Geographical Variation in Nutritional Components of Peanut: Evidence From Multi‐Region Production Areas
Bibliographic record
Abstract
ABSTRACT As an important oilseed and food material, the nutritional quality of peanut is influenced by not only the variety but also place of origin. However, research on the geographical impact remains scarce. In this study, nine peanut cultivars were planted at eight sites to determine geographical effects on quality parameters, including fatty acids, sucrose, tocopherols, and phenolic compounds. General linear model analysis showed that the environment of the producing area was a major factor for nutritional indicators except oleic acid in high‐oleic acid peanut varieties ( η 2 : genetic 51.20%, environmental 37.46%). Geographic factors accounted for 58.20%, 68.41%, and 37.15% of the variance in total tocopherols, total phenolics, and sucrose, respectively, while the corresponding varietal effects were 57.83%, 71.06%, and 20.13%. Climate–nutrient interaction analysis revealed this was primarily attributed to low‐temperature conditions promoting sucrose and phenolic compound biosynthesis (e.g., quercetin). In contrast, elevated temperature and humidity correlated with tocopherol accumulation. Furthermore, we delineated geographical characteristics: Hubei (high oleic acid/tocopherol) and Xinjiang (high sucrose/phenol). This study determined geography's impact, providing strategies for region‐specific breeding to advance the industry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".