A Review On Corn Breeding For Organic And Sustainable Agriculture
Bibliographic record
Abstract
Modern agriculture is principally focused on varieties bred for high performance under high input systems (fertilizers, water, oil, pesticides), which generally do not perform well under low-input systems. They are high yielders but, they have negative consequences as they are likely to threat sustainability. A new paradigm is required which assures food supply as per demand and being accepted in all aspects such as nutrition, economy and bolster sustainable agriculture. This can be achieved through the breeding of maize for organic conditions or low-input production systems. Organic agriculture has been imperative since the 1990s and currently, there is a surge in demand for varieties specifically adapted to organic or low input conditions. However, breeding programs specific for organic farming would entail major time, resources, and labor investment and to breed organic varieties would require specific testing conditions and different breeding tactics. Different breeding programs have been efficient in maize in organic condition centered on nutrition, economy and local environmental adaptability. This technique helps in resource optimization without compromising food sufficiency for a growing population and thus, fortifying sustainability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".