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Record W2004491074 · doi:10.4141/s99-054

Soil aeration for dairy manure spreading on forage: Effects on ammonia volatilisation and yield

2000· article· en· W2004491074 on OpenAlexvenueno aff
Robert J. Gordon, Gary T. Patterson, T. Harz, V. Rodd, John Macleod

Bibliographic record

VenueCanadian Journal of Soil Science · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAerationManureVolatilisationForageAgronomyEnvironmental scienceAmmonia volatilization from ureaSoil waterLiquid manureFertilizerYield (engineering)Animal scienceAmmoniaChemistrySoil scienceBiology

Abstract

fetched live from OpenAlex

Experiments were conducted to evaluate the effects of performing soil aeration either before or after spreading liquid manure in forage production systems. The experiments included eight trials performed in 1996 using a non-interfering diffusion method to determine ammonia (NH 3 ) flux emissions from both aerated and control plots. For all eight trials, the manure application rate was 75 355 L ha −1 . The average NH 3 loss for the aerated treatment was 67.3 kg ha −1 while the loss for the control plots was 63.0 kg ha −1 . Although differences in the NH 3 loss between treatments were low, substantial variations were observed between individual trials depending on the prevailing meteorological conditions.To further evaluate the effects of soil aeration, 11 trials were carried out on Nova Scotia dairy farms in 1996 and 1997 to identify yield effects. Manure application rates ranged from 18 000 to 64 000 L ha −1 . The average forage yield on aerated treatments was 9.4% below control treatments (i.e., manure without aeration). Of the 11 trials, 9 resulted in significantly (P < 0.05) reduced yield with soil aeration. Key words: Liquid manure, ammonia volatilisation, soil aeration

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)0.000
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 score0.909
Threshold uncertainty score0.861

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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

Citations29
Published2000
Admission routes1
Has abstractyes

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