Effects of Long-Term Nitrogen Fertilization and Application Methods on Fruit Yield, Plant Nutrition, and Soil Chemical Properties in Highbush Blueberries
Bibliographic record
Abstract
Nitrogen (N) fertilizer is routinely applied in highbush blueberry (Vaccinium corymbosum L.) production. The recommended N fertilizer rate increases as the plants mature, and is usually determined based on regional growing conditions. However, the effects of N fertilizer rates and application methods over the long term remain poorly understood. In this study, ammonium sulfate was applied as an N source at the recommended rate (100%), which corresponds to a maximum of 155 kg N ha−1 for plants older than eight years, along with higher rates at 150% and 200% of the recommended level, as well as a control treatment of no N. Treatments were applied to the blueberry cultivar ‘Duke’ as either broadcast (BROAD) or fertigation (FERT), and impacts were analyzed after 12 and 13 years of treatment. In the 14th year, the 100% N rate was uniformly applied as BROAD across all plants to separate the effects of different N rates from those caused by long-term soil condition changes. The BROAD treatment at the 100% N rate achieved the highest yield, and the FERT treatment at 200% resulted in the lowest yield in the 12th year, suggesting that excessive N rates can reduce fruit yield. However, no significant yield differences were observed in the 13th year. Higher N rates were associated with reduced titratable acidity in fruits and fewer flower buds. The soil pH declined across all N treatments, with the FERT at 200% showing the most significant reduction. All N treatments generally increased soil electrical conductivity (EC). High N rates also decreased plant accumulation of magnesium, calcium, and copper, with the latter reaching deficiency levels. These findings emphasize the importance of adhering to recommended N application rates and adjusting soil pH and EC to mitigate the adverse effects of prolonged N treatments.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".