diff --git a/.claude/skills/model-calibration/SKILL.md b/.claude/skills/model-calibration/SKILL.md
index a565be62..cd34ca5c 100644
--- a/.claude/skills/model-calibration/SKILL.md
+++ b/.claude/skills/model-calibration/SKILL.md
@@ -79,6 +79,7 @@ tools/calibrate food_demand
 tools/calibrate cost
 tools/calibrate stability
 tools/calibrate --check      # per-step staleness + provenance probe (no execution)
+tools/calibrate --record     # re-stamp the set as it stands (no execution)
 tools/calibrate --base config/<name>.yaml [all|<step>|--check]
                              # calibrate a dedicated set for another config
 ```
@@ -109,22 +110,23 @@ see [local_machine_smk_defaults.md](../../memory/local_machine_smk_defaults.md).
 tools/calibrate --check
 ```
 
-This runs a Snakemake dry-run per step and reports `[up-to-date]` or
-`[STALE]`. `tools/smk` also prints a one-line mtime-based reminder on
-every invocation, but treat that as advisory -- `git pull` touches
-mtimes and produces false positives.
+Takes ~20 s for the `default` set, ~30 s for `gbd-anchored`. It compares
+content hashes recorded in `data/curated/calibration/<source>/fingerprint.yaml`
+against the step's external DAG leaves (curated/bundled/manually-downloaded
+data, rule files, scripts), its config (YAML round-tripped, so comments don't
+count), and the artefacts the step wrote. Everything inside the artefact set is
+excluded, so the answer is stable: a completed chain reports all-green, and
+mtime churn from `git checkout` or `touch` is invisible.
 
-`--check` is more informative but is still mtime-based, and the steps
-write into a directory they all read from, so it oscillates: re-running
-any step rewrites its artefacts (often byte-for-byte identical) and the
-later steps immediately report `[STALE]`. A freshly completed full chain
-frequently still shows one or more steps stale. Treat `[STALE]` as "worth
-investigating", not "the set is wrong". The decisive test is to re-run
-the step and diff its artefacts -- if they come back identical, the flag
-was bookkeeping. Don't chase `--check` to all-green by re-running steps
-in a loop; it does not converge.
+`tools/smk` also prints a one-line mtime-based reminder on every invocation.
+That one *is* advisory and does produce false positives after `git pull`;
+silence it with `SMK_SKIP_CALIBRATION_HINT=1`.
 
-Silence the mtime hint in scripted contexts with `SMK_SKIP_CALIBRATION_HINT=1`.
+Editing any rule file or script in a step's DAG marks it stale, including
+inert changes (refactors, comments, unrelated rules). When you have
+established the artefacts are unaffected, `tools/calibrate --record`
+re-stamps the set without solving. It asserts the artefacts are current, so
+don't reach for it to make a genuine `[STALE]` go away.
 
 ### Triggers for a full re-run (start from `feed`)
 
@@ -203,7 +205,7 @@ 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` 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.
+- `data/curated/calibration/<source>/*` -- one artefact set per base config, plus its `provenance.yaml` stamp and `fingerprint.yaml` input hashes; 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-<source>-<step>/*` -- one upstream prep tree per step (~250 MB each), NOT committed.
 - `results/calibration-<source>-<step>/*` -- per-iteration solve logs, NOT committed.
 - `results/calibration-<source>-stability/calibration/deviation_penalty_trace.csv` -- per-iter Broyden trace (per-component lambda, achieved deviations, residual norm). Inspect when stability behaves oddly.
@@ -212,7 +214,7 @@ sequential (each Broyden iteration depends on the previous solve).
 
 - A config selects its set with `calibration.source` (default: `default`); all artefact paths resolve through the `{calibration_source}` placeholder at config-load time.
 - Structurally divergent configs must either calibrate their own set (`calibration.source: <name>` + `tools/calibrate --base config/<name>.yaml`), point at a compatible set, or set `calibration.accept_provenance_mismatch: true` (test/tutorial-grade escape hatch: warning instead of error).
-- The provenance check covers config drift only; code/data staleness remains `tools/calibrate --check`'s job. Both run from `tools/calibrate --check`.
+- The provenance check covers structural config drift only; input and code staleness is the fingerprint's job. Both run from `tools/calibrate --check`.
 - The stamp compares all non-solve-time leaves minus exempt machinery keys (see `PROVENANCE_EXEMPT_PREFIXES` in `workflow/validation/calibration_provenance.py`). Solve-time knobs (GHG price, value_per_yll, deviation_penalty, scenario overrides) never trip it.
 - `tests/test_calibration_provenance.py::TestDefaultStampConsistency` fails when `config/default.yaml` changes structurally without a recalibration/restamp -- that is the intended forcing function.
 
@@ -291,9 +293,9 @@ historical centre once the chain absorbs its gaps cleanly.
 - **Each step must disable the calibrations of every *later* step.** The
   chain is a single forward pass, not an iteration to a fixed point, so a
   step that consumes a successor's artefact is fit against whatever
-  vintage happens to be on disk -- and `tools/calibrate --check` then
-  reports that step permanently `[STALE]`, advising a re-run that
-  restarts the cycle forever. The disable blocks in the step configs form
+  vintage happens to be on disk. That artefact is then an external leaf of
+  the step's DAG, so `tools/calibrate --check` goes stale every time the
+  successor is regenerated, and the chain never settles. The disable blocks in the step configs form
   a lower triangle (feed disables the other four, food_waste the last
   three, and so on); preserve it when adding a calibration.
 
diff --git a/CHANGELOG.md b/CHANGELOG.md
index be239635..864cb1e4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -86,6 +86,15 @@ introduce breaking changes to configuration and outputs.
 
 ### Changed
 
+- `tools/calibrate --check` now answers by content instead of by file
+  timestamp. Each artefact set carries a `fingerprint.yaml` hashing the step's
+  external inputs (curated, bundled and manually downloaded data, the rule
+  files and scripts its DAG runs, and its config), so `git checkout`, `touch`
+  and comment-only config edits no longer report anything, and re-running one
+  step no longer marks its successors stale. New `tools/calibrate --record`
+  re-stamps a set without solving, for when a code change provably cannot move
+  the artefacts.
+
 - `planning_horizon` now defaults to 2020, matching `baseline_year`, so an
   unmodified run solves the observed year the calibration artefacts are fit
   against. Configs that previously relied on the 2030 default (including
@@ -243,6 +252,17 @@ introduce breaking changes to configuration and outputs.
 
 ### Fixed
 
+- Workflow startup no longer builds a full copy of the configuration for every
+  configured scenario when deciding whether health data is needed. On configs
+  with generated scenario ensembles this dominated DAG construction: for
+  `gsa.yaml` (16384 samples) Snakefile parsing drops from ~35 s to ~4 s, and
+  every Snakemake invocation against such a config benefits.
+
+- Solve-time calibration artefacts (feed corrections, exogenous feed and
+  forage, food-demand multipliers, the calibrated deviation penalty) are now
+  declared as inputs of `solve_model` and `calibrate_deviation_penalty`, so
+  Snakemake reruns solves when they change.
+
 - The feed calibration step no longer consumes the food-waste calibration
   artefact, which is produced by a later step in the chain. Feed was
   effectively fit against whichever vintage happened to be on disk, a full
diff --git a/REUSE.toml b/REUSE.toml
index d8b0b7a4..c33488bb 100644
--- a/REUSE.toml
+++ b/REUSE.toml
@@ -36,6 +36,14 @@ precedence = "aggregate"
 SPDX-FileCopyrightText = "2026 Koen van Greevenbroek"
 SPDX-License-Identifier = "CC-BY-4.0"
 
+# Machine-generated calibration input fingerprints (written by
+# workflow/scripts/calibration_fingerprint.py via tools/calibrate).
+[[annotations]]
+path = "data/curated/calibration/**/fingerprint.yaml"
+precedence = "aggregate"
+SPDX-FileCopyrightText = "2026 Koen van Greevenbroek"
+SPDX-License-Identifier = "CC-BY-4.0"
+
 # Root LICENSE copy so GitHub detects the project license (it only scans the
 # repository root, not LICENSES/). The canonical text lives in LICENSES/.
 [[annotations]]
diff --git a/data/curated/calibration/default/fingerprint.yaml b/data/curated/calibration/default/fingerprint.yaml
new file mode 100644
index 00000000..57bc6f88
--- /dev/null
+++ b/data/curated/calibration/default/fingerprint.yaml
@@ -0,0 +1,814 @@
+# Content fingerprint of each calibration step's external inputs, written by
+# workflow/scripts/calibration_fingerprint.py via tools/calibrate. Compared by
+# `tools/calibrate --check`; do not edit by hand.
+# Licensing: see the annotation in REUSE.toml.
+steps:
+  cost:
+    artefacts:
+      data/curated/calibration/default/animal_cost.csv: eb7c0e40dc065ffde962d06a635fd64e95c21b9b5dd2f55e9944d4a20f0422dc
+      data/curated/calibration/default/crop_cost.csv: 89ce3ca3136edbaaf0fe54f6a3259b91dfabe7def2dab4aaf79b602212fbbd8e
+      data/curated/calibration/default/grassland_cost.csv: f202991ef38df2163060e5232209490effea748c7aa827494d12d8bacdece850
+      data/curated/calibration/default/multi_crop_cost.csv: 16b75ffda24cff11340555a603499350379c917fb1266805e6bdc93e51437c80
+    code:
+      workflow/rules/animals.smk: fa07f649a53263c151e1799f63b40c66fba42ee7858a97d51ff988c406ba0024
+      workflow/rules/consumer_values.smk: 4f3fb2650b690a41b45443b322745b698910abae3cb013f70e0eea8e54ba6290
+      workflow/rules/crops.smk: 6413ed1571fe2db7bbac9d45db8a27d51a1c2955a86b4629f43cf70da1a048b0
+      workflow/rules/diet.smk: 15db54cf636dca37bea0343c2bb236ead24ffe238dc2a843b391bbd43bfacb12
+      workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943
+      workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257
+      workflow/rules/model.smk: 0e52481f84031711f0706a8c54a7e1038063e24320e52609a1fd832a977aaffd
+      workflow/rules/retrieve.smk: ecd9eb633974cd575b7a879348c763f844b3f66cdedc785c7d28977ac541a577
+      workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d
+      workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e
+      workflow/scripts/build_crop_residue_yields.py: 4bd14638cf3cadc817819fb6dd3cc43eb4f74420a44cc9338c053b53df0fbdfc
+      workflow/scripts/build_crop_yields.py: 32fa249e34b0b93e58eb3d3f36f6e25696766571e047ee7594c313b9dc5fbc5d
+      workflow/scripts/build_crop_yields_cropgrids.py: ebe6890f7346bcdc1152a63011b3984d76a30be29b6cf1192287c97282702d92
+      workflow/scripts/build_cropland_baseline_from_gaez.py: d577b66db9a4d54f14e211e96c0512abe709c18517b625aea0c235a8290bf4a5
+      workflow/scripts/build_current_grassland_area.py: 84a9c3646e7dd9a56bec62e879ae317f9e65bc2340ab0f62c1215a19d4f9d2b8
+      workflow/scripts/build_fdd_area_shares.py: b6e084b1779636547c41dada16d570ec0f98d5dc34f3d4e948e6e3943e32034a
+      workflow/scripts/build_feed_to_animal_products.py: 1eb41eaf6149524bd6c324ba39356ee09cbebe62bd97bf2e7e4520e1b14e3ba3
+      workflow/scripts/build_fodder_yield_corrections.py: eef62e7237a89f1f8b962893ed031758fd6abcc1b1c71140bc0c16e92e3fc568
+      workflow/scripts/build_frt_area_attribution.py: f91f51757808594f87d6a3151fd228b453181586bd342930b9749a4c21973bc8
+      workflow/scripts/build_grassland_yields.py: 38ed635e8e162d59f33fa15ba0c649046a047ae37136203de3db2fc72b135a2d
+      workflow/scripts/build_grazing_only_land.py: 9880aea4ffe4886ba68fdbc283b009ea984cbb0fb3159754c721b02beb3eec99
+      workflow/scripts/build_harvested_area.py: b6abc0557ed3c68948380d920ec77102f35e47b4712f4701a40677d15c972a98
+      workflow/scripts/build_harvested_area_yield_weighted.py: 2bdd700c00c2028b9e671a4614d05d0469bc7c396e2a04d42d5c0c075fc985b2
+      workflow/scripts/build_luc_carbon_coefficients.py: 17b54976b738ae30728878d7fc600d3f7242037d1376f0cd92b469586c502ce0
+      workflow/scripts/build_luc_grid.py: d8a8af444d2267b1dab55f6719bf3ade019a6410e15ed3b7888cb76d7c7061d6
+      workflow/scripts/build_luicube_grassland_yields.py: 01d2865e3c1878fc4795075680197b161f4cb50259a2fb7f8cdcb1e3deaf2191
+      workflow/scripts/build_mirca_crop_calendar.py: f44617c319c76f4e1f3d68f6d2dee14866804e498d478797e2d63612d9515e4e
+      workflow/scripts/build_model.py: 3294bfeb27cefb28ad3406f3fc40ff05400ddc6746f9944742d16c6a97c2b16d
+      workflow/scripts/build_multi_cropping.py: 6130469e316fb795e66d87fc3f9af6b8086878abba8dfe31c9b587c87478cc21
+      workflow/scripts/build_ooc_olive_area_share.py: 3c5d202eabc9cd997d3dc4989e9e86e672230ce2d139cc7d96a3f5ccf459bbd5
+      workflow/scripts/build_reforestation_mask.py: c373bb1564782ff1400d5fd717a87e67501241c5a9e09b644ea0e199a82aefa1
+      workflow/scripts/build_region_class_cell_mapping.py: c8a2971f2cd7d3d46107f7c6af1a1d8bc22eacec1cca80a6a58ec04da508ad74
+      workflow/scripts/build_region_water_aware.py: 9d6a8e8f1bf23d51d52d802c905c6460539874505546cec68d32f08a00ec2be1
+      workflow/scripts/build_region_watergap.py: 93451ae9ac67fac05fe205ca5009d0854f29012893997ed50db919ddb29ed5c5
+      workflow/scripts/build_regions.py: 08a1f3c0247bdbe4600f2b1596396a352ff6c459496566e7e24da5b2f83558a7
+      workflow/scripts/build_yield_calibration.py: 1225a0d55add24ce44acc86cde5dd3335ed9d0a233ba6025bfc8c0e5afc8375d
+      workflow/scripts/calculate_manure_emissions.py: c35ee707e5cfda0bc1cd0aa06f31c834f118b914b47dc6478a37222f10589f94
+      workflow/scripts/calibrate_food_utility_blocks.py: e20676cd1b44c4504df3c9856131fa234088b1fd77c9f242144483f6f58ad330
+      workflow/scripts/categorize_feeds.py: 575b37f430e4fbf54b8477be96f5069ce6522fa1882c2f024b9b9a25a8540140
+      workflow/scripts/compose_water_supply.py: c1fc61844079219f2381664b44b6a917f51ecbc07bb08e1318702985deaf9231
+      workflow/scripts/compute_gleam3_feed_fractions.py: 83cc4268fe7f1762932ac63fc7f05ecedda9b03bcc1b502a69d85ec6779e6289
+      workflow/scripts/compute_gleam3_me_requirements.py: 07c680c7aa5753eb838d3cbbd4078cbb0dff1947467c209acfb7de9268568b9d
+      workflow/scripts/compute_resource_classes.py: e7b5a4849be730eb1afac3b154463eecec4dfb21718afd1d5f329969366e1181
+      workflow/scripts/convert_faostat_to_parquet.py: 5dbf7e73692c2f393b20299346d2573711052b11a730a1cfc7bfe80bcdeba81d
+      workflow/scripts/derive_global_fertilizer_rates.py: c2f8957100a8e73b1cff58549087f173e520020f614bcd5376c43e2c806bb585
+      workflow/scripts/derive_mirca_multicropping.py: 4d9a9146872009abfb9a5fe9e1ff5e2291aa120c575499c16596c1e1744e3cca
+      workflow/scripts/download_figshare_file.py: 3c5873d6098a25c522fcfd63954edea8c761b551b916eff52491a897b592a8c8
+      workflow/scripts/download_luicube_raster.py: 7883a5a73c9c18f6afbbabe62044494657e69b49469ae7c97b4df18f8a584b2c
+      workflow/scripts/download_mirca_os_bag_member.py: e869f4ec2f87a03b65d7c6f875d6ad393dcce647789ba7fc0f1c2d2fa2e48049
+      workflow/scripts/download_soilgrids_ocs.py: bcb361c0ada72cd6264685eb36b2efabc1a03bc25c93918c37da8da7fe049d58
+      workflow/scripts/estimate_baseline_diet.py: 87047e7cb0db1f5ab7460eeecfacea52c7ef36802669e0aff5b8c62079e589e9
+      workflow/scripts/extract_consumer_values.py: eba7d4bd3d10632fc5fbcbc46f65acd5775f2bb74ca3aa3f5d6ff15113c3dd97
+      workflow/scripts/extract_cost_calibration.py: f7475beca73108a5f6ef63f1c284625e996abc9dadf9efc981c92e2f259d3f23
+      workflow/scripts/extract_mirca_os_archive.py: 687fe1d413ea063d585cc526ff53ce7d91c1df90e1d1ec898bfc62d19890647c
+      workflow/scripts/merge_animal_costs.py: db83828da2fad888f0bae49e3a4d19ebc8315e27d2edcd0c6fde579b736de57c
+      workflow/scripts/merge_dietary_sources.py: b647708a56e5d7c9359f78c644020e3b67e37f50d699004fc8462ed5700031a4
+      workflow/scripts/merge_grassland_yields.py: bd1f04fced7438db6ec75c4c1d3c277880c2256e31ce071d02146de225734be2
+      workflow/scripts/prepare_banana_plantain_production.py: 214cd243b0932ee9da96cf24bdd8c7c10e05c94e27c303758cc2770e0d166e3a
+      workflow/scripts/prepare_biofuel_baseline.py: de7fa63ca68961a1334e74af63b6e23a1360fcf810fa3a815e901e89cc8710ce
+      workflow/scripts/prepare_fao_edible_portion.py: cb71097d23dbe14d077c2a74fbd28bd13fcf039529979cd1af64db2c184a5c0e
+      workflow/scripts/prepare_faostat_animal_production.py: d163e7bfb272e9b542f16dcc11ebfffae4476fbe08f9bbfcb64479b2d869ac06
+      workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b
+      workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435
+      workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05
+      workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809
+      workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe
+      workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab
+      workflow/scripts/prepare_feed_baseline.py: b67255afd433f1fd9ffe446a25d8224f6a2014a86cb96ac74e2486ec0473bf79
+      workflow/scripts/prepare_fertilizer_application_rates.py: e02ebd2020d616fd6a6b5d5d758d858b923ad14cc265807f9ccce60e0bc31bd5
+      workflow/scripts/prepare_fiber_baseline.py: c9cdce9d70211b9171777f791f10a9fb63f34c4a2fc72b4f417a4a50468c8394
+      workflow/scripts/prepare_food_loss_waste.py: c2b33b5032de4fad69425f87a92109d8e6dd3164769b58cc3816e09347e42834
+      workflow/scripts/prepare_food_security_anchors.py: 374e9211799db8946bf589a8b14a4512b4bbb173f8227b92405b940e437ef4c7
+      workflow/scripts/prepare_gdd_ia_dietary_intake.py: c9b4fd5640e37644e6fb55285f86cd259427bbb60a2a4ef988cdcb82d7d4b41d
+      workflow/scripts/prepare_gleam_feed_properties.py: 792a23dace16089a8a80f320a497c85a5c0aac54c6f9bac0281cec724d888358
+      workflow/scripts/prepare_luc_inputs.py: 910ef31cbf1c5800daffd2a086f73f9afb24f6250d2a475ce5eda4199a3d65c9
+      workflow/scripts/prepare_mirca_os_calendar.py: c3f7c0443d4c02cac0c0fe78054c0a5b55c4a46e907ca344e0daf821c6cf21eb
+      workflow/scripts/prepare_nhanes_dietary_intake.py: 0b25216f9a43ebbb51d0d9d86fde5829c4e656f7f59875bdeb9afb44e969520f
+      workflow/scripts/prepare_population.py: 0fa0d60890660bd61cf4c3dd41bc17162309f142299b1965b656aa7c8418417d
+      workflow/scripts/resample_forest_carbon_accumulation.py: aa9e7d246ad15e0698db4479255b0a6f880c4772a058b9cefe6425fbbe999d30
+      workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce
+      workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb
+      workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8
+      workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9
+      workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7
+      workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b
+      workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444
+      workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc
+      workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af
+      workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb
+      workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4
+    config_files:
+      config/calibration/cost.yaml: 45abcb2e750d90543222119fdd53cbd2e7f0b639e52f8bd4897ee36ea5414801
+    inputs:
+      data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3
+      data/bundled/gleam3/feed_items_categories.xlsx: b2f5f6a8dd86648c6b1a34100dfb6531aacc1c65865d2e8c4eac3a384776de9a
+      data/bundled/gleam3/intakes.csv: 2e0664a7433dd99ac805f1c40eaaabc71309dc657943477105b47c08e745ef78
+      data/bundled/gleam3/manure_management_systems_fraction.csv: c8697e060d4ae355e26d2d7a0328fa0c5076467502ed8c8f44a48f43aa0a2a72
+      data/bundled/gleam3/production.csv: eb85e82ba9a61c71c6cd33ab36c00b5d844f2550e1caa45b6b250410adc6f2b5
+      data/curated/M49-codes.csv: 10ae52e639e62f07ce992ead08536167d0c8aa9f467870dbfcfbf465ef3aa9be
+      data/curated/biogas_crop_demand.csv: 8371d2ef0d3170f4ee145b0b0fd7a1ab4e63eec87febb78ca5574866b58cff71
+      data/curated/calibration/default/deviation_penalty.yaml: a20304c2ecfc7d1109d43aaded46fd650bdc5524f43e6d6365b56fc225faeca0
+      data/curated/calibration/default/exogenous_feed.csv: 3ebcac7797beb398a9ff227f700cab0f1e01fef27d361696d1c2b05c274cb39d
+      data/curated/calibration/default/exogenous_forage.csv: d806b2e98968a5d0e3b8618d3882152102f11e4ce62a4de233b8560e7e1aeccc
+      data/curated/calibration/default/fodder_conversion.csv: f46d89574830cefba376f9fdb86c0ae4462bdaf9889dcc25d8c15b0da1524458
+      data/curated/calibration/default/food_demand.csv: a7cc72a127b566e59057cf2cdc9a8595b79a00dfa0503a29fb55b0afa350c6e5
+      data/curated/calibration/default/food_waste.yaml: 4ef037a0318401bc676f7ed65affedf80edcfdaf60ce2edcba54308810376b77
+      data/curated/calibration/default/grassland_yield.csv: 7e2c656be823646dd9325400e3c7612388b2ff336cdaa9fbcd7831553f05eb79
+      data/curated/country_mottet_region.csv: 8c41e8d0a8ad7ec8cd89b9c9cdbb72b6870fb8e9b59dd6ba6e49be468c5651a3
+      data/curated/country_wirsenius_region.csv: 73ae37e68c61286b0312b04b0d1dbc661322d8be5c5e5e66754fff1c9dcfac6e
+      data/curated/crop_moisture_content.csv: b9e6ac4455fdbb6c28f211a291d2cde59439b7e2215d987e104544cc9eeebfe7
+      data/curated/crop_residue_specs.csv: ca79f5a599010eb1f9bc0fabfc2c3e445ff7f6207c378903c4b686f8daee060f
+      data/curated/cropgrids_crop_mapping.csv: 897a3185f076aba4d4ba62c311572ac544893539c30c3d33f26d270a4b486bb5
+      data/curated/diet_source_basis_overrides.csv: 25c6f42d2d82af4a0cff13d5dd01676eeb900ca4054e7f2377d765a712005e86
+      data/curated/fadn_animal_mapping.yaml: 394f0fe05013859432e8c8c65a49b4afc74ae1094cc1efb305de2aec771c1393
+      data/curated/faostat_animal_yield_mapping.yaml: 102eeab081d658193a8b7c36eff37335f84d602a7ea8ff41891dd3e9a70bebcb
+      data/curated/faostat_biofuel_crop_map.csv: 6d1f65e615a7e9a0fc5c7e5164af011074de98b2c706306dc69d9a6767add023
+      data/curated/faostat_cost_proxies.yaml: 49cec76e22bfa197459faf97eae149917724b24272f08a9fe2adfd126c009114
+      data/curated/faostat_crop_item_map.csv: d002cb67387d50162a43c7ded8820297576d79a680d123fe3ca8d455cceed7f0
+      data/curated/faostat_fiber_demand_map.csv: de079d7b870ba0c067203a04966ac25d894d7e31ade6aef9ad14c64f8acea6e4
+      data/curated/faostat_food_item_map.csv: ec1f5efa02a30177af468b5186e4e5ec3a826adbc88f7b40d87f37f70968e0e0
+      data/curated/faostat_food_qcl_resolution.csv: e981707efb43555bb1acb2a2cd402f09d4578e3133971999b9fe00d5de2c460b
+      data/curated/feed_ash_content.csv: 80d8eabc84af81e43892d3251e0665176b911e793ea2c2c0d0bc19d9b018cf0c
+      data/curated/feed_category_overrides.csv: 966cd9a9d6e66fe36f8a1850228b4f2e74d4de4c6fd148a7a2dad54d7408b5b3
+      data/curated/food_basis.csv: 29cd97f9ed7dd69a90e6858acb7e85923d79bb8c60e8bb9889573714429a56e6
+      data/curated/food_groups.csv: 31b05e38ceca2c9623ac730e615da31f990e5a83ac4c84dd4447106cd75bf69c
+      data/curated/food_loss_waste_overrides.csv: ce55344b9de3fea44a43a000224bd04f38798d82cb76c860ea1f9e046759bf03
+      data/curated/foods.csv: f1b924d701dcce99d6bfca548eeda25847da433e375609582551d1a3b8afdcb9
+      data/curated/gaez_crop_code_mapping.csv: 8d8a5475010b8c4a3531c060c8300920dcecf8058d8b142709d6021f91ba73d6
+      data/curated/gleam/feed_mapping.csv: f1cc0a45fb9bae5edbe358f97c87087f2b631353980f21f1f690729c5f19366b
+      data/curated/gleam/roughage_composition.csv: 66785434beabc0fd67c01dc4a1ee78d574da794ce6d52313de4201b60d2f9116
+      data/curated/ifa_fubc_crop_mapping.csv: 5775197d4b6e61c576fa9dda994feee9422f28f128ed665072c93a7ac8bed80c
+      data/curated/ipcc_enteric_methane_yields.csv: a6e3a3948df00ecaf2ffc2d091a4ddb26c72ea92ffc26e8a1a2fd86520b933b7
+      data/curated/ipcc_manure_methane_conversion_factors.csv: 4139a5e9a00f15fbc0590efe929bad30bf2ed182accb75d0ee369a4144f077c0
+      data/curated/ipcc_manure_methane_producing_capacity.csv: 5f53b798f573a71aeab5d8c6d3547b9bf799e1be827ba56429dfe93a1e053f2f
+      data/curated/ipcc_manure_n2o_emission_factors.csv: d8c1127b1d5c88e9a9f9b356e8de96821060e8588e79dd460eaef4c32b718b52
+      data/curated/luc_zone_parameters.csv: 2d042de0ee836e319a44b87210785a7ad42b4fe44aa72de0d22c43db04309869
+      data/curated/mirca_os_calendar_supplement.csv: c0da8ef71a6c98d0f0862c25c8c1addb4424be2d92ac622162bd4408d0da3d8e
+      data/curated/mirca_os_crop_mapping.csv: 961acc42e00742e5255907bb1301545e2bc5ef4bf541f1fe738e5a3b37e7d7c3
+      data/curated/mirca_os_multicropping_combinations.yaml: b0187142f2e328dede10e9fbf052c05f0adda648469070dfc2491abdedb24fe8
+      data/curated/nhanes_fped_mapping.csv: 6f2a8976b06d13a095fd6bc58d800f56ab0b42318dffd49b59c5e9ddb48ee031
+      data/curated/nutrition.csv: 22b367e8b25b5f07457d76446f0ac5c8f16b7f4f064c10c73c211afd3571ee8f
+      data/curated/seed_rates.csv: 15d5be2c24154db2904e68695e8db31baff2220fefa85c13a1cf5831525718e1
+      data/curated/supplementary_feed_properties.csv: 428d0dd8cd6c0caa58bc64cb159b18eab5a8beb1ba1c875194f7aff591460f8d
+      data/curated/usda_animal_cost_sources.csv: 7a27f0ad2e7dc08a10e3264e9aefc7ebfaa1ea360e3b198cb368738d995faf6a
+      data/curated/wirsenius_feed_energy_requirements.csv: f6961ea48f00dcd793d0b61d2939c907fe87b03ab5f28f643073252a8306455c
+      data/curated/yield_unit_conversions.csv: c1905886711b3b079bfd2761029070b874efd72f16ea5aac1f708e24d42d089f
+      workflow/scripts/build_model/animals.py: 030c2dd4b459fb856e7dc34c9dd74fe9245a340eca9055b19a6d6bf01a910f29
+      workflow/scripts/build_model/biomass.py: 78059ea9d2fb953c5890dfb789b4215373a095a087d3e888cff1fa75d04e034f
+      workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610
+      workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9
+      workflow/scripts/build_model/grassland.py: c3ee3ae3149137e8ab427f0b5584251b9c49baac821e92f318cca816df171f48
+      workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702
+      workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c
+      workflow/scripts/build_model/land.py: 160b8d97dfaaadcaeef4bc8efa3ebcd55ea8ad0aaf9469cc1387b4b0687f4422
+      workflow/scripts/build_model/nutrition.py: e94986ded9c4569cc1f3b264c352a1f7f7f72d0c3b107f2aed2ad5f44e1b875c
+      workflow/scripts/build_model/primary_resources.py: 133c60f613ad9d758063511607d068a9e7fba04a726cc9fb714e83d9b6ae882c
+      workflow/scripts/build_model/trade.py: 0fa403447f9f3013fb9efd35f48b7d33044d9a5abca1bc2fe2c05460f077992a
+      workflow/scripts/build_model/utils.py: 46fee1344183d1d2fa798b0d92ac63f3aba5989c462d0b92946bf8a83fcbee43
+      workflow/scripts/constants.py: ab1f55cfb14ae63171047643c634101962abdeab3b410624ad47570871268296
+    missing_inputs: []
+  feed:
+    artefacts:
+      data/curated/calibration/default/exogenous_feed.csv: 3ebcac7797beb398a9ff227f700cab0f1e01fef27d361696d1c2b05c274cb39d
+      data/curated/calibration/default/exogenous_forage.csv: d806b2e98968a5d0e3b8618d3882152102f11e4ce62a4de233b8560e7e1aeccc
+      data/curated/calibration/default/fodder_conversion.csv: f46d89574830cefba376f9fdb86c0ae4462bdaf9889dcc25d8c15b0da1524458
+      data/curated/calibration/default/grassland_yield.csv: 7e2c656be823646dd9325400e3c7612388b2ff336cdaa9fbcd7831553f05eb79
+    code:
+      workflow/rules/animals.smk: fa07f649a53263c151e1799f63b40c66fba42ee7858a97d51ff988c406ba0024
+      workflow/rules/crops.smk: 6413ed1571fe2db7bbac9d45db8a27d51a1c2955a86b4629f43cf70da1a048b0
+      workflow/rules/diet.smk: 15db54cf636dca37bea0343c2bb236ead24ffe238dc2a843b391bbd43bfacb12
+      workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943
+      workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257
+      workflow/rules/model.smk: 0e52481f84031711f0706a8c54a7e1038063e24320e52609a1fd832a977aaffd
+      workflow/rules/retrieve.smk: ecd9eb633974cd575b7a879348c763f844b3f66cdedc785c7d28977ac541a577
+      workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d
+      workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e
+      workflow/scripts/build_crop_residue_yields.py: 4bd14638cf3cadc817819fb6dd3cc43eb4f74420a44cc9338c053b53df0fbdfc
+      workflow/scripts/build_crop_yields.py: 32fa249e34b0b93e58eb3d3f36f6e25696766571e047ee7594c313b9dc5fbc5d
+      workflow/scripts/build_crop_yields_cropgrids.py: ebe6890f7346bcdc1152a63011b3984d76a30be29b6cf1192287c97282702d92
+      workflow/scripts/build_cropland_baseline_from_gaez.py: d577b66db9a4d54f14e211e96c0512abe709c18517b625aea0c235a8290bf4a5
+      workflow/scripts/build_current_grassland_area.py: 84a9c3646e7dd9a56bec62e879ae317f9e65bc2340ab0f62c1215a19d4f9d2b8
+      workflow/scripts/build_fdd_area_shares.py: b6e084b1779636547c41dada16d570ec0f98d5dc34f3d4e948e6e3943e32034a
+      workflow/scripts/build_feed_to_animal_products.py: 1eb41eaf6149524bd6c324ba39356ee09cbebe62bd97bf2e7e4520e1b14e3ba3
+      workflow/scripts/build_fodder_yield_corrections.py: eef62e7237a89f1f8b962893ed031758fd6abcc1b1c71140bc0c16e92e3fc568
+      workflow/scripts/build_frt_area_attribution.py: f91f51757808594f87d6a3151fd228b453181586bd342930b9749a4c21973bc8
+      workflow/scripts/build_grassland_yields.py: 38ed635e8e162d59f33fa15ba0c649046a047ae37136203de3db2fc72b135a2d
+      workflow/scripts/build_grazing_only_land.py: 9880aea4ffe4886ba68fdbc283b009ea984cbb0fb3159754c721b02beb3eec99
+      workflow/scripts/build_harvested_area.py: b6abc0557ed3c68948380d920ec77102f35e47b4712f4701a40677d15c972a98
+      workflow/scripts/build_harvested_area_yield_weighted.py: 2bdd700c00c2028b9e671a4614d05d0469bc7c396e2a04d42d5c0c075fc985b2
+      workflow/scripts/build_luc_carbon_coefficients.py: 17b54976b738ae30728878d7fc600d3f7242037d1376f0cd92b469586c502ce0
+      workflow/scripts/build_luc_grid.py: d8a8af444d2267b1dab55f6719bf3ade019a6410e15ed3b7888cb76d7c7061d6
+      workflow/scripts/build_luicube_grassland_yields.py: 01d2865e3c1878fc4795075680197b161f4cb50259a2fb7f8cdcb1e3deaf2191
+      workflow/scripts/build_mirca_crop_calendar.py: f44617c319c76f4e1f3d68f6d2dee14866804e498d478797e2d63612d9515e4e
+      workflow/scripts/build_model.py: 3294bfeb27cefb28ad3406f3fc40ff05400ddc6746f9944742d16c6a97c2b16d
+      workflow/scripts/build_multi_cropping.py: 6130469e316fb795e66d87fc3f9af6b8086878abba8dfe31c9b587c87478cc21
+      workflow/scripts/build_ooc_olive_area_share.py: 3c5d202eabc9cd997d3dc4989e9e86e672230ce2d139cc7d96a3f5ccf459bbd5
+      workflow/scripts/build_reforestation_mask.py: c373bb1564782ff1400d5fd717a87e67501241c5a9e09b644ea0e199a82aefa1
+      workflow/scripts/build_region_class_cell_mapping.py: c8a2971f2cd7d3d46107f7c6af1a1d8bc22eacec1cca80a6a58ec04da508ad74
+      workflow/scripts/build_region_water_aware.py: 9d6a8e8f1bf23d51d52d802c905c6460539874505546cec68d32f08a00ec2be1
+      workflow/scripts/build_region_watergap.py: 93451ae9ac67fac05fe205ca5009d0854f29012893997ed50db919ddb29ed5c5
+      workflow/scripts/build_regions.py: 08a1f3c0247bdbe4600f2b1596396a352ff6c459496566e7e24da5b2f83558a7
+      workflow/scripts/build_yield_calibration.py: 1225a0d55add24ce44acc86cde5dd3335ed9d0a233ba6025bfc8c0e5afc8375d
+      workflow/scripts/calculate_manure_emissions.py: c35ee707e5cfda0bc1cd0aa06f31c834f118b914b47dc6478a37222f10589f94
+      workflow/scripts/categorize_feeds.py: 575b37f430e4fbf54b8477be96f5069ce6522fa1882c2f024b9b9a25a8540140
+      workflow/scripts/compose_water_supply.py: c1fc61844079219f2381664b44b6a917f51ecbc07bb08e1318702985deaf9231
+      workflow/scripts/compute_exogenous_feed_calibration.py: e72a80a835bbf1cca799f46e861ceef67c0e03a2fdaebc4ded10d31b55d4a5bd
+      workflow/scripts/compute_gleam3_feed_fractions.py: 83cc4268fe7f1762932ac63fc7f05ecedda9b03bcc1b502a69d85ec6779e6289
+      workflow/scripts/compute_gleam3_me_requirements.py: 07c680c7aa5753eb838d3cbbd4078cbb0dff1947467c209acfb7de9268568b9d
+      workflow/scripts/compute_grassland_calibration.py: be8c246c265cb22c19939a4000dff6e16170ab78196be4380ae55ca3dd213681
+      workflow/scripts/compute_resource_classes.py: e7b5a4849be730eb1afac3b154463eecec4dfb21718afd1d5f329969366e1181
+      workflow/scripts/convert_faostat_to_parquet.py: 5dbf7e73692c2f393b20299346d2573711052b11a730a1cfc7bfe80bcdeba81d
+      workflow/scripts/derive_global_fertilizer_rates.py: c2f8957100a8e73b1cff58549087f173e520020f614bcd5376c43e2c806bb585
+      workflow/scripts/derive_mirca_multicropping.py: 4d9a9146872009abfb9a5fe9e1ff5e2291aa120c575499c16596c1e1744e3cca
+      workflow/scripts/download_figshare_file.py: 3c5873d6098a25c522fcfd63954edea8c761b551b916eff52491a897b592a8c8
+      workflow/scripts/download_luicube_raster.py: 7883a5a73c9c18f6afbbabe62044494657e69b49469ae7c97b4df18f8a584b2c
+      workflow/scripts/download_mirca_os_bag_member.py: e869f4ec2f87a03b65d7c6f875d6ad393dcce647789ba7fc0f1c2d2fa2e48049
+      workflow/scripts/download_soilgrids_ocs.py: bcb361c0ada72cd6264685eb36b2efabc1a03bc25c93918c37da8da7fe049d58
+      workflow/scripts/estimate_baseline_diet.py: 87047e7cb0db1f5ab7460eeecfacea52c7ef36802669e0aff5b8c62079e589e9
+      workflow/scripts/extract_mirca_os_archive.py: 687fe1d413ea063d585cc526ff53ce7d91c1df90e1d1ec898bfc62d19890647c
+      workflow/scripts/merge_animal_costs.py: db83828da2fad888f0bae49e3a4d19ebc8315e27d2edcd0c6fde579b736de57c
+      workflow/scripts/merge_dietary_sources.py: b647708a56e5d7c9359f78c644020e3b67e37f50d699004fc8462ed5700031a4
+      workflow/scripts/merge_grassland_yields.py: bd1f04fced7438db6ec75c4c1d3c277880c2256e31ce071d02146de225734be2
+      workflow/scripts/prepare_banana_plantain_production.py: 214cd243b0932ee9da96cf24bdd8c7c10e05c94e27c303758cc2770e0d166e3a
+      workflow/scripts/prepare_biofuel_baseline.py: de7fa63ca68961a1334e74af63b6e23a1360fcf810fa3a815e901e89cc8710ce
+      workflow/scripts/prepare_fao_edible_portion.py: cb71097d23dbe14d077c2a74fbd28bd13fcf039529979cd1af64db2c184a5c0e
+      workflow/scripts/prepare_faostat_animal_production.py: d163e7bfb272e9b542f16dcc11ebfffae4476fbe08f9bbfcb64479b2d869ac06
+      workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b
+      workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435
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+      workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab
+      workflow/scripts/prepare_feed_baseline.py: b67255afd433f1fd9ffe446a25d8224f6a2014a86cb96ac74e2486ec0473bf79
+      workflow/scripts/prepare_fertilizer_application_rates.py: e02ebd2020d616fd6a6b5d5d758d858b923ad14cc265807f9ccce60e0bc31bd5
+      workflow/scripts/prepare_fiber_baseline.py: c9cdce9d70211b9171777f791f10a9fb63f34c4a2fc72b4f417a4a50468c8394
+      workflow/scripts/prepare_food_loss_waste.py: c2b33b5032de4fad69425f87a92109d8e6dd3164769b58cc3816e09347e42834
+      workflow/scripts/prepare_food_security_anchors.py: 374e9211799db8946bf589a8b14a4512b4bbb173f8227b92405b940e437ef4c7
+      workflow/scripts/prepare_gdd_ia_dietary_intake.py: c9b4fd5640e37644e6fb55285f86cd259427bbb60a2a4ef988cdcb82d7d4b41d
+      workflow/scripts/prepare_gleam_feed_properties.py: 792a23dace16089a8a80f320a497c85a5c0aac54c6f9bac0281cec724d888358
+      workflow/scripts/prepare_luc_inputs.py: 910ef31cbf1c5800daffd2a086f73f9afb24f6250d2a475ce5eda4199a3d65c9
+      workflow/scripts/prepare_mirca_os_calendar.py: c3f7c0443d4c02cac0c0fe78054c0a5b55c4a46e907ca344e0daf821c6cf21eb
+      workflow/scripts/prepare_nhanes_dietary_intake.py: 0b25216f9a43ebbb51d0d9d86fde5829c4e656f7f59875bdeb9afb44e969520f
+      workflow/scripts/prepare_population.py: 0fa0d60890660bd61cf4c3dd41bc17162309f142299b1965b656aa7c8418417d
+      workflow/scripts/resample_forest_carbon_accumulation.py: aa9e7d246ad15e0698db4479255b0a6f880c4772a058b9cefe6425fbbe999d30
+      workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce
+      workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb
+      workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8
+      workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9
+      workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7
+      workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b
+      workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444
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+      workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af
+      workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb
+      workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4
+    config_files:
+      config/calibration/feed.yaml: dc76da31efdef7eb308b665b728b847c756883b1a3d478721baacfa3ffe64488
+    inputs:
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+      data/bundled/gleam3/production.csv: eb85e82ba9a61c71c6cd33ab36c00b5d844f2550e1caa45b6b250410adc6f2b5
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+      data/curated/calibration/default/deviation_penalty.yaml: a20304c2ecfc7d1109d43aaded46fd650bdc5524f43e6d6365b56fc225faeca0
+      data/curated/country_mottet_region.csv: 8c41e8d0a8ad7ec8cd89b9c9cdbb72b6870fb8e9b59dd6ba6e49be468c5651a3
+      data/curated/country_wirsenius_region.csv: 73ae37e68c61286b0312b04b0d1dbc661322d8be5c5e5e66754fff1c9dcfac6e
+      data/curated/crop_moisture_content.csv: b9e6ac4455fdbb6c28f211a291d2cde59439b7e2215d987e104544cc9eeebfe7
+      data/curated/crop_residue_specs.csv: ca79f5a599010eb1f9bc0fabfc2c3e445ff7f6207c378903c4b686f8daee060f
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+      data/curated/fadn_animal_mapping.yaml: 394f0fe05013859432e8c8c65a49b4afc74ae1094cc1efb305de2aec771c1393
+      data/curated/faostat_animal_yield_mapping.yaml: 102eeab081d658193a8b7c36eff37335f84d602a7ea8ff41891dd3e9a70bebcb
+      data/curated/faostat_biofuel_crop_map.csv: 6d1f65e615a7e9a0fc5c7e5164af011074de98b2c706306dc69d9a6767add023
+      data/curated/faostat_cost_proxies.yaml: 49cec76e22bfa197459faf97eae149917724b24272f08a9fe2adfd126c009114
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+      data/curated/faostat_food_qcl_resolution.csv: e981707efb43555bb1acb2a2cd402f09d4578e3133971999b9fe00d5de2c460b
+      data/curated/feed_ash_content.csv: 80d8eabc84af81e43892d3251e0665176b911e793ea2c2c0d0bc19d9b018cf0c
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+      data/curated/food_basis.csv: 29cd97f9ed7dd69a90e6858acb7e85923d79bb8c60e8bb9889573714429a56e6
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+      data/curated/foods.csv: f1b924d701dcce99d6bfca548eeda25847da433e375609582551d1a3b8afdcb9
+      data/curated/gaez_crop_code_mapping.csv: 8d8a5475010b8c4a3531c060c8300920dcecf8058d8b142709d6021f91ba73d6
+      data/curated/gleam/feed_mapping.csv: f1cc0a45fb9bae5edbe358f97c87087f2b631353980f21f1f690729c5f19366b
+      data/curated/gleam/roughage_composition.csv: 66785434beabc0fd67c01dc4a1ee78d574da794ce6d52313de4201b60d2f9116
+      data/curated/ifa_fubc_crop_mapping.csv: 5775197d4b6e61c576fa9dda994feee9422f28f128ed665072c93a7ac8bed80c
+      data/curated/ipcc_enteric_methane_yields.csv: a6e3a3948df00ecaf2ffc2d091a4ddb26c72ea92ffc26e8a1a2fd86520b933b7
+      data/curated/ipcc_manure_methane_conversion_factors.csv: 4139a5e9a00f15fbc0590efe929bad30bf2ed182accb75d0ee369a4144f077c0
+      data/curated/ipcc_manure_methane_producing_capacity.csv: 5f53b798f573a71aeab5d8c6d3547b9bf799e1be827ba56429dfe93a1e053f2f
+      data/curated/ipcc_manure_n2o_emission_factors.csv: d8c1127b1d5c88e9a9f9b356e8de96821060e8588e79dd460eaef4c32b718b52
+      data/curated/luc_zone_parameters.csv: 2d042de0ee836e319a44b87210785a7ad42b4fe44aa72de0d22c43db04309869
+      data/curated/mirca_os_calendar_supplement.csv: c0da8ef71a6c98d0f0862c25c8c1addb4424be2d92ac622162bd4408d0da3d8e
+      data/curated/mirca_os_crop_mapping.csv: 961acc42e00742e5255907bb1301545e2bc5ef4bf541f1fe738e5a3b37e7d7c3
+      data/curated/mirca_os_multicropping_combinations.yaml: b0187142f2e328dede10e9fbf052c05f0adda648469070dfc2491abdedb24fe8
+      data/curated/nhanes_fped_mapping.csv: 6f2a8976b06d13a095fd6bc58d800f56ab0b42318dffd49b59c5e9ddb48ee031
+      data/curated/nutrition.csv: 22b367e8b25b5f07457d76446f0ac5c8f16b7f4f064c10c73c211afd3571ee8f
+      data/curated/seed_rates.csv: 15d5be2c24154db2904e68695e8db31baff2220fefa85c13a1cf5831525718e1
+      data/curated/supplementary_feed_properties.csv: 428d0dd8cd6c0caa58bc64cb159b18eab5a8beb1ba1c875194f7aff591460f8d
+      data/curated/usda_animal_cost_sources.csv: 7a27f0ad2e7dc08a10e3264e9aefc7ebfaa1ea360e3b198cb368738d995faf6a
+      data/curated/wirsenius_feed_energy_requirements.csv: f6961ea48f00dcd793d0b61d2939c907fe87b03ab5f28f643073252a8306455c
+      data/curated/yield_unit_conversions.csv: c1905886711b3b079bfd2761029070b874efd72f16ea5aac1f708e24d42d089f
+      workflow/scripts/build_model/animals.py: 030c2dd4b459fb856e7dc34c9dd74fe9245a340eca9055b19a6d6bf01a910f29
+      workflow/scripts/build_model/biomass.py: 78059ea9d2fb953c5890dfb789b4215373a095a087d3e888cff1fa75d04e034f
+      workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610
+      workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9
+      workflow/scripts/build_model/grassland.py: c3ee3ae3149137e8ab427f0b5584251b9c49baac821e92f318cca816df171f48
+      workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702
+      workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c
+      workflow/scripts/build_model/land.py: 160b8d97dfaaadcaeef4bc8efa3ebcd55ea8ad0aaf9469cc1387b4b0687f4422
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+      workflow/scripts/build_model/primary_resources.py: 133c60f613ad9d758063511607d068a9e7fba04a726cc9fb714e83d9b6ae882c
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+      workflow/scripts/constants.py: ab1f55cfb14ae63171047643c634101962abdeab3b410624ad47570871268296
+    missing_inputs: []
+  food_demand:
+    artefacts:
+      data/curated/calibration/default/food_demand.csv: a7cc72a127b566e59057cf2cdc9a8595b79a00dfa0503a29fb55b0afa350c6e5
+    code:
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+      workflow/rules/crops.smk: 6413ed1571fe2db7bbac9d45db8a27d51a1c2955a86b4629f43cf70da1a048b0
+      workflow/rules/diet.smk: 15db54cf636dca37bea0343c2bb236ead24ffe238dc2a843b391bbd43bfacb12
+      workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943
+      workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257
+      workflow/rules/model.smk: 0e52481f84031711f0706a8c54a7e1038063e24320e52609a1fd832a977aaffd
+      workflow/rules/retrieve.smk: ecd9eb633974cd575b7a879348c763f844b3f66cdedc785c7d28977ac541a577
+      workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d
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+      workflow/scripts/build_crop_residue_yields.py: 4bd14638cf3cadc817819fb6dd3cc43eb4f74420a44cc9338c053b53df0fbdfc
+      workflow/scripts/build_crop_yields.py: 32fa249e34b0b93e58eb3d3f36f6e25696766571e047ee7594c313b9dc5fbc5d
+      workflow/scripts/build_crop_yields_cropgrids.py: ebe6890f7346bcdc1152a63011b3984d76a30be29b6cf1192287c97282702d92
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+      workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab
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+      workflow/scripts/prepare_fertilizer_application_rates.py: e02ebd2020d616fd6a6b5d5d758d858b923ad14cc265807f9ccce60e0bc31bd5
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+      workflow/scripts/prepare_luc_inputs.py: 910ef31cbf1c5800daffd2a086f73f9afb24f6250d2a475ce5eda4199a3d65c9
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+      workflow/scripts/prepare_population.py: 0fa0d60890660bd61cf4c3dd41bc17162309f142299b1965b656aa7c8418417d
+      workflow/scripts/resample_forest_carbon_accumulation.py: aa9e7d246ad15e0698db4479255b0a6f880c4772a058b9cefe6425fbbe999d30
+      workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce
+      workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb
+      workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8
+      workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9
+      workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7
+      workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b
+      workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444
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+      workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af
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+      workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4
+    config_files:
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+    inputs:
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+      data/curated/faostat_cost_proxies.yaml: 49cec76e22bfa197459faf97eae149917724b24272f08a9fe2adfd126c009114
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+      data/curated/food_loss_waste_overrides.csv: ce55344b9de3fea44a43a000224bd04f38798d82cb76c860ea1f9e046759bf03
+      data/curated/foods.csv: f1b924d701dcce99d6bfca548eeda25847da433e375609582551d1a3b8afdcb9
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+      data/curated/gleam/roughage_composition.csv: 66785434beabc0fd67c01dc4a1ee78d574da794ce6d52313de4201b60d2f9116
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+      data/curated/luc_zone_parameters.csv: 2d042de0ee836e319a44b87210785a7ad42b4fe44aa72de0d22c43db04309869
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+      data/curated/mirca_os_multicropping_combinations.yaml: b0187142f2e328dede10e9fbf052c05f0adda648469070dfc2491abdedb24fe8
+      data/curated/nhanes_fped_mapping.csv: 6f2a8976b06d13a095fd6bc58d800f56ab0b42318dffd49b59c5e9ddb48ee031
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+      data/curated/supplementary_feed_properties.csv: 428d0dd8cd6c0caa58bc64cb159b18eab5a8beb1ba1c875194f7aff591460f8d
+      data/curated/usda_animal_cost_sources.csv: 7a27f0ad2e7dc08a10e3264e9aefc7ebfaa1ea360e3b198cb368738d995faf6a
+      data/curated/wirsenius_feed_energy_requirements.csv: f6961ea48f00dcd793d0b61d2939c907fe87b03ab5f28f643073252a8306455c
+      data/curated/yield_unit_conversions.csv: c1905886711b3b079bfd2761029070b874efd72f16ea5aac1f708e24d42d089f
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+      workflow/scripts/constants.py: ab1f55cfb14ae63171047643c634101962abdeab3b410624ad47570871268296
+    missing_inputs: []
+  food_waste:
+    artefacts:
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+      workflow/scripts/calculate_manure_emissions.py: c35ee707e5cfda0bc1cd0aa06f31c834f118b914b47dc6478a37222f10589f94
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+      workflow/scripts/download_figshare_file.py: 3c5873d6098a25c522fcfd63954edea8c761b551b916eff52491a897b592a8c8
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+      workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab
+      workflow/scripts/prepare_feed_baseline.py: b67255afd433f1fd9ffe446a25d8224f6a2014a86cb96ac74e2486ec0473bf79
+      workflow/scripts/prepare_fertilizer_application_rates.py: e02ebd2020d616fd6a6b5d5d758d858b923ad14cc265807f9ccce60e0bc31bd5
+      workflow/scripts/prepare_fiber_baseline.py: c9cdce9d70211b9171777f791f10a9fb63f34c4a2fc72b4f417a4a50468c8394
+      workflow/scripts/prepare_food_loss_waste.py: c2b33b5032de4fad69425f87a92109d8e6dd3164769b58cc3816e09347e42834
+      workflow/scripts/prepare_food_security_anchors.py: 374e9211799db8946bf589a8b14a4512b4bbb173f8227b92405b940e437ef4c7
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+      workflow/scripts/prepare_gleam_feed_properties.py: 792a23dace16089a8a80f320a497c85a5c0aac54c6f9bac0281cec724d888358
+      workflow/scripts/prepare_luc_inputs.py: 910ef31cbf1c5800daffd2a086f73f9afb24f6250d2a475ce5eda4199a3d65c9
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+      workflow/scripts/prepare_nhanes_dietary_intake.py: 0b25216f9a43ebbb51d0d9d86fde5829c4e656f7f59875bdeb9afb44e969520f
+      workflow/scripts/prepare_population.py: 0fa0d60890660bd61cf4c3dd41bc17162309f142299b1965b656aa7c8418417d
+      workflow/scripts/resample_forest_carbon_accumulation.py: aa9e7d246ad15e0698db4479255b0a6f880c4772a058b9cefe6425fbbe999d30
+      workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce
+      workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb
+      workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8
+      workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9
+      workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7
+      workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b
+      workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444
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+      workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb
+      workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4
+    config_files:
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+    inputs:
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+      data/curated/mirca_os_multicropping_combinations.yaml: b0187142f2e328dede10e9fbf052c05f0adda648469070dfc2491abdedb24fe8
+      data/curated/nhanes_fped_mapping.csv: 6f2a8976b06d13a095fd6bc58d800f56ab0b42318dffd49b59c5e9ddb48ee031
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+      data/curated/yield_unit_conversions.csv: c1905886711b3b079bfd2761029070b874efd72f16ea5aac1f708e24d42d089f
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+      workflow/scripts/build_model/biomass.py: 78059ea9d2fb953c5890dfb789b4215373a095a087d3e888cff1fa75d04e034f
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+      workflow/scripts/constants.py: ab1f55cfb14ae63171047643c634101962abdeab3b410624ad47570871268296
+    missing_inputs: []
+  stability:
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+    code:
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+      workflow/scripts/build_model/primary_resources.py: 133c60f613ad9d758063511607d068a9e7fba04a726cc9fb714e83d9b6ae882c
+      workflow/scripts/build_model/trade.py: 0fa403447f9f3013fb9efd35f48b7d33044d9a5abca1bc2fe2c05460f077992a
+      workflow/scripts/build_model/utils.py: 46fee1344183d1d2fa798b0d92ac63f3aba5989c462d0b92946bf8a83fcbee43
+      workflow/scripts/constants.py: ab1f55cfb14ae63171047643c634101962abdeab3b410624ad47570871268296
+    missing_inputs: []
diff --git a/data/curated/calibration/gbd-anchored/fingerprint.yaml b/data/curated/calibration/gbd-anchored/fingerprint.yaml
new file mode 100644
index 00000000..283e699d
--- /dev/null
+++ b/data/curated/calibration/gbd-anchored/fingerprint.yaml
@@ -0,0 +1,834 @@
+# Content fingerprint of each calibration step's external inputs, written by
+# workflow/scripts/calibration_fingerprint.py via tools/calibrate. Compared by
+# `tools/calibrate --check`; do not edit by hand.
+# Licensing: see the annotation in REUSE.toml.
+steps:
+  cost:
+    artefacts:
+      data/curated/calibration/gbd-anchored/animal_cost.csv: 1f9b1a7759d34181f8e8dfa1c5700a879ac79b1a3a0520047bd2e05baeb711c3
+      data/curated/calibration/gbd-anchored/crop_cost.csv: d70bfb99b53f7ac56f7e549162269a0383c8553319d314e1758ff5e2009f81d0
+      data/curated/calibration/gbd-anchored/grassland_cost.csv: d6f0794380f991f11a09a49190fa65668f663a60d76c951d0c54cde3c18c10ca
+      data/curated/calibration/gbd-anchored/multi_crop_cost.csv: 898f6ac1f7393e7fb5084fdbc5dd89971962e69ea2b2efe902805972442bdcc3
+    code:
+      workflow/rules/animals.smk: fa07f649a53263c151e1799f63b40c66fba42ee7858a97d51ff988c406ba0024
+      workflow/rules/consumer_values.smk: 4f3fb2650b690a41b45443b322745b698910abae3cb013f70e0eea8e54ba6290
+      workflow/rules/crops.smk: 6413ed1571fe2db7bbac9d45db8a27d51a1c2955a86b4629f43cf70da1a048b0
+      workflow/rules/diet.smk: 15db54cf636dca37bea0343c2bb236ead24ffe238dc2a843b391bbd43bfacb12
+      workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943
+      workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257
+      workflow/rules/model.smk: 0e52481f84031711f0706a8c54a7e1038063e24320e52609a1fd832a977aaffd
+      workflow/rules/retrieve.smk: ecd9eb633974cd575b7a879348c763f844b3f66cdedc785c7d28977ac541a577
+      workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d
+      workflow/scripts/aggregate_class_areas.py: 93e9a52eec4dac86eeff03fa7e574ad4bdd91bb4049f715a321850f327aba92e
+      workflow/scripts/build_crop_residue_yields.py: 4bd14638cf3cadc817819fb6dd3cc43eb4f74420a44cc9338c053b53df0fbdfc
+      workflow/scripts/build_crop_yields.py: 32fa249e34b0b93e58eb3d3f36f6e25696766571e047ee7594c313b9dc5fbc5d
+      workflow/scripts/build_crop_yields_cropgrids.py: ebe6890f7346bcdc1152a63011b3984d76a30be29b6cf1192287c97282702d92
+      workflow/scripts/build_cropland_baseline_from_gaez.py: d577b66db9a4d54f14e211e96c0512abe709c18517b625aea0c235a8290bf4a5
+      workflow/scripts/build_current_grassland_area.py: 84a9c3646e7dd9a56bec62e879ae317f9e65bc2340ab0f62c1215a19d4f9d2b8
+      workflow/scripts/build_fdd_area_shares.py: b6e084b1779636547c41dada16d570ec0f98d5dc34f3d4e948e6e3943e32034a
+      workflow/scripts/build_feed_to_animal_products.py: 1eb41eaf6149524bd6c324ba39356ee09cbebe62bd97bf2e7e4520e1b14e3ba3
+      workflow/scripts/build_fodder_yield_corrections.py: eef62e7237a89f1f8b962893ed031758fd6abcc1b1c71140bc0c16e92e3fc568
+      workflow/scripts/build_frt_area_attribution.py: f91f51757808594f87d6a3151fd228b453181586bd342930b9749a4c21973bc8
+      workflow/scripts/build_grassland_yields.py: 38ed635e8e162d59f33fa15ba0c649046a047ae37136203de3db2fc72b135a2d
+      workflow/scripts/build_grazing_only_land.py: 9880aea4ffe4886ba68fdbc283b009ea984cbb0fb3159754c721b02beb3eec99
+      workflow/scripts/build_harvested_area.py: b6abc0557ed3c68948380d920ec77102f35e47b4712f4701a40677d15c972a98
+      workflow/scripts/build_harvested_area_yield_weighted.py: 2bdd700c00c2028b9e671a4614d05d0469bc7c396e2a04d42d5c0c075fc985b2
+      workflow/scripts/build_luc_carbon_coefficients.py: 17b54976b738ae30728878d7fc600d3f7242037d1376f0cd92b469586c502ce0
+      workflow/scripts/build_luc_grid.py: d8a8af444d2267b1dab55f6719bf3ade019a6410e15ed3b7888cb76d7c7061d6
+      workflow/scripts/build_luicube_grassland_yields.py: 01d2865e3c1878fc4795075680197b161f4cb50259a2fb7f8cdcb1e3deaf2191
+      workflow/scripts/build_mirca_crop_calendar.py: f44617c319c76f4e1f3d68f6d2dee14866804e498d478797e2d63612d9515e4e
+      workflow/scripts/build_model.py: 3294bfeb27cefb28ad3406f3fc40ff05400ddc6746f9944742d16c6a97c2b16d
+      workflow/scripts/build_multi_cropping.py: 6130469e316fb795e66d87fc3f9af6b8086878abba8dfe31c9b587c87478cc21
+      workflow/scripts/build_ooc_olive_area_share.py: 3c5d202eabc9cd997d3dc4989e9e86e672230ce2d139cc7d96a3f5ccf459bbd5
+      workflow/scripts/build_reforestation_mask.py: c373bb1564782ff1400d5fd717a87e67501241c5a9e09b644ea0e199a82aefa1
+      workflow/scripts/build_region_class_cell_mapping.py: c8a2971f2cd7d3d46107f7c6af1a1d8bc22eacec1cca80a6a58ec04da508ad74
+      workflow/scripts/build_region_water_aware.py: 9d6a8e8f1bf23d51d52d802c905c6460539874505546cec68d32f08a00ec2be1
+      workflow/scripts/build_region_watergap.py: 93451ae9ac67fac05fe205ca5009d0854f29012893997ed50db919ddb29ed5c5
+      workflow/scripts/build_regions.py: 08a1f3c0247bdbe4600f2b1596396a352ff6c459496566e7e24da5b2f83558a7
+      workflow/scripts/build_yield_calibration.py: 1225a0d55add24ce44acc86cde5dd3335ed9d0a233ba6025bfc8c0e5afc8375d
+      workflow/scripts/calculate_manure_emissions.py: c35ee707e5cfda0bc1cd0aa06f31c834f118b914b47dc6478a37222f10589f94
+      workflow/scripts/calibrate_food_utility_blocks.py: e20676cd1b44c4504df3c9856131fa234088b1fd77c9f242144483f6f58ad330
+      workflow/scripts/categorize_feeds.py: 575b37f430e4fbf54b8477be96f5069ce6522fa1882c2f024b9b9a25a8540140
+      workflow/scripts/compose_water_supply.py: c1fc61844079219f2381664b44b6a917f51ecbc07bb08e1318702985deaf9231
+      workflow/scripts/compute_gleam3_feed_fractions.py: 83cc4268fe7f1762932ac63fc7f05ecedda9b03bcc1b502a69d85ec6779e6289
+      workflow/scripts/compute_gleam3_me_requirements.py: 07c680c7aa5753eb838d3cbbd4078cbb0dff1947467c209acfb7de9268568b9d
+      workflow/scripts/compute_resource_classes.py: e7b5a4849be730eb1afac3b154463eecec4dfb21718afd1d5f329969366e1181
+      workflow/scripts/convert_faostat_to_parquet.py: 5dbf7e73692c2f393b20299346d2573711052b11a730a1cfc7bfe80bcdeba81d
+      workflow/scripts/derive_global_fertilizer_rates.py: c2f8957100a8e73b1cff58549087f173e520020f614bcd5376c43e2c806bb585
+      workflow/scripts/derive_mirca_multicropping.py: 4d9a9146872009abfb9a5fe9e1ff5e2291aa120c575499c16596c1e1744e3cca
+      workflow/scripts/download_figshare_file.py: 3c5873d6098a25c522fcfd63954edea8c761b551b916eff52491a897b592a8c8
+      workflow/scripts/download_luicube_raster.py: 7883a5a73c9c18f6afbbabe62044494657e69b49469ae7c97b4df18f8a584b2c
+      workflow/scripts/download_mirca_os_bag_member.py: e869f4ec2f87a03b65d7c6f875d6ad393dcce647789ba7fc0f1c2d2fa2e48049
+      workflow/scripts/download_soilgrids_ocs.py: bcb361c0ada72cd6264685eb36b2efabc1a03bc25c93918c37da8da7fe049d58
+      workflow/scripts/estimate_baseline_diet.py: 87047e7cb0db1f5ab7460eeecfacea52c7ef36802669e0aff5b8c62079e589e9
+      workflow/scripts/extract_consumer_values.py: eba7d4bd3d10632fc5fbcbc46f65acd5775f2bb74ca3aa3f5d6ff15113c3dd97
+      workflow/scripts/extract_cost_calibration.py: f7475beca73108a5f6ef63f1c284625e996abc9dadf9efc981c92e2f259d3f23
+      workflow/scripts/extract_mirca_os_archive.py: 687fe1d413ea063d585cc526ff53ce7d91c1df90e1d1ec898bfc62d19890647c
+      workflow/scripts/merge_animal_costs.py: db83828da2fad888f0bae49e3a4d19ebc8315e27d2edcd0c6fde579b736de57c
+      workflow/scripts/merge_dietary_sources.py: b647708a56e5d7c9359f78c644020e3b67e37f50d699004fc8462ed5700031a4
+      workflow/scripts/merge_grassland_yields.py: bd1f04fced7438db6ec75c4c1d3c277880c2256e31ce071d02146de225734be2
+      workflow/scripts/prepare_banana_plantain_production.py: 214cd243b0932ee9da96cf24bdd8c7c10e05c94e27c303758cc2770e0d166e3a
+      workflow/scripts/prepare_biofuel_baseline.py: de7fa63ca68961a1334e74af63b6e23a1360fcf810fa3a815e901e89cc8710ce
+      workflow/scripts/prepare_fao_edible_portion.py: cb71097d23dbe14d077c2a74fbd28bd13fcf039529979cd1af64db2c184a5c0e
+      workflow/scripts/prepare_faostat_animal_production.py: d163e7bfb272e9b542f16dcc11ebfffae4476fbe08f9bbfcb64479b2d869ac06
+      workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b
+      workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435
+      workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05
+      workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809
+      workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe
+      workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab
+      workflow/scripts/prepare_feed_baseline.py: b67255afd433f1fd9ffe446a25d8224f6a2014a86cb96ac74e2486ec0473bf79
+      workflow/scripts/prepare_fertilizer_application_rates.py: e02ebd2020d616fd6a6b5d5d758d858b923ad14cc265807f9ccce60e0bc31bd5
+      workflow/scripts/prepare_fiber_baseline.py: c9cdce9d70211b9171777f791f10a9fb63f34c4a2fc72b4f417a4a50468c8394
+      workflow/scripts/prepare_food_loss_waste.py: c2b33b5032de4fad69425f87a92109d8e6dd3164769b58cc3816e09347e42834
+      workflow/scripts/prepare_food_security_anchors.py: 374e9211799db8946bf589a8b14a4512b4bbb173f8227b92405b940e437ef4c7
+      workflow/scripts/prepare_gbd_food_group_intake.py: 22c0d05151b9131fa8e072ac157ad6881e1d929ec0f5ddf7cc7869f2d2b35d3f
+      workflow/scripts/prepare_gdd_ia_dietary_intake.py: c9b4fd5640e37644e6fb55285f86cd259427bbb60a2a4ef988cdcb82d7d4b41d
+      workflow/scripts/prepare_gleam_feed_properties.py: 792a23dace16089a8a80f320a497c85a5c0aac54c6f9bac0281cec724d888358
+      workflow/scripts/prepare_luc_inputs.py: 910ef31cbf1c5800daffd2a086f73f9afb24f6250d2a475ce5eda4199a3d65c9
+      workflow/scripts/prepare_mirca_os_calendar.py: c3f7c0443d4c02cac0c0fe78054c0a5b55c4a46e907ca344e0daf821c6cf21eb
+      workflow/scripts/prepare_nhanes_dietary_intake.py: 0b25216f9a43ebbb51d0d9d86fde5829c4e656f7f59875bdeb9afb44e969520f
+      workflow/scripts/prepare_population.py: 0fa0d60890660bd61cf4c3dd41bc17162309f142299b1965b656aa7c8418417d
+      workflow/scripts/resample_forest_carbon_accumulation.py: aa9e7d246ad15e0698db4479255b0a6f880c4772a058b9cefe6425fbbe999d30
+      workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce
+      workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb
+      workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8
+      workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9
+      workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7
+      workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b
+      workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444
+      workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc
+      workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af
+      workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb
+      workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4
+    config_files:
+      config/calibration/cost.yaml: 45abcb2e750d90543222119fdd53cbd2e7f0b639e52f8bd4897ee36ea5414801
+    inputs:
+      data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3
+      data/bundled/gleam3/feed_items_categories.xlsx: b2f5f6a8dd86648c6b1a34100dfb6531aacc1c65865d2e8c4eac3a384776de9a
+      data/bundled/gleam3/intakes.csv: 2e0664a7433dd99ac805f1c40eaaabc71309dc657943477105b47c08e745ef78
+      data/bundled/gleam3/manure_management_systems_fraction.csv: c8697e060d4ae355e26d2d7a0328fa0c5076467502ed8c8f44a48f43aa0a2a72
+      data/bundled/gleam3/production.csv: eb85e82ba9a61c71c6cd33ab36c00b5d844f2550e1caa45b6b250410adc6f2b5
+      data/curated/M49-codes.csv: 10ae52e639e62f07ce992ead08536167d0c8aa9f467870dbfcfbf465ef3aa9be
+      data/curated/biogas_crop_demand.csv: 8371d2ef0d3170f4ee145b0b0fd7a1ab4e63eec87febb78ca5574866b58cff71
+      data/curated/calibration/gbd-anchored/deviation_penalty.yaml: 783dc46e38dc0fa21546ffa0e3d6e0b93a16c250f7fb7e8ad650f6d1ef28c351
+      data/curated/calibration/gbd-anchored/exogenous_feed.csv: a42d3bdc2ba32d535fa50f2ba496240fa774fc9014f3b565aedf976934304fb1
+      data/curated/calibration/gbd-anchored/exogenous_forage.csv: c059074f0f18be9709ada5f6c231869047675f72b1caa7eaef7d5e14c5976875
+      data/curated/calibration/gbd-anchored/fodder_conversion.csv: f46d89574830cefba376f9fdb86c0ae4462bdaf9889dcc25d8c15b0da1524458
+      data/curated/calibration/gbd-anchored/food_demand.csv: d59ae58f60259a33fed247f16a341d1162878a37a6167a74e95b3b55d4b18f67
+      data/curated/calibration/gbd-anchored/food_waste.yaml: 3e66ca8a6b477f8856b37009c93ebb2955c69c64e29ce8b93024f3645a8d61a3
+      data/curated/calibration/gbd-anchored/grassland_yield.csv: 7e2c656be823646dd9325400e3c7612388b2ff336cdaa9fbcd7831553f05eb79
+      data/curated/country_mottet_region.csv: 8c41e8d0a8ad7ec8cd89b9c9cdbb72b6870fb8e9b59dd6ba6e49be468c5651a3
+      data/curated/country_wirsenius_region.csv: 73ae37e68c61286b0312b04b0d1dbc661322d8be5c5e5e66754fff1c9dcfac6e
+      data/curated/crop_moisture_content.csv: b9e6ac4455fdbb6c28f211a291d2cde59439b7e2215d987e104544cc9eeebfe7
+      data/curated/crop_residue_specs.csv: ca79f5a599010eb1f9bc0fabfc2c3e445ff7f6207c378903c4b686f8daee060f
+      data/curated/cropgrids_crop_mapping.csv: 897a3185f076aba4d4ba62c311572ac544893539c30c3d33f26d270a4b486bb5
+      data/curated/diet_source_basis_overrides.csv: 25c6f42d2d82af4a0cff13d5dd01676eeb900ca4054e7f2377d765a712005e86
+      data/curated/fadn_animal_mapping.yaml: 394f0fe05013859432e8c8c65a49b4afc74ae1094cc1efb305de2aec771c1393
+      data/curated/faostat_animal_yield_mapping.yaml: 102eeab081d658193a8b7c36eff37335f84d602a7ea8ff41891dd3e9a70bebcb
+      data/curated/faostat_biofuel_crop_map.csv: 6d1f65e615a7e9a0fc5c7e5164af011074de98b2c706306dc69d9a6767add023
+      data/curated/faostat_cost_proxies.yaml: 49cec76e22bfa197459faf97eae149917724b24272f08a9fe2adfd126c009114
+      data/curated/faostat_crop_item_map.csv: d002cb67387d50162a43c7ded8820297576d79a680d123fe3ca8d455cceed7f0
+      data/curated/faostat_fiber_demand_map.csv: de079d7b870ba0c067203a04966ac25d894d7e31ade6aef9ad14c64f8acea6e4
+      data/curated/faostat_food_item_map.csv: ec1f5efa02a30177af468b5186e4e5ec3a826adbc88f7b40d87f37f70968e0e0
+      data/curated/faostat_food_qcl_resolution.csv: e981707efb43555bb1acb2a2cd402f09d4578e3133971999b9fe00d5de2c460b
+      data/curated/feed_ash_content.csv: 80d8eabc84af81e43892d3251e0665176b911e793ea2c2c0d0bc19d9b018cf0c
+      data/curated/feed_category_overrides.csv: 966cd9a9d6e66fe36f8a1850228b4f2e74d4de4c6fd148a7a2dad54d7408b5b3
+      data/curated/food_basis.csv: 29cd97f9ed7dd69a90e6858acb7e85923d79bb8c60e8bb9889573714429a56e6
+      data/curated/food_groups.csv: 31b05e38ceca2c9623ac730e615da31f990e5a83ac4c84dd4447106cd75bf69c
+      data/curated/food_loss_waste_overrides.csv: ce55344b9de3fea44a43a000224bd04f38798d82cb76c860ea1f9e046759bf03
+      data/curated/foods.csv: f1b924d701dcce99d6bfca548eeda25847da433e375609582551d1a3b8afdcb9
+      data/curated/gaez_crop_code_mapping.csv: 8d8a5475010b8c4a3531c060c8300920dcecf8058d8b142709d6021f91ba73d6
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+      workflow/scripts/constants.py: ab1f55cfb14ae63171047643c634101962abdeab3b410624ad47570871268296
+    missing_inputs: []
+  feed:
+    artefacts:
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+      data/curated/calibration/gbd-anchored/grassland_yield.csv: 7e2c656be823646dd9325400e3c7612388b2ff336cdaa9fbcd7831553f05eb79
+    code:
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+      workflow/rules/crops.smk: 6413ed1571fe2db7bbac9d45db8a27d51a1c2955a86b4629f43cf70da1a048b0
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+    inputs:
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+      workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7
+      workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b
+      workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444
+      workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc
+      workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af
+      workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb
+      workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4
+    config_files:
+      config/calibration/food_demand.yaml: 75f8d91dc73aece4621d940b183457348b3c513218d06fa00e40af2b6729769a
+    inputs:
+      data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3
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+      data/bundled/gleam3/intakes.csv: 2e0664a7433dd99ac805f1c40eaaabc71309dc657943477105b47c08e745ef78
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+      data/bundled/gleam3/production.csv: eb85e82ba9a61c71c6cd33ab36c00b5d844f2550e1caa45b6b250410adc6f2b5
+      data/curated/M49-codes.csv: 10ae52e639e62f07ce992ead08536167d0c8aa9f467870dbfcfbf465ef3aa9be
+      data/curated/biogas_crop_demand.csv: 8371d2ef0d3170f4ee145b0b0fd7a1ab4e63eec87febb78ca5574866b58cff71
+      data/curated/calibration/gbd-anchored/deviation_penalty.yaml: 783dc46e38dc0fa21546ffa0e3d6e0b93a16c250f7fb7e8ad650f6d1ef28c351
+      data/curated/calibration/gbd-anchored/exogenous_feed.csv: a42d3bdc2ba32d535fa50f2ba496240fa774fc9014f3b565aedf976934304fb1
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+      data/curated/faostat_animal_yield_mapping.yaml: 102eeab081d658193a8b7c36eff37335f84d602a7ea8ff41891dd3e9a70bebcb
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+      data/curated/faostat_cost_proxies.yaml: 49cec76e22bfa197459faf97eae149917724b24272f08a9fe2adfd126c009114
+      data/curated/faostat_crop_item_map.csv: d002cb67387d50162a43c7ded8820297576d79a680d123fe3ca8d455cceed7f0
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+      data/curated/feed_ash_content.csv: 80d8eabc84af81e43892d3251e0665176b911e793ea2c2c0d0bc19d9b018cf0c
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+      data/curated/food_basis.csv: 29cd97f9ed7dd69a90e6858acb7e85923d79bb8c60e8bb9889573714429a56e6
+      data/curated/food_groups.csv: 31b05e38ceca2c9623ac730e615da31f990e5a83ac4c84dd4447106cd75bf69c
+      data/curated/food_loss_waste_overrides.csv: ce55344b9de3fea44a43a000224bd04f38798d82cb76c860ea1f9e046759bf03
+      data/curated/foods.csv: f1b924d701dcce99d6bfca548eeda25847da433e375609582551d1a3b8afdcb9
+      data/curated/gaez_crop_code_mapping.csv: 8d8a5475010b8c4a3531c060c8300920dcecf8058d8b142709d6021f91ba73d6
+      data/curated/gleam/feed_mapping.csv: f1cc0a45fb9bae5edbe358f97c87087f2b631353980f21f1f690729c5f19366b
+      data/curated/gleam/roughage_composition.csv: 66785434beabc0fd67c01dc4a1ee78d574da794ce6d52313de4201b60d2f9116
+      data/curated/ifa_fubc_crop_mapping.csv: 5775197d4b6e61c576fa9dda994feee9422f28f128ed665072c93a7ac8bed80c
+      data/curated/ipcc_enteric_methane_yields.csv: a6e3a3948df00ecaf2ffc2d091a4ddb26c72ea92ffc26e8a1a2fd86520b933b7
+      data/curated/ipcc_manure_methane_conversion_factors.csv: 4139a5e9a00f15fbc0590efe929bad30bf2ed182accb75d0ee369a4144f077c0
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+      data/curated/ipcc_manure_n2o_emission_factors.csv: d8c1127b1d5c88e9a9f9b356e8de96821060e8588e79dd460eaef4c32b718b52
+      data/curated/luc_zone_parameters.csv: 2d042de0ee836e319a44b87210785a7ad42b4fe44aa72de0d22c43db04309869
+      data/curated/mirca_os_calendar_supplement.csv: c0da8ef71a6c98d0f0862c25c8c1addb4424be2d92ac622162bd4408d0da3d8e
+      data/curated/mirca_os_crop_mapping.csv: 961acc42e00742e5255907bb1301545e2bc5ef4bf541f1fe738e5a3b37e7d7c3
+      data/curated/mirca_os_multicropping_combinations.yaml: b0187142f2e328dede10e9fbf052c05f0adda648469070dfc2491abdedb24fe8
+      data/curated/nhanes_fped_mapping.csv: 6f2a8976b06d13a095fd6bc58d800f56ab0b42318dffd49b59c5e9ddb48ee031
+      data/curated/nutrition.csv: 22b367e8b25b5f07457d76446f0ac5c8f16b7f4f064c10c73c211afd3571ee8f
+      data/curated/seed_rates.csv: 15d5be2c24154db2904e68695e8db31baff2220fefa85c13a1cf5831525718e1
+      data/curated/supplementary_feed_properties.csv: 428d0dd8cd6c0caa58bc64cb159b18eab5a8beb1ba1c875194f7aff591460f8d
+      data/curated/usda_animal_cost_sources.csv: 7a27f0ad2e7dc08a10e3264e9aefc7ebfaa1ea360e3b198cb368738d995faf6a
+      data/curated/wirsenius_feed_energy_requirements.csv: f6961ea48f00dcd793d0b61d2939c907fe87b03ab5f28f643073252a8306455c
+      data/curated/yield_unit_conversions.csv: c1905886711b3b079bfd2761029070b874efd72f16ea5aac1f708e24d42d089f
+      data/manually_downloaded/IHME-GBD_2023-death-rates-2020.csv: 8e0678a115b4f1a49cb5f0ed09eca8f20273ae5c7e066173afe06426740d9f9a
+      data/manually_downloaded/IHME_GBD_2023_RISK_EXPOSURE_DIET_1: 7792dd5bf6873bd8657c2c1f99936f6b1111a357a0de3163ca7f01d696d387ef
+      data/manually_downloaded/IHME_GBD_2023_RISK_EXPOSURE_DIET_2: e217ce5893f9041c6ff721c976b2905018e3434097282d41d41f71b9628a33be
+      workflow/scripts/build_model/animals.py: 030c2dd4b459fb856e7dc34c9dd74fe9245a340eca9055b19a6d6bf01a910f29
+      workflow/scripts/build_model/biomass.py: 78059ea9d2fb953c5890dfb789b4215373a095a087d3e888cff1fa75d04e034f
+      workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610
+      workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9
+      workflow/scripts/build_model/grassland.py: c3ee3ae3149137e8ab427f0b5584251b9c49baac821e92f318cca816df171f48
+      workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702
+      workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c
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+      workflow/scripts/constants.py: ab1f55cfb14ae63171047643c634101962abdeab3b410624ad47570871268296
+    missing_inputs: []
+  food_waste:
+    artefacts:
+      data/curated/calibration/gbd-anchored/food_waste.yaml: 3e66ca8a6b477f8856b37009c93ebb2955c69c64e29ce8b93024f3645a8d61a3
+    code:
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+      workflow/rules/crops.smk: 6413ed1571fe2db7bbac9d45db8a27d51a1c2955a86b4629f43cf70da1a048b0
+      workflow/rules/diet.smk: 15db54cf636dca37bea0343c2bb236ead24ffe238dc2a843b391bbd43bfacb12
+      workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943
+      workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257
+      workflow/rules/model.smk: 0e52481f84031711f0706a8c54a7e1038063e24320e52609a1fd832a977aaffd
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+      workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435
+      workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05
+      workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809
+      workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe
+      workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab
+      workflow/scripts/prepare_feed_baseline.py: b67255afd433f1fd9ffe446a25d8224f6a2014a86cb96ac74e2486ec0473bf79
+      workflow/scripts/prepare_fertilizer_application_rates.py: e02ebd2020d616fd6a6b5d5d758d858b923ad14cc265807f9ccce60e0bc31bd5
+      workflow/scripts/prepare_fiber_baseline.py: c9cdce9d70211b9171777f791f10a9fb63f34c4a2fc72b4f417a4a50468c8394
+      workflow/scripts/prepare_food_loss_waste.py: c2b33b5032de4fad69425f87a92109d8e6dd3164769b58cc3816e09347e42834
+      workflow/scripts/prepare_food_security_anchors.py: 374e9211799db8946bf589a8b14a4512b4bbb173f8227b92405b940e437ef4c7
+      workflow/scripts/prepare_gbd_food_group_intake.py: 22c0d05151b9131fa8e072ac157ad6881e1d929ec0f5ddf7cc7869f2d2b35d3f
+      workflow/scripts/prepare_gdd_ia_dietary_intake.py: c9b4fd5640e37644e6fb55285f86cd259427bbb60a2a4ef988cdcb82d7d4b41d
+      workflow/scripts/prepare_gleam_feed_properties.py: 792a23dace16089a8a80f320a497c85a5c0aac54c6f9bac0281cec724d888358
+      workflow/scripts/prepare_luc_inputs.py: 910ef31cbf1c5800daffd2a086f73f9afb24f6250d2a475ce5eda4199a3d65c9
+      workflow/scripts/prepare_mirca_os_calendar.py: c3f7c0443d4c02cac0c0fe78054c0a5b55c4a46e907ca344e0daf821c6cf21eb
+      workflow/scripts/prepare_nhanes_dietary_intake.py: 0b25216f9a43ebbb51d0d9d86fde5829c4e656f7f59875bdeb9afb44e969520f
+      workflow/scripts/prepare_population.py: 0fa0d60890660bd61cf4c3dd41bc17162309f142299b1965b656aa7c8418417d
+      workflow/scripts/resample_forest_carbon_accumulation.py: aa9e7d246ad15e0698db4479255b0a6f880c4772a058b9cefe6425fbbe999d30
+      workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce
+      workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb
+      workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8
+      workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9
+      workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7
+      workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b
+      workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444
+      workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc
+      workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af
+      workflow/scripts/solve_model.py: 8b13b5a820ba69e4385dcc0b4f576744d9759485a642ca43282c7b65861fe5cb
+      workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4
+    config_files:
+      config/calibration/food_waste.yaml: d003b796f7a10db3f2cb4c8269d05e6a5c76aac0b17196e9c75dbf5c3a9755f7
+    inputs:
+      data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3
+      data/bundled/gleam3/feed_items_categories.xlsx: b2f5f6a8dd86648c6b1a34100dfb6531aacc1c65865d2e8c4eac3a384776de9a
+      data/bundled/gleam3/intakes.csv: 2e0664a7433dd99ac805f1c40eaaabc71309dc657943477105b47c08e745ef78
+      data/bundled/gleam3/manure_management_systems_fraction.csv: c8697e060d4ae355e26d2d7a0328fa0c5076467502ed8c8f44a48f43aa0a2a72
+      data/bundled/gleam3/production.csv: eb85e82ba9a61c71c6cd33ab36c00b5d844f2550e1caa45b6b250410adc6f2b5
+      data/curated/M49-codes.csv: 10ae52e639e62f07ce992ead08536167d0c8aa9f467870dbfcfbf465ef3aa9be
+      data/curated/biogas_crop_demand.csv: 8371d2ef0d3170f4ee145b0b0fd7a1ab4e63eec87febb78ca5574866b58cff71
+      data/curated/calibration/gbd-anchored/deviation_penalty.yaml: 783dc46e38dc0fa21546ffa0e3d6e0b93a16c250f7fb7e8ad650f6d1ef28c351
+      data/curated/calibration/gbd-anchored/exogenous_feed.csv: a42d3bdc2ba32d535fa50f2ba496240fa774fc9014f3b565aedf976934304fb1
+      data/curated/calibration/gbd-anchored/exogenous_forage.csv: c059074f0f18be9709ada5f6c231869047675f72b1caa7eaef7d5e14c5976875
+      data/curated/calibration/gbd-anchored/fodder_conversion.csv: f46d89574830cefba376f9fdb86c0ae4462bdaf9889dcc25d8c15b0da1524458
+      data/curated/calibration/gbd-anchored/grassland_yield.csv: 7e2c656be823646dd9325400e3c7612388b2ff336cdaa9fbcd7831553f05eb79
+      data/curated/country_mottet_region.csv: 8c41e8d0a8ad7ec8cd89b9c9cdbb72b6870fb8e9b59dd6ba6e49be468c5651a3
+      data/curated/country_wirsenius_region.csv: 73ae37e68c61286b0312b04b0d1dbc661322d8be5c5e5e66754fff1c9dcfac6e
+      data/curated/crop_moisture_content.csv: b9e6ac4455fdbb6c28f211a291d2cde59439b7e2215d987e104544cc9eeebfe7
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+      data/curated/cropgrids_crop_mapping.csv: 897a3185f076aba4d4ba62c311572ac544893539c30c3d33f26d270a4b486bb5
+      data/curated/diet_source_basis_overrides.csv: 25c6f42d2d82af4a0cff13d5dd01676eeb900ca4054e7f2377d765a712005e86
+      data/curated/fadn_animal_mapping.yaml: 394f0fe05013859432e8c8c65a49b4afc74ae1094cc1efb305de2aec771c1393
+      data/curated/faostat_animal_yield_mapping.yaml: 102eeab081d658193a8b7c36eff37335f84d602a7ea8ff41891dd3e9a70bebcb
+      data/curated/faostat_biofuel_crop_map.csv: 6d1f65e615a7e9a0fc5c7e5164af011074de98b2c706306dc69d9a6767add023
+      data/curated/faostat_cost_proxies.yaml: 49cec76e22bfa197459faf97eae149917724b24272f08a9fe2adfd126c009114
+      data/curated/faostat_crop_item_map.csv: d002cb67387d50162a43c7ded8820297576d79a680d123fe3ca8d455cceed7f0
+      data/curated/faostat_fiber_demand_map.csv: de079d7b870ba0c067203a04966ac25d894d7e31ade6aef9ad14c64f8acea6e4
+      data/curated/faostat_food_item_map.csv: ec1f5efa02a30177af468b5186e4e5ec3a826adbc88f7b40d87f37f70968e0e0
+      data/curated/faostat_food_qcl_resolution.csv: e981707efb43555bb1acb2a2cd402f09d4578e3133971999b9fe00d5de2c460b
+      data/curated/feed_ash_content.csv: 80d8eabc84af81e43892d3251e0665176b911e793ea2c2c0d0bc19d9b018cf0c
+      data/curated/feed_category_overrides.csv: 966cd9a9d6e66fe36f8a1850228b4f2e74d4de4c6fd148a7a2dad54d7408b5b3
+      data/curated/food_basis.csv: 29cd97f9ed7dd69a90e6858acb7e85923d79bb8c60e8bb9889573714429a56e6
+      data/curated/food_groups.csv: 31b05e38ceca2c9623ac730e615da31f990e5a83ac4c84dd4447106cd75bf69c
+      data/curated/food_loss_waste_overrides.csv: ce55344b9de3fea44a43a000224bd04f38798d82cb76c860ea1f9e046759bf03
+      data/curated/foods.csv: f1b924d701dcce99d6bfca548eeda25847da433e375609582551d1a3b8afdcb9
+      data/curated/gaez_crop_code_mapping.csv: 8d8a5475010b8c4a3531c060c8300920dcecf8058d8b142709d6021f91ba73d6
+      data/curated/gleam/feed_mapping.csv: f1cc0a45fb9bae5edbe358f97c87087f2b631353980f21f1f690729c5f19366b
+      data/curated/gleam/roughage_composition.csv: 66785434beabc0fd67c01dc4a1ee78d574da794ce6d52313de4201b60d2f9116
+      data/curated/ifa_fubc_crop_mapping.csv: 5775197d4b6e61c576fa9dda994feee9422f28f128ed665072c93a7ac8bed80c
+      data/curated/ipcc_enteric_methane_yields.csv: a6e3a3948df00ecaf2ffc2d091a4ddb26c72ea92ffc26e8a1a2fd86520b933b7
+      data/curated/ipcc_manure_methane_conversion_factors.csv: 4139a5e9a00f15fbc0590efe929bad30bf2ed182accb75d0ee369a4144f077c0
+      data/curated/ipcc_manure_methane_producing_capacity.csv: 5f53b798f573a71aeab5d8c6d3547b9bf799e1be827ba56429dfe93a1e053f2f
+      data/curated/ipcc_manure_n2o_emission_factors.csv: d8c1127b1d5c88e9a9f9b356e8de96821060e8588e79dd460eaef4c32b718b52
+      data/curated/luc_zone_parameters.csv: 2d042de0ee836e319a44b87210785a7ad42b4fe44aa72de0d22c43db04309869
+      data/curated/mirca_os_calendar_supplement.csv: c0da8ef71a6c98d0f0862c25c8c1addb4424be2d92ac622162bd4408d0da3d8e
+      data/curated/mirca_os_crop_mapping.csv: 961acc42e00742e5255907bb1301545e2bc5ef4bf541f1fe738e5a3b37e7d7c3
+      data/curated/mirca_os_multicropping_combinations.yaml: b0187142f2e328dede10e9fbf052c05f0adda648469070dfc2491abdedb24fe8
+      data/curated/nhanes_fped_mapping.csv: 6f2a8976b06d13a095fd6bc58d800f56ab0b42318dffd49b59c5e9ddb48ee031
+      data/curated/nutrition.csv: 22b367e8b25b5f07457d76446f0ac5c8f16b7f4f064c10c73c211afd3571ee8f
+      data/curated/seed_rates.csv: 15d5be2c24154db2904e68695e8db31baff2220fefa85c13a1cf5831525718e1
+      data/curated/supplementary_feed_properties.csv: 428d0dd8cd6c0caa58bc64cb159b18eab5a8beb1ba1c875194f7aff591460f8d
+      data/curated/usda_animal_cost_sources.csv: 7a27f0ad2e7dc08a10e3264e9aefc7ebfaa1ea360e3b198cb368738d995faf6a
+      data/curated/wirsenius_feed_energy_requirements.csv: f6961ea48f00dcd793d0b61d2939c907fe87b03ab5f28f643073252a8306455c
+      data/curated/yield_unit_conversions.csv: c1905886711b3b079bfd2761029070b874efd72f16ea5aac1f708e24d42d089f
+      data/manually_downloaded/IHME-GBD_2023-death-rates-2020.csv: 8e0678a115b4f1a49cb5f0ed09eca8f20273ae5c7e066173afe06426740d9f9a
+      data/manually_downloaded/IHME_GBD_2023_RISK_EXPOSURE_DIET_1: 7792dd5bf6873bd8657c2c1f99936f6b1111a357a0de3163ca7f01d696d387ef
+      data/manually_downloaded/IHME_GBD_2023_RISK_EXPOSURE_DIET_2: e217ce5893f9041c6ff721c976b2905018e3434097282d41d41f71b9628a33be
+      workflow/scripts/build_model/animals.py: 030c2dd4b459fb856e7dc34c9dd74fe9245a340eca9055b19a6d6bf01a910f29
+      workflow/scripts/build_model/biomass.py: 78059ea9d2fb953c5890dfb789b4215373a095a087d3e888cff1fa75d04e034f
+      workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610
+      workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9
+      workflow/scripts/build_model/grassland.py: c3ee3ae3149137e8ab427f0b5584251b9c49baac821e92f318cca816df171f48
+      workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702
+      workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c
+      workflow/scripts/build_model/land.py: 160b8d97dfaaadcaeef4bc8efa3ebcd55ea8ad0aaf9469cc1387b4b0687f4422
+      workflow/scripts/build_model/nutrition.py: e94986ded9c4569cc1f3b264c352a1f7f7f72d0c3b107f2aed2ad5f44e1b875c
+      workflow/scripts/build_model/primary_resources.py: 133c60f613ad9d758063511607d068a9e7fba04a726cc9fb714e83d9b6ae882c
+      workflow/scripts/build_model/trade.py: 0fa403447f9f3013fb9efd35f48b7d33044d9a5abca1bc2fe2c05460f077992a
+      workflow/scripts/build_model/utils.py: 46fee1344183d1d2fa798b0d92ac63f3aba5989c462d0b92946bf8a83fcbee43
+      workflow/scripts/constants.py: ab1f55cfb14ae63171047643c634101962abdeab3b410624ad47570871268296
+    missing_inputs: []
+  stability:
+    artefacts:
+      data/curated/calibration/gbd-anchored/deviation_penalty.yaml: 783dc46e38dc0fa21546ffa0e3d6e0b93a16c250f7fb7e8ad650f6d1ef28c351
+    code:
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+      workflow/rules/crops.smk: 6413ed1571fe2db7bbac9d45db8a27d51a1c2955a86b4629f43cf70da1a048b0
+      workflow/rules/deviation_penalty.smk: 4c6b2723520befa5a4bfc54e40ff5e93c27e2234a1762b6f63b311de3ed78209
+      workflow/rules/diet.smk: 15db54cf636dca37bea0343c2bb236ead24ffe238dc2a843b391bbd43bfacb12
+      workflow/rules/geography.smk: 54b113f5647787bacea8d2a2f45298a24c605a84ced48630abece8b2dc887943
+      workflow/rules/luc.smk: e64fb669c0d6c40299d47c995dbb9f04a71f4d99d8edcd1fe9ce3ed700639257
+      workflow/rules/model.smk: 0e52481f84031711f0706a8c54a7e1038063e24320e52609a1fd832a977aaffd
+      workflow/rules/retrieve.smk: ecd9eb633974cd575b7a879348c763f844b3f66cdedc785c7d28977ac541a577
+      workflow/rules/water.smk: 9fe3fc69051df02bab7dde53d2057b2b64142c7212741f8a45bc0171447c951d
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+      workflow/scripts/build_grazing_only_land.py: 9880aea4ffe4886ba68fdbc283b009ea984cbb0fb3159754c721b02beb3eec99
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+      workflow/scripts/build_luc_carbon_coefficients.py: 17b54976b738ae30728878d7fc600d3f7242037d1376f0cd92b469586c502ce0
+      workflow/scripts/build_luc_grid.py: d8a8af444d2267b1dab55f6719bf3ade019a6410e15ed3b7888cb76d7c7061d6
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+      workflow/scripts/build_mirca_crop_calendar.py: f44617c319c76f4e1f3d68f6d2dee14866804e498d478797e2d63612d9515e4e
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+      workflow/scripts/calculate_manure_emissions.py: c35ee707e5cfda0bc1cd0aa06f31c834f118b914b47dc6478a37222f10589f94
+      workflow/scripts/calibrate_deviation_penalty.py: ab82cb7c0d206581c7ed3a85c473a9063e5bb64c1072d26aec247789e7ee63eb
+      workflow/scripts/categorize_feeds.py: 575b37f430e4fbf54b8477be96f5069ce6522fa1882c2f024b9b9a25a8540140
+      workflow/scripts/compose_water_supply.py: c1fc61844079219f2381664b44b6a917f51ecbc07bb08e1318702985deaf9231
+      workflow/scripts/compute_gleam3_feed_fractions.py: 83cc4268fe7f1762932ac63fc7f05ecedda9b03bcc1b502a69d85ec6779e6289
+      workflow/scripts/compute_gleam3_me_requirements.py: 07c680c7aa5753eb838d3cbbd4078cbb0dff1947467c209acfb7de9268568b9d
+      workflow/scripts/compute_resource_classes.py: e7b5a4849be730eb1afac3b154463eecec4dfb21718afd1d5f329969366e1181
+      workflow/scripts/convert_faostat_to_parquet.py: 5dbf7e73692c2f393b20299346d2573711052b11a730a1cfc7bfe80bcdeba81d
+      workflow/scripts/derive_global_fertilizer_rates.py: c2f8957100a8e73b1cff58549087f173e520020f614bcd5376c43e2c806bb585
+      workflow/scripts/derive_mirca_multicropping.py: 4d9a9146872009abfb9a5fe9e1ff5e2291aa120c575499c16596c1e1744e3cca
+      workflow/scripts/download_figshare_file.py: 3c5873d6098a25c522fcfd63954edea8c761b551b916eff52491a897b592a8c8
+      workflow/scripts/download_luicube_raster.py: 7883a5a73c9c18f6afbbabe62044494657e69b49469ae7c97b4df18f8a584b2c
+      workflow/scripts/download_mirca_os_bag_member.py: e869f4ec2f87a03b65d7c6f875d6ad393dcce647789ba7fc0f1c2d2fa2e48049
+      workflow/scripts/download_soilgrids_ocs.py: bcb361c0ada72cd6264685eb36b2efabc1a03bc25c93918c37da8da7fe049d58
+      workflow/scripts/estimate_baseline_diet.py: 87047e7cb0db1f5ab7460eeecfacea52c7ef36802669e0aff5b8c62079e589e9
+      workflow/scripts/extract_mirca_os_archive.py: 687fe1d413ea063d585cc526ff53ce7d91c1df90e1d1ec898bfc62d19890647c
+      workflow/scripts/merge_animal_costs.py: db83828da2fad888f0bae49e3a4d19ebc8315e27d2edcd0c6fde579b736de57c
+      workflow/scripts/merge_dietary_sources.py: b647708a56e5d7c9359f78c644020e3b67e37f50d699004fc8462ed5700031a4
+      workflow/scripts/merge_grassland_yields.py: bd1f04fced7438db6ec75c4c1d3c277880c2256e31ce071d02146de225734be2
+      workflow/scripts/prepare_banana_plantain_production.py: 214cd243b0932ee9da96cf24bdd8c7c10e05c94e27c303758cc2770e0d166e3a
+      workflow/scripts/prepare_biofuel_baseline.py: de7fa63ca68961a1334e74af63b6e23a1360fcf810fa3a815e901e89cc8710ce
+      workflow/scripts/prepare_fao_edible_portion.py: cb71097d23dbe14d077c2a74fbd28bd13fcf039529979cd1af64db2c184a5c0e
+      workflow/scripts/prepare_faostat_animal_production.py: d163e7bfb272e9b542f16dcc11ebfffae4476fbe08f9bbfcb64479b2d869ac06
+      workflow/scripts/prepare_faostat_crop_costs.py: be66f2aad20af7c88085b3c24e59ce2e26cfcee25b202fd96f3a47666d65ad1b
+      workflow/scripts/prepare_faostat_crop_production.py: f446a0f22d865d6dfb7aaab643ea245932169769070b7a72ab16c98b09bf7435
+      workflow/scripts/prepare_faostat_fbs_items.py: 87446f863e0e26e06b863cabcaafda362fb033f86b522b519922f86b5f80bb05
+      workflow/scripts/prepare_faostat_food_group_supply.py: e1702c7c67e7295c70b4cd1fdcf871f3d02c5a90dcb2c0c5b0c6b891e8e81809
+      workflow/scripts/prepare_faostat_pasture_area.py: 3f0d6212cf0c54edaf6f47145e75334c532e6e7a2d243a4996d4f96bda5096fe
+      workflow/scripts/prepare_faostat_yields.py: 178264f3eaa3e386a7ea6813fe607af7183663624cc0388a9df724562a5dd9ab
+      workflow/scripts/prepare_feed_baseline.py: b67255afd433f1fd9ffe446a25d8224f6a2014a86cb96ac74e2486ec0473bf79
+      workflow/scripts/prepare_fertilizer_application_rates.py: e02ebd2020d616fd6a6b5d5d758d858b923ad14cc265807f9ccce60e0bc31bd5
+      workflow/scripts/prepare_fiber_baseline.py: c9cdce9d70211b9171777f791f10a9fb63f34c4a2fc72b4f417a4a50468c8394
+      workflow/scripts/prepare_food_loss_waste.py: c2b33b5032de4fad69425f87a92109d8e6dd3164769b58cc3816e09347e42834
+      workflow/scripts/prepare_food_security_anchors.py: 374e9211799db8946bf589a8b14a4512b4bbb173f8227b92405b940e437ef4c7
+      workflow/scripts/prepare_gbd_food_group_intake.py: 22c0d05151b9131fa8e072ac157ad6881e1d929ec0f5ddf7cc7869f2d2b35d3f
+      workflow/scripts/prepare_gdd_ia_dietary_intake.py: c9b4fd5640e37644e6fb55285f86cd259427bbb60a2a4ef988cdcb82d7d4b41d
+      workflow/scripts/prepare_gleam_feed_properties.py: 792a23dace16089a8a80f320a497c85a5c0aac54c6f9bac0281cec724d888358
+      workflow/scripts/prepare_luc_inputs.py: 910ef31cbf1c5800daffd2a086f73f9afb24f6250d2a475ce5eda4199a3d65c9
+      workflow/scripts/prepare_mirca_os_calendar.py: c3f7c0443d4c02cac0c0fe78054c0a5b55c4a46e907ca344e0daf821c6cf21eb
+      workflow/scripts/prepare_nhanes_dietary_intake.py: 0b25216f9a43ebbb51d0d9d86fde5829c4e656f7f59875bdeb9afb44e969520f
+      workflow/scripts/prepare_population.py: 0fa0d60890660bd61cf4c3dd41bc17162309f142299b1965b656aa7c8418417d
+      workflow/scripts/resample_forest_carbon_accumulation.py: aa9e7d246ad15e0698db4479255b0a6f880c4772a058b9cefe6425fbbe999d30
+      workflow/scripts/resample_land_cover.py: 3a953b4c2a0a4a226688e282af9a00c778a077bfbf3d8504829a637a6281a1ce
+      workflow/scripts/resample_luicube_grassland.py: 8efad93db0c694e2b9152acc2373b356614d65c8170cf4effa08f6df9b4cc8fb
+      workflow/scripts/retrieve_cpi_data.py: ee11749f80d1ab2dd0ade2909b40ef001f7bd24c6a56fa381a251662b51f54b8
+      workflow/scripts/retrieve_eurostat_fodder.py: c90bbfbbb2c08b83144c84f2199da9ec01d17a8ecea99d2ead77a1baf6de22f9
+      workflow/scripts/retrieve_fadn_animal_costs.py: 7791580c7c04f99a474909e4e53bdaa7af0a23056c2036b36753c43076ac25f7
+      workflow/scripts/retrieve_hicp_data.py: 8375097c1fb9f18d096116dd133268e36a332373b56720790a4abb35a2b6290b
+      workflow/scripts/retrieve_ppp_rates.py: 13ff1ed85f2153d4a5a8b4b9031432c59b23e0a85eb4fc62bfd4806c32aee444
+      workflow/scripts/retrieve_usda_animal_costs.py: 5849db38933f9323fb581e5b81a17223e3e5a12b5c8471727c4b9cef4c2211cc
+      workflow/scripts/simplify_gadm.py: 9b206abc2f561840b5a964e9fc06a6f4ee6b71cb2673277d4e2c27ebf532d1af
+      workflow/scripts/validate_baseline_diet.py: 6d54d506a2d8382321c849d77698098429019e0f089f969c43ee2848becc05f4
+    config_files:
+      config/calibration/stability.yaml: 35c57a354cadf204a243a3d7bf7f3726ded6c9a81d5738da00f1ef31a8012971
+    inputs:
+      data/bundled/doi_10_5061_dryad_2rbnzs7qh__v20250311/FUBC_1_to_9_data.csv: 3b5dc29b6e2f6844807f2983c1596c28a127f48a67f7deec7e36f6a49c0b17f3
+      data/bundled/gleam3/feed_items_categories.xlsx: b2f5f6a8dd86648c6b1a34100dfb6531aacc1c65865d2e8c4eac3a384776de9a
+      data/bundled/gleam3/intakes.csv: 2e0664a7433dd99ac805f1c40eaaabc71309dc657943477105b47c08e745ef78
+      data/bundled/gleam3/manure_management_systems_fraction.csv: c8697e060d4ae355e26d2d7a0328fa0c5076467502ed8c8f44a48f43aa0a2a72
+      data/bundled/gleam3/production.csv: eb85e82ba9a61c71c6cd33ab36c00b5d844f2550e1caa45b6b250410adc6f2b5
+      data/curated/M49-codes.csv: 10ae52e639e62f07ce992ead08536167d0c8aa9f467870dbfcfbf465ef3aa9be
+      data/curated/biogas_crop_demand.csv: 8371d2ef0d3170f4ee145b0b0fd7a1ab4e63eec87febb78ca5574866b58cff71
+      data/curated/calibration/gbd-anchored/animal_cost.csv: 1f9b1a7759d34181f8e8dfa1c5700a879ac79b1a3a0520047bd2e05baeb711c3
+      data/curated/calibration/gbd-anchored/crop_cost.csv: d70bfb99b53f7ac56f7e549162269a0383c8553319d314e1758ff5e2009f81d0
+      data/curated/calibration/gbd-anchored/exogenous_feed.csv: a42d3bdc2ba32d535fa50f2ba496240fa774fc9014f3b565aedf976934304fb1
+      data/curated/calibration/gbd-anchored/exogenous_forage.csv: c059074f0f18be9709ada5f6c231869047675f72b1caa7eaef7d5e14c5976875
+      data/curated/calibration/gbd-anchored/fodder_conversion.csv: f46d89574830cefba376f9fdb86c0ae4462bdaf9889dcc25d8c15b0da1524458
+      data/curated/calibration/gbd-anchored/food_demand.csv: d59ae58f60259a33fed247f16a341d1162878a37a6167a74e95b3b55d4b18f67
+      data/curated/calibration/gbd-anchored/food_waste.yaml: 3e66ca8a6b477f8856b37009c93ebb2955c69c64e29ce8b93024f3645a8d61a3
+      data/curated/calibration/gbd-anchored/grassland_cost.csv: d6f0794380f991f11a09a49190fa65668f663a60d76c951d0c54cde3c18c10ca
+      data/curated/calibration/gbd-anchored/grassland_yield.csv: 7e2c656be823646dd9325400e3c7612388b2ff336cdaa9fbcd7831553f05eb79
+      data/curated/calibration/gbd-anchored/multi_crop_cost.csv: 898f6ac1f7393e7fb5084fdbc5dd89971962e69ea2b2efe902805972442bdcc3
+      data/curated/country_mottet_region.csv: 8c41e8d0a8ad7ec8cd89b9c9cdbb72b6870fb8e9b59dd6ba6e49be468c5651a3
+      data/curated/country_wirsenius_region.csv: 73ae37e68c61286b0312b04b0d1dbc661322d8be5c5e5e66754fff1c9dcfac6e
+      data/curated/crop_moisture_content.csv: b9e6ac4455fdbb6c28f211a291d2cde59439b7e2215d987e104544cc9eeebfe7
+      data/curated/crop_residue_specs.csv: ca79f5a599010eb1f9bc0fabfc2c3e445ff7f6207c378903c4b686f8daee060f
+      data/curated/cropgrids_crop_mapping.csv: 897a3185f076aba4d4ba62c311572ac544893539c30c3d33f26d270a4b486bb5
+      data/curated/diet_source_basis_overrides.csv: 25c6f42d2d82af4a0cff13d5dd01676eeb900ca4054e7f2377d765a712005e86
+      data/curated/fadn_animal_mapping.yaml: 394f0fe05013859432e8c8c65a49b4afc74ae1094cc1efb305de2aec771c1393
+      data/curated/faostat_animal_yield_mapping.yaml: 102eeab081d658193a8b7c36eff37335f84d602a7ea8ff41891dd3e9a70bebcb
+      data/curated/faostat_biofuel_crop_map.csv: 6d1f65e615a7e9a0fc5c7e5164af011074de98b2c706306dc69d9a6767add023
+      data/curated/faostat_cost_proxies.yaml: 49cec76e22bfa197459faf97eae149917724b24272f08a9fe2adfd126c009114
+      data/curated/faostat_crop_item_map.csv: d002cb67387d50162a43c7ded8820297576d79a680d123fe3ca8d455cceed7f0
+      data/curated/faostat_fiber_demand_map.csv: de079d7b870ba0c067203a04966ac25d894d7e31ade6aef9ad14c64f8acea6e4
+      data/curated/faostat_food_item_map.csv: ec1f5efa02a30177af468b5186e4e5ec3a826adbc88f7b40d87f37f70968e0e0
+      data/curated/faostat_food_qcl_resolution.csv: e981707efb43555bb1acb2a2cd402f09d4578e3133971999b9fe00d5de2c460b
+      data/curated/feed_ash_content.csv: 80d8eabc84af81e43892d3251e0665176b911e793ea2c2c0d0bc19d9b018cf0c
+      data/curated/feed_category_overrides.csv: 966cd9a9d6e66fe36f8a1850228b4f2e74d4de4c6fd148a7a2dad54d7408b5b3
+      data/curated/food_basis.csv: 29cd97f9ed7dd69a90e6858acb7e85923d79bb8c60e8bb9889573714429a56e6
+      data/curated/food_groups.csv: 31b05e38ceca2c9623ac730e615da31f990e5a83ac4c84dd4447106cd75bf69c
+      data/curated/food_loss_waste_overrides.csv: ce55344b9de3fea44a43a000224bd04f38798d82cb76c860ea1f9e046759bf03
+      data/curated/foods.csv: f1b924d701dcce99d6bfca548eeda25847da433e375609582551d1a3b8afdcb9
+      data/curated/gaez_crop_code_mapping.csv: 8d8a5475010b8c4a3531c060c8300920dcecf8058d8b142709d6021f91ba73d6
+      data/curated/gleam/feed_mapping.csv: f1cc0a45fb9bae5edbe358f97c87087f2b631353980f21f1f690729c5f19366b
+      data/curated/gleam/roughage_composition.csv: 66785434beabc0fd67c01dc4a1ee78d574da794ce6d52313de4201b60d2f9116
+      data/curated/ifa_fubc_crop_mapping.csv: 5775197d4b6e61c576fa9dda994feee9422f28f128ed665072c93a7ac8bed80c
+      data/curated/ipcc_enteric_methane_yields.csv: a6e3a3948df00ecaf2ffc2d091a4ddb26c72ea92ffc26e8a1a2fd86520b933b7
+      data/curated/ipcc_manure_methane_conversion_factors.csv: 4139a5e9a00f15fbc0590efe929bad30bf2ed182accb75d0ee369a4144f077c0
+      data/curated/ipcc_manure_methane_producing_capacity.csv: 5f53b798f573a71aeab5d8c6d3547b9bf799e1be827ba56429dfe93a1e053f2f
+      data/curated/ipcc_manure_n2o_emission_factors.csv: d8c1127b1d5c88e9a9f9b356e8de96821060e8588e79dd460eaef4c32b718b52
+      data/curated/luc_zone_parameters.csv: 2d042de0ee836e319a44b87210785a7ad42b4fe44aa72de0d22c43db04309869
+      data/curated/mirca_os_calendar_supplement.csv: c0da8ef71a6c98d0f0862c25c8c1addb4424be2d92ac622162bd4408d0da3d8e
+      data/curated/mirca_os_crop_mapping.csv: 961acc42e00742e5255907bb1301545e2bc5ef4bf541f1fe738e5a3b37e7d7c3
+      data/curated/mirca_os_multicropping_combinations.yaml: b0187142f2e328dede10e9fbf052c05f0adda648469070dfc2491abdedb24fe8
+      data/curated/nhanes_fped_mapping.csv: 6f2a8976b06d13a095fd6bc58d800f56ab0b42318dffd49b59c5e9ddb48ee031
+      data/curated/nutrition.csv: 22b367e8b25b5f07457d76446f0ac5c8f16b7f4f064c10c73c211afd3571ee8f
+      data/curated/seed_rates.csv: 15d5be2c24154db2904e68695e8db31baff2220fefa85c13a1cf5831525718e1
+      data/curated/supplementary_feed_properties.csv: 428d0dd8cd6c0caa58bc64cb159b18eab5a8beb1ba1c875194f7aff591460f8d
+      data/curated/usda_animal_cost_sources.csv: 7a27f0ad2e7dc08a10e3264e9aefc7ebfaa1ea360e3b198cb368738d995faf6a
+      data/curated/wirsenius_feed_energy_requirements.csv: f6961ea48f00dcd793d0b61d2939c907fe87b03ab5f28f643073252a8306455c
+      data/curated/yield_unit_conversions.csv: c1905886711b3b079bfd2761029070b874efd72f16ea5aac1f708e24d42d089f
+      data/manually_downloaded/IHME-GBD_2023-death-rates-2020.csv: 8e0678a115b4f1a49cb5f0ed09eca8f20273ae5c7e066173afe06426740d9f9a
+      data/manually_downloaded/IHME_GBD_2023_RISK_EXPOSURE_DIET_1: 7792dd5bf6873bd8657c2c1f99936f6b1111a357a0de3163ca7f01d696d387ef
+      data/manually_downloaded/IHME_GBD_2023_RISK_EXPOSURE_DIET_2: e217ce5893f9041c6ff721c976b2905018e3434097282d41d41f71b9628a33be
+      workflow/scripts/build_model/animals.py: 030c2dd4b459fb856e7dc34c9dd74fe9245a340eca9055b19a6d6bf01a910f29
+      workflow/scripts/build_model/biomass.py: 78059ea9d2fb953c5890dfb789b4215373a095a087d3e888cff1fa75d04e034f
+      workflow/scripts/build_model/crops.py: 4b8638d8902b0a688d0ab55df3e3625d0953f3b4ac63886ddae50b3bf47b4610
+      workflow/scripts/build_model/food.py: 9e53b6c847202a2192ea0e014746e62a9a5f88eedffc5a48833fc4186ef65ae9
+      workflow/scripts/build_model/grassland.py: c3ee3ae3149137e8ab427f0b5584251b9c49baac821e92f318cca816df171f48
+      workflow/scripts/build_model/health.py: 5ddc8cbae98992f67140db787946a0f9fd9ae15ad683592b4f81b4bf6a2df702
+      workflow/scripts/build_model/infrastructure.py: b792d568d0f199699f5d53bc6f175138a899e41a71f86b5d72a037de1130785c
+      workflow/scripts/build_model/land.py: 160b8d97dfaaadcaeef4bc8efa3ebcd55ea8ad0aaf9469cc1387b4b0687f4422
+      workflow/scripts/build_model/nutrition.py: e94986ded9c4569cc1f3b264c352a1f7f7f72d0c3b107f2aed2ad5f44e1b875c
+      workflow/scripts/build_model/primary_resources.py: 133c60f613ad9d758063511607d068a9e7fba04a726cc9fb714e83d9b6ae882c
+      workflow/scripts/build_model/trade.py: 0fa403447f9f3013fb9efd35f48b7d33044d9a5abca1bc2fe2c05460f077992a
+      workflow/scripts/build_model/utils.py: 46fee1344183d1d2fa798b0d92ac63f3aba5989c462d0b92946bf8a83fcbee43
+      workflow/scripts/constants.py: ab1f55cfb14ae63171047643c634101962abdeab3b410624ad47570871268296
+    missing_inputs: []
diff --git a/docs/calibration.rst b/docs/calibration.rst
index ac711bc2..3a5bc3f1 100644
--- a/docs/calibration.rst
+++ b/docs/calibration.rst
@@ -143,6 +143,7 @@ Everything is wrapped by ``tools/calibrate``:
    tools/calibrate cost
    tools/calibrate stability
    tools/calibrate --check      # per-step staleness + provenance, no execution
+   tools/calibrate --record     # re-stamp the set as it stands, no execution
    tools/calibrate --base config/<name>.yaml [all|<step>|--check]
                                 # calibrate a dedicated set for another config
 
@@ -199,8 +200,8 @@ A config with different structural assumptions has three options:
 
 Solve-time keys (GHG price, health valuation, the deviation-penalty
 block, scenario overrides, ...) never invalidate a set; the check fires
-only on structural changes. Code changes are not captured by the stamp:
-``tools/calibrate --check`` (Snakemake staleness) covers those.
+only on structural changes. Input and code changes are not captured by the
+stamp: ``tools/calibrate --check`` covers those.
 
 Consuming the calibrated values
 -------------------------------
@@ -506,8 +507,36 @@ answer run
 
    tools/calibrate --check
 
-which performs a Snakemake dry-run against each calibration target and
-reports ``[up-to-date]`` / ``[STALE]`` per step.
-
+which reports ``[up-to-date]`` / ``[STALE]`` per step, listing what moved.
 Set ``SMK_SKIP_CALIBRATION_HINT=1`` to silence the reminder in scripted
 contexts.
+
+A step is stale when re-running it would change its artefacts, which depends
+only on inputs from *outside* the artefact set. ``tools/calibrate`` therefore
+writes a ``fingerprint.yaml`` next to the artefacts, recording SHA-256 digests
+of
+
+* every external leaf of the step's Snakemake DAG (curated and bundled data,
+  manually downloaded sources, the ``build_model`` package),
+* the rule files and scripts the DAG runs,
+* the step config, hashed after a YAML round-trip so comment and formatting
+  edits do not register,
+* the artefacts the step wrote, so one edited or checked out behind the
+  chain's back is reported rather than silently trusted.
+
+The DAG is rebuilt on each check, so a dependency that a rule or code change
+adds or drops is picked up straight away. Because the fingerprint ignores the
+artefact set's own contents, re-running one step never marks its successors
+stale, and mtime changes alone (a ``git checkout``, a ``touch``) never do
+either.
+
+Editing any rule file or script in a step's DAG marks it stale, including
+changes that cannot affect the result. When you know a code change is inert,
+re-stamp the set without solving:
+
+.. code-block:: bash
+
+   tools/calibrate --record
+
+This asserts that the artefacts on disk are current, so only use it when they
+demonstrably are.
diff --git a/tests/test_calibration_fingerprint.py b/tests/test_calibration_fingerprint.py
new file mode 100644
index 00000000..10d4018a
--- /dev/null
+++ b/tests/test_calibration_fingerprint.py
@@ -0,0 +1,88 @@
+# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+"""Unit tests for calibration input fingerprinting."""
+
+from workflow.scripts.calibration_fingerprint import (
+    compare,
+    sha256_dir,
+    sha256_file,
+    sha256_yaml,
+)
+
+
+def test_yaml_hash_ignores_comments_and_formatting(tmp_path):
+    a = tmp_path / "a.yaml"
+    b = tmp_path / "b.yaml"
+    a.write_text("# a comment\nfoo:\n  bar: 1\n  baz: [1, 2]\n")
+    b.write_text("foo: {baz: [1, 2], bar: 1}\n")
+    assert sha256_yaml(a) == sha256_yaml(b)
+    assert sha256_file(a) != sha256_file(b)
+
+
+def test_yaml_hash_tracks_values(tmp_path):
+    a = tmp_path / "a.yaml"
+    b = tmp_path / "b.yaml"
+    a.write_text("foo:\n  bar: 1\n")
+    b.write_text("foo:\n  bar: 2\n")
+    assert sha256_yaml(a) != sha256_yaml(b)
+
+
+def test_dir_hash_tracks_membership_and_size(tmp_path):
+    root = tmp_path / "src"
+    (root / "nested").mkdir(parents=True)
+    (root / "nested" / "one.csv").write_text("abc")
+    before = sha256_dir(root)
+
+    assert sha256_dir(root) == before
+
+    (root / "two.csv").write_text("d")
+    added = sha256_dir(root)
+    assert added != before
+
+    (root / "two.csv").write_text("de")
+    assert sha256_dir(root) != added
+
+
+def _fingerprint(**overrides):
+    base = {
+        "config_files": {"config/calibration/feed.yaml": "cfg"},
+        "code": {"workflow/scripts/build_model.py": "code"},
+        "inputs": {"data/curated/nutrition.csv": "in"},
+        "artefacts": {"data/curated/calibration/unit/exogenous_feed.csv": "art"},
+        "missing_inputs": [],
+    }
+    base.update(overrides)
+    return base
+
+
+def test_identical_fingerprints_are_not_stale():
+    assert compare(_fingerprint(), _fingerprint()) == []
+
+
+def test_changed_input_is_reported():
+    current = _fingerprint(inputs={"data/curated/nutrition.csv": "other"})
+    (reason,) = compare(_fingerprint(), current)
+    assert "nutrition.csv" in reason and "input content changed" in reason
+
+
+def test_changed_artefact_is_reported_as_hand_modification():
+    current = _fingerprint(
+        artefacts={"data/curated/calibration/unit/exogenous_feed.csv": "other"}
+    )
+    (reason,) = compare(_fingerprint(), current)
+    assert "modified since it was generated" in reason
+
+
+def test_added_and_dropped_dependencies_are_reported():
+    current = _fingerprint(code={"workflow/scripts/build_luc.py": "code"})
+    reasons = compare(_fingerprint(), current)
+    assert any("build_luc.py" in r and "code added" in r for r in reasons)
+    assert any("build_model.py" in r and "code no longer used" in r for r in reasons)
+
+
+def test_missing_input_is_reported():
+    current = _fingerprint(missing_inputs=["data/manually_downloaded/gbd"])
+    (reason,) = compare(_fingerprint(), current)
+    assert "input missing from disk" in reason
diff --git a/tools/calibrate b/tools/calibrate
index 150a8636..b46b9be6 100755
--- a/tools/calibrate
+++ b/tools/calibrate
@@ -28,6 +28,7 @@
 #   tools/calibrate cost
 #   tools/calibrate stability
 #   tools/calibrate --check        # report which calibrations are stale (no execution)
+#   tools/calibrate --record       # fingerprint the set as it stands (no execution)
 #   tools/calibrate --base config/<name>.yaml [all|<step>|--check]
 #                                  # calibrate against a different base config;
 #                                  # the base must declare its own calibration.source
@@ -46,6 +47,7 @@ PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
 cd "$PROJECT_ROOT"
 
 PROVENANCE_SCRIPT=workflow/scripts/write_calibration_provenance.py
+FINGERPRINT_SCRIPT=workflow/scripts/calibration_fingerprint.py
 
 # Extract a global --base <config> option (anywhere in argv).
 BASE_CONFIG=""
@@ -169,6 +171,18 @@ inject_env() {
   PASSTHROUGH_ARR=(-e "$DEFAULT_PIXI_ENV" "${PASSTHROUGH_ARR[@]}")
 }
 
+# True when the caller passed a dry-run flag; nothing on disk may be written
+# then, neither fingerprint nor provenance stamp.
+is_dry_run() {
+  local arg
+  for arg in "${PASSTHROUGH_ARR[@]}"; do
+    case "$arg" in
+      -n|--dry-run|--dryrun) return 0 ;;
+    esac
+  done
+  return 1
+}
+
 run_step() {
   local label="$1" configfile="$2"
   shift 2
@@ -180,6 +194,39 @@ run_step() {
   local name_override=(--config "name=calibration-${SOURCE}-${label}")
   echo "[calibrate] $label -- tools/smk --configfile ${cfgfiles[*]} ${name_override[*]} ${PASSTHROUGH_ARR[*]} -- ${TARGETS_ARR[*]}"
   tools/smk --configfile "${cfgfiles[@]}" "${name_override[@]}" "${PASSTHROUGH_ARR[@]}" -- "${TARGETS_ARR[@]}"
+  is_dry_run || record_fingerprint "$label" "$configfile" "${cfgfiles[@]}"
+}
+
+# Record what this step was built against, so --check can compare by content.
+record_fingerprint() {
+  fingerprint_cmd record "$@"
+}
+
+# Build the argv shared by `record` and `check`: the same configs and targets
+# the step itself runs with, so the DAG matches exactly.
+fingerprint_cmd() {
+  local mode="$1" label="$2" configfile="$3"
+  shift 3
+  local args=(--step "$label" --cal-dir "$CAL_DIR"
+              --name "calibration-${SOURCE}-${label}" --hash-config "$configfile")
+  local f tgt
+  for f in "$@"; do args+=(--configfile "$f"); done
+  for tgt in "${TARGETS_ARR[@]}"; do args+=(--target "$tgt"); done
+  pixi run python "$FINGERPRINT_SCRIPT" "$mode" "${args[@]}"
+}
+
+# Fingerprint the set as it stands on disk, without solving. This asserts the
+# artefacts are current, so only run it when you know they are.
+record_step() {
+  local label="$1" configfile="$2"
+  shift 2
+  split_args "$@"
+  inject_env
+  local cfgfiles=()
+  [[ -n "$BASE_CONFIG" ]] && cfgfiles+=("$BASE_CONFIG")
+  cfgfiles+=("$configfile" "$ANCHOR_OVERLAY")
+  record_fingerprint "$label" "$configfile" "${cfgfiles[@]}"
+  printf '  [recorded]   %s\n' "$label"
 }
 
 check_step() {
@@ -190,23 +237,12 @@ check_step() {
   local cfgfiles=()
   [[ -n "$BASE_CONFIG" ]] && cfgfiles+=("$BASE_CONFIG")
   cfgfiles+=("$configfile" "$ANCHOR_OVERLAY")
-  local name_override=(--config "name=calibration-${SOURCE}-${label}")
-  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"
-    printf '%s\n' "$out" | sed 's/^/    /'
-    return 2
-  fi
-  # Snakemake prints "Nothing to be done" when everything is up-to-date.
-  if echo "$out" | grep -q "Nothing to be done"; then
-    printf '  [up-to-date] %-10s\n' "$label"
-  else
-    printf '  [STALE]      %-10s (run: tools/calibrate %s)\n' "$label" "$label"
-  fi
+  fingerprint_cmd check "$label" "$configfile" "${cfgfiles[@]}"
 }
 
 # Stamp the artefact set with the structural config it was fit against.
 stamp_provenance() {
+  is_dry_run && return 0
   pixi run python "$PROVENANCE_SCRIPT" "${BASE_ARGS[@]}"
 }
 
@@ -240,6 +276,7 @@ shift || true
 if [[ "$SOURCE" != "default" ]]; then
   case "$cmd" in
     all|feed|food_waste|food_demand|cost|stability) seed_source_set ;;
+    --record|record) ;;
     --check|check)
       for f in "${ARTEFACT_FILES[@]}"; do
         if [[ ! -f "$CAL_DIR/$f" ]]; then
@@ -257,6 +294,14 @@ case "$cmd" in
     sed -n '7,40p' "$0"
     exit 0
     ;;
+  --record|record)
+    echo "[calibrate] Recording fingerprints for the set as it stands on disk ..."
+    record_step feed        config/calibration/feed.yaml        "${FEED_TARGETS[@]}"        -- "$@"
+    record_step food_waste  config/calibration/food_waste.yaml  "${FOOD_WASTE_TARGETS[@]}"  -- "$@"
+    record_step food_demand config/calibration/food_demand.yaml "${FOOD_DEMAND_TARGETS[@]}" -- "$@"
+    record_step cost        config/calibration/cost.yaml        "${COST_TARGETS[@]}"        -- "$@"
+    record_step stability   config/calibration/stability.yaml   "${STABILITY_TARGETS[@]}"   -- "$@"
+    ;;
   --check|check)
     echo "[calibrate] Checking staleness (no execution) ..."
     rc=0
@@ -298,7 +343,7 @@ case "$cmd" in
     ;;
   *)
     echo "[calibrate] Unknown subcommand: $cmd" >&2
-    echo "Usage: tools/calibrate [--base <config>] [all|feed|food_waste|food_demand|cost|stability|--check]" >&2
+    echo "Usage: tools/calibrate [--base <config>] [all|feed|food_waste|food_demand|cost|stability|--check|--record]" >&2
     exit 2
     ;;
 esac
diff --git a/workflow/rules/common.smk b/workflow/rules/common.smk
index bb96e798..9b601298 100644
--- a/workflow/rules/common.smk
+++ b/workflow/rules/common.smk
@@ -98,7 +98,13 @@ def health_required():
     """
     if config["health"]["enabled"]:
         return True
-    return any(get_effective_config(s)["health"]["enabled"] for s in list_scenarios())
+    # Only a scenario that names the key can flip it on, so read the overrides
+    # directly; merging an effective config per scenario is prohibitively slow
+    # for generated ensembles of thousands of scenarios.
+    return any(
+        overrides.get("health", {}).get("enabled", False)
+        for overrides in load_scenario_defs().values()
+    )
 
 
 def gbd_anchoring_enabled():
@@ -320,3 +326,35 @@ def gaez_path(kind: str, water_supply: str, crop: str) -> str:
     return (
         f"{prefix}_{climate}_{period}_{climate_scenario}_{input_level}_{ws}_{crop}.tif"
     )
+
+
+def calibration_artefact_inputs(cfg):
+    """Return the calibration artefacts a solve reads, gated by their config.
+
+    Shared by ``solve_model`` and ``calibrate_deviation_penalty``: both run the
+    same solve, so both depend on the same files. Each block is skipped when its
+    calibration is disabled, which is how the generating step avoids a cycle
+    onto its own output.
+    """
+    inputs = {}
+
+    cal_cfg = cfg["grazing"]["grassland_forage_calibration"]
+    if cal_cfg["enabled"]:
+        inputs["grassland_yield_correction"] = cal_cfg["grassland_yield_correction"]
+        inputs["fodder_conversion_correction"] = cal_cfg["fodder_conversion_correction"]
+        inputs["exogenous_forage"] = cal_cfg["exogenous_forage"]
+
+    exo_feed_cal_cfg = cfg["exogenous_feed_calibration"]
+    if exo_feed_cal_cfg["enabled"]:
+        inputs["exogenous_feed"] = exo_feed_cal_cfg["exogenous_feed"]
+
+    fd_cal_cfg = cfg["food_demand_calibration"]
+    if fd_cal_cfg["enabled"]:
+        inputs["food_demand_calibration"] = fd_cal_cfg["calibration_file"]
+
+    dp_cfg = cfg["deviation_penalty"]
+    dp_cal_cfg = dp_cfg["calibration"]
+    if dp_cal_cfg["enabled"] and deviation_penalty_uses_calibrated(dp_cfg):
+        inputs["deviation_penalty_calibration"] = dp_cal_cfg["calibrated_yaml"]
+
+    return inputs
diff --git a/workflow/rules/deviation_penalty.smk b/workflow/rules/deviation_penalty.smk
index 449e5f7a..03ef2d85 100644
--- a/workflow/rules/deviation_penalty.smk
+++ b/workflow/rules/deviation_penalty.smk
@@ -20,7 +20,15 @@ if _dp_cal_cfg["generate"]:
         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 {}),
+            # The Broyden iteration solves in-process rather than through
+            # solve_model, so the artefacts those solves read are declared here
+            # as well.
+            unpack(
+                lambda w: {
+                    **(health_input_paths(name) if health_required() else {}),
+                    **calibration_artefact_inputs(config),
+                }
+            ),
             model=f"<results>/{name}/build/model.nc",
             baseline_diet=f"<processing>/{name}/baseline_diet.csv",
             m49="data/curated/M49-codes.csv",
diff --git a/workflow/rules/model.smk b/workflow/rules/model.smk
index d4fb14b6..4ef4494d 100644
--- a/workflow/rules/model.smk
+++ b/workflow/rules/model.smk
@@ -287,38 +287,7 @@ def solve_model_inputs(w):
     ):
         inputs["nutrition"] = "data/curated/nutrition.csv"
 
-    # Grassland forage calibration: include CSVs when enabled for this scenario.
-    # When generate=true, the calibration CSVs are produced from the source
-    # scenario's solve, so non-source scenarios depend on them via the DAG.
-    # The source scenario has enabled=false and skips this block.
-    cal_cfg = eff_cfg["grazing"]["grassland_forage_calibration"]
-    if cal_cfg["enabled"]:
-        inputs["grassland_yield_correction"] = cal_cfg["grassland_yield_correction"]
-        inputs["fodder_conversion_correction"] = cal_cfg["fodder_conversion_correction"]
-        inputs["exogenous_forage"] = cal_cfg["exogenous_forage"]
-
-    # Protein-feed calibration: per-country exogenous protein supply CSV.
-    # Same gating pattern as the forage calibration.
-    exo_feed_cal_cfg = eff_cfg["exogenous_feed_calibration"]
-    if exo_feed_cal_cfg["enabled"]:
-        inputs["exogenous_feed"] = exo_feed_cal_cfg["exogenous_feed"]
-
-    # Food demand calibration: include the per-food multiplier CSV when
-    # enabled. The generation config sets enabled=false to break the DAG
-    # cycle on the source scenario; solve_model then treats the calibration
-    # as absent (multiplier=1).
-    fd_cal_cfg = eff_cfg["food_demand_calibration"]
-    if fd_cal_cfg["enabled"]:
-        inputs["food_demand_calibration"] = fd_cal_cfg["calibration_file"]
-
-    # Deviation-penalty L1 calibration: include the calibrated YAML when
-    # any component's l1_cost is set to the "calibrated" sentinel. The
-    # solve resolves the sentinel via
-    # solve_model/production_stability.resolve_calibrated_l1_costs.
-    dp_cfg = eff_cfg["deviation_penalty"]
-    dp_cal_cfg = dp_cfg["calibration"]
-    if dp_cal_cfg["enabled"] and deviation_penalty_uses_calibrated(dp_cfg):
-        inputs["deviation_penalty_calibration"] = dp_cal_cfg["calibrated_yaml"]
+    inputs.update(calibration_artefact_inputs(eff_cfg))
 
     return inputs
 
diff --git a/workflow/scripts/calibration_fingerprint.py b/workflow/scripts/calibration_fingerprint.py
new file mode 100644
index 00000000..030a19a1
--- /dev/null
+++ b/workflow/scripts/calibration_fingerprint.py
@@ -0,0 +1,286 @@
+# SPDX-FileCopyrightText: 2026 Koen van Greevenbroek
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+"""Content fingerprint of a calibration step's external inputs.
+
+A calibration step is stale when re-running it would change its artefacts,
+which depends only on inputs from outside the artefact set: curated and bundled
+data, the scripts, and the step config. Those are hashed when the set is
+generated and compared when it is checked, so the set's own artefacts and the
+chain's intermediates never enter into the answer.
+
+The input list comes from the live Snakemake DAG, built through the Python API:
+every file the DAG consumes that no rule in the DAG produces is an external
+leaf. Building it afresh on each check means a dependency that a rule or code
+change adds or drops is reflected straight away.
+
+Config files are hashed after a YAML round-trip, so reformatting and comment
+edits do not register. The base config belongs to ``calibration_provenance``,
+which compares it structurally.
+"""
+
+import argparse
+import contextlib
+import hashlib
+import io
+from pathlib import Path
+import sys
+
+from snakemake.api import SnakemakeApi
+from snakemake.settings.types import (
+    ConfigSettings,
+    DAGSettings,
+    OutputSettings,
+    ResourceSettings,
+)
+import yaml
+
+FINGERPRINT_FILE = "fingerprint.yaml"
+
+HEADER = """# Content fingerprint of each calibration step's external inputs, written by
+# workflow/scripts/calibration_fingerprint.py via tools/calibrate. Compared by
+# `tools/calibrate --check`; do not edit by hand.
+# Licensing: see the annotation in REUSE.toml.
+"""
+
+
+def sha256_file(path: Path) -> str:
+    """Return the SHA-256 of a file, streamed so large inputs stay cheap."""
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1 << 20), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def sha256_dir(path: Path) -> str:
+    """Return a digest of a directory input's file manifest.
+
+    Manually downloaded sources arrive as directories of hundreds of megabytes.
+    Hashing the sorted (relative path, size) listing costs nothing and moves
+    whenever a file is added, removed or replaced.
+    """
+    entries = sorted(
+        f"{f.relative_to(path)} {f.stat().st_size}"
+        for f in path.rglob("*")
+        if f.is_file()
+    )
+    return hashlib.sha256("\n".join(entries).encode()).hexdigest()
+
+
+def sha256_yaml(path: Path) -> str:
+    """Return the SHA-256 of a YAML file's parsed content.
+
+    Hashing the loaded document rather than the raw bytes keeps comment and
+    formatting edits from registering as a change.
+    """
+    data = yaml.safe_load(path.read_text())
+    return hashlib.sha256(
+        yaml.safe_dump(data, sort_keys=True, default_flow_style=False).encode()
+    ).hexdigest()
+
+
+def dag_files(
+    configfiles: list[str], name: str, targets: list[str], workdir: Path
+) -> tuple[set[str], set[str], set[str]]:
+    """Return (inputs, outputs, code) over every job in the step's DAG.
+
+    ``code`` is the scripts the DAG's rules run plus the rule files defining
+    them; Snakemake carries those outside the input lists, so they are gathered
+    separately here.
+
+    The DAG is prepared without taking the workflow lock, so a check is safe to
+    run while something else is using the directory.
+    """
+    with SnakemakeApi(OutputSettings(dryrun=True, quiet={"all"})) as api:
+        workflow = api.workflow(
+            resource_settings=ResourceSettings(cores=1),
+            config_settings=ConfigSettings(
+                configfiles=[Path(c) for c in configfiles],
+                config={"name": name},
+            ),
+            snakefile=workdir / "workflow" / "Snakefile",
+            workdir=workdir,
+        )
+        workflow.dag(dag_settings=DAGSettings(targets=frozenset(targets)))
+        internal = workflow._workflow
+        internal._prepare_dag(
+            forceall=False,
+            ignore_incomplete=True,
+            lock_warn_only=True,
+            nolock=True,
+        )
+        internal._build_dag()
+
+        inputs: set[str] = set()
+        outputs: set[str] = set()
+        code: set[str] = set()
+        for job in internal.dag.jobs:
+            inputs.update(str(f) for f in job.input)
+            outputs.update(str(f) for f in job.output)
+            rule_file = getattr(job.rule, "snakefile", None)
+            if rule_file:
+                code.add(str(rule_file))
+                script = getattr(job.rule, "script", None)
+                if script:
+                    # Scripts are named relative to the rule file that runs them.
+                    code.add(str((Path(rule_file).parent / script).resolve()))
+    return inputs, outputs, code
+
+
+def build_fingerprint(
+    configfiles: list[str],
+    name: str,
+    targets: list[str],
+    hash_configs: list[str],
+    workdir: Path,
+) -> dict:
+    """Hash a step's external leaves, its config, and the artefacts it wrote."""
+    inputs, outputs, code = dag_files(configfiles, name, targets, workdir)
+
+    leaf_hashes = {}
+    missing = []
+    for leaf in sorted(inputs - outputs):
+        path = Path(leaf)
+        if path.is_file():
+            leaf_hashes[leaf] = sha256_file(path)
+        elif path.is_dir():
+            leaf_hashes[leaf] = sha256_dir(path)
+        else:
+            missing.append(leaf)
+
+    def relative(path: Path) -> str:
+        """Key code by repo-relative path so a fingerprint stays portable."""
+        try:
+            return str(path.relative_to(workdir))
+        except ValueError:
+            return str(path)
+
+    return {
+        "config_files": {c: sha256_yaml(Path(c)) for c in hash_configs},
+        "code": {
+            relative(Path(c)): sha256_file(Path(c))
+            for c in sorted(code)
+            if Path(c).is_file()
+        },
+        "inputs": leaf_hashes,
+        # The step's own artefacts, so that one edited or checked out behind the
+        # chain's back is reported rather than silently trusted.
+        "artefacts": {
+            tgt: sha256_file(Path(tgt))
+            for tgt in sorted(targets)
+            if Path(tgt).is_file()
+        },
+        "missing_inputs": missing,
+    }
+
+
+def load_fingerprints(cal_dir: Path) -> dict:
+    path = cal_dir / FINGERPRINT_FILE
+    if not path.is_file():
+        return {}
+    return yaml.safe_load(path.read_text()) or {}
+
+
+def save_fingerprints(cal_dir: Path, data: dict) -> None:
+    cal_dir.mkdir(parents=True, exist_ok=True)
+    path = cal_dir / FINGERPRINT_FILE
+    path.write_text(HEADER + yaml.safe_dump(data, sort_keys=True))
+
+
+def compare(recorded: dict, current: dict) -> list[str]:
+    """Return human-readable reasons the step is stale, empty when it is not."""
+    reasons = []
+
+    for kind, key in (
+        ("config", "config_files"),
+        ("code", "code"),
+        ("input", "inputs"),
+        ("artefact", "artefacts"),
+    ):
+        old, new = recorded.get(key, {}), current.get(key, {})
+        for path in sorted(set(old) | set(new)):
+            if path not in old:
+                reasons.append(f"{path} ({kind} added)")
+            elif path not in new:
+                reasons.append(f"{path} ({kind} no longer used)")
+            elif old[path] != new[path]:
+                changed = (
+                    "modified since it was generated"
+                    if kind == "artefact"
+                    else "content changed"
+                )
+                reasons.append(f"{path} ({kind} {changed})")
+
+    for path in current.get("missing_inputs", []):
+        reasons.append(f"{path} (input missing from disk)")
+
+    return reasons
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("mode", choices=["record", "check"])
+    parser.add_argument("--step", required=True, help="calibration step label")
+    parser.add_argument("--cal-dir", required=True, help="artefact set directory")
+    parser.add_argument("--name", required=True, help="workflow name override")
+    parser.add_argument(
+        "--configfile", action="append", default=[], help="DAG config (repeatable)"
+    )
+    parser.add_argument(
+        "--hash-config",
+        action="append",
+        default=[],
+        help="config whose content is part of the fingerprint (repeatable)",
+    )
+    parser.add_argument(
+        "--target",
+        action="append",
+        default=[],
+        help="artefact this step produces (repeatable)",
+    )
+    args = parser.parse_args()
+
+    workdir = Path.cwd()
+    cal_dir = Path(args.cal_dir)
+
+    # Snakemake narrates DAG construction; keep that out of the report, but
+    # hand it back if the build fails, since then it is the error message.
+    noise = io.StringIO()
+    try:
+        with contextlib.redirect_stdout(noise), contextlib.redirect_stderr(noise):
+            current = build_fingerprint(
+                args.configfile, args.name, args.target, args.hash_config, workdir
+            )
+    except Exception as exc:
+        print(f"  [error]      {args.step:<10} ({type(exc).__name__}: {exc})")
+        sys.stderr.write(noise.getvalue())
+        return 2
+
+    if args.mode == "record":
+        data = load_fingerprints(cal_dir)
+        data.setdefault("steps", {})[args.step] = current
+        save_fingerprints(cal_dir, data)
+        return 0
+
+    recorded = load_fingerprints(cal_dir).get("steps", {}).get(args.step)
+    if recorded is None:
+        print(f"  [STALE]      {args.step:<10} (no fingerprint recorded)")
+        return 1
+
+    reasons = compare(recorded, current)
+    if not reasons:
+        print(f"  [up-to-date] {args.step}")
+        return 0
+
+    print(f"  [STALE]      {args.step:<10} (run: tools/calibrate {args.step})")
+    for reason in reasons[:10]:
+        print(f"                 {reason}")
+    if len(reasons) > 10:
+        print(f"                 ... and {len(reasons) - 10} more")
+    return 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())