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Record W2126222557 · doi:10.4141/cjas10034

Review: Ammonia emissions from dairy farms and beef feedlots

2011· article· en· W2126222557 on OpenAlexaffvenue
A.N. Hristov, M.D. Hanigan, Andy Cole, Richard W. Todd, Tim A. McAllister, Pius M. Ndegwa, Al Rotz

Bibliographic record

VenueCanadian Journal of Animal Science · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsAgriculture and Agri-Food Canada
FundersU.S. Department of Agriculture
KeywordsManureAmmonia volatilization from ureaAmmoniaEnvironmental scienceFertilizerManure managementEnvironmental chemistryEutrophicationChemistryAnimal scienceAgronomyNutrient

Abstract

fetched live from OpenAlex

Hristov, A. N., Hanigan, M., Cole, A., Todd, R., McAllister T. A., Ndegwa, P. and Rotz, A. 2011. Review: Ammonia emissions from dairy farms and beef feedlots. Can. J. Anim. Sci. 91: 1–35. Ammonia emitted from animal feeding operations is an environmental and human health hazard, contributing to eutrophication of surface waters and nitrate contamination of ground waters, soil acidity, and fine particulate matter formation. It may also contribute to global warming through nitrous oxide formation. Along with these societal concerns, ammonia emission is a net loss of manure fertilizer value to the producer. A significant portion of cattle manure nitrogen, primarily from urinary urea, is converted to ammonium and eventually lost to the atmosphere as ammonia. Determining ammonia emissions from cattle operations is complicated by the multifaceted nature of the factors regulating ammonia volatilization, such as manure management, ambient temperature, wind speed, and manure composition and pH. Approaches to quantify ammonia emissions include micrometeorological methods, mass balance accounting and enclosures. Each method has its advantages, disadvantages and appropriate application. It is also of interest to determine the ammonia emitting potential of manure (AEP) independent of environmental factors. The ratio of nitrogen to non-volatile minerals (phosphorus, potassium, ash) or nitrogen isotopes ratio in manure has been suggested as a useful indicator of AEP. Existing data on ammonia emission factors and flux rates are extremely variable. For dairy farms, emission factors from 0.82 to 250 g ammonia per cow per day have been reported, with an average of 59 g per cow per day (n=31). Ammonia flux rates for dairy farms averaged 1.03 g m −2 h −1 (n=24). Ammonia losses are significantly greater from beef feedlots, where emission factors average 119 g per animal per day (n=9) with values as high as 280 g per animal per day. Ammonia flux rate for beef feedlots averaged 0.174 g m −2 h −1 (n=12). Using nitrogen mass balance approaches, daily ammonia nitrogen losses of 25 to 50% of the nitrogen excreted in manure have been estimated for dairy cows and feedlot cattle. Practices to mitigate ammonia emissions include reducing excreted N (particularly urinary N), acidifying ammonia sources, or binding ammonium to a substrate. Reducing crude protein concentration in cattle diets and ruminal protein degradability are powerful tools for reducing N excretion, AEP, and whole-farm ammonia emissions. Reducing dietary protein can also benefit the producer by reducing feed cost. These interventions, however, have to be balanced with the risk of lost production. Manure treatment techniques that reduce volatile N species (e.g., urease inhibition, pH reduction, nitrification-denitrification) are also effective for mitigating ammonia emissions. Another option for reducing ammonia emissions is capture and treatment of released ammonia. Examples in the latter category include biofilters, permeable and impermeable covers, and manure incorporation into the soil for crop or pasture production. Process-level simulation of ammonia formation and emission provides a useful tool for estimating emissions over a wide range of production practices and evaluating the potential benefits of mitigation strategies. Reducing ammonia emissions from dairy and beef cattle operations is critical to achieving environmentally sustainable animal production that will benefit producers and society at large.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.033
GPT teacher head0.246
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations429
Published2011
Admission routes2
Has abstractyes

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