Promoting Sustainable Agriculture: Experiences from India and Canada
Bibliographic record
Abstract
Agriculture growth, driven by Green Revolution, has increased the foodgrains supply, ensuring food security. The next stage, however, faces a serious challenge in terms of sustainability. While developing countries face the problem of sustainability of resource use, the challenge for developed economies is overuse of chemical inputs. These problems have increased awareness about sustainable farming and emphasised the need for moving towards it. Policies have since stressed promoting sustainable agriculture. Organic farming is a variant that is receiving special thrust under these policies. This paper examines the policy initiatives and experiences of promoting organic farming in India and Canada. In fact, the policy initiatives, if any, have emanated mainly form the viewpoint of trade concerns. There are very few studies that have gone into examining the issues of economic viability, institutional support, and market access for organic farming in India and Canada. This paper tries to fill this critical gap by examining these issues in a comparative framework. The analysis, mainly exploratory in nature, is based on the existing literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".