Herbicidal weed control and crop-year NPK fertilization improves lowbush blueberry (<i>Vaccinium angustifolium</i> Ait.) production
Bibliographic record
Abstract
Past research has shown that fertilizer applied in the vegetative year can increase yield, but not always. Fertilizer applied in the crop year without weed control also has been shown to increase yield. The present study, conducted on a natural lowbush blueberry stand for 8 yr, compared the effects of factorial combinations of two rates each of N (0, 60), P (0, 26), and K (0, 50 kg ha −1 ) applied either in the vegetative or crop year, with or without weed control. Greatest production was obtained with weed control, which increased ripe fruit yield by 247% over that from plots without weed control. Nitrogen alone or P and K with N also increased yield, but only when applied in the crop year to weed-controlled plots. Phosphorous or K alone was of little benefit. Nitrogen increased ripe fruit yield from 3910 (unfertilized plots with weed control) to 4440 kg ha −1 and in combination with P and K to 5520 kg ha −1 . Yield increases from weed control and N were due to increased berry weight and hastened maturity, but weed control also increased total berry number. The increase by P and K was due to an increase in total and ripe berry numbers. Nitrogen applied in the vegetative year, although producing more flower buds m −2 than when applied in the crop year, gave lower yields. Fruit abortion, due to insufficient nutrients in the crop year, particularly N, is suspected to be the reason for the reduced yield. Key words: Lowbush blueberry, Vaccinium angustifolium, yield flower buds, weed control, fertilizer, herbicide
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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 | 0.001 | 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 | 0.000 |
| 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".