Impact of mineral, organic, and industrial by-product fertilization on leaf nutrient status of wild lowbush blueberry
Bibliographic record
Abstract
Monitoring wild lowbush blueberry (WLB) leaf tissue during the sprout year provides adequate guidelines for fertilizer recommendations. Conventional mineral (MIN) fertilizers and dried poultry manure (organic (ORG)) are environmentally and financially costly to acquire. Pulp and paper mill sludge (PPMS) and synthetic anhydrite (SA) are two industrial by-products that could be used from an industrial ecology perspective to substitute conventional fertilizers due to their local availability and positive agronomic effects. Our study assessed the impacts of conventional MIN, ORG, PPMS, and SA applications on the nutrient leaf status of WLB in the Saguenay-Lac-Saint-Jean region (Québec, Canada). An experimental study was carried out in 2021 on 10-m2 plots that included seven fertilization treatments (MIN, ORG, PPMS, 1SA, 2SA, PPMS+1SA, PPMS+2SA) and control without fertilization. A nitrogen (N) input of 50 kg N ha‑1 was used for N-containing fertilizers. A calcium (Ca) input of 1,558 kg Ca ha‑1 was used for 1SA. Nutrients were monitored in leaf tissue analyses (N, phosphorus (P), potassium (K), Ca, magnesium (Mg), manganese (Mn), sulfur (S), carbon (C), and hydrogen (H)). Compared to the control, 1SA application (no N input) significantly increased N, P, K, and S leaf contents by 17, 40, 68, and 263%, respectively. The PPMS applied on its own had similar effects as ORG fertilization. However, N, P, K, Mn, and S leaf contents increased significantly when PPMS was combined with SA. Compared to SA fertilization on its own, Mn leaf content was lower with PPMS and ORG than other fertilization treatments. Fertilization had no detectable effects on Ca and Mg leaf contents. A combination of different by-products as fertilizers could be a promising alternative to conventional fertilizers, as their use ensures optimal leaf nutrient contents, which could be translated into WLB productivity and improved local circular economy among industries.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".