Impact of Zinc, Boron and Molybdenum Addition in Soil on Mungbean Productivity, Nutrient Uptake and Economics
Bibliographic record
Abstract
Zinc (Zn), boron (B) and molybdenum (Mo) are essential to increase the productivity of mungbean (Vigna radiata L.) and help to maintain the soil fertility but mostly ignored. Hence, an experiment was conducted during the years of 2016 and 2017 to know the impact of Zn, B and Mo on mungbean yield, nutrient uptake, economics and soil fertility improvement. The experiments were planned in randomized complete block design including of eight treatments with three replications. The treatments were T1 = Control, T2 = Zn 2 kg ha-1, T3 = B 1.5 kg ha-1, T4 = Mo 1 kg ha-1, T5 = Zn2B1.5, T6 = Zn2Mo1, T7 = B1.5Mo1 and T8 = Zn2B1.5Mo1. The other fertilizers, N, P, K and S at 20, 20, 30 and 10 kg ha-1, respectively were used in all treatments. The results indicate that the highest seed yield (1522 kg ha-1) was obtained from T8 treatment followed by T7. The highest percent seed yield increment (51.6%) over control was achieved in T8 treatment. Most of the growth and yield contributing characters of mungbean were recorded highest in T8 treatment. The maximum nodulation (37.6) and highest amount of protein (24.3%) was also obtained from T8 treatment. The T8 treatment contributed positively to attain higher total uptake of N, P, K, S, Zn and B by mungbean. The combination of Zn, B and Mo is showed more productive compare to sole or couple use of these micronutrients. The T8 (Zn2B1.5Mo1 kg ha-1) treatment exhibited helpful effects on soil organic matter, total N, available P, Zn and B. This treatment also showed economically better on the basis of net return. Results of the present study suggest that the combination of Zn, B and Mo applied at 2, 1.5 and 1 kg ha-1, respectively could be recommended for mungbean cultivation.
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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 | 0.001 |
| 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".