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Record W3160010891 · doi:10.1071/an20134

Sustainability of beef production from brigalow lands after cultivation and mining. 3. Pasture rundown, climate and grazing pressure effects

2021· article· en· W3160010891 on OpenAlexaff
J. F. Clewett, Tom Newsome, Colin J. Paton, Alice R. Melland, Jochen Eberhard, J. McL. Bennett, Craig Baillie

Bibliographic record

VenueAnimal Production Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPasture and Agricultural Systems
Canadian institutionsBarrick Gold (Canada)
Fundersnot available
KeywordsPastureGrazingContext (archaeology)ProductivityAgroforestrySustainabilityLand restorationGrazing pressureEnvironmental scienceRevegetationAgronomyGeographyLand reclamationForestryEcologyBiology

Abstract

fetched live from OpenAlex

Context The Acland Land System overlying the Walloon sandstone coal deposits in southern Queensland is generally marginal for cropping but well suited to grazing, and thus cultivated land is commonly returned to pasture. Rehabilitation of these lands after open-cut coal mining seeks to be safe, stable and self-sustaining to satisfy requirements for ecologically sustainable development. Aims The present paper evaluates the sustainability and economic viability of beef production on (a) lands retired from cultivation and then rehabilitated with sown pastures after open-cut coal mining at the New Acland mine site, and (b) similar nearby pasture lands that were not mined but were also retired from cultivation. Methods The GRASP grazing systems model was modified and calibrated with short-term (5-year) grazing trial data (soil, pasture and cattle observations), and then used with long-term (60-year) weather data to estimate effects of land type, pasture rundown, climate and grazing pressure on productivity and economic returns. The productivity of three rehabilitated sites and 15 unmined sites were evaluated, including pastures on six commercial properties. Key results Estimates of long-term mean annual growth of pastures on unmined lands retired from cultivation on three land types (Mountain Coolibah, Brigalow Uplands and Poplar Box) were 3398, 2817 and 2325 kg/ha respectively. Pasture growth was greater on rehabilitated lands; 3736 kg/ha on the site most typical of rehabilitated lands and a mean of 4959 kg/ha across three sites. Seasonal conditions had large effects on cattle liveweight gain (133–213 kg/head per year during the trial); however, pasture growth was the main driver of beef production and economic returns per hectare. In GRASP, potential nitrogen uptake was used to influence key pasture growth processes and accounted for 64% of variation in observed annual growth. The short-term lift and subsequent rundown in productivity typically associated with sown pastures was estimated to have increased mean annual pasture and cattle productivity during the 2014–2018 trial period by up to 17% and 25% respectively. Estimates of long-term mean annual beef production and economic returns for the unmined lands were less than estimated for rehabilitated lands and were 139 kg/head.year (45 kg/ha.year) and AU$154/adult equivalent. Conclusions Rehabilitated lands were found to be sustainable for beef production at grazing pressures up to 30% utilisation of annual pasture growth, and comparable with grazing systems on native and sown pastures in good condition. Pastures on unmined lands retired from cultivation had reduced productivity. Implications Overgrazing is a significant and on-going residual risk to sustainable production. Grazing regimes need to continually adjust for changes in novel landscapes, pasture condition and climate. The methods used in the present study could be applied more generally.

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)0.001
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.221
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 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

Citations6
Published2021
Admission routes1
Has abstractyes

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