The effects of bulk, biologic and nano-form fertilizers on Zea mays growth under irrigated circumstances
Bibliographic record
Abstract
In practical plant biology, nanotechnology has involvements on every step of cropping, such as early growing, maintenance, harvesting and post harvesting and it has caused remarkable changes in findings solutions for facing problems. A trial was done to study the effects of different fertilizers on maize performance. The trial compared NPK bulk fertilizer, synthetic nano-sized fertilizers (boron, zinc, and complete), and biological fertilizers. Analyzing the data through principal component (PC) analysis indicated that the PC1 and PC2 explained for 56 and 27% of the variability in the dataset. Synthetic nano-zinc and nano-boron emerged as the most promising fertilizers, showcasing superior performance in terms of yield performance and yield components. A vector-tool biplot highlighted a robust positively correlation between chlorophyll content and straw yield, along with similar trends in grain yield and number of kernels per ear. Conventional bulk fertilizer (NPK) showed relatively lower efficiency across most evaluated traits. Based on ideal trait biplot, biological yield and stem diameter exhibited similar properties like to ideal trait, while oil percentage and hundred grain weight demonstrated unfavorable performance across treatments. This analysis underscores the efficacy of the treatment × trait biplot in elucidating relationships among traits and facilitating visual comparisons between different fertilizers. Overall, the findings underscore the significant enhancement of various maize cultivation traits through the application of synthetic nano-zinc and boron fertilizers, particularly in full irrigation condition. Article history: Received 15 March 2024; Revised 16 December 2024; Accepted 5 May 2025; Available online 25 June 2025
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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) | 0.001 | 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".