<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The yield of four soybean (<italic>Glycine max</italic>, L. Merrill) genotypes under six planting dates in two years was assessed using the Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype and Genotype-by-Environment biplot models. The results of combined analysis of variance for grain yield of the four genotypes of soybean grown in 12 environments showed that soybean grain yield was significantly (<italic>P</italic> &lt; 0.01) affected by environments (E), genotypes (G) and genotype by environment interactions (GE). Genotypes and environments accounted for about 6.56% and 47.66% of the variation, respectively, while the GE explained 14.47% of the variation, which is more than double of the genotypic effects of the total variation. AMMI biplot indicated genotype TGx1485-1D and the early July 2012 environment were above average for grain yield and had positive specific interactions with each other. However, TGx1485-1D had negative interactions with the other environments while genotypesTGx14482E, TGx1987-10F and TGx1835-10E had positive interactions with all the environments except E5. In the differential yield ranking of genotypes across the twelve environments TGx1485-1D had the highest yield in seven out of the twelve environments. TGx1835-10E was the highest yielding genotype in three environments, while TGx1448-2E gave the greatest yield in two environments. Although TGx1485-1D exhibited high GEI, in the GGE biplot it was ranked as the most desirable genotype. GGE biplot identified early July 2012(E5) as the best environment. The result showed that application of AMMI and GGE biplots facilitates visual comparison and identified superior genotypes for each target set of environments.</p></abstract>

The yield of four soybean (Glycine max, L. Merrill) genotypes under six planting dates in two years was assessed using the Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype and Genotype-by-Environment biplot models. The results of combined analysis of variance for grain yield of the four genotypes of soybean grown in 12 environments showed that soybean grain yield was significantly (P < 0.01) affected by environments (E), genotypes (G) and genotype by environment interactions (GE). Genotypes and environments accounted for about 6.56% and 47.66% of the variation, respectively, while the GE explained 14.47% of the variation, which is more than double of the genotypic effects of the total variation. AMMI biplot indicated genotype TGx1485-1D and the early July 2012 environment were above average for grain yield and had positive specific interactions with each other. However, TGx1485-1D had negative interactions with the other environments while genotypesTGx14482E, TGx1987-10F and TGx1835-10E had positive interactions with all the environments except E5. In the differential yield ranking of genotypes across the twelve environments TGx1485-1D had the highest yield in seven out of the twelve environments. TGx1835-10E was the highest yielding genotype in three environments, while TGx1448-2E gave the greatest yield in two environments. Although TGx1485-1D exhibited high GEI, in the GGE biplot it was ranked as the most desirable genotype. GGE biplot identified early July 2012(E5) as the best environment. The result showed that application of AMMI and GGE biplots facilitates visual comparison and identified superior genotypes for each target set of environments.

An Assessment of Soybean (<italic>Glycine max</italic>, L. Merrill) Grain Yield in Different Environments Using AMMI and GGE Biplot Models in Humidorest Fringes of Southeast Nigeria

Department of Agronomy, Michael Okpara University of Agriculture, Umudike, Nigeria

Department of Crop, Soil and Pest Management, Federal University of Technology, Akure, Nigeria

Agricultura Tropica et Subtropica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An Assessment of Soybean (, L. Merrill) Grain Yield in Different Environments Using AMMI and GGE Biplot Models in Humidorest Fringes of Southeast Nigeria

The yield of four soybean (Glycine max, L. Merrill) genotypes under six planting dates in two years was assessed using the Additive Main Effect and...

An Assessment of Soybean (Glycine max, L. Merrill) Grain Yield in Different Environments Using AMMI and GGE Biplot Models in Humidorest Fringes of Southeast Nigeria

Pubblicato online: 22 dic 2015

Pagine: 82 - 90

Ricevuto: 05 giu 2015

Accettato: 06 nov 2015

DOI: https://doi.org/10.1515/ats-2015-0012

Parole chiave
soybean, genotype, genotype and environment interaction, selection, variation

© 2015 Agricultura Tropica et Subtropica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An Assessment of Soybean (Glycine max, L. Merrill) Grain Yield in Different Environments Using AMMI and GGE Biplot Models in Humidorest Fringes of Southeast Nigeria

Pubblicato online: 22 dic 2015

Pagine: 82 - 90

Ricevuto: 05 giu 2015

Accettato: 06 nov 2015

DOI: https://doi.org/10.1515/ats-2015-0012

Parole chiavesoybean, genotype, genotype and environment interaction, selection, variation

© 2015 Agricultura Tropica et Subtropica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
soybean, genotype, genotype and environment interaction, selection, variation