Application of rock phosphate enriched composts increases nodulation, growth and yield of chickpea
Bibliographic record
Abstract
Phosphorus (P) deficiency in agricultural land is one of the main factors which reduce the crop production and yield. To increase the crop growth, the availability of P using the rock phosphate (RP) can be enhanced using organic waste with the addition of phosphate solubilizing microorganisms (PSMs). Moreover, the combination along with time and dose application of RP and compost can also affect the P-availability through mineralization and/immobilization. To investigate the effect of different combinations of RP and compost (0:100, 25:75, 50:50, 75:25 and 100:0) and time of application (30, 15, 7, 3 and 0 days before sowing), each was conducted in two consecutive pot experiments while, the rate of application (100–1000 kg ha −1 ) was investigated under field conditions, in comparison to control (recommended P fertilizer). Rock phosphate enriched compost (RP-EC) with a combination ratio of 50:50; RP and compost applied before 7 days of sowing in pot experiments resulted in the maximum nodulation, growth and productivity of chickpea. Under field conditions, the maximum increase of 35.3% in no. nodules plant −1 , 26.7% in dry wt. of nodules plant −1 and 20.8% in grain yield (t ha −1 ) compared to control was obtained by RP-EC @ 1000 kg ha −1 . The same treatment indicated an increase of 12.9 and 4.3% in P contents in straw and grains, respectively, compared to control. However, most results were non-significant when RP-EC applied at the rate of 1000 kg ha −1 . Application of RP-EC, with the ratio of 50:50 (RP:Compost) and application rate of 800 kg ha −1 before 7 days of sowing, exhibited maximum growth and development, and can be highly recommended for optimum production of chickpea.
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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 | 0.001 |
| 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 | 0.001 | 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".