Managing catchments for multiple objectives: the implications of land use change for salinity, biodiversity and economics
Bibliographic record
Abstract
Policy developed for the management of natural resources in agricultural landscapes in recent years has emphasised the need for an integrated approach. Operationally however, natural resource objectives have been pursued independently with little consideration of the link between components of ecosystems and therefore the possibility of trade-offs between components. In the absence of this information, decision makers cannot adequately assess the cost-effectiveness of alternative strategies for improving the condition of the natural resource base. The aim of this study is to assess the extent of trade-offs between multiple catchment objectives viz. biodiversity, stream salinity, stream yield, salt load, sequestration of carbon and farm profit in the Little River Catchment in Central New South Wales. Seven scenarios describing different land use alternatives for the catchment were assessed using spatial datasets of catchment characteristics. A suite of models was used to determine the impact of land use change on these characteristics over a 50-year timeframe. The results of the analysis indicate that changes in farm production methods may deliver small improvements in some indicators of catchment health. However, significant improvements would require the establishment of large areas of woody perennials and this is only likely to occur with significant public investment, given the consequent large reduction in farm profit. Trade-offs between several catchment indicators were identified. Significantly the benefits of reducing stream salinity were outweighed by the losses resulting from reduced stream flow. Generally, the financial benefits of improving the indicators of resource condition were low relative to the investment required. It was concluded therefore that the environmental value of these improvements would need to be substantial to justify the investment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".