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Record W2050414188 · doi:10.4141/cjss06010

Effect of fertilizer nitrogen management on N<sub>2</sub>O emissions in commercial corn fields

2008· article· en· W2050414188 on OpenAlexafffundvenue
Bernie J. Zebarth, P. Rochette, David L. Burton, Morgan N. Price

Bibliographic record

VenueCanadian Journal of Soil Science · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsGovernment of New BrunswickNova Scotia Department of AgricultureAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsFertilizerAgronomyNitrateDenitrificationNitrogenAerationEnvironmental scienceAnimal scienceField experimentSoil fertilityChemistrySoil waterBiologySoil science

Abstract

fetched live from OpenAlex

This study examined the effect of rate and time of fertilizer N application to corn on N 2 O emissions in 2 yr on commercial corn fields. All treatments received starter fertilizer at 45 and 59 kg N ha -1 in 2004 and 2005, respectively, similar to grower practice. Treatments included a control, with no additional fertilizer N application, 75 or 150 kg N ha -1 banded at sidedress or 150 kg N ha -1 broadcast at emergence. There was no significant effect of N fertility treatment on corn grain or silage yield, indicating that all N applications were at or in excess of crop N requirement. Delay of fertilizer application to sidedress and reduced fertil izer N application were effective in reducing nitrate intensity, an index of soil nitrate availability calculated as the summation of daily soil NO 3 − -N concentration for the 0- to 15-cm depth. However, there was no significant effect of N fertility treatment on cumulative N 2 O emissions, and nitrate intensity explained a small proportion of the variation in cumulative N 2 O emissions. This study provides evidence that improved fertilizer N management may not result in reduced N 2 O emissions under some conditions. Key words: Zea mays, nitrate, denitrification, carbon availability, soil aeration

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.218
Teacher spread0.203 · 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

Citations63
Published2008
Admission routes3
Has abstractyes

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