Impacto das mudanças climáticas globais sobre a produtividade e distribuição geográfica da soja nos municípios brasileiros

• Ano de defesa: 2013
• Autor(a) principal: Caetano, Jordana Moura
• Orientador(a): Nabout, João Carlos
• Banca de defesa: Teixeira, Itamar Rosa, Oliveira, Guilherme de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual de Goiás
• Programa de Pós-Graduação: Programa de Pós-Graduação Stricto sensu em Engenharia Agrícola
• Departamento: UEG ::Coordenação de Mestrado em Engenharia Agrícola
• País: Brasil
• Palavras-chave em Português: Adequabilidade ambiental; Conjunto de previsões; Glycine max (L.) Merrill; Modelo de nicho; Mudanças climáticas; Ensemble forecasting
• Palavras-chave em Inglês: Environmental suitability; Glycine max (L.) Merrill; Niche model; Climate change
• Área do conhecimento CNPq: CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA; RECURSOS HIDRICOS::PLANEJAMENTO INTEGRADO DOS RECURSOS HIDRICOS; ENGENHARIA SANITARIA::SANEAMENTO AMBIENTAL
• Link de acesso: http://www.bdtd.ueg.br/handle/tede/188

Niche models are an important tool used to understand the limits of geographic range of the species and how these limits change in response to climate change. However, few studies of geographic distribution modeling that deal with the impact of climate change on agricultural species. This study aimed to estimate the impacts of global climate change on productivity of soybean in Brazil, to investigate the relationship between crop yield and climate suitability and map the sources of uncertainty among niche models and climate scenarios, and their interactions. Maps were generated from the combination of thirteen techniques niche models and five climate models, in scenarios current and future (projected for 2080). Thus, it was possible to estimate and map the frequency of occurrence of soybean in Brazil and quantitatively determine the changes in climatic suitability. The relationship (productivity and current climatic suitability) was investigated using Spearman correlation with geographically effective degrees of freedom. To quantify the sources of uncertainty of the variables was used an analysis of variance of two-way. The projection for 2080 showed that the climate suitability for cultivation of soybean in Brazil tends to decrease to 50% in the central region while the extreme south showed more favorable conditions (increase of 25% climate suitability). Soybean yield was positively correlated with the current suitability (r = 0.319, P = <0.001). The analysis of variance indicated that the methods presented more uncertainty in the forecasts (i.e., 0.75) when compared with the climate scenarios and their interaction, 0.02 and 0.23, respectively. The results point to the need to adopt mitigation measures to control the possible economic impacts that climate change might cause.