Second-crop N fertilization improves lowbush blueberry (<i>Vaccinium angustifolium</i> Ait.) production
Bibliographic record
Abstract
Most commercial blueberry growers follow a 2-yr pruning cycle because second-crop yield in a 3-yr cycle is usually too low for an economical harvest. Research was conducted to determine the extent to which second-crop production could be increased by applying fertilizer in the second-crop year. Treatments, consisting of factorial combinations of N (0, 60 kg ha-1), P (0, 26 kg ha-1), and K (0, 50 kg ha-1), were studied over two 3-yr burn-pruning cycles on a natural lowbush blueberry stand. Treatments were applied prior to, or shortly after, flower buds started to swell in the spring of the second-crop year. Nitrogen increased ripe fruit yield by 65% (3410 vs. 2070 kg ha-1) when compared with plots not previously fertilized with N, and by 43% (3410 vs. 2380 kg ha-1) when compared with plots previously fertilized with N. These results indicate that N might make the second crop economical to harvest. Phosphorus did not significantly affect yield, but K applied in combination with N in the second-crop year negatively affected production and Mg uptake. The increase in yield by N was due to more ripe berries resulting from a higher total (ripe + unripe) number of berries and hastened maturity that increased the percentage of ripe fruit. Reduced fruit abortion is suspected to be the reason for higher berry numbers. In contrast, the negative response to K applications with N was due to reduced total berry numbers. Key words: NK interaction, N × K interaction, potassium, nutrition, fertility, fruit abortion
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".