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Record W2267734107 · doi:10.1071/an15575

Modelled greenhouse gas emissions from beef cattle grazing irrigated leucaena in northern Australia

2016· article· en· W2267734107 on OpenAlexaff
C. A. Taylor, Matthew Tom Harrison, Marnie Telfer, Richard Eckard

Bibliographic record

VenueAnimal Production Science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsCarbon Engineering (Canada)
FundersAustralian GovernmentMeat and Livestock AustraliaAustralian Wool Innovation
KeywordsLeucaenaLeucaena leucocephalaGreenhouse gasGrazingAgronomyEnvironmental scienceAgroforestryBiologyCarbon sequestrationPastureEnvironmental management systemIrrigationEcologyCarbon dioxide

Abstract

fetched live from OpenAlex

Agriculture produces an estimated 14.5% of global anthropogenic greenhouse gases, with livestock emissions being the largest source of enteric methane. Reducing greenhouse gas (GHG) emissions from production and processing of beef cattle will become increasingly important with time, particularly in line with global efforts to mitigate rising GHG emissions. The present study compared several GHG emission scenarios from beef cattle grazing on irrigated Leucaena leucocephala (Lam.) de Wit cv. Cunningham (leucaena) in Queensland, Australia. Animals began grazing the leucaena paddocks when they were 16 months old and continued until ~240 days, before being sold to market. Three scenarios were modelled with cattle grazing leucaena and the resulting GHG emissions calculated, representing (1) the current leucaena paddock (current leucaena scenario), (2) clearing native vegetation and extending the leucaena paddock (extended leucaena scenario) and (3) extending the leucaena paddock onto previously cleared paddocks (alternative leucaena scenario). These were compared with a pre-scenario baseline, where the steers grazed on native vegetation until the time of sale. Herd GHG emission intensities (EI) were reduced in comparison with the baseline (EI of 8.4 tCO2-e/t liveweight sold) for all the leucaena scenarios, where reductions were modelled for the current, extended and alternative leucaena scenarios, which had an EI of 3.9, 3.7 and 3.6 tCO2-e/ t liveweight sold, respectively. Reductions were attributed to the higher growth rates of the steers on leucaena and the anti-methanogenic potential of leucaena. Where leucaena was planted on previously cleared paddocks, carbon stocks (t C/ha) nearly doubled a decade following planting, with most carbon sequestered in the soil. However, total carbon stocks on the property reduced over the modelled period (112 years), where native vegetation, e.g. eucalyptus woodland, was cleared for leucaena planting, but soil carbon yield increased. The combined sequestration of leucaena and the reduction of GHG emission intensities resulted in overall net reductions of GHG emissions for the three leucaena scenarios compared with the baseline. These results demonstrated that the use of leucaena for grazing can be an effective means for farmers to reduce the GHG emissions and increase productivity of their herds. The study also demonstrated that it would take 9 years of reduced emissions to compensate for the carbon lost as emissions from clearing the eucalyptus woodland, suggesting that farmers should use other methods of intensifying production from existing leucaena paddocks if their sole purpose is short-term emissions abatement.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.488

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.262
Teacher spread0.238 · 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 designSimulation or modeling
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

Citations65
Published2016
Admission routes1
Has abstractyes

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