Identifying major yield limiting nutrients on sorghum (Girana one) for developing site-specific nutrient management practices in low lands of Eastern Amhara
Bibliographic record
Abstract
Abstract Background: Omission trial is a passionate way of identifying yield limiting nutrients. A field experiment was conducted for identification of yield limiting nutrients through crop response in Kobo with test crop sorghum (Girana one variety) for two years in three kebeles. The design for experiment was a randomized completely block (RCBD) using farmer field as replication. Biological yield data were collected and subjected to analysis of variance (ANOVA). Whenever there was significant difference between treatments, the means were separated using LSD test at P≤0.05. Results: The results showed that the omission of nutrients from inorganic fertilizer alone or in combination resulted in significance grain yield reduction. The highest grain yield was observed with the treatment receiving NPS+FYM whereas the lowest was omission of all nutrients (control). There was no significant difference in biomass yield between treatments for both years. Omission of all nutrients from inorganic fertilizer alone or in combination significantly reduced grain yield of sorghum. Application of FYM in combination with inorganic fertilizer (NPS) and NP contained treatments (NPS, NPSK and NPSKZN) can meet nutrient requirement for sorghum. Conclusions: This experiment shows a significant yield advantage of all applied nutrients compared to omitting nutrients. Application of farm yard manure was also significantly affected grain yield compared to the control. Based on this result, nitrogen and phosphorous fertilizers with farm yard manure rather than secondary and micro nutrients can be recommended in the study area for increasing production and productivity of the sorghum.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".