1994
DOI: 10.1051/agro:19941003
|View full text |Cite
|
Sign up to set email alerts
|

Statistical analysis and interpretation of line x environment interaction for biomass yield in maize

Abstract: Summary — The maize line x environment interaction for biomass dry matter yield was analysed using a multilocal factorial mating design. Various

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

3
8
0
1

Year Published

1997
1997
2017
2017

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 18 publications
(12 citation statements)
references
References 24 publications
(23 reference statements)
3
8
0
1
Order By: Relevance
“…Hébert et al (1995) were able to explain 74% of the interaction sum of squares with an environmental covariate for root system traits in maize. For biomass yield in the same species, 55% of the interaction could be accounted for by two genotypic covariates and one environmental covariate, the rest of the interaction being not significant (Argillier et al, 1994). Three covariates (two genotypic and one environmental) accounted for 64% of the interaction sum of squares in pea yield, whereas the rest of the interaction remained significant (Biarnès-Dumoulin et al, 1996).…”
Section: Our Model Resulted In a Relatively Simple Model With Few Covmentioning
confidence: 95%
See 1 more Smart Citation
“…Hébert et al (1995) were able to explain 74% of the interaction sum of squares with an environmental covariate for root system traits in maize. For biomass yield in the same species, 55% of the interaction could be accounted for by two genotypic covariates and one environmental covariate, the rest of the interaction being not significant (Argillier et al, 1994). Three covariates (two genotypic and one environmental) accounted for 64% of the interaction sum of squares in pea yield, whereas the rest of the interaction remained significant (Biarnès-Dumoulin et al, 1996).…”
Section: Our Model Resulted In a Relatively Simple Model With Few Covmentioning
confidence: 95%
“…Two types of residual mean squares can be used for each covariate test: (i) the residual mean square of the model (Baril, 1992;Charmet et al, 1993), in which case the residual mean square changes according to the number of covariates and possibly to their nature (environment or genotype); and (ii) the residual variance of the complete interactive model, or a derived variance that takes into account the number of replications used to calculate the mean for each G ϫ E combination (Argillier et al, 1994;Hébert et al, 1995;Biarnès-Dumoulin et al, 1996;BrancourtHulmel, 1999). Only the second method is comparable to ours.…”
Section: Our Model Resulted In a Relatively Simple Model With Few Covmentioning
confidence: 99%
“…Generally, grain yields were reduced when emergence or silking was delayed. In maize, Argillier et al (1994) showed that biomass yield was related to silking date, but that the relationship depended on the location; early cultivars had an advantage in some locations, but not in others. In others species, some authors succeeded in determining key stages of yield elaboration.…”
Section: Discussionmentioning
confidence: 99%
“…In maize, some authors who have attempted to relate GEI to environmental variables succeeded in accounting for the total GEI sum of squares with only one to three covariates (Argillier et al, 1994; Hébert et al, 1995). In our case, the remaining GEI sum of squares was still significant.…”
Section: Discussionmentioning
confidence: 99%
“…Il paraît tout naturel au sélectionneur et au généticien de caractériser génétiquement le matériel végétal et de nombreux progrès sont actuellement réalisés grâce à la biologie moléculaire. En revanche, la caractérisation du milieu ne bénéficie pas d'autant d'efforts malgré la présence d'effet du milieu souvent beaucoup plus important que celui du génotype (par exemple sur blé [13], sur ray-grass [14], sur maïs [15] ou sur pois [16]) et malgré les progrès réalisés dans ce domaine (accès automatisé aux données météorologiques, développement continu de modèles agronomiques, etc.). Parmi les démarches proposées pour caractériser le milieu, une méthode s'appuie sur l'idée de restreindre à un petit nombre de génotypes les observations nécessaires à la caractérisation des milieux en termes de facteurs limitant le rendement [17,18].…”
Section: Régression Factorielle Biadditiveunclassified