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Record W2887745930 · doi:10.22077/escs.2018.381.1075

بررسی سازگاری و پایداری عملکرد دانه لاینهای گندم دوروم تحت شرایط دیم با استفاده از GGE بایپلات و AMMI

2018· article· fa· W2887745930 on OpenAlexaboutno aff
بهزاد صادق زاده, رضا محمدی, حسن احمدی, غلامرضا عابدیاصل, ملکمسعود احمدی, مهناز محمدفام, نوذر بهرامی, محمدشریف خالدیان, علیاکبر ناصریان

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languagefa
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsAmmiMathematicsBiologyGene–environment interactionGenetics

Abstract

fetched live from OpenAlex

Introduction Durum wheat (Triticum turgidum var. durum) is grown for human consumption, mainly as pasta products, e.g., spaghetti and macaroni, couscous, bulgur, frike, flat breads, etc. Worldwide, the area annually planted to durum wheat is estimated to be around 17-18 million hectares, i.e., 8 percent of total wheat area, with a production averaging about 30 million tons annually, which is 5.5 percent of total wheat production. Although durum is grown in various regions of the world, the great bulk of durum area and production is concentrated in the Mediterranean basin and North America. Eight countries (Algeria, Canada, Italy, Morocco, Syria, Tunisia, Turkey, and USA) account for nearly two thirds (2/3) of world durum area and production. In Iran, the area under durum cultivation is about 400-500 thousands hectares with an annual production of 400-500 thousand tons, which covers about 60% of country demands. In spite of the importance of durum for Iranian rural economies, the country has not all succeeded in its research and development efforts to substantially improve durum productivity. The combinations of increasing demand for durum and durum products, as a result of demographic pressure, and relatively low durum productivity partly due to abiotic stresses (i.e. cold, terminal heat, moisture and nutrient deficiency stresses) made the country to an importer of durum. These are frequently exacerbated by biotic stresses, e.g., diseases and insects that may severely inhibit crop growth. Materials and methods The main purpose of this study was to achieve high yielding durum wheat genotypes with higher yield stability in different environmental condition, tolerance to environmental stresses such as cold damage, drought and end of season heat stress. Hence, 17 durum wheat lines were evaluated for grain yeild and morphlogical traits in Maragheh, Sararood, Qamloo, Ardabil and Shirvan agricultural research stations in 2011-14. In each location, the experiments were conducted in a randomized complete block design with three replications. Results and discussion Based on combined ANOVA, there was significant difference among the environments, genotypes and G×E. GGE-biplot models showed that the 5 environments were belonged to 3 mega-environments, and different genetopes had higher yield in each mega-environments. Sumplimentary irrigation, at sowing time and flowering growth stages, could increase grain yield of lines 30 and 70 percent in Maragheh and Qamloo locations, respectively. The increase of grain yield was 42 percent for line Rascon under suplimentary irrigation. The AMMI and GGE results also confirmed genotype 5 was the most high-yielding durum line with reasonable yield stability in cold areas (Maragheh, Qamloo and Ardabil). Also, genotype 13 was the most high yielding and stable line in Sararood. Hence, these line can be candiatted to release new durum varieties for cold and moderat rainfed areas. Complementary irrigation could increase grain yield up to 30 and 70 percent in Maragheh and Sararood, respectively. Conclusions It can be concluded that finding of new stable high-yielding durum lines, with better performances than that the existed varieies, is a great progress in durum breeding programs in cold rainfed areas. Both GGE biplot and AMMI analyses could be used in grain yield stability and adaptability under rainfed conditions and sumplementary irrigations, however, the results of GGE biploet were more applicable and can be use extensively in the study of grain yield adaptability and stability under rainfed and sumplementary irrigations conditions in durum wheat breeding programs.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0620.041

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 head0.289
GPT teacher head0.527
Teacher spread0.238 · 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 designObservational
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".

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Citations3
Published2018
Admission routes1
Has abstractyes

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Same venueDOAJ (DOAJ: Directory of Open Access Journals)→Same topicWheat and Barley Genetics and Pathology→French-language works237,207→