Results 171 to 180 of about 2,756 (197)

Soybean Cultivar Evaluation via GGE Biplot Analysis

Journal of New Seeds, 2005
ABSTRACT Multi-environment trials (MET) are an important activity in agriculture to identify superior cultivars for production. Visual evaluation of MET data via GGEbiplot software makes the cultivar recommendation easy. Our objectives were to evaluate performance of 100 soybean [Glycine max (L.) Merr.] cultivars belonging to five maturity groups (MG ...
Meiqin Zhang, Manjit S. Kang, Paul F. Reese, Harbans L. Bhardwaj   +3 more
openaire   +1 more source

Evaluation of the performance of sorghum genotypes using GGE biplot

Canadian Journal of Plant Science, 2015
Gasura, E., Setimela, P. S. and Souta, C. M. 2015. Evaluation of the performance of sorghum genotypes using GGE biplot. Can. J. Plant Sci. 95: 1205–1214. In spite of sorghum's drought tolerance, it is largely affected by genotype×environment interaction (GE), making it difficult and expensive to select and recommend new sorghum genotypes for different
Edmore Gasura, Peter S. Setimela, Caleb M. Souta   +2 more
openaire   +1 more source

[Ecological regionalization of cotton varieties based on GGE biplot].

Ying yong sheng tai xue bao = The journal of applied ecology, 2014
By using the heritability-adjusted GGE biplot analysis method, and taking the trial sites Anqing, Nanyang, Huanggang, Jingzhou, Wuhan, Xiangyang, Changde, Yueyang, Nanjing, Nantong, Yancheng, Jiujiang, Jianyang, Shehong, and Cixi as the representative cotton-planting areas in the Yangtze River basin, the ecological regionalization of cotton varieties ...
Nai-Yin, Xu, Guo-Wei, Zhang, Jian, Li, Zhi-guo, Zhou   +3 more
openaire   +2 more sources

GGE Biplot vs. AMMI Analysis of Genotype‐by‐Environment Data

Crop Science, 2007
ABSTRACTThe use of genotype main effect (G) plus genotype‐by‐environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi‐environment trial (MET) data.
Weikai Yan, Manjit S. Kang, Baoluo Ma, Sheila Woods, Paul L. Cornelius   +4 more
openaire   +1 more source

Cultivar Evaluation and Mega‐Environment Investigation Based on the GGE Biplot

Crop Science, 2000
Cultivar evaluation and mega‐environment identification are among the most important objectives of multi‐environment trials (MET). Although the measured yield is a combined result of effects of genotype (G), environment (E), and genotype × environment interaction (GE), only G and GE are relevant to cultivar evaluation and mega‐environment ...
Weikai Yan, L.A. Hunt, Qinglai Sheng, Zorka Szlavnics   +3 more
openaire   +1 more source

Multivariate outliers detection on GGE Biplot

AIP Conference Proceedings, 2022
A. F. Hadi, D. Anggraeni, H. Sadiyah, D. B. C. Wicaksono   +3 more
openaire   +1 more source

Determining Selection Gains and Discriminating Environments via GGE Biplots

Journal of Crop Improvement, 2008
Abstract Genetic variation is the basis for meaningful selection; thus, the use of locations that discriminate or result in greater variation among genotypes, for a trait or trait package should promote accurate selection of superior genotypes. The objectives of this study were to quantify the gains by selection in discriminating locations using ...
Sterling Brooks Blanche, Gerald O. Myers, William D. Caldwell, Ted Wallace   +3 more
openaire   +1 more source

GGE Biplot Software – The Solution for GGE Biplot Analyses

2002
Weikai Yan, Manjit S. Kang
openaire   +2 more sources

Introduction to GGE Biplot

2002
Weikai Yan, Manjit S. Kang
openaire   +2 more sources

GGE biplot analysis of promising barley lines in the cold regions of Iran

Journal of Crop Improvement, 2022
Habibollah Ghazvini, Saeed Bagherikia, Alireza Pour-Aboughadareh   +2 more
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