JWSS - Journal of Water and Soil Science

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This experiment was conducted to study genetic variation for electrophoretic seed protein patterns and their relations with some seed characteristics such as protein and oil percentages, chemical compositions and 100-seed weight among 270 soybean (Glycine max L. Moench) genotypes.

Among different electrophoresis procedures examined, 10% and 4.5% concentrations of acrylamide for resolving and stacking gels, respectively, 13.5 mg/ml concentration of protein buffer extraction, injection of 10 microlitre sample injection into gel hollows, 2.5 miliamper and 2-hours staining period were determined as the best combination to achieve clear bands and good separation. Based on relative mobility on gel, 30 protein bands were observed, of which only 5 varied among genotypes. In general, 8 electrophoretic patterns were recognized. Cluster analysis based on qualitative evaluation of patterns grouped the genotypes in 8 clusters and classified different bands in three groups. Simple concordance (matching) coefficients between protein bands with relative mobilities of 2.5% and 49.5% were zero, which is an indication of their independent occurrence. Probably, these bands are coded with one gene that in dominant and recessive homozygous genotypes appear as a single band at different positions on the gel. Analysis of variance revealed significant differences (P<0.05) among protein patterns for protein and oil percentages. Correlation coefficients between variable protein bands and studied traits showed a positive and significant relation (P<0.05) of bands with relative mobility of 3.5 and 49.5 with protein and phosphorous contents of the seeds, respectively. Protein patterns having band with relative mobility of 49.5 had the highest phosphorous content. Black hilum color of seeds was related to protein band with relative mobility of 52.

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Pedotransfer functions are the predictive models of a certain soil property from other easily, routinely, or cheaply measured properties. The common approach for fitting the pedotransfer functions is the use of the conventional statistical regression method. Such an approach is heavily based on the crisp obervations and also the crisp relations among variables. In the modeling natural systems, like soil, we are dealing with imprecise observations and the vague relations among the variables. Therefore, we need an appropriate algorithm for modeling such a fuzzy structures. In the present study, the fuzzy regression approach was used in order to fit some chemical and physical pedotransfer functions. The optimum regression models with the fuzzy coefficients were obtained for modeling pedotransfer functions. Sensivity analysis was carried out by using the credibility level. The results indicated that the fuzzy regression might be considered, as a suitable alternative or a complement to the statistical regression, whenever a relationship between variables is imprecise and generally when dealing with the errors due to a vaguness in regression models.