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Record W2944636030 · doi:10.2134/jeq2018.12.0433

Assessing the Impacts of Climate Variability on Fertilizer Management Decisions for Reducing Nitrogen Losses from Corn Silage Production

2019· article· en· W2944636030 on OpenAlexafffundabout
Ward Smith, Brian Grant, Zhiming Qi, Wentian He, Andrew VanderZaag, C. F. Drury, X.P.C. Vergé, Hambaliou Baldé, Robert J. Gordon, Matthew J. Helmers

Bibliographic record

VenueJournal of Environmental Quality · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsWilfrid Laurier UniversityMcGill UniversityAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsEnvironmental scienceFertilizerLeaching (pedology)ManureAmmonia volatilization from ureaAgronomyDrainageSilageTile drainageTonneSurface runoffSoil waterEngineeringWaste managementSoil scienceEcology

Abstract

fetched live from OpenAlex

There is an incentive for dairy farmers to maximize crop production while minimizing costs and environmental impacts. In cold climates, farmers have limited opportunity to balance field activities and manure storage requirements while limiting nutrient losses. A revised DeNitrification DeComposition (DNDC) model for simulating tile drainage was used to investigate fertilizer scenarios when applying dairy slurry or urea on silage corn ( Zea mays L.) to examine N losses over a multidecadal horizon at locations in eastern Canada and the US Midwest. Management scenarios included timing (spring, fall, split, and sidedress) and method of application (injected [10 cm], incorporated [5 cm], and broadcast). Reactive N losses (NO 3 − from drainage and runoff, N 2 O, and NH 3 ) were greatest from broadcast, followed by incorporated and then injected applications. Among the fertilizer timing scenarios, fall manure application resulted in the greatest N loss, primarily due to increased N leaching in non‐growing‐season periods, with 58% more N loss per metric ton of silage than spring application. Split and sidedress mineral fertilizer had the lowest N losses, with average reductions of 9.5 and 4.9%, respectively, relative to a single application. Split application mitigated losses more so than sidedress by reducing the soil pH shift due to urea hydrolysis and NH 3 volatilization during the warmer June period. This assessment helps to distinguish which fertilizer practices are more effective in reducing N loss over a long‐term time horizon. Reactive N loss is ranked across 18 fertilizer management practices, which could assist farmers in weighing the tradeoffs between field trafficability, manure storage capacity, and expected N loss. Core Ideas The impacts of climate variability were often greater than the impacts of fertilizer management. Fall‐applied manure resulted in more N loss than spring‐applied, primarily from N leaching. Lowest N loss occurred from split and sidedress urea at all three locations. More NH 3 volatilization and N 2 O losses occurred from organic fertilizer, but N leaching was similar. Similar fertilizer management recommendations can be made across all three locations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.304
Teacher spread0.278 · 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

Citations24
Published2019
Admission routes3
Has abstractyes

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