The Carbon and Global Warming Potential Impacts of Organic Farming: Does It Have a Significant Role in an Energy Constrained World?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
About 130 studies were analyzed to compare farm-level energy use and global warming potential (GWP) of organic and conventional production sectors. Cross cutting issues such as tillage, compost, soil carbon sequestration and energy offsets were also reviewed. Finally, we contrasted E and GWP data from the wider food system. We concluded that the evidence strongly favours organic farming with respect to whole-farm energy use and energy efficiency both on a per hectare and per farm product basis, with the possible exception of poultry and fruit sectors. For GWP, evidence is insufficient except in a few sectors, with results per ha more consistently favouring organic farming than GWP per unit product. Tillage was consistently a negligible contributor to farm E use and additional tillage on organic farms does not appear to significantly deplete soil C. Energy offsets, biogas, energy crops and residues have a more limited role on organic farms compared to conventional ones, because of the nutrient and soil building uses of soil organic matter, and the high demand for organic foods in human markets. If farm E use represents 35% of total food chain E use, improvements shown of 20% or more in E efficiency through organic farm management would reduce food-chain E use by 7% or more. Among other food supply chain stages, wholesale/retail (including cooling and packaging) and processing often each contribute 30% or more to total food system E. Thus, additional improvements can be obtained with reduced processing, whole foods and food waste minimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it