Are Organic Standards Sufficient to Ensure Sustainable Agriculture? Lessons From New Zealand’s ARGOS and Sustainability Dashboard Projects
Bibliographic record
Abstract
Our review concludes that organic standards need to account for a broader set of criteria in order to retain claims to ‘sustainability’. Measurements of the ecological, economic and social outcomes from over 96 kiwifruit, sheep/beef and dairy farms in New Zealand between 2004 and 2012 by The Agricultural Research Group on Sustainability (ARGOS) project showed some enhanced ecosystem services from organic agriculture that will assist a “land-sharing” approach for sustainable land management. However, the efficiency of provisioning services is reduced in organic systems and this potentially undermines a “land-sparing” strategy to secure food security and ecosystem services. Other aspects of the farm operation that are not considered in the organic standards sometimes had just as much or even a greater effect on ecosystem services than restriction of chemical inputs and synthetic fertilisers. An organic farming version of the New Zealand Sustainability Dashboard will integrate organic standards and wider agricultural best practice into a broad and multidimensional sustainability assessment framework and package of learning tools. There is huge variation in performance of farms within a given farming system. Therefore improving ecosystem services depends as much on locally tuned learning and adjustments of farm practice on individual farms as on uptake of organic or Integrated Management farming system protocols.
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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 | 0.038 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".