STATISTICAL ASSESSMENT OF THE IMPACT OF NANO-CHELATED ELEMENTS AND SULFUR ON CHICKPEA PRODUCTION UNDER SUPPLEMENTAL IRRIGATION
Bibliographic record
Abstract
Chickpea is an important source of plant protein source and has a major role at people nutrition in semi-arid regions.Soils of these regions have high pH and low organic matter, which reduce the availability of most micronutrients.In order to investigate the effects of application of sulfur (0, 15, 30 kg ha -1 ) and three nano-chelated micronutrients (nano-Zn, nano-Fe and nano-Mn) on yield and some morphological traits of chickpea, a field experiment was conducted.Day to maturity (DM), first pod height (FPH), primary branch per plants (PBP), secondary branch per plant (SBP), number of pods per plant (NPP), number of empty pod per plant (EPP), number of seeds per plant (NSP), seed yield (SY), straw yield (ST), biological yield (BY), harvest index (HI), and 1000 seed weight (TSW) were measured.Results showed that the first two principal components (PC1 and PC2) were used to create a two-dimensional treatment by trait (TT) biplot that accounted percentages of 53% and 26% respectively of total variation.The vertex treatments in polygon of TT biplot were S1-Nano1, S1-Nano2, S1-Nano3, S2-Nano1, and S3-Nano1 which S3-Nano1 treatment combination indicated high performance in DM, FPH, PBP, SBP, NPP, NSP, SY, ST, BY and TSW.According to ideal treatment biplot, the S3-Nano1 (30 kg ha -1 sulfur plus nano-chelated zinc) might be used in selecting superior traits and it can be considered as the candidate treatment for chickpea production.Treatment combinations which are suitable for obtaining of high seed yield performance were identified in the vector-view biplot and showed S3-Nano1 as the best treatment suitable for obtaining of high seed yield.In conclusion, application of nano-fertilizer could increase crop yield and improve the fertilizer efficiency.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".