Maximizing Yields, Nutrient Uptake and Balance for Mustard-Mungbean-T. Aman Rice Cropping Systems through Nutrient Management Practices in Calcareous Soils
Bibliographic record
Abstract
The experiment was conducted to measure crop yields, nutrient concentration, nutrient uptake and balance by using different nutrient management practices for mustard-mungbean-T. aman rice cropping system in calcareous soil of Madaripur, Bangladesh. Different nutrient management practices were absolute nutrient control (T1); farmer’s practice (T2); AEZ based nutrient application (T3) and soil test based nutrient application (T4). The practices were compared in a randomized completely block design with three replications over two consecutive years. The average yield through application of soil test based nutrient (T4) was showed effective to get highest yields of mustard (1530 kg ha-1), mungbean (1632 kg ha-1) and T. aman rice (4729 kg ha-1). The same practices (T4) exhibited the greatest nutrients uptake by the test crops. The apparent balance of N and K was negative; however it was less negative and less deficiency detect in T4 treatment. Positive balance of P observed in all practices except in T1. There was a positive S balance (7.60 kg ha-1) in T4 but negative in T1, T2 and T3. Zinc balance was found positive in T3 and T4 and negative in T1 and T2. Boron balance in the system was neutral or slightly positive in T1 and negative in T2 but positive in T3 and T4. Organic matter, N, P, S, Zn and B status in soil was improved by T4 treatment. The results suggested that the soil test based nutrient application is viable and sustainable for mustard-mungbean-T. aman rice cropping system in calcareous soils of Bangladesh.
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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".