Potencial e lacunas de produtividade em arroz irrigado no Rio Grande do Sul

• Autor(a) principal: Ribas, Giovana Ghisleni
• Data de Publicação: 2019
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UFSM
• Texto Completo: http://repositorio.ufsm.br/handle/1/20758
• topic: Oryza sativa; Yield potential; Yield gap; Farmers; Surveys; Potencial de produtividade; Lacuna de produtividade; Lavouras; Questionários; CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA

The Rio Grande do Sul state is the main Brazilian rice producer (Oryza sativa L.). Despite continue increase of average rice yield in the last years, there is still a huge difference between yield experiments from rice research centers and actual yield in irrigated rice in the Rio Grande do Sul. The yield potential is the yield from a cultivar that gown without biotic and abiotic limitation. Among the objectives of this study highlight (i) to introduce of three conventional rice cultivar actual widely used in RS by SimulArroz and Oryza models, (ii) to estimate yield potential and yield gap in lowland rice area in the Rio Grande do Sul state, (ii) to identify the biophysics and management factors that are potentially causing the yield gap in lowland area in the Rio Grande do Sul state. Management data were collected from 324 surveys applied in three growing seasons (2015/2016, 2016/2017 e 2017/2018). The models showed a great performance and the NRMSE varied from 0,8% to 34%. The yield potential reported in RS using Oryza v3 was 14.8 t ha-1. From the point of view of best rice fields in RS was found that they are reaching 68% of yield potential, whereas the other fields are reaching 52% of yield potential in RS. This result indicates how much fields in RS still need to improve the management, where the most consistent factors to cause the gaps were sowing date, onset irrigation, pre-sowing weed control, soybean-rice rotation, and fertilizer. The combined use of farmers and strategize research and extension programs at is a great tool to capture regional variation which can help to inform.