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Record W1967653330 · doi:10.4141/p04-062

Phosphorus fertilization and asparagus yield during establishment years

2005· article· en· W1967653330 on OpenAlexvenueaboutno aff
David W. Sommerville, Joann K. Whalen

Bibliographic record

VenueCanadian Journal of Plant Science · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsnot available
Fundersnot available
KeywordsAsparagusFertilizerSowingHuman fertilizationAgronomyNutrientSoil fertilityPhosphorusBiologyAnimal scienceHorticultureChemistrySoil waterEcology

Abstract

fetched live from OpenAlex

Adequate P is important for the production of healthy asparagus (Asparagus officinalis L.) plants. This study determined how varying P fertilization rates affected asparagus yields obtained 3 and 4 yr after planting 1-yr-old crowns. The P fertilizers were applied annually at 0, 50, 75 100 and 200% of the recommended rate for Québec. The recommended rates for P fertilizer are much higher in the year that the crowns are planted than in a producing asparagus field as the goal at planting is to establish soil fertility levels that will sustain production for 15 yr or longer. Marketable yields for the various P fertility regimes ranged from 2060 to 2460 kg fresh weight (FW) ha -1 in the first year of harvest and between 3015 and 3250 kg FW ha -1 in the second year. Marketable yields were not affected by P fertilization in either year, suggesting unamended soil at the test site contained adequate P (about 160 mg Mehlich III extractable P kg -1 ) for asparagus production. The rates of P removal in the harvested asparagus spears were low compared to fertilizer P applications. All of the P fertilizer rates applied increased the Mehlich III extractable P concentration and the P saturation ratio of the soil above critical agroenvironmental limits. Further study is needed to develop P fertilizer recommendations that maintain asparagus yields and quality without contributing to environmental pollution. Key words: Mehlich III-extractable P, P nutrient management, P saturation

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.209
Teacher spread0.188 · 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

Citations5
Published2005
Admission routes2
Has abstractyes

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