MétaCan
Menu
Back to cohort
Record W4405319944 · doi:10.1016/j.fcr.2024.109708

Combining measurements and modelling to reveal long-term effects of nitrogen fertilizer application timing on N2O emissions in corn

2024· article· en· W4405319944 on OpenAlex
Jong‐Won Kang, Pedro Vitor Ferrari Machado, David C. Hooker, Brian Grant, Ward Smith, Claudia Wagner‐Riddle, Joshua Nasielski

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueField Crops Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
Fundersnot available
KeywordsEnvironmental scienceTerm (time)AgronomyFertilizerNitrogen fertilizerNitrogenAgricultural engineeringBiologyChemistryEngineering

Abstract

fetched live from OpenAlex

The impact of nitrogen fertilizer (N) application timing on nitrous oxide (N 2 O) emissions is inconsistent in the literature. This inconsistency is attributed to year-to-year weather variations, which affect soil conditions around N application time. Planting dates (PD) also vary year-to-year based on weather, and PD can influence N timing decisions. The study aims to evaluate: i) the long-term effects of different N application timings on N 2 O emissions and, ii) how variations in PD influence the relative performance of different N timing strategies. We used the DeNitirifcation-DeComposition (DNDC) model, calibrated with field measurements from Elora, Ontario, Canada, to simulate 39 growing seasons using historical weather data. Three N timing strategies were tested: spring application one day before planting, in-season application at the V6 growth stage, and a split-N strategy with N applied at both times. PDs were either dynamically adjusted each year based on rainfall or fixed to one of three typical corn ( Zea mays L.) planting dates in Ontario: April 25, May 5, and May 15. For the first objective, the long-term simulation found that average N 2 O emissions were greatest when N was applied at V6 (3.2 kg N ha −1 ) compared to when N was applied pre-plant (2.3 kg N ha −1 ) or split-applied (2.0 kg N ha −1 ). This was caused by slightly greater rainfall around V6 than planting. For the second objective, the relative performance of different N-timing strategies was affected by PD. Earlier PDs resulted in lower N₂O emissions compared to later PDs, primarily due to lower soil temperatures around the time of N fertilizer application. Earlier PDs also led to the largest differences in N 2 O emissions among the N timing strategies, with PD delays leading to smaller differences among N timing strategies. Large single N applications, particularly those applied in-season, resulted in greater N 2 O emissions than split and at-planting N applications in a long-term simulation. Early PDs consistently reduced N 2 O emissions by creating less favourable conditions for N 2 O production. Moreover, the relative performance of N timing strategies was mediated by PD. This study highlights the interconnected nature of cropping systems, where one management practice, PD, can influence a seemingly unrelated outcome, N 2 O emissions. Long-term climatic, social, economic, and technological changes that influence PD will also influence N 2 O emissions from spring and summer-applied N fertilizer. • Early planting dates reduce N 2 O emissions from nitrogen applications. • Split nitrogen applications result in the lowest average N 2 O emissions. • N 2 O emissions peak with large nitrogen applications at the V6 growth stage. • Effect of nitrogen timing on N2O emissions depends on planting date. • Long-term simulations can reveal the true effect of different fertilizer strategies for emission reduction.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.232

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 communication0.0000.000
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 head0.084
GPT teacher head0.356
Teacher spread0.272 · 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