Enhanced plant nutrient use efficiency with PGPR and AMF in an integrated nutrient management system
Bibliographic record
Abstract
A 3 year field study was conducted with field corn from 2005 to 2007 to test the hypothesis that microbial inoculants that increase plant growth and yield can enhance nutrient uptake, and thereby remove more nutrients, especially N, P, and K from the field as part of an integrated nutrient management system. The field trial evaluated microbial inoculants, which include a commercially available plant growth-promoting rhizobacteria (PGPR), arbuscular mycorrhiza fungi (AMF), and their combination across 2 tillage systems (no-till and conventional till) and 2 fertilization regimes (poultry litter and ammonium nitrate). Data were collected on plant height, yield (dry mass of ears and silage), and nutrient content of corn grain and silage. In addition, nutrient content of soil was determined, and bioavailability of soil nutrient was measured with plant root simulator probes. Results showed that inoculants promoted plant growth and yield. For example, grain yields (kg.ha(-1)) in 2007 for inoculants were 7717 for AMF, 7260 for PGPR+AMF, 7313 for PGPR, 5725 for the control group, and for fertilizer were 7470 for poultry litter and 6537 for NH4NO3. Nitrogen content per gram of grain tissues was significantly enhanced in 2006 by inoculant, fertilizer, and their interactions. Significantly higher amounts of N, P, and K were removed from the plots with inoculants, based on total nutrient content of grain per plot. These results supported the overall hypothesis and indicate that application of inoculants can lead to reduction in the build up of N, P, and K in agricultural soils. Further studies should be conducted to combine microbial inoculants with reduced rates of fertilizer.
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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 | 0.001 | 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 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".