Trade‐offs in the performance of alternative farming systems
Bibliographic record
Abstract
Abstract Numerous alternative farming systems are proposed as solutions to the sustainability challenges of today's conventional farming systems. In this paper, we review the production, environmental, and socioeconomic performance of three widely discussed and promoted alternative farming systems—organic, smallholder, and urban agriculture. We show that both organic and smallholder agricultures have some benefits, but also entail important trade‐offs; organic has environmental benefits, and also livelihood, health, and nutritional benefits for producers and consumers, but is hampered by lower yields and higher prices. Smaller farms have higher yields and host higher biodiversity, but are hampered by lower incomes to farmers. Urban agriculture can take some pressure off rural landscapes, provide nutritional benefits to the urban poor, and engage urban dwellers in addressing food system challenges, but it simply cannot scale up to be a substantial solution in and of itself. We suggest that instead of focusing on alternative systems, we should identify pathways to sustainable farming for all systems, reforming conventional systems where they perform poorly, and transitioning to alternative systems in contexts where they perform best.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".