Comparative Impact of Bio-Organic and Inorganic Fertilizer Application on Soil Health, Grain Quality and Yield Stability in Nutrient Deficient Regions
Bibliographic record
Abstract
Abstract. Soil fertility limitations in arid regions restrict wheat productivity and grain nutritional quality, with zinc (Zn) deficiency being a major concern. Sustainable soil amendments combining organic and microbial inputs offer potential to address these constraints. This study aimed to evaluate the effectiveness of bio-organic fertilization in enhancing wheat growth, yield, grain Zn biofortification, and soil fertility under deficient arid field conditions. Two field trials were conducted in Bahawalpur and Bahawalnagar, Pakistan, using a randomized complete block design. Treatments included compost, ZnO (2 %), ZnSO4, zinc-solubilizing bacteria (ZSB), and their combinations. Wheat growth, yield, grain nutrient concentrations, and soil fertility indicators (organic matter, microbial biomass nitrogen (MBN), microbial biomass carbon (MBC), and nutrient availability) were measured. Microbial populations were determined through colony-forming units. Correlation and principal component analysis (PCA) were applied to explore associations among variables. The integrated application of compost + ZnO + ZSB significantly improved wheat height (19 %), biomass (20 %), yield attributes (10 %), and grain Zn concentration (39 %) compared with the control. Soil fertility parameters also increased (organic matter, 39 %; MBN, 32 %; MBC, 27 %). Correlation and PCA highlighted strong positive relationships among microbial populations, soil fertility, and crop performance. Bio-organic fertilization provides an eco-friendly and effective strategy to improve wheat yield, Zn biofortification, and soil fertility in arid agroecosystems.
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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".