Effects of Tree Age and Soil Property on the Nutritional Status and Fruit Quality of Shenzhou Nane (<i>Prunus Salicina</i> Var. <i>taoxingli</i>)
Bibliographic record
Abstract
The purpose of this study was to determine the effects of tree age and soil property on the nutritional status and fruit quality of Shengzhou nane ( Prunus Salicina Var. taoxingli ). The contents of the soil nutrients, leaf nutrients during two rapid fruit growth stages and fruit quality indexes of Shengzhou nane with different tree ages in Jinting town, Shengzhou, were measured. The results showed the highest values of the mass per fruit (MPF), the contents of total soluble solids (TSS), soluble sugar (SS), and vitamin C (Vc), and the ratios of TSS/TA and SS/TA, and the lowest value of the titratable acid (TA) content were detected in the fruit of 10~12 year old trees. However, the lowest values of the MPF, fruit firmness (FF), the contents of TSS, and the ratios of TSS/TA were detected in the fruit of 38~40 year old trees. Moreover, the soil of all the orchards detected was acidic or strongly acidic, and the soil pH values decreased with the increase of tree ages. Moreover, all the soil was short of organic matter and alkali-hydrolyzed nitrogen (N), while, the contents of available phosphorus (P), available iron (Fe), available manganese (Mn), and available zinc (Zn) were sufficient. Further, the contents of available potassium (K), available calcium (Ca), available magnesium (Mg), available copper (Cu), and available zinc (Zn) in the rhizosphere of 38~40 year old trees were lack. The results of correlation analysis between the contents of soil nutrients and the fruit quality indexes showed that the fruit quality was mainly affected by the contents of available Cu, available Ca, and available P, and pH value and organic matter content in the soil. In both rapid fruit growth stages, there were significant differences in nutrient contents in the leaves with different tree ages. The highest contents of most nutrients were detected in the leaves of 10~12 year old trees during the fruit ripening stage. The results of correlation analysis between the contents of leaves nutrients during two rapid fruit growth stages and the fruit quality indexes showed that, only the contents of total N, total K, total Ca total Fe, and total Zn in the leaves during the fruit expanding stage significantly affected fruit quality, while, during the fruit ripening stage, the contents of all the nutrients except total Mn all significantly affected the fruit quality. In conclusion, the fruit quality of Shengzhou nane decreased with the increase of tree ages. In order to keep high yield and fruit quality, more organic matter, fertilizers of Ca, Cu, and P should be applied in all the orchards. Further, sufficient fertilizers supply during the fruit ripening stage is very necessary because the nutrient contents in the leaves during this stage had a great influence on the fruit quality.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".