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Record W2957302033 · doi:10.2134/jeq2018.11.0414

Understanding the Fertilizer Management Impacts on Water and Nitrogen Dynamics for a Corn Silage Tile‐Drained System in Canada

2019· article· en· W2957302033 on OpenAlexafffundabout
Wentian He, Ward Smith, Brian Grant, Andrew VanderZaag, Emily A. Schwager, Zhiming Qi, D H B REYNOLDS, Claudia Wagner‐Riddle

Bibliographic record

VenueJournal of Environmental Quality · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of GuelphMcGill UniversityAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsTile drainageEnvironmental scienceLeaching (pedology)ManureFertilizerAgronomyDrainageSilageSoil waterSoil scienceEcologyBiology

Abstract

fetched live from OpenAlex

Effective management of dairy manure is important to minimize N losses from cropping systems, maximize profitability, and enhance environmental sustainability. The objectives of this study were (i) to calibrate and validate the DeNitrification‐DeComposition (DNDC) model using measurements of silage corn ( Zea mays L.) biomass, N uptake, soil temperature, tile drain flow, NO 3 − leaching, N 2 O emissions, and soil mineral N in eastern Canada, and (ii) to investigate the long‐term impacts of manure management under climate variability. The treatments investigated included a zero‐fertilizer control, inorganic fertilizer, and dairy manure amendments (raw and digested). The DNDC model overall demonstrated statistically “good” performance when simulating silage corn yield and N uptake based on normalized RMSE (nRMSE) < 10%, index of agreement ( d ) > 0.9, and Nash–Sutcliffe efficiency (NSE) > 0.5. In addition, DNDC, with its inclusion of a tile drainage mechanism, demonstrated “good” predictions for cumulative drainage (nRMSE < 20%, d > 0.8, and NSE > 0.5). The model did, however, underestimate daily drainage flux during spring thaw for both organic and inorganic amendments. This was attributed to an underestimation of soil temperature and soil water under frequent soil freezing and thawing during the 2013–2014 overwinter period. Long‐term simulations under climate variability indicated that spring applied manure resulted in less NO 3 − leaching and N 2 O emissions than fall application when manure rates were managed based on crop N requirements. Overall, this study helped highlight the challenges in discerning the short‐term climate interactions on fertilizer‐induced N losses compared with the long‐term dynamics under climate variability. Core Ideas A new tile drainage mechanism implemented in DNDC was successfully evaluated. DNDC performed well in simulating silage corn biomass, N uptake, and soil inorganic N. Soil temperature and tile drainage was sometimes underestimated in the overwinter period. Seasonal N losses were better simulated than nonseasonal N losses. Long‐term simulation indicated lower N leaching and N 2 O for spring than fall manure.

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 score0.037
Threshold uncertainty score0.999

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 head0.024
GPT teacher head0.226
Teacher spread0.202 · 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

Citations22
Published2019
Admission routes3
Has abstractyes

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