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To evaluate the significant sources contributing to water quality parameters, we used principal component analysis (PCA) for the interpretation of a large complex data matrix obtained from the Kandla creek environmental monitoring program. The data set consists of analytical results of a seasonal sampling survey conducted over 2 years at four stations. PCA indicates five principal components to be responsible for the data structure and explains 76% of the total variance of the data set. The study stresses the need to include new parameters in the analysis in order to make the interpretation of principal components more meaningful. The PCA could be applied as a useful tool to eliminate multi-collinearity problems and to remove the indirect effect of parameters.

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Studies on seasonal patterns of okra leafhopper, Amrasca biguttula biguttula Ishida were conducted for two years. Time series analyses were adopted for working out weekly seasonal index of okra leafhopper. Low incidence of leafhopper was recorded from June to mid-January. Results further revealed that incidence of leafhopper was highly correlated with minimum temperature. There was no discernible time lag for population increase of leafhoppers. Prediction of leafhoppers at different crop growth stages for various months of sowing was done using dummy variable technique.Resume-Des etudes ont ete faites pendant une periode de 2 ans sur les evolutions saisonnieres des cicadelles, Amrasca biguttula biguttula Ishida, qui affectent la plante okra. Les analyses de series relatives au temps ont ete adoptees pour calculer l'index saisonnier hebdomadaire de ces cicadelles. Une incidence basse des cicadelles a ete relevee de juin au mi-janvier. Les resultats ont d'ailleurs montre que l'incidence des cicadelles etait nettement correlee avec la temperature minimale. II n'y avait pas de decalage perceptible de temps pour l'accroissement de population des cicadelles. Utilisant la technique de variables simulees, on pouvait predire la population des cicadelles aux etapes differentes de la croissance des plantes, c'est a dire, on pouvait predire la population relative aux mois differents apres les semailles.