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The most defensible variety choice combines multi-environment field trials with genomic prediction. Field data show how candidates actually perform under target management and weather; genomic data help fill gaps when trials are sparse, candidates are new, or some environments are untested. Neither a single-season average nor a genomic score is sufficient on its own. Build the comparison around the intended product, target environments, genotype-by-environment interaction, and a validation design that matches how the prediction will be used.
What a rigorous variety evaluation must answer
A variety evaluation is a decision about expected performance, not a ranking of numbers from one experiment. Before comparing candidates, establish four things:
- Target population of environments: the regions, seasons, soils, planting dates, irrigation regimes, and management systems where the variety will be grown.
- Product purpose: for example, grain yield, forage, processing quality, stress adaptation, disease resistance, or a combination.
- Selection traits: the measured traits that determine value for that purpose. Trait weights and release rules are crop- and jurisdiction-specific; there is no universal formula.
- Decision timing: whether you are choosing among established lines, advancing new lines, filling missing plots in an incomplete trial, or predicting performance in an environment not yet represented in the training data.
These definitions determine which field records to include, which genomic model is appropriate, and what “accuracy” means. A prediction validated for new lines is not automatically evidence that the same model will predict an untested location.
Why multi-environment field trials remain the foundation
Traditional breeding evaluates phenotypes in several environments because the observed result is a combination of genetic potential, environment, and management. A line that leads in a cool, irrigated trial may not lead under heat, drought, disease pressure, or a different soil type. Replicated, multi-location and multi-year trials expose those changes in performance.
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Genomic selection adds marker-derived predictions to this process. It does not turn an unphenotyped candidate into a proven field winner. The prediction is conditional on the phenotypic and genotypic records used for training, the relatedness between training and candidate lines, the environments represented, the trait definition, and the model.
Use field observations to estimate performance in the target population of environments. Use genomic prediction to prioritize candidates, estimate missing values, or extend inference to lines and environments that cannot all be tested. Keep the two evidence streams identifiable so that a high genomic score is not mistaken for direct field confirmation.
Step 1: Define the target product and environments
Specify the target population of environments
Write down the locations and seasons that matter, rather than using a broad label such as “dryland” or “temperate.” Include the environmental factors that can change variety ranking: rainfall pattern, temperature extremes, soil constraints, planting window, irrigation, fertility, disease pressure, and management intensity. If the product will be grown across contrasting zones, treat those zones as separate or partially connected decision environments.
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Set the trait list before looking at rankings
List primary traits, constraints, and any minimum requirements. A processing cultivar may require quality thresholds even when yield is high; a forage cultivar may need persistence and digestibility; a stress-adapted line may be selected for stable yield rather than maximum yield in the best site. The available evidence does not establish universal trait weights, so document the weights or thresholds used by your breeding program or release authority.
Define the prediction task
State whether the immediate decision concerns:
- newly developed lines with no field phenotype in the current cycle;
- incomplete or sparse trials where some line-by-environment observations are missing;
- an environment absent from the training data; or
- ordinary advancement based primarily on observed multi-environment performance.
This classification determines the validation scheme and prevents an accuracy estimate from being applied to the wrong operational question.
Step 2: Audit the field-trial data
Map entries to locations, seasons, and management
Create a record for every plot or entry-by-environment observation. At minimum, retain variety or line identifier, location, year or season, trial design, replication, management regime, measured trait, and any quality-control flags. Record the environmental covariates needed to explain unusual results, such as temperature, rainfall, soil moisture, or disease score.
Check balance and connectivity
A balanced trial observes every line in every environment, but breeding programs commonly use sparse allocations. Determine which lines overlap across environments and which were tested only once. Overlap connects environments statistically: shared lines provide information about whether a location’s performance is related to another location’s performance.
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Historical records can expand the data set, but differences in year, location, management, and germplasm composition can alter the relationship between environments. Do not treat a large historical file as automatically equivalent to a balanced contemporary trial.
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Inspect replication and uncertainty
Review plot-level variation, missing plots, outliers, and trial means. Keep standard errors or other uncertainty measures with estimated means. A two-kilogram difference with high uncertainty should not be presented as a decisive ranking. Note whether environmental effects, block effects, and spatial trends were modeled; otherwise, apparent variety differences may partly reflect trial layout.
Step 3: Model genotype-by-environment interaction explicitly
Genotype-by-environment interaction (G×E) occurs when varieties respond differently to environments, causing rank changes or changes in the size of their differences. It is central to both variety recommendation and genomic prediction. A global average can hide a line that is excellent in one target zone and poor in another.
Analyze environment means and variety-by-environment deviations, then inspect whether candidate rankings are stable. Consider environmental clusters when the same groups of lines perform similarly across locations or seasons. Report the environments included and whether the target decision is broad adaptation or specific adaptation.
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Step 4: Understand what genomic prediction contributes
Genomic prediction uses marker data from a training population whose members have both genotypes and phenotypes. A model estimates genomic values, often called genomic estimated breeding values, for candidates with marker data. The training population may be assembled from multi-location trials; CIMMYT describes using such a population to predict values for unphenotyped maize lines within a breeding pipeline.
Prediction quality depends on several connections between training and deployment:
- Genetic relatedness: candidates should be represented by, or related to, the training germplasm.
- Environmental relevance: training environments should resemble the target environments or provide a valid correlation structure.
- Trait definition: a genomic model for plot yield is not automatically a model for processing quality or disease severity.
- Data structure: missingness, unbalanced testing, replication, and marker density affect what can be learned.
- Model specification: models differ in how they represent main genetic effects, markers, and G×E terms.
Use the genomic result as an estimate with uncertainty and a stated scope. A score without the training population, environments, trait, and validation design is not interpretable as a general probability of success.
Step 5: Match validation to the deployment scenario
Cross-validation is useful only when the withheld data resemble the information that will be missing at deployment. A chickpea study illustrates three distinct scenarios:
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| Validation scenario | What is withheld | Operational question |
|---|---|---|
| CV1 | Observations for new lines | Can the model prioritize newly developed candidates before they receive full phenotyping? |
| CV2 | Part of the field data for lines that are otherwise represented | Can the model predict missing observations in incomplete or sparse trials? |
| CV0 | An environment not represented in training for the relevant prediction task | Can the model generalize to an untested location or season? |
Do not compare an accuracy from CV1 with one from CV0 as though they measured the same capability. Report the crop, lines, locations, seasons, traits, marker platform, model, training-test split, and metric for every estimate. The chickpea study considered 320 breeding lines, eight traits, three seasons, two locations, and 13 models. Its lines were phenotyped and genotyped with DArTseq (1.6 K SNPs) and genotyping-by-sequencing (GBS; 89 K SNPs). Those counts describe that study design; they are not universal sample-size or marker-density recommendations.
Step 6: Design sparse trials for connected information
Sparse testing can increase the number of environments or the selection intensity within a fixed budget, but it changes the information structure. A 2021 wheat study described allocations in which some lines were observed in all environments (overlap) while others were tested in only one environment (non-overlap). The resulting predictions depend on how well the observed pieces connect.
A CIMMYT summary of sparse genomic testing in spring wheat reported that substantial line overlap, genetic correlations between environments, and genomic relationships helped drive prediction accuracy in that setting. The analysis identified genomic best linear unbiased prediction as the best predictor of true breeding value among the methods evaluated and proposed it as an early-stage selection metric. Treat these as findings from that wheat data set, not as proof that the same model or overlap level is optimal for every crop.
Practical checks for a sparse design
- Calculate the proportion of entries shared across each pair of environments.
- Identify environments connected only through genomic relationships rather than direct line overlap.
- Check whether overlap is concentrated in a narrow genetic group, which can weaken inference for unrelated candidates.
- Simulate or cross-validate the exact missingness pattern expected in the next cycle.
- Retain a set of lines or environments as independent checks instead of using every observation for training.
Step 7: Compare candidate varieties on decision-relevant axes
Once field estimates and genomic predictions are available, compare candidates in a matrix that keeps direct evidence separate from inferred evidence.
| Decision axis | Questions to answer | Evidence to record |
|---|---|---|
| Target-trait performance | Does the candidate meet required thresholds and show useful mean performance? | Environment-specific means, uncertainty, and trait definitions |
| Stability and adaptation | Does its rank change across target environments or seasons? | G×E estimates, environment clusters, rank correlations |
| Field evidence strength | How many relevant environments, years, and replications were observed? | Testing coverage, balance, missingness, and standard errors |
| Genomic relevance | Is the candidate related to the training population and are target environments represented? | Marker data, relationship structure, training-set description |
| Prediction validity | Does validation mimic the intended use? | CV1, CV2, CV0 or another explicitly defined split |
| Uncertainty and risk | Could a small estimated advantage disappear with new data? | Prediction intervals, resampling variation, sensitivity analyses |
Use a documented decision rule after the matrix is complete. For example, a candidate might advance only if it meets a quality threshold, has acceptable performance in each target zone, and has either sufficient field evidence or a genomic prediction validated for the specific missing-data scenario. The rule should be written before final selection so that a favorable score does not silently change the criteria.
A repeatable evaluation workflow
- Describe the deployment: crop, target regions, seasons, management, intended use, and decision date.
- Define traits and constraints: identify primary traits, minimum thresholds, and any crop- or jurisdiction-specific release requirements.
- Assemble the field file: link line IDs, environments, years, replications, management, phenotypes, and quality flags.
- Audit trial structure: quantify balance, overlap, missing plots, replication, and environmental covariates.
- Estimate field performance: model environmental effects, design effects, uncertainty, and G×E; retain environment-specific results.
- Build or inspect the training population: document phenotypes, genotypes, marker platform, relatedness, and environments.
- Choose the prediction task: new lines, incomplete trials, or untested environments, then select a matching validation scheme.
- Validate under deployment-like splits: repeat resampling where possible and report the metric, uncertainty, and all withheld dimensions.
- Compare candidates: combine target-trait performance, adaptation, evidence strength, genomic relevance, and uncertainty without collapsing them into an unexplained single score.
- Decide and monitor: advance candidates that satisfy the predefined rule, then retain new field observations to update and test the model in subsequent cycles.
Analysis resources and data organization
CIMMYT lists META-R for multi-environment trial analysis and GEA-R for genotype-by-environment interaction analysis. Its data portal also points to trial resources including International Maize Trials Network data and IWIN field books. Availability, access terms, and suitability differ by crop and region, so verify the current documentation before adopting a resource.
Whatever software is used, keep a reproducible data dictionary. Define environment IDs, trait units, missing-value codes, marker filtering, genotype identifiers, and the exact train-test split. Store the analysis script or workflow with the result so that a reported accuracy can be reconstructed rather than treated as an unqualified property of the model.
Common failure modes and fixes
Ranking by one grand mean
Problem: a global average hides rank changes and makes a broadly adapted line look identical to a specifically adapted line.
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Fix: show environment-specific estimates, G×E patterns, and the target environments that drive the recommendation.
Calling a genomic score a phenotype
Problem: a predicted value is reported as if the candidate had been observed under the target conditions.
Fix: label it as a prediction, identify the training context, and state the validation scenario and uncertainty.
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Using random cross-validation for a structured deployment
Problem: random splits can place near-duplicate lines or records from the same environment in both training and test sets, overstating performance.
Fix: withhold whole lines, environments, or line-by-environment records according to the actual deployment question.
Assuming more historical data always improves prediction
Problem: historical trials may differ in weather, management, design, and genetic composition; G×E can make old relationships less relevant.
Fix: test historical subsets, inspect environmental correlations, and report how time and place were handled.
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Problem: no reviewed evidence establishes one best model, trial size, or acceptable accuracy for every crop and use.
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Fix: compare plausible models under the deployment-like validation scheme and judge whether the resulting uncertainty is adequate for the actual selection decision.
Ignoring local release rules
Problem: a statistically strong candidate may fail registration, quality, disease, or identity requirements in its jurisdiction.
Fix: obtain the applicable crop- and jurisdiction-specific primary requirements and include them as explicit decision constraints.
How to report an evaluation so others can judge it
A useful report lets a reader reproduce the decision and understand its limits. Include:
- crop, germplasm, target region, seasons, and management conditions;
- locations, trial designs, replications, line overlap, and missing-data pattern;
- trait definitions, units, transformations, and any selection thresholds;
- field model, environmental effects, G×E treatment, uncertainty estimates, and quality-control exclusions;
- genotyping platform, marker count after filtering, training-population composition, and relationship structure;
- prediction model, interaction terms, and software or workflow version;
- validation scheme, number of resamples or folds, evaluation metric, and uncertainty;
- candidate-level results for target environments rather than only an overall average; and
- the final decision rule and which evidence was direct observation versus prediction.
Presenting these details prevents a result from being generalized beyond the population, environments, traits, and missing-data pattern that generated it.
What the evidence supports—and what it does not
Across the cited wheat, maize, and chickpea examples, the consistent practical lesson is to connect genomic prediction to well-described field data and to treat environment structure as part of the prediction problem. Field phenotypes across environments remain the reference for observed adaptation. Genomic methods can increase selection intensity, support sparse testing, and prioritize candidates earlier, particularly when overlap and genetic relationships provide a connected information network.
The evidence does not establish a universal accuracy cutoff, trait-weighting formula, trial size, marker density, or model choice. Results from a 320-line chickpea experiment, a spring-wheat sparse-testing analysis, or historical semi-arid wheat trials should be transferred only after checking whether the crop, germplasm, environments, traits, and deployment scenario are comparable.
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Start with the target product and environments, then use multi-environment field trials to estimate real performance and G×E. Add genomic prediction to address clearly defined gaps—new lines, incomplete trials, or untested environments—and validate it with a split that reproduces that gap. A defensible variety choice is the candidate that meets the stated trait and release requirements with strong, relevant field evidence, a connected and appropriate genomic context, and uncertainty that is acceptable for the decision.
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