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Record W2187326686 · doi:10.1093/jexbot/52.357.821

Towards optimization of growth via nutrient supply phasing: nitrogen supply phasing increases broccoli (Brassica oleracea var. italica) growth and yield

2001· article· en· W2187326686 on OpenAlexaff
Roger Nkoa, Yves Desjardins, Nicolas Tremblay

Bibliographic record

VenueJournal of Experimental Botany · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBrassica oleraceaYield (engineering)NitrogenNutrientAgronomyPhaserEnvironmental scienceChemistryBiologyMaterials scienceEngineering

Abstract

fetched live from OpenAlex

A greenhouse experiment on broccoli (Brassica oleracea var. italica, cvs Windsor and Arcadia) was carried out in order to demonstrate that supplying nitrogen (N) to meet the nitrogen demands of plant growth stages, through N phasing, improves plant growth and yield, as compared to fertilizing at the conventional, optimal, constant N rate. Two broccoli cultivars and two rates of starter nitrogen fertilizer (optimum, 250 mg l(-1) and sub-optimum, 150 mg l(-1)), were combined with three timings of fertigation change. Shifting N rate, at 60% and 75% of the market plant growth cycle significantly increased shoot dry weight and head fresh weight, compared to the constant-N rates treatments (controls). The highest yield and shoot dry weight were obtained when the N-rate was switched from the optimum level (250 mg l(-1)) to the sub-optimum level (150 mg l(-1)) at inflorescence initiation. The nitrogen-to-growth-stage-fitness effect was determined and partitioned into rate effect and phasing effect. The phasing effect was greatest, on both shoot dry weight and head fresh weight, at inflorescence initiation, and subsequently decreased until harvest time. None of the interactions was significant. The results demonstrated the superiority of nitrogen supply phasing over the conventional fixed-rate-supply method.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.232
Teacher spread0.216 · 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 designBench or experimental
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

Citations17
Published2001
Admission routes1
Has abstractyes

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