Genetic Variation in Yield and Fruit Weight Among Strawberry (<i>Fragaria</i> ×<i>ananassa</i>) Cultivars and the Interaction With Year and Location Effect
Bibliographic record
Abstract
ABSTRACT Strawberry ( Fragaria × ananassa ) is an important crop in the United States. In this study, we analysed strawberry trials established across 7 years and two locations. Our objective was to estimate genetic parameters and predict breeding values for yield. Linear mixed models were used for unbalanced data across years and locations and study the genotype by year and location interactions. The results suggested significant differences between cultivars for fruit weight but not for total and marketable yield. Fruit weight was controlled by genetic factors, shown by the high heritability of cultivar means (0.93), whereas marketable yield and total yield showed lower heritability estimates (0.48 and 0.63, respectively), indicating a substantial environmental influence. For fruit weight, cultivars exhibited stability across years and locations, supported by the high additive genetic correlations observed for cultivar‐by‐year ( r = 0.90) and cultivar‐by‐location interactions ( r = 0.98). Fruit weight and total yield were genetically independent as suggested near zero additive genetic correlation (−0.06 ± 0.416) between two traits, and they should be considered for an index selection in the strawberry breeding strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".