Soil fertility response to pruning, fungicide, and fertilization in lowbush blueberry
Bibliographic record
Abstract
Management improves the growth and fruit yield of cultivated lowbush blueberries, but it remains to be seen how the pruning method, fertilizers, and fungicide applications affect soil fertility. This study investigates the impact of pruning, fungicide, and fertilization management practices on key soil parameters related to soil fertility, namely: soil organic matter (SOM) content, soil pH, nitrogen and phosphorus mineralization, nitrification, and phosphorus saturation index (PSI). A split–split–plot experiment was established, including two pruning methods (mechanical and thermal), two fungicide regimes (with or without), and three types of fertilizer applications (mineral, organic, or none). Mineral fertilizer applications significantly and strongly affected most soil fertility indicators, with increased nitrogen (+77 kg ha −1 ) and phosphorus (+117 kg ha −1 ) mineralization and SOM (+34 g kg −1 ), while reducing soil pH (−0.18) and nitrification (−46 kg ha −1 ). Thermal pruning decreased nitrification (−26 kg ha −1 ), soil pH (−0.12), and SOM concentration (−29 g kg −1 ). Fungicide applications showed no significant impact on soil fertility. While mineral fertilizer improves soil fertility, repeated application of organic fertilizer increases soil pH (+0.34), nitrification (+53 kg ha −1 ), phosphorus mineralization (+161 kg ha −1 ), and the soil phosphorus saturation index at undesirable levels (PSI > 2.8%) in lowbush blueberry production systems. The loss of SOM with thermal pruning is noteworthy and highlights the management impact and need for regular monitoring to maintain soil fertility in such fields.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".