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Record W2005438146 · doi:10.5539/jas.v4n10p115

Effect of Salicylic Acid Pretreatment on Yield, Its Components and Remobilization of Stored Material of Wheat under Drought Stress

2012· article· en· W2005438146 on OpenAlexvenueno aff
Mehran Sharafizad, A Naderi, S. A. Siadat, Tayeb Sakinejad, Shahram Lak

Bibliographic record

VenueJournal of Agricultural Science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsSalicylic acidYield (engineering)AgronomyDrought stressGrain yieldCultivarHorticultureBiologyChemistryMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Due to higher needs of food in growing populations leads to accelerate the efforts of food production now days. Yield which is obtained from cereal farm is not at the amount of what we expected from their genetic potential. So it is possible to use different agro-techniques to increase total yield and help the crops to reach their genetic potential. In order to investigate the effect of salicylic acid on total yield and yield component of wheat under stress condition an experiment was conducted base on split factorial design with three replications. Treatments were drought stress at three levels (control, drought stress in mid florescence and drought stress in grain filling stage). Second treatment was application of salicylic acid as a priming agent, foliar application at beginning of tillering and foliar application of salicylic acid at beginning of flowering, and the third treatment was different dosage of salicylic acid (0, 0.7, 1.2 and 2.7 mmol). Results of experiment showed that drought stress significantly decreased grain yield, efficiency of material distribution while the highest grain yield was obtained at non-stressed condition with application of 0.7 mmol Salicylic acid. The highest redistribution of stored material, redistribution efficiency and partitioning was at time of salicylic application in vegetative stage, whereas the highest proportion of the metabolism in the grain yield observed in control condition (without stress). Grain yield exhibited high and positive correlation with number of spikes in m2, number of grain in spike, biological yield and harvest index.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.133

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.246
Teacher spread0.225 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

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