Challenges, solutions and research priorities for sustainable rangelands
Bibliographic record
Abstract
Australia’s rangeland communities, industries, and environment are under increasing pressures from anthropogenic activities and global changes more broadly. We conducted a horizon scan to identify and prioritise key challenges facing Australian rangelands and their communities, and outline possible avenues to address these challenges, with a particular focus on research priorities. We surveyed participants of the Australian Rangeland Society 20th Biennial Conference, held in Canberra in September 2019, before the conference and in interactive workshops during the conference, in order to identify key challenges, potential solutions, and research priorities. The feedback was broadly grouped into six themes associated with supporting local communities, managing natural capital, climate variability and change, traditional knowledge, governance, and research and development. Each theme had several sub-themes and potential solutions to ensure positive, long-term outcomes for the rangelands. The survey responses made it clear that supporting ‘resilient and sustainable rangelands that provide cultural, societal, environmental and economic outcomes simultaneously’ is of great value to stakeholders. The synthesis of survey responses combined with expert knowledge highlighted that sustaining local communities in the long term will require that the inherent social, cultural and natural capital of rangelands are managed sustainably, particularly in light of current and projected variability in climate. Establishment of guidelines and approaches to address these challenges will benefit from: (i) an increased recognition of the value and contributions of traditional knowledge and practices; (ii) development of better governance that is guided by and benefits local stakeholders; and (iii) more funding to conduct and implement strong research and development activities, with research focused on addressing critical knowledge gaps as identified by the local stakeholders. This requires strong governance with legislation and policies that work for the rangelands. We provide a framework that indicates the key knowledge gaps and how innovations may be implemented and scaled out, up and deep to achieve the resilience of Australia’s rangelands. The same principles could be adapted to address challenges in rangelands on other continents, with similar beneficial outcomes.
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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 | 0.036 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.020 | 0.022 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 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".