Improvement in Soil Characteristics of Sandy Loam Soil and Grain Quality of Spring Maize by Using Phosphorus Solublizing Bacteria
Bibliographic record
Abstract
Unavailability of balanced nutrients in nutrient-deficient soils is the key reason in reduced yields of spring maize. After application to soil, most of the phosphorus (80–90%) is lost in the environment because of runoff losses and chemically bonding. So, this makes the phosphorus unavailable for plant use. However, soil microorganisms may provide a biological rescue system which is able to solubilize the soil-bound phosphorus (p). Keeping this in view, the present study is designed to meet the following objectives; (1) to improve physico-chemical properties of soil (e.g., soil water retention, soil enzyme activities), and (2) to improve growth and yield of spring maize (cv. Hybrid YSM-112) through the inoculation of phosphorus solubilization bacteria (PSB). A pot experiment was carried out with the following treatments; T1: control (uninoculated control, CT), T2: inoculation with PSB (Enterobacter sakazakii J129), T3: recommend level of NPK fertilizers (RNPK), T4: PSB + RNPK fertilizers, T5: rock phosphate (RP), T6: PSB + RP. Results showed that the addition of PSB together with RNPK improved the yield and yield-related characteristics of spring maize grown in sandy soil. Moreover, it also enhanced dry mater characteristics and maize grain quality. Soil fertility in the context of P-solubilization, soil organic acids, soil organic matter, enzyme activities, PSB colony, and rhizosphere moisture contents were significantly improved with PSB inoculation together with recommended dose of NPK fertilizers (RNPK) compared to PSB alone, rock phosphate (RP) alone, or PSB together with rock phosphate and control treatment. Maize digestibility attributes such as DM, CP, CF, EE (by 35%, 20%, 33%, and 28% respectively) and grain quality such as NPK, Mg, Ca, Fe, Mn, Cu, and Zn (by 88%, 92%, 71%, 68%, 78%, 90%, 83, 69%, 92%, 48%, and 90% respectively) were improved compared to control. In conclusion, improvement in maize crop yield and soil characteristics are more prominent and significant when RNPK is supplemented and inoculated. The present study suggests that PSB, together with RNPK, would improve the maize plant growth and soil fertility in sandy soil.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".