| name | fcr-experimental-design |
| description | Use when designing or defending the field-experiment or modelling design of a Field Crops Research (FCR) manuscript — multi-environment trials, randomization and replication, blocking and split-plot layouts, genotype-by-environment (G×E) structure, and crop-model calibration/validation. FCR expects field experiments to span at least two seasons and/or multiple environments. Strengthens the design; it does not write code. |
Experimental Design (fcr-experimental-design)
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.
When to trigger
- Planning a multi-environment trial (MET) or a season×site×treatment layout
- A reviewer questioned randomization, replication, blocking, or G×E inference
- Deciding how to characterise environments (soil, weather, phenology)
- Designing a crop-modelling study (calibration/validation, scenario design)
Field-experiment design essentials
- Multi-environment by design. Plan ≥ 2 seasons and/or multiple sites; define what an
"environment" is (site×year, managed water/N regime). State why the set spans the target population
of environments.
- Randomization & replication. Use a proper randomized design (RCBD, resolvable
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."
- Blocking & spatial control. Block against known field gradients; plan for spatial analysis
(e.g., row/column or P-spline) where fields are large or heterogeneous.
- Plot management detail. Plot size, borders/guard rows, sowing density, dates, and the management
applied — enough to reproduce the experiment.
- Environment characterisation. Record soil and weather and present weather in relation to
crop phenology; this is what lets readers interpret G×E.
Genotype/treatment × environment (G×E)
- Decide up front how G×E will be modelled: factorial structure, which effects are fixed vs.
random, and how environments enter (random sample vs. fixed targets).
- Plan stability/adaptation analyses (Finlay–Wilkinson regression, AMMI, GGE biplot) where
ranking across environments is the question.
Crop-modelling design
- State the model and version, the cultivar coefficients, and the calibration vs.
validation split (independent data, not the same trials).
- Justify the scenario/factor design and the environments simulated; report what the model adds
beyond the field data (extrapolation, yield-gap decomposition, generalisation).
The generalisation test (FCR-specific)
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.
Design-choice decision table (match layout to the question)
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 |
Sizing anchors (illustrative, hedged)
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.
Worked design vignette (illustrative)
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.
Anti-patterns
- One site, one season presented as sufficient (fails the multi-environment expectation)
- "Randomized" asserted with no design named, no replication count, no layout
- Treating a controlled-environment study as the main evidence (out of scope — see
fcr-topic-selection)
- Ignoring spatial heterogeneity in large fields; pseudoreplication (sub-samples treated as reps)
- Calibrating and validating a model on the same data
Output format
【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
Supplementary resources
