ABSTRACT
Studies were conducted to determine the effect of osmotic and matric stress on germination and growth of two Fusarium solani strains, the etiological agent responsible of peanut brown root rot. Both strains had similar osmotic and matric potential ranges that allowed growth, being the latter one narrower. F. solani showed the ability to grow down to -14 MPa at 25 °C in non-ionic modified osmotic medium, while under matric stress this was limited to -8.4 MPa at 25 °C. However, both strains were seen to respond differently to decreasing osmotic and matric potentials, during early stages of germination. One strain (RC 338) showed to be more sensitive to matric than osmotic (non ionic) and the other one (RC 386) showed to be more sensitive to osmotic than matric imposed water stress. After 24 h of incubation, both isolates behaved similarly. The minimum water potential for germination was -8.4 MPa on glycerol amended media and -5.6 MPa for NaCl and PEG amended media, respectively. The knowledge of the water potential range which allow mycelia growth and spore germination of F. solani provides an inside to the likely behaviour of this devastating soilborne plant pathogen in nature and has important practical implications.
Subject(s)
Fusarium/growth & development , Osmotic Pressure , Water/metabolism , Arachis/microbiology , Fusarium/drug effects , Fusarium/radiation effects , Glycerol/metabolism , Plant Diseases/microbiology , Polyethylene Glycols/metabolism , Soil Microbiology , Sodium Chloride/metabolism , TemperatureABSTRACT
O trabalho teve como objetivo apresentar uma proposta de metodologia para estimativa da curva de retenção de água, para solos do Estado do Rio Grande do Sul, a partir do uso de redes neurais artificiais. Para o desenvolvimento do trabalho, foi montado um banco de dados com informações disponíveis na literatura, de textura e estrutura dos solos do Estado do Rio Grande do Sul. Para o desenvolvimento das redes, utilizou-se o software Matlab, no qual foram treinadas diferentes arquiteturas, variando os números de neurônios na camada de entrada e camada intermediária. A eficiência das redes foi analisada graficamente pela relação 1:1, entre os dados estimados versus os observados, por meio de indicadores estatísticos. Observou-se, a partir dos resultados, que a arquitetura com melhor capacidade preditiva foi: 4-24-7, com classificação do índice de desempenho "ótimo". Assim, pode-se inferir que o uso de redes neurais, para estimativa da curva de retenção de água no solo, é uma ferramenta com alta capacidade preditiva e que trará grande contribuição ao setor agrícola.
The study aims to propose a methodology for estimating the water retention curve for soils of the State of Rio Grande do Sul, by using artificial neural networks. For the development of the research it was assembled a database with information available in the literature, texture and structure of soils of Rio Grande do Sul. The modeling was developed using the software Matlab, where the networks were trained with different architectures, varying the numbers of neurons in the input layer and the hidden layer. The efficiency of the network was analyzed graphically by the ratio 1:1 between the estimated versus the observed data by means of statistical indicators. It was observed from the results that the architecture with best predictive performance was the 4-24-7, with index classification of "great" performance. Thus it can be inferred that the use of neural networks to estimate the water retention curve of the soil is a tool with high predictive ability which will bring great contribution to the agricultural sector.