Role of soil nutrient elements transport on Camellia oleifera yield under different soil types
Bibliographic record
Abstract
BACKGROUND: Most of Camellia oleifera forests have low fruit yield and poor oil quality that are largely associated with soil fertility. Soil physical and chemical properties interact with each other affecting soil fertility and C. oleifera growing under different soil conditions produced different yield and oil composition. Three main soil types were studied, and redundancy, correlation, and double-screening stepwise regression analysis were used for exploring the relationships between C. oleifera nutrients uptake and soil physical and chemical properties, shedding light on the transport law of nutrient elements from root, leaves, and kernel, and affecting the regulation of fruit yield and oil composition. RESULTS: In the present study, available soil elements content of C. oleifera forest were mainly regulated by water content, pH value, and total N, P and Fe contents. Seven elements (N, P, K, Mg, Cu, Mn and C) were key for kernel's growth and development, with N, P, K, Cu and Mn contents determining 74.0% the yield traits. The transport characteristics of these nutrients from root, leaves to the kernel had synergistic and antagonistic effects. Increasing oil production and unsaturated fatty acid content can be accomplished in two ways: one through increasing N, P, Mg, and Zn contents of leaves by applying corresponding N, P, Mg, Zn foliar fertilizers, while the other through maintaining proper soil moisture content by applying Zn fertilizer in the surface layer and Mg and Ca fertilizer in deep gully. CONCLUSION: Soil type controlled nutrient absorption by soil pH, water content and total N, P and Fe content. There were synergistic and antagonistic effects on the inter-organ transport of nutrient elements, ultimately affecting N, P, K, Cu and Mn contents in kernel, which determined the yield and oil composition of C. oleifera.
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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".