RESUMO
Germination parameters of the response to temperature and water potential from four common bean (Phaseolus vulgaris) lines based on thermal-time and hydrotime concepts were estimated to verify to what extent they can predict germination under different thermal and water conditions. The cultivars IPR Uirapuru and IAPAR 81 (drought-tolerant), and Grauna and Carioca (not tolerant) were used. The isothermal assays were performed in a temperature gradient block, and the assays with different osmotic potentials (PEG 6000) were performed in germination chambers. Seeds from drought-tolerant cultivars spent less time to germinate at supra-optimum temperatures than non-tolerant ones, and the cultivar Uirapuru (drought-tolerant) germinated faster in response to reduced Ψ and low temperatures. The parameter Ψb(50) did not discriminate between drought-tolerant and non-tolerant lines at the infra-optimum temperature range, but it can be used to identify drought-tolerant lines at high temperatures. In general, the hydrotime model reproduced the actual germination data relatively well, chiefly at higher temperatures. This study evidenced that the hydrotime model can be used to describe the germination of common bean seeds under reduced water potentials, and as a screening tool for drought-tolerant bean genotypes.
Neste trabalho foram determinados alguns parâmetros térmicos e hídricos da germinação de quatro cultivares de feijão comum (Phaseolus vulgaris), com base nos modelos graus.dia (thermal-time) e psi.dia (hydrotime), testando-se sua adequação em descrever a germinação em diferentes condições de temperatura e água. Foram analisadas as cultivares IPR Uirapuru e IAPAR 81 (tolerantes à seca), e Grauna e Carioca (não-tolerantes). Os ensaios isotérmicos foram realizados em bloco termogradiente, enquanto que os experimentos com diferentes potenciais osmóticos (PEG 6000) foram realizados em câmaras de germinação. Sementes das cultivares tolerantes à seca germinaram mais rapidamente do que as não-tolerantes em temperaturas supraótimas, sendo que a cultivar Uirapuru (tolerante à seca) germinou mais rápido do que as demais em resposta ao Ψ reduzido e temperaturas baixas. O parâmetro Ψb(50) não discriminou entre cultivares tolerantes e não-tolerantes à seca na faixa térmica infraótima, embora ele possa ser usado para identificar cultivares tolerantes em temperaturas supraótimas. Em geral, o modelo hydrotime descreveu bem o comportamento germinativo, principalmente em temperaturas altas. Este trabalho mostra que o modelo pode ser usado para descrever a germinação de feijão comum em potenciais de água reduzidos, e como ferramenta para identificar genótipos de feijoeiro tolerantes à seca.
Assuntos
Fito-Hemaglutininas , Umidade do Solo , TemperaturaRESUMO
Germination parameters of the response to temperature and water potential from four common bean (Phaseolus vulgaris) lines based on thermal-time and hydrotime concepts were estimated to verify to what extent they can predict germination under different thermal and water conditions. The cultivars IPR Uirapuru and IAPAR 81 (drought-tolerant), and Grauna and Carioca (not tolerant) were used. The isothermal assays were performed in a temperature gradient block, and the assays with different osmotic potentials (PEG 6000) were performed in germination chambers. Seeds from drought-tolerant cultivars spent less time to germinate at supra-optimum temperatures than non-tolerant ones, and the cultivar Uirapuru (drought-tolerant) germinated faster in response to reduced ط and low temperatures. The parameter طb(50) did not discriminate between drought-tolerant and non-tolerant lines at the infraoptimum temperature range, but it can be used to identify drought-tolerant lines at high temperatures. In general, the hydrotime model reproduced the actual germination data relatively well, chiefly at higher temperatures. This study evidenced that the hydrotime model can be used to describe the germination of common bean seeds under reduced water potentials, and as a screening tool for drought-tolerant bean genotypes.(AU)
Neste trabalho foram determinados alguns parâmetros térmicos e hيdricos da germinaçمo de quatro cultivares de feijمo comum (Phaseolus vulgaris), com base nos modelos graus.dia (thermal-time) e psi.dia (hydrotime), testando-se sua adequaçمo em descrever a germinaçمo em diferentes condiçُes de temperatura e لgua. Foram analisadas as cultivares IPR Uirapuru e IAPAR 81 (tolerantes à seca), e Grauna e Carioca (nمo-tolerantes). Os ensaios isotérmicos foram realizados em bloco termogradiente, enquanto que os experimentos com diferentes potenciais osmَticos (PEG 6000) foram realizados em câmaras de germinaçمo. Sementes das cultivares tolerantes à seca germinaram mais rapidamente do que as nمotolerantes em temperaturas supraَtimas, sendo que a cultivar Uirapuru (tolerante à seca) germinou mais rلpido do que as demais em resposta ao ط reduzido e temperaturas baixas. O parâmetro طb(50) nمo discriminou entre cultivares tolerantes e nمo-tolerantes à seca na faixa térmica infraَtima, embora ele possa ser usado para identificar cultivares tolerantes em temperaturas supraَtimas. Em geral, o modelo hydrotime descreveu bem o comportamento germinativo, principalmente em temperaturas altas. Este trabalho mostra que o modelo pode ser usado para descrever a germinaçمo de feijمo comum em potenciais de لgua reduzidos, e como ferramenta para identificar genَtipos de feijoeiro tolerantes à seca.(AU)
Assuntos
Phaseolus/citologia , Sementes , Temperatura , Água/análiseRESUMO
Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R(2)) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R(2) in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies.