Frequency, depth and rate of phosphorus fertilizer application effects on alfalfa seed yields
Bibliographic record
Abstract
Zhang, T., Kang, J., Zhao, Z., Guo, W. and Yang, Q. 2014. Frequency, depth and rate of phosphorus fertilizer application effects on alfalfa seed yields. Can. J. Plant Sci. 94: 1149–1156. Efficient phosphorus (P) management is important for alfalfa (Medicago sativa L.) seed production and is necessary in order to alleviate the negative environmental effects of excess P application. The study was conducted on both very low-P and optimum-P soils to evaluate the effects of two frequencies (annual and triennial applications), two depths (7–8 and 15–16 cm), and three rates (15, 30, and 45 kg P ha−1) of P fertilization on seed yield, total P uptake (TPU), P-use efficiency of applied P (PUE), and recovery of P fertilizer (PR). There was a zero-P control. Under the model of annual application, the highest seed yields were obtained with the rates of 30 or 45 kg P ha−1. Under the model of triennial application, however, the seed yield decreased linearly with increasing rates in the first year. Averaged across frequency and depth, PUE and PR decreased linearly but TPU increased linearly with increasing P application rate. Triennial application of 45 kg of P led to higher mean seed yield than annual application of 15 kg of P, and its PUE and PR values were higher than triennial application of 90 and 135 kg of P. However, on low-P or optimum-P soil, annual application of 30 or 45 kg of P resulted in the highest mean seed yields compared with low PUE and PR. Thus, triennial application of a low P rate leads to a high alfalfa seed yield and has potential economic and environmental benefits.
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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".