Investigating of N and K Fertilizers on Yield and Components of Soybean (Glycine max (L.) Merr.)
Bibliographic record
Abstract
Nitrogen and potassium fertilization have given variable results in increasing soybean yield. More information is needed about optimum potassium (K) and nitrogen (N) fertilizers placement for soybean. This study investigated the effect of different amounts of nitrogen and potassium on yield and its components on soybean cultivar DPX. Treatments include nitrogen (from urea) - Potash (potassium sulfate) in seven levels (N0-K0, N50-K0, N0-K20, N50-K20, N100-K50, N200-K100 and N250-K150 kg/ha) and factor inoculated and non-inoculated at two levels. Some growth parameters such as seed number, 100 seed weight, pod number, yield and harvest index were analyzed. There was significant difference between seed number and 100 seed weight. When the seeds were inoculated with bacteria, treatments N0-K0 and N250-K150 have a minimum and maximum number of seeds in these conditions, respectively. Also, the results showed that 100 seed weight in treatments inoculated with bacteria, N250-K150 most (24 g per plant) and N0-K0 minimal (14 g per plant), respectively. In the absence of inoculation with bacteria treated N0-K0 also had the lowest 100 seed weight. There was a positive correlation between number of pod per plant, yield and harvest index and N rate. Consequently the results demonstrated that increases in yields were necessarilyrelated to increase in plant N and K content and inoculated with bacteria had a marginal effect.
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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 | 0.000 | 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 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".