Environmental impacts of organic agriculture in temperate regions.
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
Abstract Can organic agriculture elaborate a scientifically based, resource-efficient and agroecological approach to low-input farm management? This review examines the literature from temperate regions, with a particular emphasis on Canadian and US studies that relate to environmental and ecological impacts of organic agriculture with respect to (i) soil organic matter storage, (ii) soil quality/soil health, (iii) nutrient loading and risks of off-farm nutrient and agrochemical losses, (iv) biodiversity and (v) energy use and global warming potential. The context and implications of semi-arid conditions and low soil P levels, common to many organic farms in North America, and widespread adoption of genetically engineered crops in conventional production, is also considered. The consensus of the data available to date indicates the distinctiveness of cropping, floral and habitat diversity, soil management regime, nutrient intensity and use efficiency, and energy, and pesticide use in organic farming confer important environmental and ecological benefits. These include maintenance of soil organic matter and added return of carbon to soil, improved soil health, reduced off-farm nitrogen and phosphorus losses, enhanced vegetative and wildlife (bird) biological diversity, extended sometimes to other taxa depending on landscape context, improved support for pollinators and pollination and reduced energy use and improved energy efficiency. The continued evolution of organic agriculture to a more outcomes-based, agroecological production system will require an expanded multi-disciplinary research effort, linked ideally to support from consumers and policy-makers on the basis of renewed understanding of its potential contribution to global environmental sustainability.
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 | 0.000 | 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 it