Microbiological and physical-chemical water quality of the rice fields in Sinos River's basin, Southern Brazil

In this study, we evaluate spatial and temporal variations of the water's quality used in rice fields. Every 15 days during the different phases of cultivation of the rice-vegetative, reproductive, and maturity-samples were collected from the main irrigation channel, from the surface water in the field, and from the excess-water drainage channel. The differences in the values of 13 variables were analyzed by the analysis of variance system and by the Principal Component Analysis (PCA) technique. The results demonstrate that the values observed for the presence of total coliforms, heat-tolerant coliforms, and pH were higher in the irrigation water and that the calcium and magnesium components were greater in the drainage water. The PCA results indicate that three groups of variance exist and that these three account for 77 % of the observed variances. The first principal component, (39 % of the variances), includes the variables pH, phosphorus, potassium, carbon, and turbidity; the second (28.1 %), calcium, magnesium, and conductivity; while the third accounts for only 9.9 % of the variation and incorporates the variable biological thermotolerant coliforms. The spatial pattern resulting from the distribution of the sampling locations as regards the first two principal components indicates a difference between the irrigation and drainage waters. The variables of the first and third items (except for the turbidity in the second component) reach higher values in the irrigation water, while the variables associated with the second component have higher values in the drainage water. The information provided is important for the analysis of the influence exercised by plantation management decisions on the microbiological, physical, and chemical quality of the water. The results confirm the ability of paddy rice field to filter out some of the chemicals and coliforms from the irrigation water as it passes through the agro ecosystem.