Impact of Zero Tillage and Tillage Practice in Chickpea Production
Bibliographic record
Abstract
The study was carried out at the field of Regional Pulses Research Station, Madaripur under Bangladesh Agricultural Research Institute (BARI) during Rabi season (winter) to evaluate the effect of different tillage practices on growth, yield attributes, nutrient uptake and yield of chickpea, and to compare between the zero tillage and tillage practice.The experiment was planned with five different tillage practices viz. Zero tillage (T1), Single tillage (T2), Two tillage (T3), Three tillage (T4) and Four tillage (T5), and it was laid out in a randomized complete block design (RCBD) with three replications. Blanket dose of fertilizers of N, P, K, S, Zn and B at 20, 21, 30, 10, 3 and 1.5 kg ha-1, respectively were used in all tillage treatments. The highest mean seed yield achieved (1395 kg ha-1) in zero tillage (T1) followed by four tillage (T5) practice. The maximum number of branches plant-1 and more number of pods plant-1 was recorded in zero tillage treatment. Nutrient content showed non-significant effect across most of the treatment. The highest nodulation and nutrient (N, P, K and S) uptake was also obtained from T1 treatment. Tillage practices exhibited positive effects on soil properties. The highest soil organic carbon, total N, available P, K, S, Ca, Mg, Zn and B were found in zero tillage (T1). Based on profitability, the zero tillage was economically viable with compared to tillage practice. Although this practice (T1) saves the money about 3.8% to 13.7% and time also saves minimum 8 days for succeeding crops. So, the present study suggests that zero tillage practice could be implemented in the high and medium high land for chickpea cultivation. Low income farmers may practice the zero tillage technology. Future research should be carried out to evaluate the suitable rate of nutrient in zero tillage (conservative tillage) practice for yield maximization of chickpea and sustaining soil fertility.
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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 | 0.001 | 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 | 0.002 |
| 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".