Effect of synthetic and natural water‐absorbing soil amendments on photosynthesis characteristics and tuber nutritional quality of potato in a semi‐arid region
Bibliographic record
Abstract
BACKGROUND: The effect of water-absorbing soil amendments on photosynthesis characteristics and tuber nutritional quality was investigated in a field experiment in a semi-arid region in northern China in 2010-2012. Treatments included two synthetic water-absorbing amendments, potassium polyacrylate (PAA) and polyacrylamide (PAM), and one natural amendment, humic acid (HA), both as single amendments and compound amendments (HA combined with PAA or PAM), and a no amendment control. RESULTS: Soil amendments had a highly significant effect (P ≤ 0.01) on photosynthesis characteristics, dry biomass, crop root/shoot (R/S) ratio and tuber nutritional quality. They improved both dry biomass above ground and dry biomass underground in the whole growing season by 4.6-31.2 and 1.1-83.1% respectively in all three years. Crop R/S ratio was reduced in the early growing season by 2.0-29.4% and increased in the later growing season by 2.3-32.6%. Soil amendments improved leaf soil plant analysis development value, net photosynthesis rate, stomatal conductance and transpiration rate by 1.4-17.0, 5.1-45.9, 2.4-90.6 and 2.0-22.6% respectively and reduced intercellular CO2 concentration by 2.1-19.5% in all three years. CONCLUSION: Amendment treatment with PAM + HA always had the greatest effect on photosynthesis characteristics and tuber nutritional quality among all amendment treatments and thus merits further research.
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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 | 0.000 | 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 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".