Impact of rhizobial inoculum and inorganic fertilizers on nutrients (NPK) availability and uptake in wheat crop
Bibliographic record
Abstract
Experiments were conducted to evaluate the effect of rhizobial inoculums and inorganic fertilizers on NP availability, soil microbial activity, wheat NPK concentration and uptake. These experiments were consisted of two factors, four inoculums (no, lentil, peas, and chickpeas) and two NPK doses (120:90:60 and 96:72:48 kg ha −1 ). Inoculums significantly increased plant total NPK concentration by 39, 57, and 37%, and their uptake by 66, 86, and 56%, respectively. Peas inoculum was most efficient in wheat NPK concentration and uptake. The interactive effect of inoculums and NPK demonstrated that peas and lentil inoculums with 20% less NPK had statistically better role than full NPK without inoculation. AB-DTPA extractible P and mineral N were progressively increased with incubation periods and exhibited significant differences between inoculated and uninoculated treatments during all incubation intervals for NP except at day 7 for N. Peas inoculum showed maximum mean net NP availability of 131.5 and 3.48 mg kg −1 over 56 d of incubation, respectively. Significantly higher cumulative CO 2 of 1429 mg kg −1 with a net increase of 866 mg kg −1 was recorded for pea’s inoculums during 12 d of incubation interval. It is concluded that peas rhizobium could be used as a plant-growth-promoting rhizobacteria for wheat and other cereal crops.
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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) | 0.001 | 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) | 0.001 | 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".