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Record W2795272607

Impact of stabilized urea fertilizers on gaseous nitrogen losses during forage seed production in Saskatchewan

2016· dissertation· en· W2795272607 on OpenAlexaboutno aff
Nils Konstantin Ernst Yannikos

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2016
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsForageProduction (economics)AgronomyNitrogenUreaEnvironmental scienceAgroforestryBiologyChemistryEconomics
DOInot available

Abstract

fetched live from OpenAlex

Forage seed production requires significant fertility inputs, and differs from forage feed production in that fertilizer strategies focus on seed rather than biomass yield. Nitrogen fertilizer management is a particular challenge, because perennial grasses vary in their response to N due to differences in flower induction. Because forages grasses are typically grown for three or more years, N fertilizer typically is broadcast into standing vegetation. Unfortunately, the surface application of urea—the most commonly used form of N fertilizer in Western Canada—is subject to a variety of losses, such as volatilization of ammonia (NH3) and gaseous emissions of nitrous oxide (N2O), resulting in a decrease in N use efficiency (NUE) and causing a risk for the environment. Furthermore, if fertilizers are applied in the fall, subsequent spring snowmelt can promote N2O losses. One promising method to reduce these losses is to use stabilized fertilizers. Stabilized fertilizers contain either a urease or a nitrification inhibitor, or a combination of both, thereby blocking key pathways in the N cycle involved in NH3 volatilization and N2O emissions. The performance of stabilized fertilizers in soils of the Boreal Transition Zone, particularly under forage seed production management, is not well understood. The performance of stabilized N fertilizers in reducing gaseous N losses was investigated by quantifying and comparing gaseous NH3 and N2O losses in forage seed production systems. A novel and cost-effective closed, dynamic flux chamber (CDFC) system for measuring NH3 emissions in remote field sites was developed and validated. Utilizing the CDFC system, a field study was conducted to assess the efficacy of surface-applied stabilized urea fertilizers in reducing gaseous NH3 and N2O losses from forage seed production sites after application either in fall or spring. The study identified application timing (i.e., fall vs. spring) as a dominant factor governing the magnitude of gaseous N losses, with the majority of NH3 losses occurring after spring application, whereas N2O losses were greatest from fall-applied fertilizers during spring snowmelt. Soil properties influenced the potential for gaseous N losses, and stabilized fertilizers containing urease inhibitors reduced NH3 emissions significantly when the loss potential was high. The effect of stabilized fertilizers on N2O emissions, on the other hand, varied strongly between field sites. Soils were collected from the field sites and used in a series of bench-scale experiments to assess the efficacy of stabilized urea fertilizers in reducing NH3 losses under different soil environmental conditions (i.e., soil pH, moisture, and temperature). The study identified strong differences between the NH3 loss potential of the soils. Enhanced urea hydrolysis rates coupled with lower soil water content were the dominant factor governing the magnitude of NH3 losses. Stabilized fertilizers containing both urease and nitrification inhibitors were most effective in reducing NH3 losses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.145
Threshold uncertainty score0.293

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.178
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2016
Admission routes1
Has abstractno

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