Value of Locations for Representing Mega‐Environments and for Discriminating Yield of Watermelon in the U.S.
Bibliographic record
Abstract
In a crop breeding program, multiple‐location trials can be used to define target regions and mega‐environments that, in turn, will help the breeder develop stable cultivars. In addition, locations can be chosen that are efficient for distinguishing among cultivars (genotypes) and that are good representatives of the target regions. The objectives of this study were to study mega‐environments and identify test locations that were both discriminating and representative of target regions. Watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai] fruit yield and yield components were evaluated in 3 yr and eight locations using replicated, multiple‐harvest trials. Data were analyzed using genotype main effect and genotype × environment interaction (GGE) biplot model as well as other methods for stability analysis. Marketable yield and percentage early fruit had a nonrepeatable crossover pattern and thus, formed a single and complex mega‐environment. Two key locations, (Kinston, NC, and Charleston, SC) were efficient representatives of two mega‐environments for fruit count. Locations at Woodland, CA, and College Station, TX, can be used interchangeably for identifying genotypes with high percentage cull fruit. There was only one mega‐environment for fruit size. Identification of mega‐environments for watermelon in the southern United States has implications for future breeding and genotype evaluation in the United States including the use of specialized genotypes for high performance in specific locations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".