Investigation of foliar application of nano-micronutrientfertilizers and nano-titanium dioxide on some traits of barley
Bibliographic record
Abstract
Nanofertilizers are new products which contain readily available nutrients in the nanoscale range and are preferred largely due to their efficiency. To investigate the effects of micronutrient nanofertilizers (iron and zinc) as F factor and nano-titanium dioxide (nano-TiO2) solution as n factor on yield performance and some morphologic traits of barley, a field experiment was carried out with treating with chelated nanoscale zinc oxide (nano-ZnO) and ferric oxide (nano-Fe2O3) suspensions during three phonological stages. The treatment combination by trait (TT) biplot was used for two-way dataset as various treatment combinations (Fn) with multiple traits. The GT biplot explained 83% of the total variation of the standardized data. The polygon view of TT showed five vertex treatment combinations as F1n1, F1n2, F2n2, F3n1, and F3n2. Therefore, it seems that F3n1 treatment combination had the highest values for all of the days to maturity, grain yield, straw yield, harvest index, spike length, number of fertile tillers, and grain weight per plant traits. The most prominent relations of tester-view biplot were: a strong positive association between days to maturity and straw yield and among grain yield, harvest index and spike length, while there was a near zero correlation of the number of tillers per plants with days to maturity and straw yield. It can be concluded that TiO2 nanoparticles and nano-micronutrient fertilizers (iron nano-chelate and zinc nano-chelate) could increase yield and yield components of barley. Our findings suggest that the foliar application of nanoparticles resulted in sustainable and high crop production, and among six treatment combinations, F3n2 (zinc nano-chelate with 2000 ppm nano-TiO2) indicated the best performance for most of the measured traits of barley.
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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 | 0.000 |
| 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 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".