Strawberry Nursery Plant Propagation in Relation to Soil Phosphorus and Water Variation
Bibliographic record
Abstract
Strawberry nursery plants require chilling and also a high acquisition of phosphorus nutrition for helping capture and transfer energy for its rapid top growth. Soil phosphorus availability can be reduced in presence of high soil Fe, Ca and Mg concentrations in the adsorption process, which can vary with soil water content (SWC) and pH levels. A study was conducted in two commercial nursery plant production fields in the temperate Atlantic coastal areas in 2008. The objectives were to examine soil P availability, strawberry nursery plant propagation and P acquisition in relation to different soil pH, SWC and Ca, Mg and Fe concentrations. The cultivar ‘Strawberry Festival’ was used in the study and measurements were systematically taken along transects. The strawberry nursery plant propagation and productivity expressed using runners and daughter plants were significantly related to soil P availability, soil water and pH levels (0.50 < R2 < 0.61, P < 0.05). Soil soluble P concentrations were associated with soil water, pH and Ca, Mg and Fe concentrations. Strawberry produced 25 runners and 17 daughter plants in the soil with the optimal pH (6.5) and higher SWC and P availability, a 60% higher nursery productivity than that in the acidic soil (pH 5.7). Soil P concentrations were decreased while Ca, Mg and Fe cations were high in the acidic conditions. It was suggested that soil P could be fixed rapidly and thus result in low strawberry nursery productivity.
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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) | 0.001 | 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".