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Ammonia volatilization trends following liquid hog manure application to forage land

2003· article· en· W1815983109 on OpenAlexaboutno aff
B.D. Lambert, Edward W. Bork

Bibliographic record

VenueJournal of Soil and Water Conservation · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRangelandManureEnvironmental sciencePastureForageAgronomyGrazingAmmonia volatilization from ureaAmmoniaLivestockAgroforestryLitterFertilizerBiologyEcology

Abstract

fetched live from OpenAlex

Recent changes in the livestock industry of western Canada have included the increased establishment of intensive hog operations in semiarid regions where the availability of cultivated land for manure disposal is limited. Instead, permanent forage lands, including both tame pasture and native rangeland, are being considered for manure application. Given the inability of manure to be incorporated on these areas with cultivation, this study tested and successfully utilized static sorber traps as a relatively easy and inexpensive method to assess ammonia (NH3) losses on forage lands following different rates and methods of liquid hog manure application. Comparisons among treatments indicated ammonia loss increased with rate of manure application, with relatively greater losses on tame pasture than native rangeland. Coulter injection resulted in less ammonia loss compared to surface banding, with the greatest benefit on tame pastures, presumably due to the lack of surface litter and associated abundance of bare soil, factors that would increase ammonia volatilization. We conclude that the use of injection is beneficial in reducing ammonia loss on forage lands, particularly tame pastures at greater rates of manure application, but concede the economic benefits based on the amount of nitrogen conserved may be limited.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.260

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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

Citations12
Published2003
Admission routes1
Has abstractyes

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