Organic Carrot (Daucus carota L.) Production Has an Advantage over Conventional in Quantity as Well as in Quality
Bibliographic record
Abstract
Organic production is one of the fastest growing food sectors globally. However, average yield in organic vegetable production is up to 33% lower than in conventional production. This difference could be due to higher fertilization rates in conventional, compared to organic, farming. We aimed to compare yield and quality characteristics of carrots produced under equal nitrogen fertilization rates over four years in organic and conventional conditions. We found a 14.5% higher marketable, and 10.0% lower discarded, yield in the organic compared to the average conventional treatments. In addition, carrots managed organically had 14.1% lower nitrate and 10.0% higher vitamin C content than carrots managed conventionally. There were no convincing effects of cultivation system on the nitrogen, total sugar, or dry matter content of carrots. Organically managed carrots were free of pesticide residues, while several residues were found in carrots managed conventionally. Our study reveals that organic management of carrots may exceed that of conventional methods in yield and several quality characteristics, while being free of pesticide residues. Organic fertilizer gave an advantage over mineral fertilizer, when equal rates of nitrogen were used in both production systems.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".