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Record W2124517507 · doi:10.5539/jas.v7n3p49

Assessment of Multiple Tolerance Indices for Salinity Stress in Bread Wheat (Triticum aestivum L.)

2015· article· en· W2124517507 on OpenAlexvenueno aff
Sanjay Singh, RS Sengar, Neeraj Kulshreshtha, D. Datta, R. S. Tomar, Vivekanand P. Rao, Deepa Garg, Ashish Ojha

Bibliographic record

VenueJournal of Agricultural Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetics and Plant Breeding
Canadian institutionsnot available
Fundersnot available
KeywordsSalinityHectarePrincipal component analysisMathematicsCropProductivityYield (engineering)Index (typography)Crop yieldAgronomyStatisticsBiologyEcology

Abstract

fetched live from OpenAlex

Salinity is one of the major factors reducing plant growth and productivity worldwide and affects about 7% of world’s total land area. In India about 6.73 million hectare of land area is salt affected. Wheat is the second most important crop after rice in India and occupies approximately 28.5 million hectare area. Several tolerance indices comprising of mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI), stress stability index (SSI), tolerance index (TOL), yield index (YI) and yield stability index (YSI) were calculated in this investigation for salinity and its ability to understand which one or more predictor among studied indices based on correlation, principal component analysis and cluster analysis. Ten wheat genotypes were evaluated in two successive growing seasons (2012-2014), with complete randomized design with three replications under both salinity stress and non-salinity to identify salt tolerant genotypes to the target environment. Multiple indices for salt tolerance were calculated based on the potential yield (Yp) under non-stress and yield (Ys) under stress conditions. The Ys and Yp showed highest significant and positive correlations with GMP, MP and STI among indices studied. Therefore, these indices were considered as a better predictor of Ys and Yp than TOL, SSI and YSI. Principal component analysis classified the genotypes into two groups. The first two PCs with eigen values >1 contributed 99.74% of the variability amongst genotypes. PC1 accounted for about 5.24% of the variation in salt tolerance indices and PC2 for 3.74%. The first PC was related to Ys, Yp, MP, GMP, STI and YI whereas the second PC related to Yp, TOL and SSI. The cluster analysis sequestrated ten genotypes into two clusters based on Ward’s method. According to results, salinity significantly reduced the yield of some genotypes while some were found tolerant to stress indicating sufficient genetic variability for salinity tolerance among the studied genotypes. It could be implicated in selection of salinity tolerant wheat genotypes for the development of bread wheat varieties.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.053
GPT teacher head0.279
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations69
Published2015
Admission routes1
Has abstractyes

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