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Record W4214922364 · doi:10.5267/j.ccl.2021.12.002

Effect of irrigation intervals and foliar spray of zinc and silicon treatments on maize growth and yield components of maize

2022· article· en· W4214922364 on OpenAlexvenueno aff
A. Abdelgalil, Akhmad Mustafa, S. A. M. Ali, Omar M. Yassin

Bibliographic record

VenueCurrent Chemistry Letters · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsZincIrrigationSowingField experimentYield (engineering)AgronomySiliconGrain yieldCropIrrigation schedulingHorticultureMathematicsChemistryBiologyMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Field experiments were carried out for two consecutive seasons at the Experimental Shandaweel Agricultural Research Station, Sohag Governorate, Upper Egypt, during the growing seasons of 2013 and 2014, to study the Effect of Irrigation intervals and foliar spray of zinc and silicon treatments on Maize growth and yield components of maize. Results indicated that, scheduling at every 10 days produced the highest plant height, Flag Leaf area (cm²), Cob length (cm), Weight of 100-grains (g), Biological yield (T/fad.) and Grain yield (ard./fed.) followed by irrigation at 15 and 20 days interval, in contrast irrigation at 25 days interval produced the lowest values and foliar spray of zinc and silicon treatments produced the highest plant height-improved yield and yield components of maize crop. The best yield was obtained from zinc + silicon treatments followed by zinc, silicon treatments. In contrast, untreated treatments produced the lowest values. It can be concluded that the scheduling at every 10 days and application of foliar spray of zinc + silicon treatments as the effective one could be recommended for scheduling irrigation at every 10 days with application of foliar spray of zinc + silicon treatments of maize crop at Shandaweel Agricultural Research Station, Sohag Governorate, Upper Egypt to obtained the best results from maize grain yield.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.229
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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