Effect of variety, soil fertility status and agronomic treatments on carrot mineral and phytochemical composition and consumer liking of flavor traits
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the effects of variety (Nantes and Imperator), soil fertility status (high and low) and agronomic treatments on yield and quality traits of carrot composition and sensory factors. The treatments compared synthetic nitrogen at conventionally recommended amounts with compost-sourced nitrogen (high and low rates) and a range of amendments (compost, compost tea, micronutrients and foliar treatments). Additionally, we intended to identify factors affecting polyacetylene accumulation in carrots, owing to the growing interest in their health effects and paucity of agronomic information on their bioaccumulation in carrots. RESULTS: Imperator accumulated more minerals, produced more phytochemicals and had higher antioxidant capacity than Nantes, which had more carotenoids. However, consumers preferred the flavor of Nantes over Imperator. High-fertility soil produced carrots of superior nutritional quality than did low-fertility soil, regardless of year-of-application amendments, the effects of which were often variety-dependent. High soil biological activity was able to overcome low fertility status and stimulate greater yield. Carrot phosphorus was correlated with falcarindiol biosynthesis. Chlorogenic acid and falcarindiol were correlated with antioxidant capacity, but not falcarinol or total phenolic compounds. CONCLUSION: Carrots were not strongly affected by agronomic treatments in terms of yield or phytochemical status; however, soil biological activity overcame a soil nutrient deficit in terms of yield. Phenolic compounds and antioxidant status were generally higher in the high-fertility site, whereas polyacetylenes were variety-dependent. Chlorogenic acid and falcarindiol were associated with antioxidant capacity, but not total phenolic compounds and carotenoids. © 2019 Society of Chemical Industry.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".