Global trends in population, energy use and climate: implications for policy development, rangeland management and rangeland users
Bibliographic record
Abstract
Increasing world human population, declining reserves of cheaply extracted fossil fuels, scarcity of supplies of fresh water and climatic instability will put tremendous pressure on world rangelands as the 21st century progresses. It is expected that the human population of the world will increase by 40% by 2050 but fossil fuel and reserves of fresh water will be drastically reduced. Avoiding food shortages and famine could be a major world challenge within the next 10 years. Under these conditions, major changes in policies relating to economic growth and use of natural resources seem essential. Stabilisation of the human population, development of clean and renewable energy, enhanced supplies of water and its quality, increased livestock production, and changed land-use policies, that minimise agricultural land losses to development and fragmentation, will all be needed to avoid declining living conditions at the global level. The health and productivity of rangelands will need to receive much more emphasis as they are a primary source of vital ecosystem services and products essential to human life. Changes in tax policies by developed, affluent countries, such as the United States, Australia and Canada, are needed that emphasise saving and conservation as opposed to excessive material consumption and land development. Extreme levels of debt and chronic deficits in trade by the United States and European Union countries need to be moderated to avoid a devastating collision of debt, depletion of natural resources, and environmental degradation. Over the next 10 years, livestock producers of the rangelands will benefit from a major increase in demand and prices for meat. Rapidly increasing demand for meat in China and other Asian countries is driving this trend. Rangeland managers, however, will also likely encounter greater climatic, financial, biological and political risks. Higher interest rates, higher production costs and higher annual variability in forage resources are major challenges that will confront rangeland managers in the years ahead. Under these conditions, a low risk approach to livestock production from rangelands is recommended that involves conservative stocking, use of highly adapted livestock, and application of behavioural knowledge of livestock to efficiently use forage resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
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".