Production efficiency in catfish (Clarias gariepinus) Burchell, 1822 in Cross River State , Nigeria

In the study, the production efficiency of catfish in Cross River State was determined. Data was obtained from 120 fish farmers were randomly selected from Cross River Agricultural Zones, using a multistage random sampling technique. Multiple regression analysis model was the main tool of data analysis where different functions were tried. The results indicated that Cobb-Douglass production function had the best fit in explaining the relationship between output of catfish and inputs used, the coefficient of multiple determinant (R2 = 0.61) indicates that sixtyone percent of the variability in output of catfish is explained by the independent variables. The results also indicate that farmers’ educational level positively influence their level of efficiency in catfish production in the study area. The F-value of 16.427 indicates the overall significance of the model at 1 percent level, indicating that there is a significant linear relationship between the independent variables taken together and the yield of catfish produced in Cross River State. The marginal value products of fish pond size (farm size), labour and feed (diet) were N67.50, N 178.13 and N 728.00 respectively, while allocative efficiency for (farm size), labour and feed (diet) were (0.09 over utilized, 2.85 under utilized and 0.99 over utilized), respectively, there existed allocative in-efficiency, there is a high potential for catfish farmers to increase their yields and income. Based on the findings of this study, it is recommended that fish farmers should expand fish farms, improving on production efficiency and adopting new technologies. Regular awareness campaign about new technologies in fish farming should be embarked by extension agents to make fish farmers know the importance of adopting new technologies. KEYWORDS: Production efficiency, Catfish, Cobb-Douglass, Production function, Cross River State

Preparation of environmental monitoring plan pattern for surface water quality in Iran (Case study: Gharehsou River)

Using water quality management programs is a necessary and inevitable way for preservation and sustainable use of water resources. One of the important issues in determining the quality of water in rivers is designing effective quality control networks, so that the measured quality variables in these stations are, as far as possible, indicative of overall changes in water quality. One of the methods to achieve this goal is increasing the number of quality monitoring stations and sampling instances. Since this will dramatically increase the annual cost of monitoring, deciding on which stations and parameters are the most important ones, along with increasing the instances of sampling, in a way that shows maximum change in the system under study can affect the future decision-making processes for optimizing the efficacy of extant monitoring network, removing or adding new stations or parameters and decreasing or increasing sampling instances. This end, the efficiency of multivariate statistical procedures was studied in this thesis. Multivariate statistical procedure, with regard to its features, can be used as a practical and useful method in recognizing and analyzing rivers’ pollution and consequently in understanding, reasoning, controlling, and correct decision-making in water quality management. This research was carried out using multivariate statistical techniques for analyzing the quality of water and monitoring the variables affecting its quality in Gharasou river, in Ardabil province in northwest of Iran. During a year, 28 physical and chemical parameters were sampled in 11 stations. The results of these measurements were analyzed by multivariate procedures such as: Cluster Analysis (CA), Principal Component Analysis (PCA), Factor Analysis (FA), and Discriminant Analysis (DA). Based on the findings from cluster analysis, principal component analysis, and factor analysis the stations were divided into three groups of highly polluted (HP), moderately polluted (MP), and less polluted (LP) stations Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective river water quality management. This study also shows the effectiveness of these techniques for getting better information about the water quality and design of monitoring network for effective management of water resources. Therefore, based on the results, Gharasou river water quality monitoring program was developed and presented.