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Enregistrement W6888791190 · doi:10.22067/ijpr.v13i2.2106-1009

Identification and selection of superior dryland Lentil (Lens culinaris Medik) genotypes using boundary lines in Iran cold areas

2022· article· en· W6888791190 sur OpenAlexaboutno aff

Notice bibliographique

RevueDOAJ (DOAJ: Directory of Open Access Journals) · 2022
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueSports Analytics and Performance
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSelection (genetic algorithm)CropIdentification (biology)AgricultureDomesticationYield (engineering)Crop yieldLegume

Résumé

récupéré en direct d'OpenAlex

IntroductionPulse crops play important role in supplying human food needs. Lentil (Lens culinaris Medik.) is probably the oldest grain legume to be domesticated and one of the most important pulse crops worldwide due its nutritional characteristics. Lentil is an integral part of dryland agriculture, mainly because of its ability to thrive comparatively well under water-limiting environments. As a result, the crop which provides protein-rich food and animal feed is largely grown under rainfed conditions. The major lentil producing countries are India, Canada, Turkey, USA, Nepal, Australia, Syria, China, Bangladesh, Iran, etc. Iran ranked fourth in area after India, Turkey and Canada and ninth in the production. The aim of any breeding program working for unpredictable and rainfed environments is to develop varieties with high and stable yields. Breeders take advantage of the selection for several traits to achieve maximum economic yield. The selection of genotypes based on indices using yield components was used by breeders for a long time. Breeders believe that obtaining a linear function for measurable traits could lead the selection of genotypes with better genetic values, but a need to include economic value and weights in this function express by many researchers. Simultaneous selection using characteristics with important and heritable economic values is more effective. Crop yield is a function of multiple variables inter-related to each other and can not be defined only by a single-variable equation. One of the most effective of these methods is boundary lines. Feiziasl et al (2003 and 2010) used this method for the first time to determine the optimum levels of plant (dryland wheat) characteristics and yield stability analysis. In this paper, “Boundary Lines” and averaging methods and are used to determine optimum levels for some traits of dryland lentil in cold areas. Materials and Methods More than 8300 data for each trait were collected from national and international trials conducted under rainfed conditions in Maragheh, Zanjan, Ardabil and Shirvan (North Khorasan Province) DARI experiment stations 20 years (1996-2016). The traits considered in the analysis were: days to heading, days to physiological maturity, grain filling period, plant height, 100 seed weight and grain yield. The Excel software was used to develop a scatter diagram showing the relationship between each trait with grain yield in each location. Two methods were used to determine the optimum value for a given characteristic. One is based on the boundary lines method where the maximum grain yield and the optimum value for the trait considered coincide with the crossing point of the two boundary lines. “Boundary Lines” method was used to determine the maximum limits of each crop characteristic. The scatter diagram is surrounded by two regression lines, one on the left and the other on the right called boundary lines. Then Maximum yield is obtained at the intersection of both boundary lines. The other approach, called averaging method, is based on subdividing the data into two groups: high and low yielding groups. Results and DiscussionThe boundary lines method allowed the determination of optimum levels for days to flowering, days to physiological maturity, duration of grain filling, plant height and 100 seed weight which were 55.4 days, 92.6 days, 33.8 days, 27.8 centimeters and 5.4 grams, respectively. By averaging of high yielding group method optimums for days to flowering, days to physiological maturity, duration of grain filling, plant height and 100 seed weight characteristics were 62.0 days, 92.6 days, 30.7 days, 23.7 centimeters, 4.9 grams, respectively. These optimums were closely equivalent to those determined using the averaging method for one days to physiological maturity characteristic. But for other plant characteristics there were a lot of difference and boundary line estimated its amplitude on average 3 times wider than the averaging method. Principle component analysis (PCA) show that, the most important traits for selection of lentil varieties were days to flowering and number of days to physiological maturity. These methods could help breeders to determine the optimum and range for characteristics determining the adaptation of genotypes to given environments. Boundary lines method is more suitable for determining of characteristic’s optimum levels in comparison with the averaging method. ConclusionIt can be concluded that, determining the optimum levels of some dryland lentil characteristics were closely equivalent in two boundary lines and averaging methods. However, in some other characteristics, the optimum levels were a lot of difference in estimating of boundary lines and averaging of high yielding groups. Because boundary lines method is considered the data distribution process and gap data in databank, therefore, its estimates in the optimum levels of the characteristics are more accurate and more realistic than the averaging of the high yielding group.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,227
Tête enseignante GPT0,454
Écart entre enseignants0,228 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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