Sustainability and future food security—A global perspective for livestock production
Bibliographic record
Abstract
Abstract Grasslands are the predominant forage source for grazing animals and cover more of the Earth's land than any other major vegetation type. Their values are not always recognised, and conversion to other uses is continuing at a high rate leading to greater environmental and socio‐economic problems. Overgrazing is one of the main drivers of productivity decline of grasslands, reflecting the pressures from excessive human populations and a demand for food. Some 20% of the world's grasslands are in a severely degraded state; others have suffered shifts to less‐desirable species. Biodiversity and greenhouse gas production have also been particular concerns. Estimates of productivity change all show a decline over recent decades, yet animal numbers continue to increase, particularly in the developing world. This paper critically reviews the projected demands for livestock products, driven largely by human population growth; the current health of the world's grasslands and how current livestock systems that depend on land conversion and overexploitation of grassland are inappropriate and need to be improved. Central to this argument is that small holders in the developing world will be responsible for a large amount of the future red meat production, and this can be achieved through more efficient livestock production systems using lower stocking rates. The Australian sheep industry is provided as an example of how livestock production and reduced environmental impacts can be achieved with improved efficiency. Changes will require smallholders to transition to a competitive, market‐oriented livestock industry, which will provide challenges.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".