Modelagem dos efeitos da ferrugem asiática no rendimento da cultura da soja em cenário de mudanças climáticas globais no Estado de Minas Gerais

• Ano de defesa: 2011
• Autor(a) principal: Rodrigues, Rafael de ávila
• Orientador(a): Costa, Luiz Cláudio
• Banca de defesa: Gomide, Reinaldo Lúcio , Heinemann, Alexandre Bryan , Marin, Fábio Ricardo , Lemos, Carlos Fernando
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Doutorado em Meteorologia Agrícola
• Departamento: Agrometeorologia; Climatologia; Micrometeorologia
• País: BR
• Palavras-chave em Português: Soja; Ferrugem asiática; Mudanças climáticas; Minas Gerais
• Palavras-chave em Inglês: Soybeans; Asian rust; Climate change; Minas Gerais
• Área do conhecimento CNPq: CNPQ::CIENCIAS AGRARIAS::AGRONOMIA::AGROMETEOROLOGIA
• Link de acesso: http://locus.ufv.br/handle/123456789/1520

In recent years, there has been a large increase in the use of simulation models in agriculture. Currently, DSSAT (Decision Support System for Agro-technology Transfer), is an important tool for modeling and simulation of crop growth. However, one of its limitations is the fact that simulation models do not take into account the effect on pests/diseases. Soybean is among the cultures that showed the greatest growth in a Brazilian agribusiness segment area, because of its economic value and cultivation potential under different environmental conditions. Among the diseases that occur in soybean culture, the Asian Soybean Rust, Phakopsora pachyrhizi, caused by the fungus Sydow & Sydow, is considered the most destructive one in Brazil,mainly in the states of Mato Grosso, Goiás, Minas Gerais and Mato Grosso do Sul. Temperature, relative humidity and leaf wetness period favor fungal development. Thus, this work aimed to calibration and validarion of CSM-CROPGRO-Soybean n estimating the growth, development and yield of soybean culture in the state of Minas Gerais, in relation to three cultivars (BRS6101, BRS Valiosa and MGBR-46 Conquista); evaluate the coupling module of Asian Soybean Rust to CSM-CROPGRO-Soybean, considering on and off soybean culture yield simulations; and analyze the impact of soybean rust in a climate change scenario, using the model ECHAM5/MPI-OM IPCC. Experimental data for evaluation, testing and fitting genetic coefficients of the model for all three soybean cultivars were obtained in Viveiro do Café, an experimental area owned by the Department of Plant Pathology of the Federal University of Viçosa (UFV) in Viçosa, Minas Gerais, Brazil (20o44 48.5 S, 42o50 58.4 W, alt. 670 m), during the 2006 and 2010 growing seasons. Validation was performed by Vasconcelos (2009) and GLUE (Generalized Likelihood Uncertainty Estimation) was used to estimate genetic coefficients. The pedo-transference functions were used to estimate soil physical parameters. The soybean yield simulations were based on the meteorological data of 42 Conventional Stations of the National Institute of Meteorology (INMET) and 12 points of the ECHAM5/MPI-OM model for the years 2020, 2030 and 2040. The model presented high sensitivity to genetic coefficient variation by means of the different cultivars analyzed, as well as phenological development and grain yield, under the soil and climate conditions in Viçosa MG. Analyzing the effects of Asian rust,cultivar BRS 6101 (medium) presented a smaller effect on yield for cultivar BRS Valiosa (medium to medium late), which had increased yield break. As for global climate change, the use of data of the model ECHAM5/MPI-OM for the years 2020, 2030 and 2040 showed that the simulations based on INMET model and ECHAM data presented good fits in some years. However, for other years, the fit was not found to be satisfactory. The A1B scenario indicates a decrease in yield up to around 15% in Oeste Mineiro until 2040. The year 2030 appears to be a producer of large soybean crop yields, except in Vale do Mucuri and Campo das Vertentes. Thus, it is concluded that the model can be an important tool for understanding and analyzing yield in different meso-regions of the state of Minas Gerais using historical weather data, soils and global climate change scenarios for each location.