Resilient and sustainable natural resource production: how are farmers and foresters coping?
Bibliographic record
Abstract
Adapting to the anthropogenic environmental change while transitioning to a more sustainable and more productive natural resource management places unprecedented demands on natural resource production. Meeting this complex challenge without unwarranted environmental degradation or loss of livelihoods requires understanding and managing the resilience of properties that produce natural resources. However, insufficient attention has been paid in research and natural resource governance to the capacity of natural resource producers to adapt and achieve sustainable outcomes at the property-level, potentially leading to unintended environmental and social outcomes. We used a large and detailed survey data of farmers, foresters, and growers in New Zealand to identify factors that correlate with property-level outcomes that are desirable from the perspective of sustainable natural resource production: strong environmental performance, good financial situation, and high well-being. The results detail how these outcomes correlate with diverse individual traits and outlooks, property-level agroecosystem characteristics, economic resources, and social interactions. However, different factors drive individual outcomes, and a factor that is positively correlated with one desirable outcome may negatively correlate with another. The only factor that positively correlated with all three outcomes was the goal to have strong environmental performance in future, which may reflect optimism as a resilience determinant. Thus, the difficulty of achieving good outcomes across all three dimensions may arise from conflicting effects of different factors on property-level environmental, economic, and well-being outcomes. In conclusion, our results indicate that natural resource governance must more carefully consider interdependencies between environmental, financial, and well-being outcomes at the property-level to support the ability of natural resource producers to meet society’s demands.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".