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Record W4416997511 · doi:10.1007/s12230-025-10030-w

Economic Assessment of Potato Response to Struvite and Conventional Phosphorus Fertilizer in Eastern Canada

2025· article· en· W4416997511 on OpenAlexafffundabout
Mohammad Khakbazan, Judith Nyiraneza, Rim Benjannet, Athyna N. Cambouris, Keith Fuller, Stephan Hann, Noura Ziadi, Debabrata Biswas

Bibliographic record

VenueAmerican Journal of Potato Research · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of FrederictonHôtel-Dieu de QuébecBrandon UniversityUniversité LavalHealth PEIUniversity of Prince Edward IslandAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsStruvitePhosphate fertilizerDiammonium phosphatePhosphorusFertilizerPhosphateAgricultureProfitability index

Abstract

fetched live from OpenAlex

Abstract Phosphorus (P) fertilizers are essential but potentially inefficient, especially in shallowrooted potatoes. A 3-year field experiment (2016-2018) was conducted in four distinct sites in Eastern Canada to investigate economic and environmental impacts of struvite-based slow-release and conventional P fertilizers in potato ( Solanum tuberosum L.). Marginal return (MR = gross revenue-total operating cost) was assessed for different P treatments (0, 60, 120, 180, and 240 kg P 2 O 5 ha -1 ) from triple super phosphate (TSP) and for different mixtures of struvite (25%, 50%, and 75%) and TSP at 180 P 2 O 5 kg ha -1 . Comparable MRs to TSP at a rate of 180 kg P2O5 ha- 1 were obtained with a mixture of 75% TSP and 25% struvite across sites and years. Results demonstrated that a struvite-based mixture can be a viable alternative to conventional P fertilizers, improving profitability and lowering potential cumulative environmental costs associated with P application by $8-$24 ha -1 year -1 for potato production.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.333
Teacher spread0.319 · 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 teacher head, 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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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