Effects of Starter-N plus Topdressing N on K Accumulation and Distribution in Soybean Plants
Bibliographic record
Abstract
A pot experiment was conducted to assess the effects of starter-N plus topdressing N on K accumulation and distribution in soybean plants.Five treatments under the same fertilizer application rates of N 50 kg·ha-1,P2O5 40 kg·ha-1,K2O50 kg·ha-1 and different N application time were set:all N as basal fertilizer(N50),all N as topdressing at R3/R4 stage(N0+50R3/R4),N 15 kg·ha-1 as basal fertilizer and 35 kg·ha-1 as topdressing at stage R3/R4(N15+35R3/R4).At R6stage,compared to N50,K content and K accumulation in leaves,petioles,stems,pods increased by 21.3% and 36.0%(P0.01),18.0% and 16.2%(P0.01),8.24% and 10.57%(P0.05),4.60% and 14.1%(P0.05) for N15+35R3,respectively,and increased by 33.6% and 42.3%(P0.01),24.0% and 17.9%(P0.01),17.5% and 25.6%(P0.05),6.30% and 23.6%(P0.05) for N15+35R4,respectively.N15+35R4 achieved the highest yield and increased yield by 30.5%(P0.01) compared to N50,however,there was no significant difference between N15+35R4 and N15+35R3.There was a significant positive correlation between K accumulation during R4-R6 stages and yield(P0.01).Results indicated that using starter-N plus topdressing N could lay a foundation for higher yield of soybean by significantly increasing K content and K accumulation in soybean organs after R5 stage.
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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) | 0.001 | 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 | 0.001 |
| 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".