بررسی سازگاری و پایداری عملکرد دانه لاینهای گندم دوروم تحت شرایط دیم با استفاده از GGE بایپلات و AMMI
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,062 | 0,041 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».