Merging Interest for Sustainable Agenda: Is There a Link between Sustainable Agriculture Practices and Farm Efficiency?
Bibliographic record
Abstract
There is a global movement towards a sustainable agricultural system, given serious concerns for the food security of future generations. However, despite this push, adopting sustainable agriculture practices has been poor, given that their positive effect is not directly evident to the farmers. In countries like Fiji, where the majority of the land is leased, not undertaking sustainable agriculture practices can lead to a crisis of food insecurity and degraded low-quality land returning to the land owners and future generations. This study utilizes the latest Agriculture Census data from Fiji to construct a non-parametric production frontier from which to estimate the levels of efficiency of each farmer. These efficiency scores are then decomposed to farmers engaged in Sustainable Agriculture Practices vis-à-vis those not undertaking any Sustainable Agriculture Practice (SAP). The results from efficiency analysis provide efficiency and productivity scores for each farmer. Further decomposing it by SAPs reveals the marked difference in efficiency and productivity scores between farmers who undertake SAPs and those who do not. The results demonstrate that those farmers who undertake SAP have efficiency and productivity levels substantially higher than those who do not. To push the sustainable agriculture agenda amongst the farmers and landowners, policymakers must demonstrate to the farmers that undertaking SAPs will not only maintain the quality of the foundation input soil but will have a significant positive effect on their farm efficiency, productivity, and thus profitability. By doing so, the interests of all stakeholders are merged, making adopting sustainable agriculture practices easier on all farms.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".