Selection of rice genotypes based on agronomic traits and commercial grain quality
Adriano Udich Bester,
Ivan Carvalho,
Murilo Vieira Loro,
Paulo Henrique Karling Facchinello,
Leonardo Cesar Pradebon,
Jaqueline Piesanti Sangiovo,
Gabriel Almeida Aguiar,
Eduardo Anibele Streck,
Ariano Martins de Magalhães Júnior
Affiliations
Adriano Udich Bester
Universidade Regional do Noroeste do Estado do Rio Grande do Sul – Unijuí, Ijuí/RS – Brasil
Ivan Carvalho
Universidade Regional do Noroeste do Estado do Rio Grande do Sul – Unijuí, Ijuí/RS – Brasil
Murilo Vieira Loro
Doutorando em Agronomia, Universidade Federal de Santa Maria - UFSM, Santa Maria - Brasil.
Paulo Henrique Karling Facchinello
Biotrop Soluções Biológicas, Vinhedo/SP - Brasil
Leonardo Cesar Pradebon
Doutorando em Agronomia, Universidade Federal de Santa Maria - UFSM, Santa Maria/RS - Brasil.
Jaqueline Piesanti Sangiovo
Mestranda em Sistemas Ambientais e Sustentabilidade. Universidade Regional do Noroeste do Estado do Rio Grande do Sul - UNIJUI, Ijuí/RS - Brasil
Gabriel Almeida Aguiar
Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul - IFRS, Campus Sertão/RS - Brasil
Eduardo Anibele Streck
Instituto Federal de Educação, Ciência e Tecnologia Farroupilha - IFFar, Campus São Vicente do Sul/RS - Brasil
Ariano Martins de Magalhães Júnior
Empresa Brasileira de Pesquisa Agropecuária - Embrapa, Centro de Pesquisa Agropecuária de Clima Temperado, Estação Experimental Terras Baixas, Campus Universitário, Pelotas/RS - Brasil
The study aimed to select rice genotypes based on agronomic traits and commercial grain quality. The study was carried out in the 2014/15 and 2015/16 harvests in eight growing environments, located in the state of Rio Grande do Sul, Brazil. Fourteen rice genotypes were sown in each environment. The experiment was conducted in a randomized block design with four replications. The following agronomic traits were evaluated: number of days to flowering and grain yield as well as commercial grain quality traits: percentage of whole grains and percentage of chalky area in the grains. AMMI, GGE and MGIDI methodologies were used to select the best performing genotypes. Using the AMMI, GGE and MGDI methods, genotypes G9 and G1 performed close to the ideal ideotype for the number of days to grain flowering, yield and quality.
