${cfgfiles[@]}" "${NAME_OVERRIDE[@]}" "${PASSTHROUGH_ARR[@]}" --dry-run --quiet -- "${TARGETS_ARR[@]}" 2>&1); then
     printf '  [error]      %-10s (dry-run failed; see output below)\n' "$label"
diff --git a/workflow/Snakefile b/workflow/Snakefile
index 8b4e08cd..83e8fd06 100644
--- a/workflow/Snakefile
+++ b/workflow/Snakefile
@@ -66,7 +66,16 @@ include: "rules/common.smk"
 include: "rules/retrieve.smk"
 include: "rules/geography.smk"
 include: "rules/diet.smk"
-include: "rules/health.smk"
+
+
+# Health data-prep rules are only needed when the health module is enabled in
+# the base config or any scenario. When off, none of the manually-downloaded
+# IHME GBD data is required (see docs/health.rst and diet.anchor_groups_to_gbd).
+if health_required():
+
+    include: "rules/health.smk"
+
+
 include: "rules/animals.smk"
 include: "rules/crops.smk"
 include: "rules/water.smk"
@@ -116,13 +125,10 @@ ALL_RULE_TARGETS = [
     "<results>/{name}/plots/food_consumption_comparison.csv",
     "<results>/{name}/plots/system_cost_comparison.pdf",
     "<results>/{name}/plots/system_cost_comparison.csv",
-    "<results>/{name}/plots/relative_risk_curves.pdf",
     # results plots
-    "<results>/{name}/plots/scen-default/consumption_balance.pdf",
     "<results>/{name}/plots/scen-default/objective_breakdown.pdf",
     "<results>/{name}/plots/scen-default/objective_breakdown.csv",
     "<results>/{name}/plots/scen-default/emissions_breakdown.pdf",
-    "<results>/{name}/plots/scen-default/yll_global_by_cause.pdf",
     # "<results>/{name}/plots/scen-default/health_risk_map.pdf",
     # "<results>/{name}/plots/scen-default/health_risk_by_region.csv",
     # "<results>/{name}/plots/scen-default/health_baseline_map.pdf",
@@ -145,13 +151,25 @@ ALL_RULE_TARGETS = [
     "<results>/{name}/plots/scen-default/water_use_by_region.csv",
     # "<results>/{name}/plots/scen-default/water_value_map.pdf",   # Current not working
     "<results>/{name}/plots/scen-default/food_consumption.pdf",
-    "<results>/{name}/plots/scen-default/food_consumption_map.pdf",
-    "<results>/{name}/plots/scen-default/food_consumption_baseline_map.pdf",
     # marginal damages analysis
     "<results>/{name}/plots/scen-default/marginal_ghg_global.pdf",
-    "<results>/{name}/plots/scen-default/marginal_yll_global.pdf",
 ]
 
+# Plots that depend on the health module: the YLL/risk plots, the marginal-YLL
+# damages plot, and the consumption maps/balance that aggregate countries into
+# health clusters (produced by prepare_health_costs). These are only requested
+# by `rule all` when health is enabled; the model still builds, solves, and
+# produces every other output with health off and no IHME GBD data on disk.
+if health_required():
+    ALL_RULE_TARGETS += [
+        "<results>/{name}/plots/relative_risk_curves.pdf",
+        "<results>/{name}/plots/scen-default/consumption_balance.pdf",
+        "<results>/{name}/plots/scen-default/yll_global_by_cause.pdf",
+        "<results>/{name}/plots/scen-default/food_consumption_map.pdf",
+        "<results>/{name}/plots/scen-default/food_consumption_baseline_map.pdf",
+        "<results>/{name}/plots/scen-default/marginal_yll_global.pdf",
+    ]
+
 # Solve all configured scenarios without triggering plotting targets.
 SOLVE_ALL_SCENARIOS_TARGETS = [
     f"<results>/{{name}}/solved/model_scen-{scenario}.nc"
diff --git a/workflow/rules/analysis.smk b/workflow/rules/analysis.smk
index c606c619..33e112ee 100644
--- a/workflow/rules/analysis.smk
+++ b/workflow/rules/analysis.smk
@@ -173,15 +173,19 @@ else:
         passing intermediate DataFrames in memory rather than writing/re-reading them.
         """
         input:
+            # Health processing inputs only when this scenario enables health;
+            # otherwise analyze_model writes empty health outputs.
+            unpack(
+                lambda w: (
+                    health_input_paths(w.name)
+                    if get_effective_config(w.scenario)["health"]["enabled"]
+                    else {}
+                )
+            ),
             network="<results>/{name}/solved/model_scen-{scenario}.nc",
             food_groups="data/curated/food_groups.csv",
             m49_codes="data/curated/M49-codes.csv",
-            risk_breakpoints="<processing>/{name}/health/risk_breakpoints.csv",
-            health_cluster_cause="<processing>/{name}/health/cluster_cause_baseline.csv",
-            health_cause_log="<processing>/{name}/health/cause_log_breakpoints.csv",
-            health_clusters="<processing>/{name}/health/country_clusters.csv",
             population="<processing>/{name}/population.csv",
-            tmrel="<processing>/{name}/health/tmrel.csv",
             analysis_scripts=_ANALYSIS_SCRIPTS,
         params:
             ghg_price=lambda w: get_effective_config(w.scenario)["emissions"][
@@ -189,6 +193,9 @@ else:
             ],
             ch4_gwp=config["emissions"]["ch4_to_co2_factor"],
             n2o_gwp=config["emissions"]["n2o_to_co2_factor"],
+            health_enabled=lambda w: get_effective_config(w.scenario)["health"][
+                "enabled"
+            ],
             value_per_yll=lambda w: get_effective_config(w.scenario)["health"][
                 "value_per_yll"
             ],
diff --git a/workflow/rules/common.smk b/workflow/rules/common.smk
index d0bb3d30..c7765a89 100644
--- a/workflow/rules/common.smk
+++ b/workflow/rules/common.smk
@@ -20,6 +20,9 @@ from workflow.scripts.solve_namespace import (
     _is_solve_time_key,
     _leaf_keys,
     deviation_penalty_uses_calibrated,
+    health_input_paths,
+    resolve_gbd_anchoring,
+    resolve_pathvars,
     validate_scenario_config_schemas,
     validate_scenario_overrides,
 )
@@ -70,6 +73,77 @@ def get_effective_config(scenario_name):
     return eff_config
 
 
+def health_required():
+    """True if the health module is enabled in the base config or any scenario.
+
+    The build step is scenario-independent, so health data-prep rules, health
+    stores, and downstream health analysis/plots are needed whenever *any*
+    configured scenario (or the base config) enables health. health.enabled is
+    a solve-time-overridable key.
+    """
+    if config["health"]["enabled"]:
+        return True
+    return any(get_effective_config(s)["health"]["enabled"] for s in list_scenarios())
+
+
+def gbd_anchoring_enabled():
+    """Resolve diet.anchor_groups_to_gbd for this run (see resolve_gbd_anchoring)."""
+    return resolve_gbd_anchoring(config)
+
+
+def gbd_data_required():
+    """True if the manually-downloaded IHME GBD data is needed by this run.
+
+    The GBD intake/mortality prep (and thus the IHME source files) is required
+    whenever the baseline diet is anchored to GBD or the health module is
+    enabled in any scenario.
+    """
+    return gbd_anchoring_enabled() or health_required()
+
+
+def assert_gbd_data_available():
+    """Fail early with actionable guidance if GBD data is needed but absent.
+
+    Snakemake would otherwise report a terse "missing input" deep in the DAG.
+    Only enforced when gbd_data_required(); a health-off, anchoring-off run
+    needs none of these files.
+    """
+    if not gbd_data_required():
+        return
+    year = config["baseline_year"]
+    required = {
+        f"data/manually_downloaded/IHME-GBD_2023-death-rates-{year}.csv": (
+            "IHME GBD mortality / national-location list"
+        ),
+        "data/manually_downloaded/IHME_GBD_2023_RISK_EXPOSURE_DIET_1": (
+            "IHME GBD dietary risk-exposure archive (part 1)"
+        ),
+        "data/manually_downloaded/IHME_GBD_2023_RISK_EXPOSURE_DIET_2": (
+            "IHME GBD dietary risk-exposure archive (part 2)"
+        ),
+    }
+    missing = [(p, desc) for p, desc in required.items() if not Path(p).exists()]
+    if not missing:
+        return
+    reasons = []
+    if health_required():
+        reasons.append("health.enabled is true (base config or a scenario)")
+    if gbd_anchoring_enabled():
+        reasons.append("diet.anchor_groups_to_gbd resolves to true")
+    listing = "\n".join(f"  - {p}  ({desc})" for p, desc in missing)
+    raise FileNotFoundError(
+        "This run needs the manually-downloaded IHME GBD data because "
+        + " and ".join(reasons)
+        + ", but the following are missing:\n"
+        + listing
+        + "\n\nEither place the files (see data/manually_downloaded/README.md "
+        "for the GBD Results Tool queries), or run without GBD data by setting "
+        "health.enabled: false and diet.anchor_groups_to_gbd: false in your "
+        "config. Note that disabling anchoring changes the baseline diet "
+        "(see docs/current_diets.rst)."
+    )
+
+
 # SOLVE_TIME_CONFIG_PREFIXES, _leaf_keys, _is_solve_time_key, and
 # validate_scenario_overrides are imported from workflow.scripts.solve_namespace
 # above so cluster manifest export, in-process calibration drivers, and this
@@ -78,6 +152,7 @@ def get_effective_config(scenario_name):
 
 validate_scenario_overrides(load_scenario_defs())
 validate_scenario_config_schemas(config, load_scenario_defs(), Path.cwd())
+assert_gbd_data_available()
 
 
 def scenario_override_hash(scenario_name):
diff --git a/workflow/rules/deviation_penalty.smk b/workflow/rules/deviation_penalty.smk
index c9c4266b..449e5f7a 100644
--- a/workflow/rules/deviation_penalty.smk
+++ b/workflow/rules/deviation_penalty.smk
@@ -18,17 +18,13 @@ if _dp_cal_cfg["generate"]:
 
     rule calibrate_deviation_penalty:
         input:
+            # Health inputs only when health is enabled (the solve prices health
+            # only then); omitted otherwise so no IHME GBD data is required.
+            unpack(lambda w: health_input_paths(name) if health_required() else {}),
             model=f"<results>/{name}/build/model.nc",
             baseline_diet=f"<processing>/{name}/baseline_diet.csv",
             m49="data/curated/M49-codes.csv",
             food_groups="data/curated/food_groups.csv",
-            health_risk_breakpoints=f"<processing>/{name}/health/risk_breakpoints.csv",
-            health_cluster_cause=f"<processing>/{name}/health/cluster_cause_baseline.csv",
-            health_cause_log=f"<processing>/{name}/health/cause_log_breakpoints.csv",
-            health_cluster_summary=f"<processing>/{name}/health/cluster_summary.csv",
-            health_clusters=f"<processing>/{name}/health/country_clusters.csv",
-            health_tmrel=f"<processing>/{name}/health/tmrel.csv",
-            health_cluster_risk_baseline=f"<processing>/{name}/health/cluster_risk_baseline.csv",
             nutrition="data/curated/nutrition.csv",
         output:
             calibrated_yaml=_dp_cal_cfg["calibrated_yaml"],
@@ -40,10 +36,18 @@ if _dp_cal_cfg["generate"]:
             tolerance=_dp_cal_cfg["tolerance"],
             max_iter=_dp_cal_cfg["max_iter"],
             trust_region_log=_dp_cal_cfg["trust_region_log"],
-            # Warm-start path is passed as a param (not an input) so Snakemake
-            # doesn't create a self-loop on calibrated_yaml. The script loads
-            # it iff the file exists on disk at run time.
-            previous_yaml=_dp_cal_cfg["calibrated_yaml"],
+            # Warm-start seed: a side copy of the previous calibrated yaml,
+            # written by the script next to the trace. It must not be the
+            # calibrated_yaml output itself (Snakemake deletes outputs before
+            # the job runs, so that warm start would never engage) nor a
+            # declared input/output (self-loop / same deletion). Pathvars are
+            # resolved explicitly since params are not path-expanded. The
+            # script loads it iff the file exists on disk at run time.
+            previous_yaml=str(
+                Path(resolve_pathvars(_trace_csv, PATH_ROOTS)).with_name(
+                    "deviation_penalty_warm.yaml"
+                )
+            ),
             name=name,
         resources:
             # Per-iteration solves use mem_mb / runtime configured in the
diff --git a/workflow/rules/diet.smk b/workflow/rules/diet.smk
index 6e039723..2d9b1464 100644
--- a/workflow/rules/diet.smk
+++ b/workflow/rules/diet.smk
@@ -317,7 +317,14 @@ rule estimate_baseline_diet:
     """
     input:
         dietary_intake="<processing>/{name}/dietary_intake.csv",
-        gbd_exposure="<processing>/{name}/gbd_food_group_intake.csv",
+        # Only required when the baseline diet anchors risk-factor groups to
+        # GBD intake exposure; otherwise the GDD/FAOSTAT estimate is used and
+        # the IHME GBD data is not needed. See diet.anchor_groups_to_gbd.
+        gbd_exposure=(
+            "<processing>/{name}/gbd_food_group_intake.csv"
+            if gbd_anchoring_enabled()
+            else []
+        ),
         kcal_target="<processing>/{name}/gdd_ia_kcal_target.csv",
         nutrition="data/curated/nutrition.csv",
         fbs_items="<processing>/{name}/faostat_fbs_items.csv",
@@ -344,7 +351,10 @@ rule estimate_baseline_diet:
         food_groups_included=config["food_groups"]["included"],
         byproducts=config["byproducts"],
         fbs_override_foods=config["diet"]["fbs_override_foods"],
-        gbd_anchored_groups=config["health"]["risk_factors"],
+        # Empty when anchoring is off -> baseline diet uses GDD/FAOSTAT only.
+        gbd_anchored_groups=(
+            config["health"]["risk_factors"] if gbd_anchoring_enabled() else []
+        ),
         source_basis=config["diet"]["source_basis"],
         weight_conversion=config["weight_conversion"],
         # Used to drop foods whose entire production graph is unreachable
diff --git a/workflow/rules/model.smk b/workflow/rules/model.smk
index ff717356..1927216b 100644
--- a/workflow/rules/model.smk
+++ b/workflow/rules/model.smk
@@ -115,6 +115,23 @@ def build_model_cost_calibration_input(wildcards):
     return {}
 
 
+def _build_model_health_inputs(wildcards):
+    """The health-cluster store inputs and the GBD baseline-diet consistency
+    check, needed only when health is enabled (in the base config or any
+    scenario, since the build is scenario-independent)."""
+    if not health_required():
+        return {}
+    prefix = f"<processing>/{wildcards.name}/health"
+    return {
+        "health_cluster_summary": f"{prefix}/cluster_summary.csv",
+        "health_cluster_cause": f"{prefix}/cluster_cause_baseline.csv",
+        "health_clusters": f"{prefix}/country_clusters.csv",
+        "baseline_diet_risk_comparison": (
+            f"<processing>/{wildcards.name}/baseline_diet_risk_comparison.csv"
+        ),
+    }
+
+
 rule build_model:
     input:
         unpack(yield_inputs),
@@ -126,6 +143,7 @@ rule build_model:
         unpack(build_model_cost_calibration_input),
         unpack(build_model_biofuel_baseline_input),
         unpack(build_model_fiber_baseline_input),
+        unpack(_build_model_health_inputs),
         feed_baseline="<processing>/{name}/feed_baseline.csv",
         feed_to_products="<processing>/{name}/feed_to_animal_products.csv",
         fertilizer_n_rates="<processing>/{name}/global_fertilizer_n_rates.csv",
@@ -149,7 +167,6 @@ rule build_model:
         population="<processing>/{name}/population.csv",
         baseline_diet="<processing>/{name}/dietary_intake.csv",
         baseline_diet_validation="<processing>/{name}/baseline_diet_validation.csv",
-        baseline_diet_risk_comparison="<processing>/{name}/baseline_diet_risk_comparison.csv",
         food_loss_waste="<processing>/{name}/food_loss_waste.csv",
         costs="<processing>/{name}/faostat_crop_costs.csv",
         animal_costs="<processing>/{name}/animal_costs.csv",
@@ -161,9 +178,6 @@ rule build_model:
         faostat_pasture_area="<processing>/{name}/faostat_pasture_area.csv",
         current_grassland_area="<processing>/{name}/luc/current_grassland_area_by_class.csv",
         grazing_only_land="<processing>/{name}/land_grazing_only_by_class.csv",
-        health_cluster_summary="<processing>/{name}/health/cluster_summary.csv",
-        health_cluster_cause="<processing>/{name}/health/cluster_cause_baseline.csv",
-        health_clusters="<processing>/{name}/health/country_clusters.csv",
         build_scripts=expand(
             "workflow/scripts/build_model/{script}",
             script=[
@@ -204,6 +218,9 @@ rule build_model:
         validation=config["validation"],
         deviation_penalty=config["deviation_penalty"],
         netcdf=config["netcdf"],
+        # Add health-cluster stores when health is enabled in the base config or
+        # any scenario (the build is shared across scenarios).
+        health_enabled=health_required(),
     output:
         network="<results>/{name}/build/model.nc",
     group:
@@ -227,19 +244,19 @@ def solve_model_inputs(w):
     inputs = {
         "network": f"<results>/{w.name}/build/model.nc",
         "m49": "data/curated/M49-codes.csv",
-        "health_risk_breakpoints": f"<processing>/{w.name}/health/risk_breakpoints.csv",
-        "health_cluster_cause": f"<processing>/{w.name}/health/cluster_cause_baseline.csv",
-        "health_cause_log": f"<processing>/{w.name}/health/cause_log_breakpoints.csv",
-        "health_cluster_summary": f"<processing>/{w.name}/health/cluster_summary.csv",
-        "health_clusters": f"<processing>/{w.name}/health/country_clusters.csv",
-        "health_tmrel": f"<processing>/{w.name}/health/tmrel.csv",
-        "health_cluster_risk_baseline": f"<processing>/{w.name}/health/cluster_risk_baseline.csv",
         "food_groups": "data/curated/food_groups.csv",
         "baseline_diet": f"<processing>/{w.name}/baseline_diet.csv",
     }
 
-    # Add food incentives input if enabled for this scenario
     eff_cfg = get_effective_config(w.scenario)
+
+    # Health processing inputs are only consumed when this scenario enables
+    # health (add_health_objective / post-hoc evaluation). Omitted otherwise so
+    # the solve needs none of the GBD-derived health artefacts.
+    if eff_cfg["health"]["enabled"]:
+        inputs.update(health_input_paths(w.name))
+
+    # Add food incentives input if enabled for this scenario
     if eff_cfg["food_incentives"]["enabled"]:
         sources = eff_cfg["food_incentives"]["sources"]
         if not sources:
diff --git a/workflow/scripts/analysis/analyze_model.py b/workflow/scripts/analysis/analyze_model.py
index 4b9e65ac..793a7029 100644
--- a/workflow/scripts/analysis/analyze_model.py
+++ b/workflow/scripts/analysis/analyze_model.py
@@ -82,18 +82,19 @@ def run_analysis(
     output_paths: dict[str, str],
     food_groups_path: str,
     m49_codes_path: str,
-    risk_breakpoints_path: str,
-    health_cluster_cause_path: str,
-    health_cause_log_path: str,
-    health_clusters_path: str,
     population_path: str,
-    tmrel_path: str,
     ghg_price: float,
     ch4_gwp: float,
     n2o_gwp: float,
     value_per_yll: float,
     health_risk_factors: list[str],
     logger: logging.Logger,
+    health_enabled: bool,
+    risk_breakpoints_path: str | None = None,
+    health_cluster_cause_path: str | None = None,
+    health_cause_log_path: str | None = None,
+    health_clusters_path: str | None = None,
+    tmrel_path: str | None = None,
 ) -> None:
     """Run all analysis extractions on a solved network.
 
@@ -164,25 +165,34 @@ def run_analysis(
     ghg_attribution_totals = compute_ghg_totals(ghg_attribution)
 
     # --- Health impacts ---
-    logger.info("Extracting health impacts...")
-    risk_factors = list(health_risk_factors)
-    health_data = load_health_data(
-        {
-            "risk_breakpoints": risk_breakpoints_path,
-            "health_cluster_cause": health_cluster_cause_path,
-            "health_cause_log": health_cause_log_path,
-            "health_clusters": health_clusters_path,
-            "population": population_path,
-            "tmrel": tmrel_path,
-        }
-    )
-    health_marginals = compute_health_marginals(n, health_data, risk_factors)
-    health_marginals = add_health_monetary_value(health_marginals, value_per_yll)
-    health_marginals = health_marginals.sort_values(
-        ["country", "food_group"]
-    ).reset_index(drop=True)
-    health_totals = extract_yll_totals(n)
-    health_attribution = compute_health_attribution(n, health_data, risk_factors)
+    # Only computed when health is enabled for this scenario; otherwise the
+    # network carries no YLL stores and the GBD-derived health processing
+    # inputs are absent, so the health outputs are written empty.
+    if health_enabled:
+        logger.info("Extracting health impacts...")
+        risk_factors = list(health_risk_factors)
+        health_data = load_health_data(
+            {
+                "risk_breakpoints": risk_breakpoints_path,
+                "health_cluster_cause": health_cluster_cause_path,
+                "health_cause_log": health_cause_log_path,
+                "health_clusters": health_clusters_path,
+                "population": population_path,
+                "tmrel": tmrel_path,
+            }
+        )
+        health_marginals = compute_health_marginals(n, health_data, risk_factors)
+        health_marginals = add_health_monetary_value(health_marginals, value_per_yll)
+        health_marginals = health_marginals.sort_values(
+            ["country", "food_group"]
+        ).reset_index(drop=True)
+        health_totals = extract_yll_totals(n)
+        health_attribution = compute_health_attribution(n, health_data, risk_factors)
+    else:
+        logger.info("Health disabled for this scenario; writing empty health outputs.")
+        health_marginals = pd.DataFrame()
+        health_totals = pd.DataFrame()
+        health_attribution = pd.DataFrame()
 
     # Write all outputs
     output_dir = Path(output_paths["crop_production"]).parent
@@ -232,8 +242,7 @@ def main() -> None:
     network_path = Path(snakemake.input.network)
     if network_path.stat().st_size == 0:
         logger.warning(
-            "Network file is empty (solve failed or timed out). "
-            "Writing empty outputs."
+            "Network file is empty (solve failed or timed out). Writing empty outputs."
         )
         write_empty_outputs(snakemake.output)
         return
@@ -267,23 +276,33 @@ def main() -> None:
         and getattr(snakemake.output, attr).endswith(".parquet")
     }
 
+    health_enabled = bool(snakemake.params.health_enabled)
     run_analysis(
         n,
         output_paths=output_paths,
         food_groups_path=snakemake.input.food_groups,
         m49_codes_path=snakemake.input.m49_codes,
-        risk_breakpoints_path=snakemake.input.risk_breakpoints,
-        health_cluster_cause_path=snakemake.input.health_cluster_cause,
-        health_cause_log_path=snakemake.input.health_cause_log,
-        health_clusters_path=snakemake.input.health_clusters,
         population_path=snakemake.input.population,
-        tmrel_path=snakemake.input.tmrel,
         ghg_price=float(snakemake.params.ghg_price),
         ch4_gwp=float(snakemake.params.ch4_gwp),
         n2o_gwp=float(snakemake.params.n2o_gwp),
         value_per_yll=float(snakemake.params.value_per_yll),
         health_risk_factors=list(snakemake.params.health_risk_factors),
         logger=logger,
+        health_enabled=health_enabled,
+        risk_breakpoints_path=(
+            snakemake.input.health_risk_breakpoints if health_enabled else None
+        ),
+        health_cluster_cause_path=(
+            snakemake.input.health_cluster_cause if health_enabled else None
+        ),
+        health_cause_log_path=(
+            snakemake.input.health_cause_log if health_enabled else None
+        ),
+        health_clusters_path=(
+            snakemake.input.health_clusters if health_enabled else None
+        ),
+        tmrel_path=snakemake.input.health_tmrel if health_enabled else None,
     )
 
 
diff --git a/workflow/scripts/build_model.py b/workflow/scripts/build_model.py
index 091a4065..6cb5a24f 100644
--- a/workflow/scripts/build_model.py
+++ b/workflow/scripts/build_model.py
@@ -1189,25 +1189,31 @@ def _apply_yield_corrections(corr_df: pd.DataFrame, source_label: str) -> None:
         hub_centers=hub_centers,
     )
 
-    health.add_health_stores(
-        n,
-        snakemake.input.health_cluster_summary,
-        snakemake.input.health_cluster_cause,
-        snakemake.config["health"],
-    )
+    # Health-cluster stores are only added when health is enabled (in the base
+    # config or any scenario). When disabled, the health processing inputs are
+    # absent and no health data is needed on disk.
+    if snakemake.params.health_enabled:
+        health.add_health_stores(
+            n,
+            snakemake.input.health_cluster_summary,
+            snakemake.input.health_cluster_cause,
+            snakemake.config["health"],
+        )
 
-    # Compute and store health cluster populations from country populations
-    cluster_map_df = read_csv(snakemake.input.health_clusters)
-    cluster_lookup = (
-        cluster_map_df.set_index("country_iso3")["health_cluster"].astype(int).to_dict()
-    )
-    pop_df = population.reset_index()
-    pop_df.columns = ["iso3", "population"]
-    pop_df["cluster"] = pop_df["iso3"].str.upper().map(cluster_lookup)
-    pop_df = pop_df.dropna(subset=["cluster"])
-    pop_df["cluster"] = pop_df["cluster"].astype(int)
-    cluster_pop = pop_df.groupby("cluster")["population"].sum().to_dict()
-    n.meta["population"]["health_cluster"] = cluster_pop
+        # Compute and store health cluster populations from country populations
+        cluster_map_df = read_csv(snakemake.input.health_clusters)
+        cluster_lookup = (
+            cluster_map_df.set_index("country_iso3")["health_cluster"]
+            .astype(int)
+            .to_dict()
+        )
+        pop_df = population.reset_index()
+        pop_df.columns = ["iso3", "population"]
+        pop_df["cluster"] = pop_df["iso3"].str.upper().map(cluster_lookup)
+        pop_df = pop_df.dropna(subset=["cluster"])
+        pop_df["cluster"] = pop_df["cluster"].astype(int)
+        cluster_pop = pop_df.groupby("cluster")["population"].sum().to_dict()
+        n.meta["population"]["health_cluster"] = cluster_pop
 
     # Store build-time regional_limit in metadata so solve_model can rescale
     n.meta["land_regional_limit"] = float(land_cfg["regional_limit"])
diff --git a/workflow/scripts/calibrate_deviation_penalty.py b/workflow/scripts/calibrate_deviation_penalty.py
index 2ebb801d..872794ff 100644
--- a/workflow/scripts/calibrate_deviation_penalty.py
+++ b/workflow/scripts/calibrate_deviation_penalty.py
@@ -335,7 +335,20 @@ def _record(iter_id: int, x_vec: np.ndarray, d_vec: np.ndarray, r_vec: np.ndarra
         x = x_new
         r = r_new
 
-    return x, trace
+    # Out of iterations: return the best iterate seen rather than the last.
+    # With a discontinuous deviation response (LP basis switches near the
+    # target), the final Broyden step can land worse than an earlier iterate.
+    best = min(trace, key=lambda row: row["resid_inf"])
+    if best["iter"] != trace[-1]["iter"]:
+        logger.info(
+            "Not converged; returning best iterate %d (|r|_inf=%.4f) "
+            "instead of last (%.4f)",
+            best["iter"],
+            best["resid_inf"],
+            trace[-1]["resid_inf"],
+        )
+    x_best = np.array([np.log(best[f"lambda_{c}"]) for c in components])
+    return x_best, trace
 
 
 def main() -> None:
@@ -407,7 +420,9 @@ def evaluate_fn(lambdas: dict[str, float], *, iter_id: int) -> dict[str, float]:
         components=components,
     )
     l1_costs = {c: float(np.exp(x_final[i])) for i, c in enumerate(components)}
-    final_resid = trace[-1]["resid_inf"]
+    # The residual of the returned iterate: the converged row on success,
+    # otherwise the best iterate that broyden_iterate falls back to.
+    final_resid = min(row["resid_inf"] for row in trace)
 
     converged = final_resid < tol
     if not converged:
@@ -449,6 +464,12 @@ def evaluate_fn(lambdas: dict[str, float], *, iter_id: int) -> dict[str, float]:
         sort_keys=False,
     )
     out_path.write_text(header + body)
+    # Side copy for warm-starting the next calibration: Snakemake deletes the
+    # calibrated_yaml output before rerunning this rule, so the warm-start
+    # seed must live outside the rule's outputs.
+    prev_path = Path(smk.params.previous_yaml)
+    prev_path.parent.mkdir(parents=True, exist_ok=True)
+    prev_path.write_text(header + body)
     logger.info(
         "Wrote calibrated L1 costs to %s (%s, iters=%d, converged=%s)",
         out_path,
diff --git a/workflow/scripts/estimate_baseline_diet.py b/workflow/scripts/estimate_baseline_diet.py
index 4d72b505..dcdd5876 100644
--- a/workflow/scripts/estimate_baseline_diet.py
+++ b/workflow/scripts/estimate_baseline_diet.py
@@ -222,20 +222,27 @@ def load_group_totals(
     gdd_totals = intake.set_index(["country", "food_group"])["value"]
 
     # Load GBD dietary risk exposure (aggregate duplicates if any) and
-    # convert to the model's basis where it differs.
-    gbd = pd.read_csv(gbd_exposure_path)
-    gbd = convert_intake(
-        gbd,
-        source="gbd",
-        value_column="consumption_g_per_day",
-        group_column="food_group",
-        country_column="country",
-        source_basis=source_basis,
-        source_basis_country_overrides=source_basis_country_overrides,
-        target_basis_by_key=group_basis,
-        factors=weight_conversion,
-    )
-    gbd_totals = gbd.groupby(["country", "food_group"])["consumption_g_per_day"].mean()
+    # convert to the model's basis where it differs. Skipped entirely when no
+    # groups are anchored (anchoring off): the GBD intake file is then not an
+    # input and the GDD/FAOSTAT estimate is used for every group.
+    if gbd_anchored_groups:
+        gbd = pd.read_csv(gbd_exposure_path)
+        gbd = convert_intake(
+            gbd,
+            source="gbd",
+            value_column="consumption_g_per_day",
+            group_column="food_group",
+            country_column="country",
+            source_basis=source_basis,
+            source_basis_country_overrides=source_basis_country_overrides,
+            target_basis_by_key=group_basis,
+            factors=weight_conversion,
+        )
+        gbd_totals = gbd.groupby(["country", "food_group"])[
+            "consumption_g_per_day"
+        ].mean()
+    else:
+        gbd_totals = pd.Series(dtype=float)
 
     # Build combined group totals: GBD-anchored groups prefer GBD when
     # available, else fall back to GDD/FAOSTAT; non-anchored groups
@@ -261,7 +268,11 @@ def load_group_totals(
                 }
             )
 
-    log_gdd_gbd_agreement(gdd_totals, gbd_totals, gbd_anchored_groups)
+    # Cross-validation logging only applies when groups are anchored to GBD;
+    # with anchoring off gbd_totals is empty (no MultiIndex) and there is
+    # nothing to compare.
+    if gbd_anchored_groups:
+        log_gdd_gbd_agreement(gdd_totals, gbd_totals, gbd_anchored_groups)
     return pd.DataFrame(results)
 
 
@@ -355,10 +366,16 @@ def apply_kcal_normalisation(
     otherwise overshoot the GDD-IA target by 20-35% per country and
     inflate Sub-Saharan Africa's per-capita intake by ~170 kcal/day.
 
-    Anchored groups: those in ``gbd_anchored_groups`` plus ``grain``
-    (which is set by the cereal residual fix and shouldn't be rescaled).
+    Anchored groups: those in ``gbd_anchored_groups``, plus ``grain`` when
+    the cereal residual fix ran (i.e. when whole_grains is anchored): the
+    fix sets refined grain from the cereal energy budget, so rescaling it
+    would undo that. With anchoring off, grain is an ordinary GDD/FAOSTAT
+    estimate and is rescaled like every other group -- pinning it would
+    force the (largest) grain kcal share onto the rest of the diet.
     """
-    anchored = set(gbd_anchored_groups) | {GRAIN_GROUP}
+    anchored = set(gbd_anchored_groups)
+    if WHOLE_GRAINS_GROUP in gbd_anchored_groups:
+        anchored |= {GRAIN_GROUP}
     targets = kcal_target_df.set_index("country")["kcal_target_modelled"]
 
     df = baseline_diet.copy()
@@ -1154,7 +1171,7 @@ def _within_bucket_split(bucket_foods: list[str]) -> dict[str, float]:
                 weights = {f: w / total for f, w in weights.items()}
             return weights
         equal = 1.0 / len(bucket_foods)
-        return {f: equal for f in bucket_foods}
+        return dict.fromkeys(bucket_foods, equal)
 
     shares: dict[str, float] = {}
     if total_production > 0:
@@ -1547,13 +1564,18 @@ def main():
     # gdd_ia_kcal_target.csv.
     kcal_target_df = pd.read_csv(kcal_target_path)
     nutrition_df = pd.read_csv(nutrition_path)
-    kcal_per_g_group = build_kcal_per_g_group(food_groups_df, nutrition_df)
     kcal_per_g_food = build_kcal_per_g_food(nutrition_df)
-    group_totals = apply_cereal_residual_fix(
-        group_totals,
-        kcal_target_df,
-        kcal_per_g_group,
-    )
+    # The cereal residual fix only compensates for energy lost to GBD's narrow
+    # whole_grain anchor, so it applies only when whole_grains is anchored.
+    # With anchoring off the GDD/FAOSTAT whole_grains total already carries the
+    # broad cereal energy and no reallocation to refined grain is needed.
+    if WHOLE_GRAINS_GROUP in gbd_anchored_groups:
+        kcal_per_g_group = build_kcal_per_g_group(food_groups_df, nutrition_df)
+        group_totals = apply_cereal_residual_fix(
+            group_totals,
+            kcal_target_df,
+            kcal_per_g_group,
+        )
 
     # Step 1c is performed after per-food disaggregation (Step 3) so the
     # kcal accounting uses food-level energy densities rather than a
diff --git a/workflow/scripts/solve_and_analyze_model.py b/workflow/scripts/solve_and_analyze_model.py
index 86991842..41545063 100644
--- a/workflow/scripts/solve_and_analyze_model.py
+++ b/workflow/scripts/solve_and_analyze_model.py
@@ -39,23 +39,33 @@ def main() -> None:
         and getattr(snakemake.output, attr).endswith(".parquet")
     }
 
+    health_enabled = bool(snakemake.params.health_enabled)
     run_analysis(
         n,
         output_paths=output_paths,
         food_groups_path=snakemake.input.food_groups,
         m49_codes_path=snakemake.input.m49,
-        risk_breakpoints_path=snakemake.input.health_risk_breakpoints,
-        health_cluster_cause_path=snakemake.input.health_cluster_cause,
-        health_cause_log_path=snakemake.input.health_cause_log,
-        health_clusters_path=snakemake.input.health_clusters,
         population_path=snakemake.input.population,
-        tmrel_path=snakemake.input.health_tmrel,
         ghg_price=float(snakemake.params.ghg_price),
         ch4_gwp=float(snakemake.params.ch4_gwp),
         n2o_gwp=float(snakemake.params.n2o_gwp),
         value_per_yll=float(snakemake.params.health_value_per_yll),
         health_risk_factors=list(snakemake.params.health_risk_factors),
         logger=logger,
+        health_enabled=health_enabled,
+        risk_breakpoints_path=(
+            snakemake.input.health_risk_breakpoints if health_enabled else None
+        ),
+        health_cluster_cause_path=(
+            snakemake.input.health_cluster_cause if health_enabled else None
+        ),
+        health_cause_log_path=(
+            snakemake.input.health_cause_log if health_enabled else None
+        ),
+        health_clusters_path=(
+            snakemake.input.health_clusters if health_enabled else None
+        ),
+        tmrel_path=snakemake.input.health_tmrel if health_enabled else None,
     )
 
     logger.info("Solve-and-analyze complete.")
diff --git a/workflow/scripts/solve_namespace.py b/workflow/scripts/solve_namespace.py
index 7b82a3e0..a15382a3 100644
--- a/workflow/scripts/solve_namespace.py
+++ b/workflow/scripts/solve_namespace.py
@@ -72,6 +72,47 @@ def _is_solve_time_key(key):
     return any(key == p or key.startswith(p + ".") for p in SOLVE_TIME_CONFIG_PREFIXES)
 
 
+# Health data-prep outputs consumed at solve/analysis/calibration time, keyed
+# by input name; values are file names under <processing>/{name}/health/.
+# Single source of truth shared by the Snakemake rules (model.smk,
+# analysis.smk, deviation_penalty.smk) and the cluster manifest exporter
+# (build_scenario_entry below). These inputs are only wired up when the health
+# module is enabled, so that no IHME GBD data is required otherwise.
+HEALTH_INPUT_FILES = {
+    "health_risk_breakpoints": "risk_breakpoints.csv",
+    "health_cluster_cause": "cluster_cause_baseline.csv",
+    "health_cause_log": "cause_log_breakpoints.csv",
+    "health_cluster_summary": "cluster_summary.csv",
+    "health_clusters": "country_clusters.csv",
+    "health_tmrel": "tmrel.csv",
+    "health_cluster_risk_baseline": "cluster_risk_baseline.csv",
+}
+
+
+def health_input_paths(name: str) -> dict[str, str]:
+    """The health data-prep input map for a given config name (unresolved
+    <processing> pathvar, as used in Snakemake rule inputs)."""
+    return {
+        key: f"<processing>/{name}/health/{filename}"
+        for key, filename in HEALTH_INPUT_FILES.items()
+    }
+
+
+def resolve_gbd_anchoring(config: dict) -> bool:
+    """Resolve diet.anchor_groups_to_gbd to a bool.
+
+    The sentinel "match_health" follows health.enabled. Anchoring is applied
+    in the scenario-independent baseline-diet prep, so it always resolves
+    against the base config (never per-scenario). Single source of truth
+    shared by the Snakemake rules (workflow/rules/common.smk), the calibration
+    provenance snapshot, and tools/calibrate.
+    """
+    value = config["diet"]["anchor_groups_to_gbd"]
+    if value == "match_health":
+        return bool(config["health"]["enabled"])
+    return bool(value)
+
+
 def validate_scenario_overrides(scenario_defs: dict) -> None:
     """Raise if any scenario in scenario_defs overrides a structural config key.
 
@@ -319,23 +360,17 @@ def rp(path: str) -> str:
     inputs: dict = {
         "network": rp("<results>/{name}/build/model.nc"),
         "m49": "data/curated/M49-codes.csv",
-        "health_risk_breakpoints": rp(
-            "<processing>/{name}/health/risk_breakpoints.csv"
-        ),
-        "health_cluster_cause": rp(
-            "<processing>/{name}/health/cluster_cause_baseline.csv"
-        ),
-        "health_cause_log": rp("<processing>/{name}/health/cause_log_breakpoints.csv"),
-        "health_cluster_summary": rp("<processing>/{name}/health/cluster_summary.csv"),
-        "health_clusters": rp("<processing>/{name}/health/country_clusters.csv"),
-        "health_tmrel": rp("<processing>/{name}/health/tmrel.csv"),
-        "health_cluster_risk_baseline": rp(
-            "<processing>/{name}/health/cluster_risk_baseline.csv"
-        ),
         "food_groups": "data/curated/food_groups.csv",
         "baseline_diet": rp("<processing>/{name}/baseline_diet.csv"),
     }
 
+    # Health processing inputs only when this scenario enables health (mirrors
+    # solve_model_inputs in workflow/rules/model.smk).
+    if eff["health"]["enabled"]:
+        inputs.update(
+            {key: rp(path) for key, path in health_input_paths("{name}").items()}
+        )
+
     if eff["food_incentives"]["enabled"]:
         sources = eff["food_incentives"]["sources"]
         if not sources:
diff --git a/workflow/scripts/write_calibration_provenance.py b/workflow/scripts/write_calibration_provenance.py
index 2cc5aebb..b1cf54b8 100644
--- a/workflow/scripts/write_calibration_provenance.py
+++ b/workflow/scripts/write_calibration_provenance.py
@@ -25,7 +25,10 @@
 PROJECT_ROOT = Path(__file__).resolve().parents[2]
 sys.path.insert(0, str(PROJECT_ROOT))
 
-from workflow.scripts.solve_namespace import load_merged_config  # noqa: E402
+from workflow.scripts.solve_namespace import (  # noqa: E402
+    load_merged_config,
+    resolve_gbd_anchoring,
+)
 from workflow.validation.calibration_provenance import (  # noqa: E402
     diff_snapshots,
     load_provenance,
@@ -63,6 +66,15 @@ def main() -> int:
         action="store_true",
         help="Print the resolved calibration.source and exit (used by tools/calibrate)",
     )
+    parser.add_argument(
+        "--print-anchoring",
+        action="store_true",
+        help=(
+            "Print the base config's resolved diet.anchor_groups_to_gbd "
+            "('true'/'false') and exit (used by tools/calibrate to pin every "
+            "calibration step to the base config's baseline diet)"
+        ),
+    )
     args = parser.parse_args()
 
     configfiles = [PROJECT_ROOT / "config" / "default.yaml"]
@@ -75,6 +87,10 @@ def main() -> int:
         print(source)
         return 0
 
+    if args.print_anchoring:
+        print("true" if resolve_gbd_anchoring(config) else "false")
+        return 0
+
     snapshot = structural_snapshot(config)
 
     if args.check:
diff --git a/workflow/validation/calibration_provenance.py b/workflow/validation/calibration_provenance.py
index 241937ad..b6ca7287 100644
--- a/workflow/validation/calibration_provenance.py
+++ b/workflow/validation/calibration_provenance.py
@@ -24,7 +24,7 @@
 from snakemake.logging import logger
 import yaml
 
-from workflow.scripts.solve_namespace import _is_solve_time_key
+from workflow.scripts.solve_namespace import _is_solve_time_key, resolve_gbd_anchoring
 
 CALIBRATION_DIR = Path("data/curated/calibration")
 PROVENANCE_FILENAME = "provenance.yaml"
@@ -95,6 +95,13 @@ def walk(node: dict, prefix: str) -> None:
                 snapshot[full] = v
 
     walk(config, "")
+    # diet.anchor_groups_to_gbd may hold the sentinel "match_health", which
+    # resolves through health.enabled -- a solve-time (and therefore exempt)
+    # key. Snapshot the resolved boolean so two configs with different
+    # resolved anchoring (and thus different baseline diets) never stamp
+    # identically.
+    if "diet.anchor_groups_to_gbd" in snapshot:
+        snapshot["diet.anchor_groups_to_gbd"] = resolve_gbd_anchoring(config)
     return snapshot