fcr-experimental-design — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited fcr-experimental-design (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
FCR is demanding about field-experimental rigour. The design must credibly connect the agronomic question to evidence that generalises across environments. The single most important FCR-specific rule: field experiments should, unless exceptional circumstances apply, span at least two seasons and/or multiple locations/environments. Design for that from the start.
"environment" is (site×year, managed water/N regime). State why the set spans the target population of environments.
incomplete-block / alpha-lattice**, split-plot for factor hierarchies, strip-plot, augmented for many genotypes). State replication per environment and the randomization procedure — not "plots were arranged."
(e.g., row/column or P-spline) where fields are large or heterogeneous.
applied — enough to reproduce the experiment.
crop phenology**; this is what lets readers interpret G×E.
random**, and how environments enter (random sample vs. fixed targets).
ranking across environments is the question.
validation** split (independent data, not the same trials).
beyond the field data (extrapolation, yield-gap decomposition, generalisation).
For your design, write one sentence: "These environments represent ___, so the result is expected to hold for ___ (and not for ___)." If you cannot, the design does not yet support a general, FCR-worthy claim — add environments or scope the claim.
FCR referees expect the layout to follow from the agronomic question and the field's structure, with a named design and stated randomization. Pick — and justify — before committing plots.
| Situation | Design FCR expects | Note |
|---|---|---|
| One factor, field gradient | RCBD, blocks across the gradient | name blocks, give replication |
| Many genotypes, few reps | Resolvable incomplete block / alpha-lattice | recover inter-block information |
| Factor hierarchy (irrigation × N) | Split-plot (water = whole-plot) | report whole-plot + sub-plot error |
| Large/heterogeneous field | RCBD/lattice + spatial model (row–column, P-spline) | pre-plan the spatial term |
| Genotype ranking across environments | MET, environments a random sample | enables AMMI/GGE, stability inference |
No universal minimum exists, but FCR's ≥2-seasons/-environments expectation points to norms worth calibrating against — as illustrative anchors (confirm against your own variance): MET genotype trials often run ≥6–8 site-years before stability inference is credible; replication is commonly 3–4 blocks per environment; a response curve wants ≥4–5 levels.
Illustrative; the logic is the lesson. A team wants to claim a new wheat cultivar yields more under reduced N. A weak design — 1 site, 1 season, cultivar unreplicated — cannot separate cultivar from field position and yields no G×E information. The FCR-grade redesign: 2 seasons × 4 sites (8 environments) on a soil-N gradient, split-plot (N as whole-plot, cultivar as sub-plot), 4 blocks per environment, 5 N levels for a response curve, and a row–column spatial term — making the cultivar × N × environment surface identifiable and testable across environments.
fcr-topic-selection)【Design】RCBD / alpha-lattice / split-plot / MET / modelling
【Environments】#seasons × #sites; what they represent
【Randomization & replication】procedure + reps per environment
【G×E plan】fixed/random structure; stability analysis if relevant
【Environment characterisation】soil + weather vs. phenology recorded? [Y/N]
【Generalisation sentence】represents ___ → holds for ___
【Next】fcr-data-analysis../../resources/external_tools.md — design packages (agricolae, FielDHub) and crop models (APSIM, DSSAT, STICS)../../resources/official-source-map.md — the ≥2-seasons/-environments rule and reproducibility expectations~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.