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Record W2512357456 · doi:10.5539/jas.v8n10p131

Effects of Nitrogen on Seedling Growth of Wheat Varieties under Salt Stress

2016· article· en· W2512357456 on OpenAlexvenueno aff
Muhi Eldeen Hussien Ibrahim, Xinkai Zhu, Guisheng Zhou, Eltayib H. M. A. Abidallhaa

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSeedlingSalinityShootDry weightAgronomyNitrogenSoil salinityBiologyHorticultureFertilizerChemistry

Abstract

fetched live from OpenAlex

<p>Wheat growth is hampered by various environmental stresses including salinity. The aim of the present study was to evaluate the nitrogen effect on seedling emergence and growth under salinity conditions. For this reason the seeds of wheat (<em>Triticum aestivum </em>L.) varieties Argine, and Elnilein from Sudan and Xumai 30, and Yang 10-13 from China were cultured under four NaCl solutions containing (0, 100, 150, and 200 mM NaCl) and three nitrogen (N) fertilizer levels (N0 = 0, N1 = 105, and N2 12 = 210 kg N/h). Emergence percentage and early growth were determined. There were significant differences among salinity and N levels for emergence percentage shoot and root length, dry weight, salt tolerance index, and seedling vigor index. At all salinity levels, the varieties showed similar salt resistance, but each responded differently. Nitrogen affected positively on the seedling characteristics under saline soil. Elnilein had a better emergence percentage, shoot and root length, dry weight, salt tolerance index, and seedling vigor index than the other varieties. Elnilein is recommended for saline soils. We suggest that a simple seedling test would be a useful selection tool in order to develop productive new wheat lines on saline soils.</p>

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.119

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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