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Record W2584968243 · doi:10.21273/hortsci.39.4.795b

Effect of Nitrogen Rate and Residual Soil Nitrogen on Yield and Quality of Carrots

2004· article· en· W2584968243 on OpenAlexaboutno aff
Sean M. Westerveld, A.W. McKeown, Mary Ruth McDonald

Bibliographic record

VenueHortScience · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsNitrogenMineralization (soil science)Yield (engineering)AgronomySoil waterEnvironmental scienceGrowing seasonChemistryAnimal scienceBiologySoil science

Abstract

fetched live from OpenAlex

Residual nitrogen (N) in agricultural soils is an environmental concern. Nitrogen requirements for carrots were examined over a two-year period in Ontario, Canada. Carrots, cvs. `Idaho' and `Fontana' were seeded into organic and mineral soil in 2002 and 2003 in the same plots. In 2002, N was applied at 0%, 50%, 100%, 150%, and 200% of current Ontario recommendations. Each experimental unit was split in half in 2003 with one half getting the same N rate as in 2002, and the second half getting no applied N. At harvest, total and marketable yield, weight per root, and quality were assessed. Yield and quality were unaffected by N rate in both mineral soil in 2002 and organic soil in both years. On mineral soil in 2003 there was a linear increase in total and marketable yield and weight per root with increasing N rate, with total yield ranging between 36 and 55 t/ha. However, in the treatments given no N in 2003, yield also increased from 36 to 47 t/ha between the 2002 no N and high N treatments. Consequently, a large portion of the increase in yield in 2003 on mineral soil was due to residual N from the previous season. Carrots are capable of taking up very high amounts of N from the soil over a growing season, but may only require additional N for maximum total yield if soil N content is low due to low mineralization rates or depletion by a previous crop. However, low N rates can reduce the ability to harvest the crop due to increased carrot leaf blight severity even when yield is unaffected.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.482

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 head0.034
GPT teacher head0.269
Teacher spread0.235 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

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