manifesta/verified-agronomy-17k

Verified Agronomy — 17,199 rows

The arithmetic a working agronomist actually does — fertiliser rates, growing degree days, irrigation scheduling, seeding rates, grain moisture, sprayer calibration, forage budgeting — with every formula traced to a citable published source, and every number checked.

And 1,796 rows (10.4%) that cannot be answered at all, where the correct response is to say so and name the missing input.

Why this exists

Agronomy questions look like general knowledge and are actually arithmetic with domain constants. Get the constant wrong and the answer is confidently, precisely wrong — a fertiliser rate that is out by the P₂O₅→P factor is still a plausible-looking number.

So this dataset takes the same stance as a verified math corpus: a row earns its place by passing a check. Two checks, in fact — the formula is checked against its source, and the arithmetic is checked against itself.

The two-layer verification

Layer 1 — the formulas match their published sources. build/test_agri_calc.py checks every calculator against a worked example published by the source it cites. 25/25 match, including:

Check	Source	Published value	Ours
50 kg N/ha via urea 46-0-0	WSU extension	108.7	108.7
Moisture shrink factor to 15.5%	Pioneer	1.1834	1.1834
P from P₂O₅	Cornell CSS412	×0.437	×0.437
Corn GDD, 95°F/45°F day	NDSU NDAWN	18.0 (both clamps)	18.0
ETc = ETo × Kc	FAO-56	identity	identity

Citing a formula is not evidence you implemented it correctly. Matching the source's own numbers is. This caught a real error: P₂O₅→P is 0.437, not the 0.436 that would otherwise have propagated into thousands of rows.

Layer 2 — the arithmetic in every solution actually computes. build/check_agri.py re-parses the finished dataset, extracts every arithmetic expression written into the worked solutions, evaluates it independently, and compares. 30,710 expressions checked, 0 inconsistent. If a step says 31.8 x 10 x 20 = 6,360, that has been confirmed.

Each row also passed a generator-time check appropriate to its family — an inverse recomputation, a nutrient balance, a dimensional round-trip, or a dry-matter conservation check.

What is in it

Family	Rows	Share	Example
Fertiliser	3,772	21.9%	product rate from a soil-test recommendation; N-P-K blends crediting DAP's nitrogen
Growing degree days	1,980	11.5%	accumulation with the published caps and floors
Grain	1,845	10.7%	moisture shrink, bushels at 15.5%, test-weight adjustment
Unanswerable	1,796	10.4%	cannot be determined without <input>
Seeding	1,603	9.3%	kg/ha from target population, TKW, germination, emergence
Irrigation	1,577	9.2%	ETc = ETo × Kc → depth → volume → run time
Spraying	1,428	8.3%	600-rule calibration, product per tank
Economics	1,240	7.2%	gross margin, breakeven yield, cost per kg nutrient
Livestock	1,238	7.2%	intake, utilisation, grazing days
Units	720	4.2%	kg/ha ↔ lb/ac, ha ↔ ac

15,403 rows (89.6%) are exactly gradable — see exactly_gradable. Difficulty: 3,784 hard, 8,462 medium, 4,953 easy.

The refusal slice

Fertiliser rate needs a current soil test. Irrigation run time needs site-specific reference evapotranspiration. Seeding rate in kg/ha needs the thousand-kernel weight of the actual seed lot. Product rates come from the registered label. Manure timing is governed by local regulation.

These questions are unanswerable by construction — and many include real but irrelevant context (field size, previous crop, soil texture, growth stage) precisely to test whether plausible detail gets mistaken for the input the calculation requires:

"The block is 18 ha. How many days can I graze the home paddock?" → cannot be determined without the current forage dry matter on offer

Field size is genuinely relevant to grazing days and still does not determine them without forage on offer. That is the trap, on purpose.

Output contract

Every row closes with one uniform line:

The final answer is $\boxed{ANSWER}$.

Unit inside the box. This is deliberate and makes the corpus machine-gradable without an LLM judge. Verified on 100% of rows, and the boxed content matches the answer field on 100% of rows.

Loading

from datasets import load_dataset

ds = load_dataset("manifesta/verified-agronomy-17k", split="train")
print(ds[0]["question"], ds[0]["worked_solution"])

refusals = ds.filter(lambda r: r["family"] == "refusal")        # 1,796
gradable = ds.filter(lambda r: r["exactly_gradable"])           # 15,403

Field	Notes
question	The scenario. Carries the numeric inputs the verified answer depends on — do not paraphrase.
worked_solution	Stepwise calculation ending in the boxed final answer
answer	The final answer alone
family / subtask	See the table above
answer_type · answer_unit · numeric_tolerance	numeric/text; tolerance is relative (0.02 = ±2%)
exactly_gradable	False for refusal rows (free text)
citation	The published source for that row's formula
verified_by · verification_note	Which check that row passed, and what it proved
difficulty	easy · medium · hard

Licensing — CC0

Every row was generated for this dataset. No third-party text, no scraped content, no images. The formulas are facts from public extension and FAO literature (facts are not copyrightable), and the sources are cited so you can check them. Released CC0-1.0 — no restrictions.

Limitations — read these

• It is arithmetic, not agronomy judgement. This teaches a model to compute correctly and to refuse honestly. It does not teach what rate to recommend — that is a soil-test-, variety-, season- and jurisdiction-specific decision, which is exactly what the refusal slice says.
• 100% generated. Realistic and internally verified, but not drawn from field records. There is no observational data here.
• Constants are largely North-American / FAO conventions — 56 lb/bu corn, 15.5% market moisture, °F degree-day bases alongside metric rates. A model trained on this inherits those conventions.
• Kc values are FAO-56 mid-season single coefficients. Real scheduling uses stage-specific and often dual coefficients; this is the simplified standard case.
• The refusal share (10.4%) is a design choice. A model trained on this may skew toward refusal; downsample that family if you want a more answer-eager model.
• Verification proves the arithmetic, not the agronomy. Every number computes and every formula matches its source. Whether a given scenario is agronomically sensible was spot-checked by hand, not reviewed at 17,199-row scale.
• No public agronomy benchmark exists to decontaminate against, so there is no contamination check to show. The set is internally exact- and near-deduplicated (MinHash 0.85, 1,701 removed).

Citation

@misc{verified_agronomy_17k,
  title  = {Verified Agronomy: 17,199 source-cited agricultural calculations with a refusal slice},
  author = {Aivaras Navardauskas},
  year   = {2026},
  url    = {https://huggingface.co/datasets/manifesta/verified-agronomy-17k}
}

Formula sources: FAO-56 · NDSU NDAWN · Cornell CSS412 · WSU extension · Purdue · Ohio State · Pioneer.

Built with Adaptive Data by Adaption.