AMMI and GGE Biplot Analyses of Multi-Environment Test in Rice (Oryza sativa L.) for Irrigated Lowland Conditions in the Philippines

作者：Manigbas Norvie L^*; Lambio Leslie Angela F; Magat Monaliza B; Bartolome Violeta I

来源：Philippine Journal of Crop Science, 2016, 41(1): 60-69.

• AMMI
• GEI
• GGE
• multi-environment test
• rice

Genotype by Environment Interaction (GEI) was investigated on grain yield of 10 rice genotypes in 12 lowland rice growing environments in the Philippines. Biplots were used in selecting and recommending the top-performing genotypes on different environments. Additive Main Effect and Multiplicative Interaction (AMMI) and Genotype by Genotype Environment (GGE) models were used in the analyses. The ANOVA for grain yield was significant (P<0.05) for genotypes, environments and their interaction. Environments accounted for the greater proportion (80.73%) of variation in grain yield, followed by GEI (12.80%), and genotypes (6.69%), indicating the need for multi-year and multi-location testing of rice varieties. The first two terms of the AMMI models explained 62.10% of the GEI while GGE biplot analysis accounted for 72.4 % of the total GEI variation. Based on AMMI and GGE biplots, no single variety has superior performance across environments. However, G2 (PSB Rc82) and G5 (PR37273-5-16-5-2-1-2-1) were the best genotypes having high and stable yields. The GGE model identified Nueva Viscaya State University as the most discriminating environment, while Naujan, Oriental Mindoro as the most representative environment. These environments are good test environments for selecting adapted genotypes. In this study both AMMI and GGE biplots were evaluated as effective tools for the analysis of GEI and visualization of genotype performance under varying test environments. Findings in this study are useful inputs in breeding rice varieties that are adapted to certain recommendation domains.

• 出版日期2016-4
• 单位中国水稻研究所