Analysis of the impact of nano-zinc, nano-iron, and nano-manganese fertilizers on chickpea under rain-fed conditions
Bibliographic record
Abstract
<p>Nanotechnology is an emerging field of science widely exploited in many scientific fields but its application in agriculture is rarely studied in the world. In the current study, application of nanotechnology in agricultural via the application of some micronutrient nano-fertilizers (nano-zinc, nano-iron, and nano-manganese) and different sulfur fertilizers have been investigated. Three levels of sulfur fertilizer (S1: no application, S2: 15 Kg ha-1, S3: 30 Kg ha-1) and three micronutrients nano-fertilizer (Nano1: nano-chelated zinc, Nano2: nano-chelated iron, and Nano3: nano-chelated manganese) were studied on some morphophysiological traits of chickpea. Results showed that the first two principal components of treatment × trait (TT) biplot accounted to 56% and 18% respectively of total variation. The vertex treatments in polygon biplot were S1-Nano2, S1-Nano3, S2-Nano1, S3-Nano1, and S3-Nano2 which S3-Nano1 treatment indicated high performance in day to maturity, plant height, first pod height, primary branch per plants, secondary branch per plant, number of pods per plant, number of seeds per plant and 1,000 seed weight. According to vector-view biplot, seed yield was positively associated with the number of pods per plant, harvest index and day to maturity. The ideal treatment identified the S3-Nano1 (30 kg ha-1 sulfur plus nano-chelated zinc) that might be used in selecting superior traits and it can be considered as the candidate treatment. The ideal trait of biplot showed that seed yield had the highest discriminating ability and they were the most representative and as the final target trait of producers, it has the ability of discrimination among different treatm ents. The best fertilizer treatment for obtaining of high seed yield was identified in the vector-view function of TT biplot as S3-Nano1 (30 kg ha-1 sulfur plus nano-chelated zinc).</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".