Carbon and Nitrogen Reserves in Perennial Strawberry Affect Plant Growth and Yield
Bibliographic record
Abstract
Early spring growth of perennial strawberry ( Fragaria × ananassa Duch.) plants is supported by the carbohydrate and nitrogen (N) reserves accumulated from the previous growing season. The limitations of these reserves on the initial spring growth and yield of perennial strawberries have not been studied in detail, particularly the influence of N reserves. Differential N fertigation (0 to 20 m m N) was applied to potted strawberries during the growing season and a supplemental foliar urea application was applied to a portion of the plants in the fall to modify reserve N during dormancy. Plant N content and spring vegetative growth the year after fertigation increased nearly twofold with increasing N fertigation. Photosynthesis per unit leaf area also increased up to 10 m m of fertilizer N and then stabilized through 20 m m . Foliar urea application in fall further increased total plant N content and size, decreased carbohydrate concentration, and also decreased yield in plants with the most total N. Nitrogen fertigation was resumed on a portion of these plants in early spring, but new growth and subsequent yield were unaffected by spring N application. In a second experiment, CO 2 enrichment with and without soil and foliar N application in the fall was used to vary carbon (C) and N reserves. CO 2 enrichment in fall increased plant size and yield the next July by ≈20%, but total nonstructural carbohydrate and N concentrations were unaffected. Foliar urea application also increased N and C reserves (but not concentration) as well as yield in both enriched and unenriched plants. Although foliar urea in fall decreased carbohydrate concentration, total reserve levels were unaffected because treated plants were larger. In this experiment, spring N increased plant size by ≈50%, but yield was increased only 12%, suggesting that yields are mostly dependent on reserves. Increasing N reserves with a late fall foliar application is one strategy growers can use to efficiently enhance growth and yield in low to moderately fertilized plants.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".