Processing tomato phosphorus utilization and post-harvest soil profile phosphorus as affected by phosphorus and potassium additions and drip irrigation
Bibliographic record
Abstract
Liu, K., Zhang, T. Q. and Tan, C. S. 2011. Processing tomato phosphorus utilization and post-harvest soil profile phosphorus as affected by phosphorus and potassium additions and drip irrigation. Can. J. Soil Sci. Sci. 91: 417–425. Phosphorus (P) applied in agricultural land not only affects crop P utilization, but can also cause environmental concerns when excessive P applied moves off-site to surrounding water systems. A 2-yr study, 2007–2008, was conducted to determine the effects of fertilizer P (four rates: 0, 30, 60, and 90 kg P ha−1) and potassium (K) (four rates: 0, 200, 400, and 600 kg K ha−1) additions and drip irrigation on crop P utilization and post-harvest agronomic (i.e., Olsen P) and environmental [i.e., water extractable P (WEP)] soil test P under processing tomato in loamy sand soils. Plant P uptake increased, but apparent P recovery decreased with increases in fertilizer P rate. Cumulative soil WEP in the 0- to 100-cm soil profile and Olsen P in the 0- to 20-cm depth increased linearly with increases in fertilizer P rate, regardless of water management. No effects of K were found on plant P utilization, soil WEP, or soil Olsen P. Drip irrigation increased plant P uptake by 35% and apparent P recovery by 44%, relative to non-irrigation. Drip irrigation consequently decreased the post-harvest soil profile WEP by 14% and Olsen P by 6.5% averaged across the 2 yr, compared with non-irrigation. Drip irrigation reduced the potential for post-harvest soil P losses by improving P utilization. the addition of fertilizer P needs to be optimized by considering crop P needs in association with actual yield production to ensure processing tomatoes are produced in an environmentally sustainable manner.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".