Desempenho do modelo Simanihot em ambiente tropical

• Ano de defesa: 2021
• Autor(a) principal: Nascimento, Moises de Freitas do
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Agronomia; UFSM; Programa de Pós-Graduação em Agronomia; Centro de Ciências Rurais
• Palavras-chave em Português: Manihot esculenta; Modelagem; Potencial de produtividade; Lacuna de produtividade; Crop model; Yield potential; Yield gap; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/29077

Appropriate tools and methodologies are required to quantify a country's ability to produce food in a context of food security. The use of crop simulation models makes possible to assess the interaction of genotype, environment and management practices, allowing the identification of the main causes responsible for reducing and limiting the productivity of agricultural crops. The climate is one of the main causes of productivity variability, and the main cause of climate variability is the El Niño Southern Oscillation (ENSO), which has a worldwide impact. Cassava is an important source of calories, and Brazil is one of the largest producers of cassava in the world. Thus, the present study aims to evaluate the performance of the Simanihot model to represent the cassava producing regions of Brazil in a tropical environment. Simulations were performed in the yield potential (Yp) and water-limited yield potential (Yw) in 20 locations in Brazil during the period from 1980 to 2017. Cassava root yield data published in the literature were used to validate the model's performance to estimate yield potential (Yp). The model was exposed to sensitivity tests to capture the effects of the ENSO phenomenon and to identify Brazilian biomes. The performance of the model was analyzed using the statistics of root-mean-square error (RMSE), normalized root-meansquare error (RMSEn), the BIAS index and the dw agreement index. It was identified that the model satisfactorily estimates the yield potential with a normalized error of 17.54% (RMSEn). The model also showed sensitivity in: (i) capturing Brazilian biomes in terms of apparent water balance; (ii) capture the reduction in yield due: delay in the planting date, and the lower available water capacity (AWC) for the soil types of sandy soil, loam soil and clay soil; and (iii) did not identify an impact of the ENSO phenomenon on the yield of cassava roots.