How strawberry plants cope with limited phosphorus supply: Nursery‐crop formation and phosphorus and nitrogen uptake dynamics
Bibliographic record
Abstract
Abstract Healthy, well‐rooted planting stocks are important for profitable fruit production of strawberry ( Fragaria × ananassa Duch.). Adequate nutrient inputs and crop‐rotation practices are among the most important measures to insure a successful, rapid development of strawberry nursery plants. However, relationships between macronutrient use and strawberry‐nursery‐plant formation in different rotation environments are not well understood. Our objectives were to assess strawberry plant P : N nutrition and nursery development under various limited and unlimited P inputs applied at different growth stages and to examine how nursery plants cope with limited P inputs. The field studies were conducted in a wheat–ryegrass–ryegrass–strawberry (WRRS) system in 2008 and in another corn–ryegrass–ryegrass–strawberry (CRRS) system in 2009 in Nova Scotia, Canada. The nursery crop was cv. Strawberry Festival and the experimental treatments consisted of three mother‐stock P (MSP) rates (0, 6.6, and13.2 P kg ha –1 ) and five daughter‐plant P (DPP) rates (0, 13.2, 26.4, 39.6, and 52.8 kg P ha –1 ), representing 0%–125% of the regional recommendations for strawberry nursery. The P treatments were arranged with three blocks in a split‐plot design in each field. The results showed that the effects and interactions of the MSP and DPP treatments were significant ( P < 5%) on whole‐plant P and N acquisition and nursery productivity. Two‐year whole‐plant total P and N acquisition varied between (13.2 ± 2.0) kg P ha –1 and (46 ± 7.3) kg N ha –1 (n = 270) in both nursery systems. There was a quadratic regression relationship between nursery runners and daughter plants with plant P and N acquisition (0.33 < R <?h.3> 2 < 0.42, P < 5%). Soil pH levels declined with time and were positively correlated with nursery‐plant formation. Too many runners (18–22 per mother stock) might reduce nursery‐plant formation. Limited P inputs (37.5%–62.5% of regular recommendation) might result in a suitable plant P : N ratio (0.12–0.13) and adequate daughter plant‐to‐runner ratios (1.4–1.7) for optimum nursery formation (14–16 daughter plants per mother stock). Nutrient P was the single controlled factor influencing the N acquisition of nursery plants. High P inputs (> 39.6 P ha –1 or > 75% of regular recommendation) may harm the crops. Therefore, this study implies that low‐input horticulture can improve the nutrition management of strawberry nursery.
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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 | 0.001 | 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 | 0.001 |
| 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".