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Record W2914833424 · doi:10.1139/cjss-2018-0150

Effect of three nitrogen fertilizer sources on denitrification rate under irrigated potato production on sandy soils

2019· article· en· W2914833424 on OpenAlexafffundvenueabout
Isabelle Perron, Athyna N. Cambouris, Bernie J. Zebarth, Philippe Rochette, Noura Ziadi

Bibliographic record

VenueCanadian Journal of Soil Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPotato Plant Research
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsDenitrificationSowingAgronomyFertilizerSoil waterLeaching (pedology)Ammonium nitrateNitrogenAmmonium sulfateNitrateEnvironmental scienceChemistryAnimal scienceBiologySoil science

Abstract

fetched live from OpenAlex

Large inputs of nitrogen (N) are required to optimize yield and quality of potato (Solanum tuberosum L.), and it may result in a high potential for N losses including denitrification. This 5 yr study compared the effect of three N fertilizer sources [ammonium nitrate (AN), ammonium sulfate (AS), and polymer-coated urea (PCU)] at 200 kg N ha −1 (N200) and an unfertilized control (N0) on denitrification rate (DR) from irrigated potato production on a coarse-textured soil in eastern Canada. Fertilizer was banded all at-planting (PCU) or split 40% at-planting and 60% at-hilling (AN and AS). The DR was measured biweekly from planting to harvest at two locations (ridge and furrow) using the acetylene blockage technique. The mean annual DR, averaged across N treatments, ranged from 0.8 to 8.0 μg N 2 O-N kg dry soil −1 d −1 , and it was most closely related to the water inputs in the 72 h before DR measurements. Mean DR averaged across year was greater for N200 than for N0 (4.2 vs. 3.4 μg N 2 O-N kg dry soil −1 d −1 ) but did not differ among N sources. Our results suggest that choice of N fertilizer source in sandy soils is more important in controlling losses of N by leaching than by denitrification.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.993

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 science0.0010.000
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 head0.023
GPT teacher head0.241
Teacher spread0.218 · 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

Citations12
Published2019
Admission routes4
Has abstractyes

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