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

Argillier, O.; Hébert, Y.; Barrière, Yves

doi:10.1051/agro:19941003

Cited by 18 publications

(12 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

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%

“…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%

See 1 more Smart Citation

Identification of Factors Affecting Trait Stability via Factorial Regression and Environmental Variance

Robert¹,

Bérard²,

Brancourt‐Hulmel³

2007

Journal of Crop Improvement

View full text Add to dashboard Cite

The stability basis in bread wheat still remains to be understood. Our objective was to determine factors affecting the stability of end-use quality in wheat, using a method developed in a companion paper that combined environmental variance and factorial regression. Kernel protein percentage (% protein), strength (W), tenacity (P) and extensibility (L) of dough were studied. Twenty-five environmental Nathalie Robert is affiliated with Institut Polytechnique LaSalle Beauvais, 19 rue Pierre Waquet, BP 30313, F-60026 Beauvais Cedex, France. Pierre Bérard is affiliated with INRA, UMR Amélioration et santé des plantes, 234 avenue du Brézet, 63039 Clermont-Ferrand cedex, France. Maryse Brancourt-Hulmel is affiliated with UMR INRA/USTL, 1281 SADV (Stress abiotiques et différenciation des végétaux cultivés), Estrées-Mons BP 50136, ). The authors thank P. Brabant and N. Galic, J. Le Gouis and M. Niarquin, M. Trottet and J.-Y. Morlais for running trials at Le Moulon, Mons and Rennes in 1999. The authors also thank Simon Hawkins for editorial assistance.covariates characterized grain formation and nitrogen yields, dry matter and nitrogen quantities at anthesis, grain-filling, and post-anthesis temperatures. Earliness was a genotypic covariate. For each trait, the factorial regression, introducing the two environmental covariates that best discriminated for stability and best explained the interaction, was selected. The best factorial regressions explained 31.5%, 53.3%, 49%, and 46.3% of the interaction sum of squares for the % protein, W, P, and L, respectively. The selected covariates indicated that nitrogen availability at anthesis, nitrogen grain-filling rate, and duration were involved in the quality stability. The stable varieties appeared to use poorly environments favorable to nitrogen accumulation, and to behave well in unfavorable ones. Such information will be helpful in future breeding programs.

show abstract

Section: Our Model Resulted In a Relatively Simple Model With Few Covmentioning

confidence: 95%

Section: Our Model Resulted In a Relatively Simple Model With Few Covmentioning

confidence: 99%

Identification of Factors Affecting Trait Stability via Factorial Regression and Environmental Variance

Robert¹,

Bérard²,

Brancourt‐Hulmel³

2007

Journal of Crop Improvement

View full text Add to dashboard Cite

show abstract

“…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%

Genotype × Environment Interactions in Maize Hybrids from Temperate or Highland Tropical Origin

Giauffret¹,

Lothrop

Dorvillez³

et al. 2000

Crop Science

View full text Add to dashboard Cite

Exotic germplasm is often used in maize (Zea mays L.) breeding programs. However, the occurrence of genotype × environment interactions (GEIs) may mask the potential utility of exotic material. Our objective was to understand better GEIs for temperate, temperate × highland, and highland tropical maize genotypes cultivated under temperate or highland tropical conditions. We related yield instability to GEIs observed for vegetative or flowering traits, and determined the response of these vegetative or flowering traits to temperature and photoperiod. Forty‐one hybrids grown in four environments were observed for several pre‐flowering, flowering, and yield traits. Grain yield variation was related to two major adaptation factors: disease resistance and planting‐to‐silking duration (PSD). Yield variations were also related to variation in traits measured before flowering, such as emergence duration or early growth. For emergence duration, leaf stage at a given date, or leaf size, one simple temperature covariate accounted for more than half of the interaction sum of squares. Temperate × highland tropical hybrids had an intermediate behavior and were more stable than the pure temperate or pure highland tropical hybrids. For total number of leaves, photoperiod at tassel initiation explained a higher proportion of the interaction sum of squares than temperature. The response of highland tropical hybrids to photoperiod was larger than for the temperate hybrids. We recommend breeders who wish to introduce exotic material into adapted material utilize mass selection and/or advanced backcrossing, with marker assisted selection for specific traits with low heritability, such as cold tolerance.

show abstract

“…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

SELECTION VARIETALE ET MILIEU Sélection pour l’adaptation au milieu et prise en compte des interactions génotype/milieu

Brancourt‐Hulmel

2000

OCL

View full text Add to dashboard Cite

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

Cited by 18 publications

References 24 publications

Identification of Factors Affecting Trait Stability via Factorial Regression and Environmental Variance

Identification of Factors Affecting Trait Stability via Factorial Regression and Environmental Variance

Genotype × Environment Interactions in Maize Hybrids from Temperate or Highland Tropical Origin

SELECTION VARIETALE ET MILIEU Sélection pour l’adaptation au milieu et prise en compte des interactions génotype/milieu

Contact Info

Product

Resources

About