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Impact of mineral, organic, and industrial by-product fertilization on leaf nutrient status of wild lowbush blueberry

2025· article· W4416629265 on OpenAlexaboutno aff
Anthony J. Pelletier, Patrick Faubert, Jean Lafond, Normand Bertrand, Jean Legault, Rock Ouimet, David E. Pelster, André Pichette, Claude Villeneuve, Noura Ziadi, Maxime C. Paré

Bibliographic record

VenueActa Horticulturae · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsnot available
Fundersnot available
KeywordsHuman fertilizationNutrientYield (engineering)Crop yieldCultivar

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.827

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.289
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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