Influence of Crop Establishment Techniques, Fertilization and Microbial Consortia on Potassium Nutrition of Wheat
Bibliographic record
Abstract
A field experiment was conducted for 2 years (2013-14 and 2014-15) during winter (Rabi) season at Research Farm of ICAR-Indian Agricultural Research Institute, New Delhi, India with an objective to study the significance of two microbial consortia inoculations, zinc (Zn) fertilization (5 kg Zn ha-1 through ZnSO4∙7H2O soil application in each crop at sowing) and three rates of nitrogen (N) and phosphorus (P) fertilization on potassium (K) concentration, uptake and as well as ammonium acetate (NH4OAC)-extractable K content in soil at different growth stages in wheat. The microbial consortia used were Anabaena sp. (CR1) + Providencia sp. (PR3) and Anabaena-Pseudomonas biofilmed bio-fertilizer; while rate of fertilization were 0, 75% and 100% of recommended rate of nutrients (RDN) (120 kg N ha-1 and 25.8 kg P ha-1). The concentration and uptake of K was significantly higher in zero tillage wheat (ZTW) than conventional drill-sown wheat (CDW) and system of wheat intensification (SWI) at all observations except at 30 days after sowing. The application of 100% RDN and Zn fertilization have significant and positive effect on K uptake. The microbial consortia increase K concentration and uptake by 0.09-0.12 mg kg-1 and 9.9-12.7 kg ha-1 in straw and 0.08-0.11 mg kg-1 and 3.8-5.6 kg ha-1 in grain. The soil ammonium acetate (NH4OAC)-extractable K decreased by 87-108 kg ha-1 and 19-44 kg ha-1 in first and second year, respectively over initial soil K even after application of recommended rate of K (49.8 kg ha-1). Our study concludes the significant increase in K uptake due to ZTW and use of microbial consortia and there is the need for redeciding K fertilization in wheat for sustained productivity.
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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 | 0.001 |
| 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 | 0.001 | 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".