Effects of industrial agriculture on climate change and the mitigation potential of small-scale agro-ecological farms.
Bibliographic record
Abstract
Abstract According to the Intergovernmental Panel on Climate Change (IPCC), agriculture is responsible for 10-12% of total global anthropogenic emissions and almost a quarter of the continuing increase of greenhouse gas (GHG) emissions. Not all forms of agriculture, however, have equivalent impacts on global warming. Industrial agriculture contributes significantly to global warming, representing a large majority of total agriculture-related GHG emissions. Alternatively, ecologically based methods for agricultural production, predominantly used on small-scale farms, are far less energy-consumptive and release fewer GHGs than industrial agricultural production. Besides generating fewer direct emissions, agro-ecological management techniques have the potential to sequester more GHGs than industrial agriculture. Here, we review the literature on the contributions of agriculture to climate change and show the extent of GHG contributions from the industrial agricultural system and the potential of agro-ecological smallholder agriculture to help reduce GHG emissions. These reductions are achieved in three broad areas when compared with the industrial agricultural system: (1) a decrease in materials used and fluxes involved in the release of GHGs based on agricultural crop management choices; (2) a decrease in fluxes involved in livestock production and pasture management; and (3) a reduction in the transportation of agricultural inputs, outputs and products through an increased emphasis on local food systems. Although there are a number of barriers and challenges towards adopting small-scale agro-ecological methods on the large scale, appropriate incentives can lead to incremental steps towards agro-ecological management that may be able to reduce and mitigate GHG emissions from the agricultural sector.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".