Variable Rate Fertilization in Wild Blueberry Fields to Improve Crop Productivity and Reduce Environmental Impacts
Bibliographic record
Abstract
Two wild blueberry fields were selected to evaluate the impact of variable rate (VR) fertilization on crop productivity, surface and subsurface water quality. Management zones were delineated based on slope variability, and different fertilizer rates were applied according to prescription maps. Runoff collectors were place in the fields to measure the nutrient losses in surface runoff, while lysimeters were installed to evaluate the impact of VR fertilization (VRF) on subsurface water quality. The VR treatment significantly decreased phosphorus and nitrogen loadings in surface runoff as compared to uniform treatment. The concentrations of nutrients in subsurface water samples were also significantly lower for VR treatment as compared to uniform treatment. The excessive nutrients enhanced vegetative growth in low lying areas of uniform fertilization, while berry yield was less. Based on these results, it can be concluded that VRF in wild blueberry fields improved the crop productivity and potential environmental impacts.
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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 | 0.000 |
| 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".